Approaches to Scaling of Trace Gas Fluxes in Ecosystems (Developments in Atmospheric Science)

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Developments in Atmospheric Science 24 A P P R O A C H E S TO SCALING OF TRACE GAS FLUXES IN ECOSYSTEMS

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Developments in Atmospheric Science 24

A P P R O A C H E S TO SCALING OF TRACE GAS FLUXES IN ECOSYSTEMS Edited by

A.F. B O U W M A N National Institute of Public Health and Environment Protection, P.O. Box 1, 3720 BA Bilthoven, The Netherlands

Report of the workshop

"Scaling of trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere" Organized

by

International Soil Reference and Information Centre (ISRIC)

ELSEVIER AMSTERDAM

- LAUSANNE

- NEW YORK - OXFORD - SHANNON - SINGAPORE - TOKYO

ELSEVIER SCIENCE B.V. Sara Burgerhartstraat 25 P.O. Box 211, 1000 AE Amsterdam, The Netherlands

9 1998 Elsevier Science B.V. All rights reserved.

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TABLE OF CONTENTS

Foreword

vii

Acknowledgements

ix

Towards reliable global bottom-up estimates of temporal and spatial patterns of emissions of trace gases and aerosols from land-use related and natural sources A.F. Bouwman, R.G. Derwent and F.J. Dentener

Methods for stable gas flux determination in aquatic and terrestrial systems

27

R.L. Lapitan, R. Wanninkhof and A.R. Mosier

Some recent developments in trace gas flux measurement techniques

67

O.T. Denmead, R. Leuning, D. W.T. Griffith and C.P. Meyer

Working group report: How can fluxes of trace gases be validated between different scales?

85

W.A.H. Asman, M.O. Andreae, R. Conr:ld, O.T. Denmead, L.N. Ganzeveld, W. Helder, T. Kaminski, M.A. Sofiev and S.E. Trurnbore

Experimental designs appropriate for flux determination in terrestrial and aquatic ecosystems

99

D. Fowler .

Toward the use of remote sensing and other data to delineate functional types in terrestrial and aquatic systems

123

J.E. Estes and T.R. Loveland ,

Working group report: How can we best define functional types and integrate state variables and properties in time and space ?

151

S. Seitzinger, J.-P. Malingreau, N.H. Batjes, A.F. Bouwman, J.P. Burrows, J.E. Estes, J. Fowler, M. Frankignoulle and R.L. Lapitan

8.

Modelling carbon dioxide in the ocean: A review

169

D. Archer

Simulation models of terrestrial trace gas fluxes at soil microsites to global scales

185

D.S. Schimel and N.S. Panikov

10.

The application of compensation point concepts in scaling of fluxes R. Conrad and F.J. Dentener

203

vi Working group report: Relations between scale, model approach and model parameters

11.

217

s Middelburg, P.S. Liss, F.J. Dentener, T. Kaminski, C. Kroeze, s Malingreau, M. Nov6k, N.S. Panikov, R. Plant, M. Starink and R. Wanninkhof

12.

Validation of model results on different scales M.A. Sofiev

233

13.

Role of isotopes and tracers in scaling trace gas fluxes S.E. Trumbore

257

14.

Inverse modelling approaches to infer surface trace gas fluxes from observed atmospheric mixing ratios

275

M. Heimann and T. Kaminski

15.

Working group report: How should the uncertainties in the results of scaling be investigated and decreased ? R.G. Derwent, A.R. Mosier, S. Bogdanov, s Dzo,zer, V. Garcon, S. Houweling, M.A. Softer, H.A.C. Denier van der Gon, F. Wania and R. Wanninkhof

16.

Current and future passive remote sensing techniques used to determine atmospheric constituents J.P. Burrows

297

315

Participants and contributing authors

349

Index

353

vii

FOREWORD

The world's terrestrial and aquatic ecosystems are important sources of a number of greenhouse gases and aerosols which cause atmospheric pollution and disturb the energy balance of the Earth-atmosphere system. In recent decades the measurement techniques and instrumentation for quantifying gas fluxes have been improved considerably. Yet, the uncertainties in the regional and global budgets for a number of atmospheric compounds have not been reduced due to the large spatial heterogeneity and temporal variability of the factors that control gaseous fluxes in ecosystems. Techniques used for extrapolating measurements or properties and constraining results between different temporal and spatial scales are nowadays referred to as "scaling". All scaling methods are embedded in the data. Apart from uncertainties associated with the data used, errors may be caused by generalization of the basic data (e.g. in soil maps, ocean maps). Moreover, much of the spatial and temporal variation at a detailed level is obscured as a result of aggregation. Possible errors caused by the use of aggregated or generalized data in models are generally not explicitly analyzed. An important step in scaling of gas exchanges between ecosystems and the atmosphere is the delineation of functional types where distinct differences in structure, composition or properties of landscapes or water bodies coincide with functions or processes relevant for gas fluxes. Delineation reduces the variability of state variables, and therefore functional types form a good basis for measurement strategies and model development. Models are widely used tools in bottom-up scaling approaches. Models can also be used to calculate flux values for regions where less intensive or no measurement data are available. One of the challenges in model development is the integration of properties or variables in space and time, accounting for the spatial and temporal variability of processes involved in gas production, consumption and exchange. Scaling not only comprises bottom-up approaches, but also top-down methods, such as inverse modelling to calculate from the atmospheric concentrations back to the sources. Topdown scaling in general involves the validation of estimates obtained at a lower scale level against constraints given at a higher level of scale. Hence, scaling requires uncertainty analysis at all levels considered. The present book is a collective effort of a diverse group of scientists to review the stateof-the art in the field of scaling of fluxes of greenhouse gases and ozone and aerosol precursors. It focuses on identification of gaps in knowledge, and on finding solutions and determining future research directions. The book is the result of an international workshop on "Scaling of trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere", held from 18-22 January 1998 at kasteel "Hoekelum", Bennekom, the Netherlands. The workshop was organized by the International Soil Reference and Information Centre (ISRIC) as a follow-up to the international conference on "Soils and the Greenhouse Effect" which ISRIC organized in 1989. The overall goal of the workshop was to investigate approaches to reduce uncertainties in estimates of fluxes of trace gases and aerosols between terrestrial and aquatic ecosystems and the atmosphere at the landscape, regional and global scale. To achieve that goal, the participants concentrated on: (i) Identification of data gaps in scaling approaches between

viii field, landscape, regional and global scales; (ii) Development of procedures to bridge process level information between different scales; (iii) Assessment of methods for integration, aggregation and other data operations; and, (iv) Assessment of approaches to uncertainty analysis in bottom-up and top-down scaling. The workshop was one of researchers with many different backgrounds, including soil science, microbiology, oceanography, rec.ote sensing and atmospheric sciences. The group included experts in the determination of gas fluxes, modellers, specialists in the use of isotopes and tracers, and researchers working on the compilation of regional and global inventories and maps of soils, vegetation, land use and emissions. Twelve invited background papers, providing a review of the field, were distributed prior to the workshop, but were not presented at the meeting. Instead, the scientific programme of the workshop consisted of five days of discussions according to the well-known Dahlem workshop model. The participants were divided in four interdisciplinary working groups which met to address the workshop aims and give concise and practical recommendations, concentrating on the following questions: (i) How can fluxes of trace gas species be validated between different scales ?; (ii) How can we best define functional types and integrate state variables and properties in time and space ?; (iii) What is the relation between scale, the model approach and the model parameters selected ?; (iv) How should the uncertainties in the results of scaling be investigated ? The four group reports are included in this volume as separate chapters together with the peer-reviewed background papers. The organizing committee for the workshop, which started discussions Jn 1996, included the following members: A.F. Bouwman (National Institute of Public Health and the Environment, Bilthoven), N.H. Batjes (International Soil Reference and Information Centre, Wageningen), H.A.C. Denier van der Gon (Soil Science and Geology Department, Wageningen Agricultural University), F.J. Dentener (Institute for Marine and Atmospheric Research, Utrecht University), J. Duyzer (TNO Institute of Environmental Sciences, Energy Research and Process Innovation, Apeldoorn), W. Helder (Netherlands Institute for Sea Research, Den Burg), J. Middelburg (Netherlands Institute of Ecology, Centre for Estuarine and Coastal Ecology, Yerseke). The organization of the workshop was made possible through funds of the Commission of the European Communities (CEC-DG XII), European IGAC Office (EIPO), International Fertilizer Industry Association (IFA), Kemira Agro Oy, National Institute of Public Health and the Environment (RIVM), Norsk Hydro, Netherlands Royal Academy of Arts and Sciences (KNAW), Shell Nederland b.v., and the Netherlands Organization for Applied Scientific Research (TNO). Cooperating organizations were the Intemational Society of Soil Science (ISSS), International Geosphere-Biosphere Programme (IGBP), International Global Atmospheric Chemistry Programme (IGAC), Global Emission Inventories Activity (GEIA), Centre for Climate Research (CKO), and the Climate Change and Biosphere Programme of the Wageningen Agricultural University (CCB) Dr. L.R. Oldeman Director, Intemational Soil Reference and Information Centre (ISRIC) October 1998

ix

ACKNOWLEDGEMENTS

This volume is the result of an international workshop on "Scaling of trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere", held from 18-22 January 1998 at kasteel "Hoekelum", Bennekom, the Netherlands. It is a collective effort of a diverse group of scientists. The choice of topics, identification of authors for the invited background papers, and the scientific programme around four key questions is the result of discussions in the organizing committee. Thanks are due to the members of this committee, Niels Batjes, Hugo Denier van der Gon, Frank Dentener, Jan Duyzer, Wim Helder and Jack Middelburg. Thanks to the enthusiastic involvement of the committee members the workshop became a very successful one. I wish to thank the chairmen and rapporteurs of the four working groups for leading the discussions and summarizing the various contributions of the working group members in four reports which are included in this book: Andi Andreae and Willem Asman (group I), JeanPaul Malingreau and Sybil Seitzinger (group 2), Peter Liss and Jack Middelburg (group 3), Arvin Mosier and Dick Derwent (group 4). I am indebted to all participants for reviewing the invited background papers. Carl Brenninkmeier, who could not attend the meeting, was so kind to provide a review of one of the papers. I am also thankful to Niels Batjes for critically reading two papers, and to Ruth de Wijs-Christensen of RIVM for editing one of the background papers. I very much appreciated the support and ideas of Roel Oldeman Hans van Baren of ISRIC during the preparation of the workshop. Special thanks are due to Jan Brussen, Yolanda Karpes-Liem and Hans Berendsen for their enthusiasm and input during the preparations of the workshop and the workshop. Finally, I am grateful to Wouter Bomer for designing the workshop logo (also presented on the cover of this book) and for preparing some of the figures in the book. Finally, I wish to thank Fred Langeweg of RIVM for giving me the opportunity to work on this projectl Lex Bouwman, editor October 1998

This Page Intentionally Left Blank

Chapter 1

TOWARDS RELIABLE GLOBAL B O T T O M - U P ESTIMATES OF T E M P O R A L AND SPATIAL PATTERNS OF EMISSIONS OF TRACE GASES AND AEROSOLS F R O M LAND-USE RELATED AND NATURAL SOURCES

A.F. Bouwman, R.G. Derwent and F.J. Dentener


Approaches to scaling a trace gas fluxes in ecosystems A.F. Bouwman, editor 9 Elsevier Science B.V. All rights reserved

TOWARDS RELIABLE GLOBAL BOTTOM-UP ESTIMATES OF TEMPORAL AND SPATIAL PATTERNS OF EMISSIONS OF TRACE GASES AND AEROSOLS FROM LAND-USE RELATED AND NATURAL SOURCES

A.F. Bouwman l, R.G. Derwent 2 and F.J. Dentener 3 ~National Institute of Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands 2 Meteorological Office, London Road, Bracknell, RG 12 2SZ Berkshire, UK 3 University of Utrecht, Institute for Marine and Atmospheric Research, Princetonplein 5, 3584 CC Utrecht, The Netherlands

I. I n t r o d u c t i o n

Emission inventories play a dual role in global air pollution issues. Firstly, they can be used directly to establish the more important source categories, to identify trends in emissions and to examine the impact of different policy approaches. Secondly, emission inventories are used to drive atmospheric models applied to assess the environmental consequences of changing trace gas emissions and concentrations and to provide advice to policy makers. This second role contributes to the atmospheric modelling community being an important user of emission inventories. The assessment process for global air pollution problems has a number of identifiable steps: (i) it quantifies the changes in trace gas composition of the atmosphere; (ii) it quantifies changes in atmospheric chemistry, transport, deposition and radiative forcing; (iii) it identifies the climate responses to the changes in atmospheric composition of the radiatively active trace gases; and, (iv) it quantifies the biological and ecological responses to the predicted changes in climate. The atmospheric modelling community will need a hierarchy of emission inventories to complete an assessment of global air pollution problems based on these steps over the next decade or so. In their simplest form, atmospheric models merely require no more than fixed global emission fields of each relevant species. However, in their most complex form, future atmospheric models will require emission fields whose spatial patterns and magnitudes will respond in a wholly self-consistent manner to changes in economic prosperity, demography, land use, climate change and policy. The requirements placed on the emission inventories will change from the provision of fixed fields to the implementation of emission algorithms within the modelling system. Gridded emission fields may slowly change from being the essential input to being the output of the modelling system. Alongside this anticipated increase in complexity in moving towards a process-based approach to emission inventories, there is a growing interest in the emissions of a wider range of species. For example, in climate change research at the start of the Intergovernmental Panel on Climate Change (IPCC) process, assessment work was performed with present-day and doubled atmospheric carbon dioxide (CO2) concentrations. This "steady state" or "equilibrium" approach has now been replaced by the transient scenario approach in which CO2 concentrations increase with time in response to emission projections and carbon cycle modelling. Further scenarios have been added to deal with the other major radiatively active trace gases: methane (CH4), nitrous oxide (N20), tropospheric ozone (03), stratospheric ozone

4

A.F. B o u w m a n , R.G. D e r w e n t a n d F.J. D e n t e n e r

Table 1. Some recent global-scale chemistry-transport r Model

Type

GETTM GEOS-I DAS GFDL/GCTM GISS/CTM GRANTOUR IMAGES IMARU

.tels (types, spatial and time scales and che :'ical mechanisms),

Spatial resolution of meteorological data

Time resolution of meteorological data

Chemical mechanism

Reference a

Eulerian Eulerian Eulerian Eulerian Lagrangian Eulerian Eulerian

2.8~215176215 18 levels 2.5%2~215 levels 265 km• kmxl I levels 4~215 levels 7.5~215 12 levels 5%5~215 levels 3.75%3.75% 19 levels

3-hourly 6-hourly 6-hourly 4-hourly to 5-day 12-hourly monthly 1/2-hourly

1 2 3 4 5 6 7

KFA/GISS KNMI/CTMK MCT/UiB MOGUNTIA

Eulerian Eulerian Eulerian Eulerian

10%8% 15 levels 2.5%2.5~ 15 levels 150 kmx 150 kmx 10 levels 10~ 10% 10 levels

8-hourly 12-hourly hourly monthly

MOZART RGLK

Eulerian Eulerian

2.8%2.8% 18 levels 10~ 10% 0 levels

6-hourly Monthly

STOCHEM UiO/GISS

Lagrangian Eulerian

3.75~215 8~ 10~

radioactive decay Simplified Simplified DMS chemistry 76 species 47 species CH4 and NO• chemistry Simplified 13 species 51 species CH4 and NOx chemistry 45 species SO2, NOx and NH3 chemistry 70 seecies 50 species

19 '. ~els levels

6-hourly 8-hourly to 5-day

8 9 10 11 12 13 14 15

a 1, Li and Chang (1996); 2, Allen et al. (1996); 3, Moxim et al. (1996); 4, Chin et al. (1996); 5, Penner et al. (1994); 6, Mt~ller and Brasseur (1995); 7, Roelofs and Lelieveld (1995); 8, Kraus et al. (1996); 9, Wauben et al. (1997); 10, Flatoy and Hov (1996); 11, Dentener and Crutzen (1993); 12, Brasseur et al. (1997); 13, Rodhe et al. (1995); 14, Collins et al. (1997); 15, Berntsen et al. (1996).

and chlorofluorocarbons (CFCs). More recently, sulphur dioxide (SO2), dimethylsulphide (DMS), ammonia (NH3) and other aerosol species have been incorporated into the scenario approach to take into account the climate consequences of man-made fuel combustion and biomass burning. There has therefore been an increasing interest in the details of the emission inventories of a wider range of trace gases and aerosols. Emission inventories are implemented in current atmospheric models to represent the processes by which trace gases and aerosols are discharged into the model atmosphere. The models, commonly three-dimensional chemistry transport models (CTMs), then simulate the dispersion, diffusion and advection of t h i material away from its source r~'gions in response to a continuously varying turbulent and chaotic atmospheric flow. At some point, the material may be removed from the atmospheric circulation by dry or wet deposition or uptake in the oceans or it may undergo chemical transformation. An immense amount of meteorological, chemical and process information is required to drive current CTMs. This information can be made available from archived meteorological analyses or from the global climate models (GCM) used to predict future climate change. The CTM may be incorporated within the GCM, in which case the atmospheric model is "on-line"; alternatively, the model is referred to as "off-line" if the GCM and CTM are separated. By way of example, details of the time and spatial resolution of 15 of the current CTMs are provided in Table 1. At present some 20 CTMs are being used to assess global air pollution problems. Currently, CTMs use emission inventories for the trace gases and aerosol species listed in Table 2. Each emission is usually subdivided into up to about 10 major source categories. Most source categories have different spatial grids applied and work with different seasonal and sometimes diurnal variations. We will focus here on the issues of "scaling" in the implementation of emission inventories in current and future CTMs. Scaling comprises all techniques used for extrapolating measurements or properties and constraining results between different temporal and spatial scales. Very similar problems of scaling occur across various disciplines, such as ecology (Ehleringer and Field, 1993), soil science (Wagenet and Hutson, 1995; Hoosbeek and Bryant,

Towards reliable global estimates of emissions of trace gases and aerosols

5

Table 2. Some of the trace gases and aerosol species handled by current chemistry transport models (CTMs) for the

assessment of global air pollution problems. Radiatively active gases

Aerosols

CO2 N20 CFCs: 11,12,113 HCFCs: 22, 141b, 142b HFCs: 134a, 152a Perfluoro molecules: SF6, CF4, C2F6,C4F8

Black carbon Organic particles Wind-blown dust Sea-salt Resuspended material Volcanic emissions Biomass burning

Ozone precursor and depleting gases

Aerosol precursor gases

CO NOx H2 Synthetic hydrocarbons: light C2 - C~o hydrocarbons, oxygenates Biogenic hydrocarbons: isoprene, terpenes CH3CC13,C2C14,C2HC13, CH2C12 CH3Br CH3CI Synthetic bromine compounds: 12B 1, 13B 1

SO2 DMS H2S NH3

CH4

1992) and global change research in general (O'Neill, 1988). Two approaches are used for scaling gas fluxes: bottom-up and top-down scaling. Bottomup approaches, calculated from smaller to larger scales, involve extending calculations from an easily measured and reasonably well understood unit to more encompassing processes. In bottom-up scaling, various problems occur, such as how to aggregate the spatial and temporal variation of properties or fluxes. Other problems are the various data uncertainties involved, and translating mechanisms and processes between different scales. Top-down approaches can mean using the measurements at a higher scale level which set the boundary conditions for problem identification, and stimulate the testing of general relationships for specific cases (see Heimann and Kaminski, 1999). Examples of observations at a higher level of scale that are used to constrain flux estimates include atmospheric concentrations and mixing-ratios of stable isotopes (see Trumbore, 1999). Comparison of the concentrations or deposition velocities simulated by transport models with observations can result in an expression of scientific confidence or a warning that crucial !r,formation is still missing. Between these two extremes, top-down scaling can be very useful for testing hypotheses and identifying missing information. A number of methods exist to scale information, the most important being aggregation, generalization, stratification and modelling. Aggregation involves the collection or uniting of information into an aggregate unit, generally by counting and addition. Aggregated results can be presented as the mean or median coupled with statistical information such as standard deviations. Generalization is the description of a group on the basis of properties of a sub-unit or member of the group considered to be representative, commonly based on expert knowledge. This method is generally used when observational or statistical data on individual members of the group are scarce. The reverse action of aggregation, whereby the aggregate is subdivided into different components, may be the classification of a system into functional units with similar properties and environmental and management conditions that regulate trace gas fluxes (see Seitzinger et al. (1999). This is sometimes referred to as "stratification" (Matson et al. (1989).

6


Models break down a system into its main components and describe the behaviour of the system through the interaction of those components. A discussion of the different types of models used can be found in Archer (1999) for aquatic systems and Schimel and Panikov (1999) for terrestrial systems. We will focus here on bottom-up scaling approaches for trace gas fluxes between aquatic and terrestrial ecosystems (including agroecosystems) and the atmosphere used in the development of global gridded emission inventories. The discussion will be primarily on emission inventories prepared for scientific purposes such as atmospheric modelling. Although our findings may also hold for other types of inventories, we will not discuss these inventories explicitly on the country or provincial (sub-national) scale. Such inventories are now being prepared for non-scientific purposes (e.g. national communications in the United Nations Framework Convention on Climate Change). The first, and major, part of this paper discusses the uncertainties and problems of aggregation, generalization, stratification and modelling in the compilation of inventories. Next, the available global emission inventories for land-use related and natural sources of trace gases will be discussed on the basis of their spatial and temporal resolution. Finally, the spatial and temporal resolution of current CTMs will be confronted with the available emission inventories.

2. Uncertainties in emission estimates Among the various approaches to estimating fluxes, the major ones in use are the emission factor approach and modelling. In emission factor approaches emission estimates are derived by combining measurement data with geographic and statistical information on the ecosystem processes and economic activity. This can be represented as: E = A • Eu

(1)

where E is the emission, A the activity level (e.g. area of a functional unit, animal population, fertilizer use, burning of biomass) and EU the emission factor (e.g. the emission per unit of area, animal, unit of fertilizer applied or biomass burnt). When using the emission factor approach, both the stratification scheme for delineating functional types (e.g. management systems, ecosystems, environmental provinces or entities) as a basis fol scaling, and the reliability of the emission factor determine the accuracy of the flux estimates. The firmest basis for scaling is developing an understanding of the mechanisms that regulate spatial and temporal patterns of processes, and describing these mechanisms in models. Models are used to break down a system into its component parts and describe the behaviour of the system through their interaction. In general, trace gas flux models include descriptions of the processes responsible for the cycling of carbon or nitrogen and the fluxes associated with these processes. Various types of models exist, including regression models, empirical and process (or mechanistic) models. In the following sections the various sources of uncertainty in the estimates of emission rates for the emission factor approach, trace gas flux models and farm-scale models will be discussed, followed by uncertainties associated with the spatial and temporal distribution of the data underlying flux estimates. We will not discuss uncertainties in the measurement data. This problem will be examined in more detail by Lapitan et al. (1999), Fowler (1999), Denmead et al. (1999) and Sofiev (1999).


7

2.1. U n c e r t a i n t i e s in the e m i s s i o n rates

2.1.1. Emission factor approach

Uncertainty ranges for emission inventories are usually presented for the global total emission only, and not on a regional or grid-by-grid basis. Uncertainties in emission inventories may be caused by uncertainties in the environmental and economic activity data used and in the measurement data themselves. Uncertainties can also result from the lack of representative measurem'ents to resolve the full range of ,mvironmental conditions occurrhag in the systems considered and in the models used. These sources of uncertainty will be discussed for the different approaches to flux estimation on the basis of a number of examples for different scales. - M e a s u r e m e n t data. In a review of measurement data for biomass burning, Andreae (1991) proposed emission factors for several gas species. Although the ranges in measured fluxes in field and laboratory experiments varied by more than a factor of 2 for most species as a result of differences in fuel and burning conditions, one single emission factor was proposed for each gas species, representing the aggregated flux for smouldering and flaming fires for all fuel types (grass, wood, crop residues, etc.). For biomass burning it is difficult to delineate the types of fires and the different techniques used may introduce systematic differences, especially where reactive and difficult-to-measure species (such as NOx and NH3) are involved. Clearly, one emission factor cannot describe all the burning conditions and fuel

types. Another example illustrating the lack of measurement data concerns the emission coefficients used for animal housings in Europe. In housings with mechan!cal ventilation the gas flux can be easily determined from the gas concentration in the ventilation air and the flow rate. The trace gas emissions from naturally ventilated housings can only be determined indirectly and with greater uncertainty. In such "open" housings the emission depends on the opening and closing of doors. In large parts of Europe, housings for cattle - the most important category- are naturally ventilated (Asman, 1992). Besides being scant, the available measurement data need not be representative. For example, the NH3 ammonia emission per animal may vary by a factor of 4 within the same type of housing (Pedersen et al., 1996). This may be caused by differences in the ventilation over the slurry between housings and by differences in waste management practices such as cleaning. Guenther et al. (1995) were also confronted by a lack of measurement data in their global invemory of fluxes of volatile organic compounds (VOC) from vegetation. Measurements represented only 26 of the 59 global land-cover types considered; the remaining land-cover types, including tropical seasonal forests and savannas, were assigned an emission on the basis of expert knowledge. In this database, most of the simulated VOC emissions come from systems where very few or no measurements are available. - Functional types. Guenther et al. (1994) proposed emission factors for VOC for 91 woodland landscapes in the USA by combining emissions from 49 genera of plants. In their global modelling ofVOC, Guenther et al. (1995) used emission factors on the basis of the 59 land-cover types defined by Olson et al. (1985). This aggregation causes considerable loss of information, as the detailed estimates for the USA vary by as much as a factor of 5 for various aggregated landscapes on the global scale. Yienger and Levy II (1995) used a combination of emission factors and modelling approaches to estimate global emissions of NO from soils. They first calculated "biome factors" based on NO flux estimates from the literature. These biome-dependent average fluxes were modified by an algorithm to account for pulse events of NO production following

8


wetting of dry soil and another algorithm to account for the effect of varying temperature. Yienger and Levy (1995) also made an attempt to model the effects of NOx uptake by plants on net NOx emission to the atmosphere. They calculated absorption factors based on leaf area indices, and then multiplied these absorption factors by the estimated soil emissions to calculate net ecosystem emissions. The model of Yienger and Levy has some mechanistic components, such as the wetting and temperature functions, but is primarily based on averaged biome factors that are not substantially different from an emission factor approach. Davidson and Kingerlee (1997) also derived emission factors based on data for biomes from the literature. Although in their study more soil NOx measurement data were used in comparison to Yienger and Levy's study (1995), the major differences between the two studies are the stratification scheme and t;L,~coupling of environmental con.~ition descriptions at the measurement sites with the functional types distinguished. Davidson and Kingerlee (1997) presented a global annual emission which exceeds the estimate of Yienger and Levy (1995) by a factor of 2. It is clear that the differences between the two studies described will have an enormous impact on the results of atmospheric models. 2.1.2. Regression approaches

Bouwman et al. (1993) calculated the N20 emission from soils under natural vegetation using a simple global model describing the spatial and temporal variability of the major controlling factors of N20 production in soils. The basis for the model is the strong relationship between N20 fluxes and the amount of nitrogen (N) being cycled through the soil-plant-microbial biomass system. The model calculates the monthly N20 production potential from five indices representing major regulators of N20 production (soil fertility, organic matter input, soil moisture status, temperature and soil oxygen status). These five indices were combined in the final N20 index (Figure 1). Comparison of the N20 index with reported measurements for about 30 locations in six ecosystems correlated with an r2 o f - 0.6 (Figure l a). The resulting regression equation was used to calculate emissions on a l ~ 1~ resolution. However, the correlation coefficient is not a robust statistical method (see Sofiev, 1999), and minor differences in only one of the measurement sites can cause major shifts in the correlation coefficient (Bouwman et al., 1993). A major problem causing unreliability of the regression equation is the lack of measurement data, particularly for a number of important ecosystems and world regions that have not been sampled at all. It is not known how the model performs in these areas (Figure 1b). 2.1.3. Process models

Reliable regional or global estimates of trace gas emissions depend on an examination of methodologies to reduce the current high uncertainty in the estimates. One potential way to do this is to develop predictive flux models. Such models have been developed for different processes and gas species on different scales. Examples will be given of the magnitude of the uncertainties in global models, the value 9f models developed for speci~c ecosystems for extrapolation and the problem of selecting the appropriate scale of process descriptions in models. Finally, the advantages of using a range of models on different scales will be discussed. Uncertainties in global flux models. Here, examples for oceanic flux models will be given, although very similar examples also exist for terrestrial models. In aquatic systems, fluxes can generally not be directly determined. Models commonly used describe fluxes on the basis of wind speed and anomalies of concentrations between surface water and air. Nevison et al. (1995) calculated the air-sea exchange using three different relationships for the NzO-air -


a. R e l a t i o n b e t w e e n

550

o

measured

NzO fluxes and modelled

9

N20

index

Measured flux Regression equation

450

-rr

350 o

0

E z

25O

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6 z ~

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150 o 0

50

oO 0

-50

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o.........oi" s

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b. L o c a t i o n of m e a s u r e m e n t

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index

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Figure 1. a) Relationship between measured N20 flux and simulated N20 index; and b) the location of the measurement sites. Figure 1a was modified from Bouwman et al. (1993) with kind permission from the American Geophysical Union.

gas transfer velocity from global N 2 0 surface anomalies. The highest N20 fluxes were obtained using a quadratic function of wind speed for the transfer velocity, while linear functions yielded much lower values. An intermediate relationship was the stability-dependent method based on the occurrence of whitecaps, also used by Guenther et al. (1995) for estimating the VOC emission inventory for oceans. The uncertainty in the global emission is illustrated by the difference of more than a factor of 4 between the lowest and the highest global emission estimate. - Limitations of e c o s y s t e m m o d e l s . Although models developed for specific ecosystems may show fewer uncertainties than global models, their value for extrapolation may be

10


limited. Mosier and Parton (1985) developed a model for the estimation of N20 fluxes over large areas of semi-arid grassland soils, accounting for spatial and interannual variability. Model parameters were developed by relating N20 flux to soil moisture and temperature for two sites representing much of the variability in the Colorado shortgrass ecosystem. Because no time-series data of NO3 and NH4+ are available on the target scale of the study, the model was simplified with an empirical multiplier representing N availability. It is especially empirical multipliers like these that cause problems when models are applied to other ecosystems with different environmental and climatic conditions. Scale of process descriptions. Some models seem to include an imbalance in the detail and the particular scale on which different processes are described. For example, Li et al. (1992) developed a model to simulate N20 fluxes from decomposition and denitrification in soils on the field scale. The model can also describe NO• fluxes by using soil, climate and data on management to drive three submodels (i.e. thermal-hydraulic, denitrification and decomposition submodels). The management practices considered include tillage timing and intensity, fertilizer and manure application, irrigation (amount and timing), and crop type and rotation. One of the processes simulated by the model is microbial growth. Since model results appear to be dominated by the effect of temperature and soil moisture, which operate at nearly all levels in the model, the question arising is whether there is an imbalance in the scales according to which processes are described. The similarity of the results obtained for shortgrass ecosystems by Mosier and Parton (1985) with their simple approach to those of Li

Simulated (g m"2) 75-

(a)

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model correspondence (rA2=0.905, n=36)

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~

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(b)

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~176

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9

9e

15

30

45

60

75

Measured (g m2) Figure 2. Comparison of simulated and measuredtotal seasonal methane emissions from Texas flooded rice paddy soils during the 1991-95 growing seasons employingconsecutively (a) the simulation model and (b) the simplified model. Model correspondence is the regression line of simulated vs. measured methane emissions. Reprinted from Huang et al. (1998) with kind permission of Blackwell Science Ltd.


11

et al. (1992) illustrates the need to match the scale of process description with that of the scale

at which the model is applied. Comparisons of different models to predict N20 fluxes from fields (Frolking et al., 1997) reveal major differences in the simulated N gas fluxes from soils. Apparently, the major problem in developing trace gas flux models is the description of soil processes that operate in "hot spots" in field models. - M o d e l s at different scales. Apart from the above-mentioned problem of the scale on which processes operate, a very practical problem is formed by the available model input data. To overcome this problem, sometimes summary models are developed on the basis of the detailed process model. These summary models can be used to predict fluxes in regions with limited data availability. Progress with the use of models on different scales for flooded rice paddy fields was made by Huang et al. (1998). Understanding the processes of methane production, oxidation and emission in flooded rice fields enabled them to develop a semiempirical model. They also derived a simplified (summary) version of the model for application to a wider range of conditions but with limited data sets. Huang et al. (1998) hypothesized methanogenic substrates as being primarily derived from rice plants and added organic matter. Rates of methane production in flooded rice soils are determined by the availability of methanogenic substrates a,~d the influence of environmemal factors. Model validation against observations from single-rice growing seasons in Texas (USA) demonstrated that the seasonal variation of methane emission is regulated by rice growth and development. A further validation of the model against measurements from irrigated rice paddy soils in various regions of the world, including Italy, China, Indonesia, Philippines and the United States, suggested that methane emission could be predicted from rice net productivity, cultivar character, soil texture and temperature, and organic matter amendments. The detailed model and the summary model gave similar results (Figure 2), illustrating the advantage of using simplified models. 2.1.4. F a r m nutrient balance models

On the farm scale, trace gas fluxes occur in the stable, during grazing or during and after spreading of animal manure. A model is therefore required to describe farm-scale processes and cycles. For example, the model of Hutchings et al. (1996) describes NH3 losses from animal housings, stored slurry, application of slurry and urine patches. The model builds on knowledge acquired from various experiments and model studies of animal housing, waste storage and farming practices. The model tracks the N input as animal feed until it is lost as NH3. The problem of applying farm-scale models is the variety in management styles occurring within groups of farms. Representative farms or averages for a group of farms have to be used to obtain aggregated data. Differences in fluxes as a result of differences in management may disappear due to this aggregation.

2.2. U n c e r t a i n t i e s in the spatial distributions

The environmental spatial data used as a basis for stratification schemes for delineation of functional types underpins the emission factor approach and, if sufficient attribute data are available, drives flux models. When no spatial data are available to distribute activities or emissions, a proxy or surrogate distribution has to be used. Clearly, this introduces an unknown uncertainty in the spatial distribution. We will give a number of examples of databases that describe environmental conditions in aquatic and terrestrial ecosystems, emphasizing their uncertainties. A comprehensive review of the data required for global terrestrial modelling can be found in Cramer and Fischer (1996). The list of examples given

12


here is not intended to be complete, but does illustrate data limitations and aggregation problems. The weaknesses and strong points in the databases discussed may serve to improve future database development. The examples considered include databases for climate, oceans, soils and vegetation/land cover, as well as the problem of surrogate spatial distributions. 2.2.1. Climate

The example of a database on current climate for a global terrestrial 0.5 ~ x 0.50 grid given by Leemans and Cramer (1991; update in preparation) includes average monthly, average minimum and maximum air temperature, precipitation and cloudiness values. Data limitations. The weather records were usually limited to at least five observational years from the period of 1931-1960. Not all stations considered have complete coverage. Based on selection criteria, the final number of stations worldwide was found to be 6280 for temperature and 6090 for precipitation. The cloudiness data set, defined as the number of recorded bright sunshine hours as a percentage of potential number, was based on fewer stations and often derived from estimated rather than recorded data. Aggregation. To aggregate the point data to a spatial grid an interpolation onto 0.5 ~ grid boxes was done using a triangulation network followed by smooth surface fitting. For regions with no primary data, the temperature val.aes were corrected for altitude using an estimated moist adiabatic lapse rate and a global topography data set, while precipitation was not corrected; this was due to the more complex relationships between precipitation and altitude. - Uncertainty. The major problem is the inappropriate data coverage for large areas of the world. The uncertainty of temperatures is particularly high in mountainous areas because there are only a few weather stations in these regions and none of them are located on a clear altitudinal gradient. The average moist adiabatical lapse rate for mountainous areas may result in underestimation of temperatures for these areas. The spatial precipitation patterns resulting from straight interpolation of measured values causes great uncertainty in areas with sparse data coverage. Although the major annual cloud dynamics are represented, the regional reliability of the cloudiness data is low. -

-

2.2.2. Oceans

The best known chemo-physical global ocean data sets are included in the World Ocean Atlas (Conkright et al., 1994; Levitus and Boyer, 1994a, b; Levitus et al., 1994). This database includes spatial information on a l~ 1~ grid at various depths between 0 and 5500 m below the surface for ocean temperature, salinity, dissolved oxygen, apparent oxygen utilization, oxygen saturation, phosphate, and nitrate and silicate. Data for temperature and salinity have a monthly time resolution and apply to depths between 0 and 1000 m below the surface; those for dissolved oxygen, apparent oxygen utilization and oxygen saturation are on a seasonal temporal scale and phosphate; nitrate and silicate concentrations taken on an annual basis. Data limitations. The World Ocean Atlas is based on many observations. For example, the temperature data set is based on 4.5 million profiles. Although the number of observations is much higher than that used to produce the soil, vegetation/land cover and climate databases, there is a problem of areas with a low density or absence of observations; furthermore, the timing of the measurements may differ between profiles. - Aggregation. The data at the observed depth were interpolated to standard depths. The accuracy of the observed and standard level data was checked and flagged using a number of procedures. The point data for depth profiles were interpolated onto a 1o grid. There are many regions where measurements are scant or even absent. To describe the density of observations, there are accompanying mask files for all the data listed -

-

U n c e r t a i n t y .


13

above, containing the number of grid points with data within the radius of influence surrounding each grid box. If a grid box contains three or fewer observations within its radius of influence, the mask value for that 1~ grid box will be zero. This file is used in plotting routines to "mask" or cover up areas with three or fewer observations. 2.2.3. Soils

Soil fertility, and soil chemical and physical parameters, play an important role in the production and exchange of trace gases. Recently, a 0.5 ~ • 0.5 ~ global soil database was developed on the basis of an edited version of the 1:5 million scale FAO Soil Map of the World (FAO, 1991), combining geographic information on soil types with a set of representative soil profiles held in a profile-attribute database (Batjes and Bridges, 1994). Data limitations. The density of available soil profile data varies from one region to the other. Important geographic gaps are in China, the New Independent States and the Northwest Territories of Canada. Similarly, a number of soil units are underrepresented in the profile database; these units account for about 28% of the terrestrial globe of which total Lithosols (shallow soils) account for about 40%. - Aggregation. The FAO Soil Map of the World is a compilation of many national and regional soil maps. Therefore coverage is not spatially constant. The soil profile information for each soil unit was coupled to the soil units distinguished region-wise. Based on the number of profiles available, statistical analysis was performed by Batjes (1997), allowing refinement of ratings for soil quality in global environmental studies. Uncertainty. The variability of the reliability of the spatial information has already been mentioned. The attribute files containing soil profile data in Batjes and Bridges (1994) represent a major improvement on the FAO soil map as such. However, this aggregation may not realistically describe the variability actually occurring within a soil unit in regions where the density of observations is low. -

-

2.2.4. Vegetation~Land cover

Similar to the soil information, land-use and land-cover information is required to scale up information from the field to landscapes or ecosystems. Two examples of widely used vegetation/land-cover maps are those compiled by Matthews (1983) and Olson et al. (1985) with 1~ and 0.5 ~ spatial resolution, respectively. A recent development is the creation of a global 1-km resolution global land-cover characterization (Loveland et al., 1997) based on remotely sensed data. For the pan-European region (from Gibraltar to the Ural and from the North Cape to Athens) a land-cover database with a 10% 10 minutes resolution was developed (Veldkamp et al., 1996). Data limitations. Matthews (1983) used the Unesco (1973) vegetation classification scheme, while the database by Olson et al. (1985) is based on a land systems grouping. Estimates of the extent of vegetation/land-cover types excluding cultivated land show a considerable difference between the two databases. The global area of cultivated land is similar in all the maps and corresponds well with FAO statistics, although regional discrepancies may exist. The Olson and Loveland et al. databases include estimates for carbon stocks in each land-cover type. Apart from definitional problems, there is generally a major lack of observational data describing the properties of the vegetation/land-cover types distinguished. As in the soil database of Batjes and Bridges (1994), the map unit characteristics will be included in attribute files, allowing use of the data for different purposes in a variety of models. Aggregation. The Matthews and Olson databases were compiled from maps, atlases and -

-

14


other information available. For spatial aggregation satellite observations may form a considerable improvement. The 10~ x 10~ resolution for the pan-European region (Veldkamp et al., 1996) includes eight classes produced from a combination of spatial data in vector format (based on various sources, including satellite data) and tabular statistical data. A calibration routine was used to ensure that no land-use class deviated more than 5% from the statistical information. The Loveland et al. database is derived from 1-km Advanced Very High Resolution Radiometer (AVHRR) d:,,a, spanning a 12-month period (April 1992-March 1993). It is based on seasonal land-cover region concepts, which provide a framework for presenting the temporal and spatial patterns of vegetation in the database. Uncertainty. Major uncertainties in the traditional databases, such as Matthews (1983) and Olson et al. (1985), are seen in the classification scheme used, the underlying data and the aggregation method, which is illustrated by the disagreement in the spatial distributions between these two databases. The database of Veldkamp et al. (1996) may suffer from the small number of types distinguished; this may not allow a proper description of the observed variability necessary for ecosystem and trace gas studies. However, the combination with soil and climate data may form an improvement here. The database also lacks data on the characteristics of the vegetation type itself in the form of attribute data. Since the Loveland et al. database is still in development, its uncertainty is as yet unknown. A review of the use of remote sensing and other data in vegetation mapping is given by Estes and Loveland (1999) -

2.2.5. Surrogate distributions

When the exact location or distribution of an activity or process is not known, surrogate distributions are used to distribute activities, volumes or emissions over the grids. For example, the grassland distribution is generally used to distribute cattle populations, while for other animal categories the rural human population distribution or the distribution of arable land is used as a surrogate distribution. However, the human population distribution is generally not well known in rural areas, as statistics and atlases give data on populations in major towns only. Using surrogate distributions may be realistic in some regions. However, in others with specific stratifications of management, environmental or demographic conditions, surrogate distributions may cause major errors (see, for example, the dairy cattle discussed in 2.4). 2.2. 6. General remarks

The major uncertainties in databases are generally related to the scarcity of data, and variable density of data coverage and quality. With reference to the data problem, the mask files (containing the number of grid points for data within the radius of influence surrounding each grid box) provided in the ocean database form a good tool for describing the data density and the point-by-point accuracy or reliability in other databases as well. Compared to the classification schemes for vegetation and land cover in the traditional maps and databases, satellite observations may provide a more flexible way of describing ecosystem characteristics. Attribute files with descriptive data of the map units distinguished (e.g. in the soil database of Batjes and Bridges, 1994) are very useful for modellers. These attribute data also enable performance of statistical analysis of the data by unit. Furthermore, correction of the satellite data with actual statistical information is a good way to improve the accuracy of the spatial data. Finally, a combination of vegetation/land-cover data with climate and soil information may provide a basis for classification into functions.


|5

2.3. Uncertainties in the economic data on land use

The major forms of economic land use activities generating emissions of trace gases include livestock production, crop production and forestry. Livestock production is the most complex system. In livestock production systems, trace gas fluxes can be determined in a stable fi~r either individual animals or a group. The comp.ete production system, from feeu to excretion and emission in the stable and during grazing, has to be known for extrapolation of these measurements. For example, to estimate NH3 emissions from animal manure during storage and during and after application as a fertilizer, we need to know the number of animals in each animal category (e.g. dairy cattle) according to age class, live weight; N content and relative share of the various amino acids, N use efficiency (feed conversion to milk and meat); housing system and period of confinement, and form, mode and period of storage of manure. Further, we need to know weather conditions during spreading (turbulence, air temperature, air humidity and rainfall), properties of the soil to which the manure is applied, amount of manure per unit area, mode of manure application and the period between application and cultivation. Outside Europe and North America all these data are scant. Data on animal populations by category, and within a category (according to age and weight class) are almost non-existent. For many countries only the total number of animals within a category is available for a specific year. Data are not available on some animal categories, such as house pets, horses, buffalo, donkeys, camels, or on housing, and the type and form of manure. Estimates for regions within countries may be availai~:e, but do not always correspo,d to the official statistics or are outdated. Data on the coverage of stored manure, which may highly vary in effectiveness, are lacking. Geographic data on the application rate and timing of manure application, soil conditions, and weather conditions during application are not available. In addition to spatial variability, manure application rates, and mode and timing of application, show a strong interannual variability, which is not easy to include in scaling exercises. Storage and spreading of manure are regulated by law to reduce emissions in a number of countries. It is difficult to obtain information on the actual observance of these laws and the emission reductions achieved. Data on crop production systems that are essential for estimating trace gas fluxes envelop fertilizer use (including animal manure) by type and by crop, timing and mode of fertilizer application, amount and timing of field-residue burning, animal waste management, number of rice crops per year combined with soil and water management practices and fertilizer application rates. Such data may be available for regions within countries but may not always correspond to the official statistics or may be outdated. Global forestry data are available from FAO statistics and assessments ~z.g. FAO, 1995). However, information on the species planted and forest management are difficult to obtain. In assessments of trace gas fluxes it is generally important to know the amount of above- and below ground carbon in a certain forested area. Global data on carbon in vegetation can be obtained from Olson et al. (1985), for example, and carbon in soils from such sources as Batjes (1996). In summary, the economic and attribute data generally have to be inferred from aggregated country totals for the three land-use systems. Where the geographic distributions within countries are not directly available, data have to be distributed over a spatial grid or subnational regions. In this case surrogate distributions will have to be used (see section 2.2).

2.4. Uncertainties in the temporal distribution

Temporal patterns of trace gas fluxes vary in space. This poses difficulties for integration of

16


fluxes over spatial units. Spatial aggregation causes considerable loss of information on temporal flux patterns. However, the paucity of measurement data often makes generalizations unavoidable. Generalization is usually done by treating a landscape as a composite of representative soils or farms with average waste characteristics, management and weather conditions, or by treating populations as a group of identical members. Such generalizations may lead to errors in temporal distributions due to averaging procedures. The temporal pattern of estimates derived for a group of average farms may differ from the sum of all individual farms. Generally, different grazing systems co-exist within regions. For example, in dairy production systems part of the production takes place in stables only. The animal waste collected in the stables is at~plied to grassland or croplands at different times. Hence, the temporal pattern of gas fluxes is determined by the grazing systems occurring in the landscape considered. Errors caused by aggregation of groups of farms may be particularly large for N gas species. This was shown by Schimel et al. (1986), who analyzed the cycling and volatile loss of N derived from cattle urine at lowland and upland sites in a shortgrass steppe in Colorado, USA. The NH3 losses were measured in microplots representing three soil types typical for the shortgrass steppe landscape. Seasonal rates of urine and faeces deposition were mapped by landscape position, allowing for simulation of responses of animals to microclimate and forage availability, and differential use of upland and lowland pastures. This provided variation in the proportion of total excretion vulnerable to loss. Urine deposition was higher during the growing season when forage-N levels were high, and highest in lowland soils. Simple aggregation of the spatial patterns of deposition and loss would have resulted in a calculated loss of NH3 of a factor of 7 higher than for sophisticated stratification on the basis of the observed seasonal and spatial variability. Studies of gaseous fluxes are vulnerable to this type of error because fluxes can be intermittent and patchily distributed in space. Methane fluxes from rice fields are also extremely variable in time and space. Measurements for individual fields indicate diurnal and seasonal patterns caused by rice growth and development (e.g. Huang et al., 1998), which can best be described using process models (see above). Additional pulses caused by management practices are more difficult to describe in flux models or emission factor approaches because the statistical information on management is sparse and often absent, as discussed above. An attempt to distinguish seasonal variability in rice global cropping patterns was made by Matthews et al. (1991), who presented cropping calendars for rice production worldwide. This stratification serves as a basis for applying flux models with the corresponding data on soil, water and crop management. In summary, there is a problem in scaling-up of loss of information on temporal variability due to spatial aggregation or generalization. This problem may occur on any scale. Sophisticated and carefully chosen stratification schemes for the delineation of functional types within landscapes may help in reducing the aggregation loss of information on temporal variability. Temporal patterns can best be described by using process models.

3. Spatial and temporal resolution of current emission inventories and CTMs 3.1. Emission inventories In the previous sections we discussed a number of major problems that occur during the process of scaling-up data using different approaches on different scales. In this section we will present a number of global and regional inventories for selected trace gas species and sources of emissions which have been developed for scientific purposes. We will not discuss these

Towards reliable g l o b a l estimates o f emissions o f trace gases a n d aerosols

17

Table 3. Global inventories of emissions of trace gases and aerosols from aquatic and terrestrial ecosystems for a number

of gas species with a spatial resolution of 1o • 1o longitude-latitude representative for the period around 1990. Category

CO 2

CH4

N20

NO•

NH3

1 (m)

2 (m)

3 (h-d)

4 (y)

5 (y) 6(m)

2 (m)

CO

VOC

S/SO• Aerosols Black carbon

Land-use related sources

Crops, fertilized fields Animals (including enteric fermentation, animal waste) Biomass burning (including waste and fuelwood combustion Deforestation Post-clearing effects Landfills

7 (y)

7 (y)

7 (y)

2 (m)

4 (y)

7 (y)

4 (y)a

8 (y)

__

b

4(y) 2(m) 7 (y)

Natural sources

Soils under natural vegetation (including wetlands) Natural vegetation Oceans Lightning Volcanic activity

9 (y) 6(m)

2 (m)* 3 (d/m)3. 4 (y)* 10(h/m)*

11 (m)*

8 (y)

4 (m) 12(m)

8 (y) 8 (y)

13 (y) Wind erosion

14 (m)d*

The reference is indicated by a number and the temporal resolution in parenthesis by y (year), s (season), m (month) or h (hour). Inventories marked with an asterix (*) are model based; all other inventories are based on emission factor approaches. References: 1, Matthews et al. (1991); 2, Bouwman and Taylor (1996); 3, Yienger and Levy (I995); 4, Bouwman et al. (1997); 5, Lerner et al. (1988); 6, Fung et al. (1991); 7, Olivier et al. (1996); 8, Spiro et al. (1992); 9, Matthews and Fung (1987); 10, Guenther et al. (1995); 11, Nevison et al. (1995); 12, Lee et al. (1997); 13, Benkovitz and Mubaraki (1996); 14, Tegen and Fung (1995). a Inventory based on estimates of burnt dry matter burnt can also be used for other gases. b Inventory could be based on Bouwman et al. (1997). c Inventory is in fact based on emission factors for biomes coupled with a mechanistic model to produce temporal patterns of fluxes. d Soil dust emissions and transport are simulated on the basis of GCM-based wind fields.

inventories on the country or provincial (subnational) scale being prepared for non-scientific purposes (e.g. national communications in the United Nations Framework Convention on Climate Change). The inventories listed in Tables 3 and 4 represent data for the early 1990s or late 1980s. These lists are not intended to be complete but merely to illustrate the current "state-of-the-art" emission inventories. We have not presented earlier work, assuming that the methodology of early inventories is incorporated into the more recent ones. Some of the global inventories were based on regional data or inventories, and their spatial and temporal resolutions are not lower than those in the regional inventories. The reported spatial resolution for most regional and global inventories is 1~ 1o (Table 3). However, in many cases the real spatial resolution is much lower. For example, when inventories are based on the emission factor approach for vegetation types or biomes, the spatial detail is the biome and not the grid size. Emission factor approaches were used in many inventories, including all those for CH4, VOC, NO• and NH3. As discussed above, some of these inventories use simple rules or models to distribute fluxes over time. The most common temporal resolution of the inventories is one year. Some inventories have a monthly distribution; the inventory of NO• fluxes from soils has a temporal resolution of one day. This database was compiled by using the emission factor approach combined with

18


Table 4. Regional and continental inventories that include land-use related and biogenic emissions of a number of gas

species with different spatial and temporal resolutions. Region North America Europe

Europe, Russian Federation, United States of America Europe

Species/sources

Spatial scale

Temporal scale

Reference

CO, CH4, VOC, NOx. NH3, SO2, HCI for all known sources SO2, NOx, NH3, NMVOC, CH4, CO, N20, CO2 for all known sources SO2, NOx, NH3, NMVOC, CH4, CO, NzO , CO 2 for all known sources SO2, NO• NH3, VOC. CO for all known sources

80 • 80 km

h

1

Nuts regions, converted to 50• km grids + point sources 50• km grids + point sources

ya

2

ya

3

2~215 ~ grids (Ion. • lat.)

Ya

4

The temporal resolution is indicated by y (year), or h (hour). References: 1, EPA (1993); 2, EEA (1997); 3. UN (1995); 4. Veldt et al. (1991). a with time profiles for conversion to monthly or shorter time periods

a simple model based on temperature and precipitation data from one particular GCM. Some regional inventories include rules for distributing emissions in time, for example, on a daily or hourly basis (Table 4). National inventories will be produced in the framework in the IPCC Methodology for National Inventories. Most of these inventories will be compiled on the basis of default annual emission rates, as measurement data are not available in most countries. This temporal resolution of one year is similar to that of most of the global inventories.

3.2. Atmospheric models It is difficult to be definite about the current state of the art in CTMs since they continue to be developed as scientific understanding grows and as computers increase in soeed and capacity. Meteorological data with a time resolution of 1-6 h are typical of data used, while the spatial resolution in the models is typically a few degrees latitude and longitude. Models have typical runs of a few seasonal cycles: this is considered a mere snapshot when used for climate calculations. Model processes are usually handled with the same spatial and temporal resolution as the meteorological processes. It is important for two main reasons to accurately assess the trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere in CTMs. Firstly, CTMs need to describe these trace gas fluxes realistically so as to accurately assess the trace gas life cycle on the global or regional scale. Secondly, the CTMs may need to give an accurate representation of the trace gas flux for a particular ecosystem or region. In the first case, the spatial distribution of the flux may not be so crucial but it is important to achieve the correct total burden. In the second case, the flux to particular sensitive ecosystems may be a more important variable in the model than the total global flux. In considering model estimates of trace gas fluxes to terrestrial and aquatic ecosystems and their unce~ainties, there are a number of issues to consider. The CTM needs to describe the transport of the trace gas to the ecosystem and to present the trace gas to the ecosystem at the correct concentration level and on the correct time scale. Clearly, the greater the distance travelled from the point of emission and the smaller the area of the ecosystem, the greater the associated uncertainty. For regional-scale transport close to the planetary boundary layer, current CTMs should produce concentrations that are within the range o f - a factor of 4 or more for primary


19

pollutants as monthly or seasonal averages in flat terrain 10-100 km downwind of sources (Jones, 1986). However, trace gas fluxes may often involve some form of chemical processing in the atmosphere downwind of the point of emission, which may contribute considerable additional uncertainty in modelled trace gas fluxes. Figure 3 illustrates some of the issues on validation of current generation CTMs against observational data for the short-lived trace gas, sulphur dioxide (SO2). The figure shows the annual average model SO2 concentration for the 5~ • 5 ~ grid square covering much of England along with the monthly mean observations for 19 monitoring stations. On this scale, there is significant spatial variability between the individual measurement sites, which in itself covers a range of up to a factor of 8. Such a range is likely to be significantly larger than the uncertainty in emissions. Furthermore, variability is significant at a finer time resolution e.g. daily or hourly. The uncertainty in coarse-resolution CTMs operating at 5 ~ x 5 ~ which approximates the state of the art CTMs, is likely close to a factor of 4 up and down for short-lived trace gases with significant ecosystem sources and sinks and existing in a complex terrain. The representation of the trace gas exchange processes in the CTMs at the ecosystem scale will introduce further uncertainties, the magnitudes of which are crucially dependent on the nature of the exchange process involved. Dry deposition processes are thought to be the simplest processes representing the concept of a dry deposition velocity. In this way, many of the problems of scaling trace gas fluxes can be side-stepped with a simple parameterization. Clearly, there is a huge gap in scale between the available dry deposition studies on the leaf or canopy scale and the coarse grid squares of the CTM. Wet deposition is a sporadic process which is difficult to describe adequately in models. The coarse spatial resolution of the models is certainly an issue but perhaps more important is their neglect of the detailed microphysical and chemical processes thought to be occurring in rain clouds. Simulated global- or regional-scale wet deposition fluxes are available with reasoSOa concentration

(ppb)

25

20 -

25 STOCHEM Ladybower Sunderland Newcastle Liverpool Leicester

-- -O-- ~ -- ~ ..... --t- ..... ...... x- .....

- -+-- --x-- - -0-- -- ~ ..... al$ ..... ...... [ 3 .....

..... O ..... Swansea

Bridge Race Bloomsbun/ Bamsley Leeds Birmingham East Southampton

...... 9..... Middlesborough

- -11(-- -- -q3- - -U-- ~ ..... < ) ..... ...... ~ , .....

Lullington Heath Birmingham Cardiff Bristol Hull Bexley

9 t~.3' ..-]r

-20

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~" I I

15-

10-

N

,.',_ 9 ~9 . \ >*.'-\ ~. i i .

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.__ .

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.

..........

9 '. ~_..

. .. ~ l i .. .

. .Ik.^ .

i

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o..--. r F ' . : , . . . o . . . - " / i ".;~:.~".....-'" . # i . .". i " ~.. . - ' ~

~

.....i ~ . . . .~: ~

=-.-'~-:~'~--i-='.C_

i

Jan

I

Feb

~ i

Mar

" ~ - .=~ I

Apr

I

May

i

i

Jun

Jul

7-

~_.._ -~-I

Aug

I

Sept

i

Oct

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-

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-5

"'_-,m a:.'~.~-~," -~=~':":'-"~-.'='0

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i

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Figure 3. Simulated concentration of SO2 using the STOCHEM model for the 5 x 50 grid square covering most of the U.K. and monthly mean observations for 19 monitoring stations. Source: Stevenson et al. (1998).

20


nable accuracy, but this accuracy deteriorates as spatial scales decrease to the catchment or landscape scales. Topography is a crucial factor in driving the orographic enhancement of wet deposition. In coarse-resolution CTMs the topography of all but the highest mountain ranges is necessarily averaged out, thus removing a major influence on model wet deposition fluxes to sensitive catchments. There is a consequential reduction in model estimates of cloud water deposition as the topography is smoothed out by model spatial resolution. Trace gas exchange with terrestrial and aquatic ecosystems is not always a one-way process, as emission and resuspension may occur simultaneously (see Conrad and Dentener, 1999). Ammonia emissions are difficult to represent accurately in models because they are sporadic and depend on local factors, which are highly variable. Soil moisture and animal husbandry are two such factors which are difficult to be specific about, but which have a significant influence on ammonia emissions (Bouwman et al., 1997). Resuspension of seasalts and wind-blown dust is often driven by high winds, which can be adequately represented in CTMs. However, the state of the terrestrial surfaces, whether wet or recently ploughed, may have a pronounced influence on resuspension, and these local factors are not often welldefined on the coarse scales used in the CTMs.

3.3. Comparison of CTMs with emission inventories With the exception of the spatial resoh".:.on of the emission inventories which meet the requirements of current CTMs, there are major inconsistencies to remain between the CTMs and the emission inventories which drive them. The most striking discrepancy between CTMs and inventories is in the temporal scale, which is generally one year for the inventories and 16 hours for the CTMs. Most CTMs include routines based on hypotheses on temporal flux distributions at the model scale, or assumptions on temporal patterns are provided with the emission inventories (see Table 4). Another way is to incorporate the trace gas flux model in the atmospheric model, as done for example in some CTMs for NOx from soils. For reactive species with short atmospheric lifetimes such as NH3, NOx and VOC, the temporal scale gap is a more serious problem than for long-lived species. An additional gap between inventories and CTMs is the number of VOC species; here, some of the mechanisms describing the chemistry in CTMs require a much larger number of species than included in current inventories. A general major problem is that it is not always possible to ensure that consistent land use and meteorological data are used throughout the modelling system including the emission inventorie~. Furthermore, there are scaling problems with all aspects of CTI~ input data, some of which are caused by limited computer resources, others by the focus of the modelling system and yet others by lack of current understanding. Turning to validation of emission inventories, the emission fields for long-lived trace gases can be tested using CTMs on the basis of concentrations, trends, and seasonality and spatial gradients of concentrations, as the chemistry is less crucial for long-lived species with fewer fluctuations over the year. For other species, deposition rates can be used to validate model results. A discussion of validation tools is, however, outside the scope of this paper. We refer to Heimann and Kaminski (1999) for a review of inverse modelling and atmospheric monitoring networks, Trumbore (1999) for a review of the use of isotopes and tracers in validation and scaling of trace gas fluxes, and to Sofiev (1999) for a discussion on validation and representativeness of measurement data. A review of the use of remote sensing techniques to determine atmospheric concentrations is given by Burrows (1999).


4. Conclusions

and

21

recommendations

A comparison of the spatial and temporal scales of the present state-of-the-art CTMs and emission inventories for terrestrial and aquatic ecosystems indicates a wide gap when it comes to temporal resolution. The most common temporal resolution of emission inventories is one year, while CTMs describe processes with a time step of 1 to 6 hours. This discrepancy is particularly" important for gas species with a short atmospheric lifetime (less tt, an one day). It should be possible to produce estimates for most species and sources with a greater temporal resolution. However, the key problem involved in increasing the temporal resolution is the sparsity of data for use as a basis for flux estimates and a lack or even absence of independent data to validate fluxes. Available data may be appropriate to validate the temporal variability or the functional relationships between environmental conditions and fluxes. In general, it becomes increasingly difficult to find tools for validation as the level of detail of the temporal scale increases. In some cases such data are inadequate or even absent (e.g. deposition fluxes, concentrations of short-lived species). The spatial resolution of inventories in our review suggests the level of detail as being adequate for current CTMs. However, the real spatial resolution of most inventories is much lower than suggested by the 1~ reported. This is caused by the use of emission factors for biomes and functional types, and by the uncertainty and resolution of the environmental spatial data used. The major recommendations following from the examples discussed can be summarized as follows: - E m i s s i o n factor approaches. Where emissions are described with emission factor or regression approaches, variability can be used instead of the usual practice of averaging out the heterogeneity. This is done, for example, by presenting frequency distributions for regions or functional types, or the standard deviation for grid boxes,. In many cases the point-by-point uncertainty is not known. However, even the indication of the maximum and minimum values could be more helpful than the mean alone for sensitivity and quantitative uncertainty analysis. - F l u x m o d e l s . Flux models should be used where possible to replace traditional emission factor approaches. Firstly, models, which are descriptions of current process knowledge, are preferred above simple rules such as those applied in CTMs to produce temporal distributions. Secondly, intemal consistency of CTMs is improved by incorporating the flux models. In trace-gas flux models there is often an imbalance between the level of detail by which different processes are described. The relationship between scale, the model approach and the model parameters selected is very important in this respect. On a higher scale the data availability, generally poses a problem when using detailed process models. In this case, simplified or summary models are expected to interpret field experiments with limited information. The aim of simplifications is to make the model applicable to a wider area with limited data sets. Developing such ranges of models from the micro-scale to field scale and summary models to be used for extrapolation to other sites with different conditions is extremely useful. Summary models will also help to develop a better understanding of how to select the key variables to be used for specific scales. - Environmental d a t a . The spatial data on climate, oceans, soils, land cover and land use which are commonly used as a basis for scaling of trace fluxes have four general characteristics: (i) their uncertainty is regionally variable but generally unknown in the spatial distributions; (ii) data classifications are always aggregations (iii) classifications used are generally not easily translated into other classifications; (iv) classifications cannot be easily translated into properties or regulating factors of trace gas fluxes. In view of these

22

A.F. Bouwman, R. G. Derwent and F.J. Dentener

characteristics the use of common databases should be promoted. Geographic databases coupled with attribute files for the various map units distinguished is one way to at least describe the heterogeneity of the properties within a class. Examples of this approach are the soil database and the land-cover characterization discussed in this paper. Combining vegetation/land-cover data with climate and soil information may provide a basis for classification according to function. Finally, there is a need for compensation and recognition of so-called data collectors to encourage continued critical data collection, harmonization and analysis. - Functional types. Where distinct and easily identified differences in structure and composition of aquatic and terrestrial ecosystems coincide with the functions or management conditions relevant to trace-gas fluxes at the scale considered, the delineation of functionally different types or production/management systems provides a useful basis both for measurement strategies and scaling. Appropriate selection of classes may lead to reducing the number of sites to be sampled so as to derive a reliable flux estimate. Maps provide a useful basis for delineation, and in recent years remote sensing of ecosystem characteristics has been used increasingly for classification and modelling (see Estes and Loveland, 1999). Such approaches use the variability of a system or landscape instead of ignoring it, sometimes with unexpected consequences. It is very important to select appropriate stratification schemes for functional types, both for the scale of the exercise and the available spatial data. - Aggregation. Aggregation always leads to a loss of information. The variability in space is reduced and the uncertainty in the temporal patterns is increased by spatial aggregations. The problem of errors in temporal distributions as a result of spatial aggregation can be reduced by delineating functional types within a system. Scaling based on delineations with finer spatial data may be different from that derived from data with lower resolution. In general, it is better to aggregate model results than to aggregate the spatial data before modelling. Aggregation in the form of delineation of functional types as a basis for scaling generally decreases the uncertainty, and allows one to determine the uncertainties as discussed above. - Interannuai variability. Some processes in terrestrial and aquatic ecosystems show considerable year-to-year variation. Hence, in such systems with large interannual variability, inventories representing the long-range average have less value than time series of flux estimates. This paper has reviewed the uncertainties in estimating emissions from land-use-related, and natural terrestrial and aquatic sources. A comparison has also been drawn up between the available inventories and the requirements of state-of-the-art CTMs. We have shown a number of weaknesses and problems in current methods for estimating emissions. We have also presented several possibilities for improving flux estimates, hoping that these recommendations will stimulate further study and discussion on the reduction of uncertainties in flux estimates.

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23

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25

Levitus, S. and T. Boyer (1994a) Worm Ocean Atlas 1994, Volume 4: Temperature. NOAA Atlas NESDIS 4, US Department of Commerce, Washington DC. Levitus, S. and T. Boyer (1994b) Worm Ocean Atlas 1994, Volume 2: Oxygen. NOAA Atlas NESDIS 2, US Department of Commerce, Washington DC. Levitus, S., R. Burgett and T. Boyer (1994) Worm Ocean Atlas 1994, Volume 3: Nutrients. NOAA Atlas NESDIS 3, US Department of Commerce, Washington DC. Li, C., S. Frolking and T.A. Frolking (1992a) A model of nitrous oxide evolution from soil driven by rainfall events: I. Model structure and sensitivity. Journal of Geophysical Research 97:9759-9776. Li, Y. and J.S. Chang (1996) A three-dimensional global episodic tracer transport model 1. Evaluation of its transport processes by radon 222 simulations. Journal of Geophysical Research 101:2539125947. Loveland, T.R., D.O. Ohlen, J.F. Brown, B.C. Reed, Z. Zhu, J.W. Merchant and L. Yang and J.W. Merchant (1997) Western hemisphere land-cover progress toward a global land-cover characteristics data base. Pecora 13 Prr~reedings, American Society of Pbotogrammetry and Remote Sensing, Bethesda, MD (in press). Matson, P.A., P.M. Vitousek and D.S. Schimel (1989) Regional extrapolation of trace gas flux based on soils and ecosystems. In: M.O. Andreae and D.S. Schimel (Eds.) Exchange of trace gases between terrestrial ecosystems and the atmosphere. Wiley and Sons, Chichester, pp. 97-108. Matthews, E. (1983) Global vegetation and land use: New high-resolution data bases for climate studies. Journal of Climate and Applied Meteorology 22:474-487, Matthews, E. and I. Fung (1987) Methane emission from natural wetlands: Global distribution, area, and environmental characteristics of sources. Global Biogeochemical Cycles 1:61-86. Matthews, E., I. Fung and J. Lerner (1991) Methane emission from rice cultivation: Global distribution, area, and environmental characteristics of sources. Global Biogeochemical Cycles 5:3-24. Mosier, A.R. and W.J. Parton (1985) Denitrification in a shortgrass prairie: a modeling approach In: D.E. Caldwell, J.A. Brierley and C.L. Brierley (Eds.) Planetary Ecology. Van Nostrand Reinhold Co., New York, pp. 441-451. Moxim, W.J., H. Levy, H. and K. Kasibhatla (1996) Simulated global troposheric PAN: Its transport and impact on NOx. Journal of Geophysical Research 101:12621-12638. Mtiller, J-F. and G. Brasseur G. (1995) IMAGES: A three-dimensional chemical transport model of the global troposphere. Journal of Geophysical Research 100:16445-16490. Nevison, C.D., R.F. Weiss and D.J. Erickson III (1995) Global oceanic emissions of nitrous oxide. Journal of Geophysical Research 100:15809-15820. Olivier, J.GJ., A.F. Bouwman, C.W.M. van der Maas, J.J.M. Berdovski, C. Veldt, J.P.J. Bloos, A.J.H. Visschedijk, P.Y.J. Zandveld and J.L. Haverlag (1996) Description of Edgar Version 2.0: A set of global emission inventories of greenhouse gases and ozone depleting substances for all anthropogenic and most natural sources on a per country basis and on l~ ~ grid. Report 771060002, National Institute of Public Health and the Environment, Bilthoven, 141 pp. Olson, J., J.A. Watts and L.J. Allison (1985) Major world ecosystem complexes ranked by carbon in live vegetation: A database. Oak Ridge National Laboratory, Oak Ridge, TN, 164 pp., NDP-017. O'Neill, R.V. (1988) Hierarchy theory and global change. In: T. Rosswali R.G. Woodmansee and P.G. Risser (Eds.) Scales and global change, Wiley and Sons, Chichester, pp. 29-45. Pedersen, S., H. Takai, J. Johnsen and H. Birch (1996) Ammoniak og stov i kv~eg-, svine- og fjerkr~estalde II. Internal Report No. 65, Danish Institute of Animal Science, Horsens, Denmark, 54 PP. Penner, J.E., C.S. Atherton and T.E. Graedel (1994). Global emission~ and models of photochemically active compounds. In: R.G. Prinn (Ed.) Global Atmospheric-Biospheric Chemistry. Plenum Press, New York, pp. 223-247. Rodhe, H., J. Langner, L. Gallardo, and E. Kjellstrom, (1995). Global scale transport of acidifying pollutants. Water, Air and Soil Pollution 85:37-50. Roelofs, G-J. and J. Lelieveld (1995) Distribution and budget of 03 in the troposphere calculated with a chemistry general circulation model. Journal of Geophysical Research 100:20983-20998.

26

A.F. Bouwman,R.G. Derwentand F.J. Dentener

Schimel, D.S., W.J. Parton, F.J. Adamsen, R.G. Woodmansee, R.L. Senft and M.A. Stillwell (1986) The role of cattle in the volatile loss of nitrogen from a shortgrass steppe. Biogeochemistry 2:39-52. Seitzinger, S., J.-P. Malingreau, N.H. Batjes, A.F. Bouwman, J. Burrows, J.E. Estes, J. Fowler and R.L. Lapitan (1999) How can we best define functional types and integrate state variables and properties in time and space? In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 151-167. Sofiev, M. (1999) Validation of model results on different scales. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp.233-255. Spiro, P.A., D.J. Jacob and J.A. Logan (1992) Global inventory of sulphur emissions with l~ ~ resolution. Journal of Geophysical Research 97:6023-6036. Stevenson, D.S., C.E.Johnson, W.J.Collins and R.G. Derwent (1998) .Three-dimensional (STOCHEM) model studies of regional and global scale formation of troposheric oxidants and acidifying substances. Turbulence and Diffusion Note No." 246, Atmospheric Processes Research, Met Office, Bracknell, UK. Tegen, I. and I. Fung (1995) Contribution to the atmospheric mineral aerosol load from land surface modification. Journal of Geophysical Research 100:18707-18726. Trumbore, S. (1999) Role of isotopes and tracers in scaling trace gas fluxes. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 257-274. UN (1995) Strategies and policies for air pollution abatement. 1994 major review prepared under the convention of long-range transboundary air pollution. Report ECE/EB.AIR/44, United Nations/Economic Commission for Europe, Geneva, 138 pp. Unesco (1973) International classification and mapping of vegetation. Unesco, Paris, 93 pp. Veldkamp, J.G., V.F. Van Katwijk, W.S. Faber and R.J. Van De Velde (1996) Enhancements on the European land use database. Report 724001001, National Institute of Public Health and Environmental Protection, Bilthoven. 62 p. Veldt, C. (1991) Emissions of SOx, NOx, VOC and CO from East European countries. Atmospheric Environment 25A:2683-2700. Wagenet, R.J. and J.L. Hutson (1995) Consequences of scale dependency of chemical leaching models. In: A.L. EI-Kadi (Ed.) Groundwater models for resources analysis and management, CRC Press, Boca Raton FL, pp. 169-184. Wauben, V~.M.F., P.F.J. Van Velthoven and !,. Kelder (1997) A3D chemistry trar.sport model study of changes in atmospheric ozone due to aircraft emissions. Atmospheric Environment 31:18191836. Yienger, J.J. and H. Levy II (1995) Empirical model of global soil-biogenic NOx emissions. Journal of Geophysical Research 100:11447-11464.

Chapter 2

M E T H O D S F O R STABLE GAS FLUX D E T E R M I N A T I O N IN AQUATIC AND T E R R E S T R I A L SYSTEMS



Approaches to scaling a trace gas fluxes in ecosystems A.F. Bouwman, editor 1999 Elsevier Science B.V.

METHODS FOR STABLE GAS FLUX DETERMINATION IN AQUATIC

AND TERRESTRIAL SYSTEMS

R.L: Lapitan l, R. Wanninkhote and A.R. Mosier 3 1Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523 USA 2 National Oceanographic and Atmospheric Administration, Miami, FL 33149 USA 3 Correspondence to: U.S. Department of Agriculture, Agricultural Research Service, P.O. Box E, Ft. Collins, CO 80522 USA


Despite the world's keen awareness of the potential global warming effects of greenhouse gases, atmospheric loading of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20) from anthropogenic sources, and aquatic and terrestrial components of the biosphere continues at very high rates. Estimates made in 1994 showed increases of 1.6 ppmv yr -1 8.0 ppbv yr -1 and 0.8 ppbv yr -1 for CO2, CH4 and N20, respectively (Houghton et al., 1995). The present atmospheric concentrations of these gases and rates of increase could lead to irreversible climate change. To increase our confidence in projections of trace gas increases we must improve mass balance accounting of sources and sinks of these gases, on a large scale (> 1 km). The uncertainties in the estimates can be attributed to the wide spatial and high temporal heterogeneity at the surface (e.g. soil, vegetation, water) - atmosphere interface, inadequate accounting, and assessments of the source-sink strengths of these gases (Bouwman, 1990). The problems contributing to the latter factor are the unavailability of sensitive analytical devices for field measurements of trace gas fluxes, lack of effective sampling design for reducing variabilities in point measurements, and lack of proven mechanistic tools for reconciling flux measurements taken at different spatial and temporal scales. Additionally, it should be pointed out that existing models, used for extrapolating small-scale fluxes to regional and/or global scales, contained intrinsic uncertainties in their assumptions, parameterization, and analytical/numerical representations of the control processes that can further magnify the uncertainties of estimates. The primary considerations in the choice of method for measuring gas fluxes include the objectives of the study, type of ecosystem under study, cost, infrastructure, and logistical support, gas species in question, and availability of precise analytical instruments having appropriate response rates for measuring gas fluxes. Fluxes of trace gases from terrestrial systems have been measured by enclosure or micrometeorological techniques. Because of the enclosure and limited spatial resolution of the closed-chamber method it was found suitable for detecting small fluxes of trace gases (e.g. N20), studying processes, and identifying sources of spatial variations controlling gas fluxes (Hutchinson and Mosier, 1981; Mosier, 1989; Livingston and Hutchinson, 1995). Micrometeorological methods provide nondestructive, integrated measurements of gas fluxes over large areas, but generally require large, uniform fetch. Tower-based and airborne eddy flux correlation methods require expensive fast-response sensors and logistical support. Depending on the gas species in

30


question, analytical instruments must be sensitively accurate to detect one-tenth of the mean concentration difference between updrafts and downdrafts for typical CO2 (-2x 10.6 kg m 2 s-l), CH4 (1 x 10-9 kg m -2 s-l), and N20 (4x 10-l~ kg m -2 s-1) fluxes (Denmead, 1979; Desjardins et al., 1993). It should be pointed out that the measured range of fluxes of CO2, CH4 and N20 gases vary among terrestrial ecosystems (Table 1). Thus, depending on the vegetation and physiographic characteristics of the system, accompanying analytical methods must be able to detect the wide range of fluxes of the gas species desired. The field methodologies for measuring trace gas fluxes in terrestrial systems have been developed and have changed little for the past 15 years. The strategies of field sampling and computational procedures for deriving trace gas fluxes using these methodologies have been well documented; such as, for chambers (Mosier, 1989; Hutchinson and Livingston, 1993; Livingston and Hutchinson, 1995), micrometeorological (Fowler and Duyzer, 1989; Denmead and Raupach, 1993; Desjardins et al., 1993), and aircraft-based (Desjardins and MacPherson, 1989; Desjardins, 1992; Desjardins et al., 1993; Choularton et al., 1995). More recent advances made in surface and atmospheric trace gas flux measurements were seen with the employment of fast-response high resolution spectrometers, such as the tunable diode laser differential absorption (TDL) and Fourier-transform infrared (FTIR) spectroscopy. Specifically, these sensitive spectrometers extend the capability of existing methods to detecting small in situ episodic fluxes and gradients of CH4 and N20 trace gases (Kolb et al., 1995). Hence, in this paper discussions of the methods of measuring gas fluxes in terrestrial systems are limited to providing a brief overview of the methods with examples taken from more recent studies employing advanced spectrometers. Descriptions of the individual methods are focussed on their merits and limitations as these would determine the suitability of the method's application to a system, given the extent of the spatial heterogeneity observed. The exchange of nonreactive trace gases between water or soil and the atmosphere are regulated by the same chemical, biological, and physical parameters. However some methods of measurement differ because aqueous systems are, in general, relatively more homogeneous than terrestrial systems and fluxes of scalar entities are often smaller. Mass balance approaches in the water column are often used to quantify air-water fluxes because micrometeorological techniques are difficult to employ. Refinements of the latter methodologies are continually being developed particularly in accurately estimating the gas transfer velocity. A fundamental treatment of the principles and governing equation of air-water gas exchange is helpful as a priori reference for discussions on how and wl~ere the different methods provide improvement in gas flux. The application of these methods to measuring gas fluxes, including problems and uncertainties, in aquatic systems is described in section 7.

2. Scales of spatial and temporal heterogeneities in gas flux measurements The scale of spatial heterogeneity in the landscape limits the extent of applicability of a method and validity of its assumptions for gas flux measurements. Since flux measurements of gases are one-dimensional, a priori knowledge of the extent of horizontal variabilities of the dynamic factors affecting surface-air gas exchange is important for accurate identification of sources of gas entities. Breaks in horizontal continuities of surface properties, spatial pattem of climate variabilities, and magnitude of gas exchanges between the surface and the atmosphere define the boundaries of natural systems. Types of land cover, mountain barriers, and sea-land transition account for the largest horizontal scale of variability in gas fluxes, extending to the order of > 1 km. It should be appreciated that gas flux measurements at this


31

scale are taken as the mean integrated response from the more commonly persistent features of the landscape. Atmospheric transport of gases encomp_'..ssing areas of this space scale follows large-scale atmospheric mixing phenomena, such as the vertical transport due to convective boundary layer flow, cloud mass flux, and synoptic-scale transport by trade winds and storm systems (Raupach and Finnigan, 1995). Around the transition edge between the land and marine environment, transport of gases to the atmospheric boundary layer is coupled with the heat and water exchanges associated with the cyclic land breeze - sea breeze system (Merrill, 1985). Qualitative in situ identification of gas sources has been made using hydrodynamic and radioactive tracers (Reiter, 1972;1978), but quantitatively, the temporal variations in gas concentrations and effects of atmospheric mixing (e.g., inversion) on the budget of gases in the atmospheric boundary layer can only be resolved using numerical models (Raupach, 1991; Raupach et al., 1992; Denmead et al.,1996). Within a region or at the ecosystem level, the composition and properties of the surface and surface cover align with the climate (i.e., temperature and precipitation) variabilities and land management systems. Horizontal gradient of scalar entities (e.g., heat, water vapor, and gas) can be of the order of 102 to 103 m depending on the persistence of uniform surface terrain, type of vegetation, and surface cover. Transport of gas entities downstream from the source is determined by the rate of turbulent mixing as wind blows steadily over the surface. In situ identification of the source and trajectory monitoring of gas fluxes can be accomplished using non-dispersive release gas (e.g., SF6) in conjunction with micrometeorological techniques and sensitive analytical devices (IAEA, 1992). The smallest scale of variability in gas flux measurements is of the order of < 1 m. The variations in gas flux at this space scale can be attributed to patchiness and type of ground cover, plant species composition, and differences in soil properties driving the biogeochemical processes effecting soil-air gas exchange. Transport of gas from the source can be laminar or turbulent depending on the prevailing wind field. In an unobstructed vegetative system, the sources and sinks of the gas can be inferred from the variations in gas concentration profile and vertical wind velocity within the canopy, such as following the inverse Lagrangian dispersion model developed by Raupach (1989). At this space scale, the use of enclosures permits isolation of the source of gas fluxes and eliminates the uncertainties associated with numerical calculations and estimates of the turbulent eddy transfer coefficients required by micrometeorological techniques. For reconciling flux measurements at various scales of heterogeneity, a flux footprint which describes the contributions, per unit emission, of each element in the area observed by the sensor located at a fixed height above the surface has been suggested (Shuepp et al., 1990; Horst and Weil, 1992). The flux footprint is determined by the surface properties (e.g. roughness length, vegetation height), wind speed, and atmospheric stability. Flux footprint can be obtained analytically (Schuepp et al., 1990) or numerical modelling approach (Leclerc and Thurtell, 1990; Horst and Weil, 1992); either approaches provide closely similar footprint estimates (Hargreaves et al., 1996). The estimated flux footprints for the different methods of measuring gas fluxes are given in Table 2. By weighting the area-integrated flux with the flux footprint, the sources of spatial variations in gas flux measurements from micrometeorological measurements can be identified (Hargreaves et al., 1996), potential errors and differences between micrometeorological methods can be determined (Wienhold et al., 1995), and comparative analysis between chambers and integrative methods of measuring gas fluxes can be made (Christensen et al., 1996). The temporal fluctuations in ambient atmospheric conditions can be as significant a source of uncertainties as the surface horizontal heterogeneities in gas flux measurements. Sudden,

Table 1. Annual atmospheric inputs(-)/sequestration (+), and typical observed fluxes of CO2, CH4 and N 2 0 from terrestrial and aquatic ecosystems. Numbers in parenthesis and Superscripted indicate references c. Ecosystems

Terrestrial Ecosystems - net emissions

from tropical land use - (uptake - respiration) Temperate croplands

Grassland Tropical Temperate (moist) Shortgrass Tallgrass b Pasture Savannas Tropical forest Temperate forest Tundra

Desert

CH4

COz Annual Input (1012kg y-I)

Method a

Net flux (10 "6 kg m "2 s "l)

Annual input (1012g y-l)

Method a

N20 Flux

Annual input

(10-11kg m-2 s-i)

(1012g yr-i)

Method ~

Flux (10-1 ikg m-2s-l)

-4.03 (34) 5.13 (34) A C C

0.12-0.18 (3~) 0.47-0.64 (31) 0.21-0.85 (38)

A C C A C

-0.001 - -0.14 (36) V 001-0.09 (37) S -0.02- -0.11 (37) 0.53-0.65 (3~) 0.69-0.97 (31)

0.7 (24)

C

0.017 (32)

3.2 (23)

A

0.02-0.14 (17)

A A

3.3-4.2 (26`27) 0.6-1.0 (23)

-1.4- -14.0 (l'2"s) -0.02- -0.16 (16'23)

A

-0.04- -0.12 (17)

A A

-0.003- -1.8 (28'29'3~ -0.03- -0.07 (23)

C

-4.0 (2) ,,.,.

5.6 (5) 0.4_17.1 (11) 0.4_5.6 (1~) - 10- -3 8 (19'20'21"22)

6.2 (12)

1.4 (5)

A A C D E A

0.2-0.7 (1~ 0.3-4.4 0~ -28.9(19) -57.8 (19) - 7 0 . 0 (3)

1.4(5)

t~

r~

Table 1. Continued. Ecosystems

CO2 Annual Input (1012kg y-i)

Method

CH4

Net flux (10 -6 kg m -2 s-I)

Annual input (1012g y-J)

Method

N20

Flux

Annual input

(10-1tkg m-2 s-l)

(1012g yr -I)

Method

Flux (lO-llkg m-2s-I)

Aquatic ecosystems Rice paddies

- 100(4`5,9)

Natural wetlands/ Swamps/marshlands Lake Oceans

-3 5- -84 (t2) C 7.3 (34)

-0.13

- 1- -4 (~) -0.005- -0.05 (34)

A,C C A

-361- -1100 (7'8) -600- - 7 0 0 0 (33) -11.1- -600 (~)

,..,.

-5.5- -600 (~) -0.004-

- 0 . 0 1 6 (34)

aA, closed chamber, B, open chamber, C, micrometeorology, D, aircraft-based sensors and E, convective boundary layer budget. Positive values of annual input and fluxes indicate u~take and negative values indicate emissions of the gas. v, vegetative stage, s, senescent stage. el, Denmead et al. (1979); 2, Galle et al. (1993); 3, Ritter et al. (1992); 4, Denmead (1993); 5, Seiler et al. (1984); 6, Cicerone and Oremland (1988); 7, Schtitz et al. (1989); 8, Sass et al. (1990); 9, Lauren and Duxbury (1993); 10, Keller et al. (1986); 11, Schtitz et al. (1990); 12, Aselman and Crutzen (1989); 13, Harris et al. (1982); 14, Houghton et al. (1983); 15, Lauenroth and Milchunas (1992); 17, Bronson and Mosier (1993); 19, Fan et al. (1992); 20, Mathews and Fung (1987); 21, Whalen and Reeburgh (1988); 22, Whalen and Reeburgh (1990); 23, Mosier et al. (1996); 24, Sommerfeld et al. (1993); 25, Houghton et al. (1992); 26, Steudler et al. (1989); 27, Crill (1991); 28, Schmidt et al (1988); 29, Bowden et al. (1990); 30, Brumme and Beese (1992); 31, Dugas et al. (1997); 32, Baldocchi et al. (1997); 33, Simpson et al. (1995); 34, IPCC (1995); 35, Meyers et al. (1996); 36, Mosier unpublished data; 37, Ham and Knapp (1997); 38, Ruimy et al. (1995).

r~

r~

k~

34


Table 2. General characteristics of the current methods and corresponding formulations for calculating surface gas fluxes in terrestrial systems. Method

Footprint a (m)

Maximum instrument frequency response a'b (Hz)

Formulations for calculating gas flux (F)

Closed chamber

1

1 x 10 .3 - 3 x 10 .3

F=

Open-top chamber

1

1 x 10 .3- 3 x 10 .3

F = J (Cg(o) - Cg(i ))

Chamber

V dCg

Adt A

Mass balance

20

3 X 10 -3

1

F=

Xo Micrometeorological Eddy correlation

2 0 0 - 1 x 103

2.5 - 10.0

F=w'Cg'+ Cg p.

Flux gradients

2 0 0 - 1 x 10 3

2 X 10 -2

F=- K2

rl

1+q[3

E+

u

H c,T

du dC~ d[ln(z-d)] d[ln(z-d)] *-~'~hl

or

F =-

~cU.z dCg ~h dz

Eddy accumulation/ Eddy relaxation

2 0 0 - 1 x 10 3

Energy balance

200 - 1 xl 03

CBL budget Aircraft

3 x 10 .3

F=b(Cg -Cg) F=(R.~,-G ~Cg-C-g ) Cp(I+YXT§

3 x 104

2 x 10 .2

5 X 10 3 - 1.5 x 10 4

10.0

200

2 X 10 -2

-)

F = z L dCL - (CL+- C O)

dt

dZL.. dt

Non-isotopic tracer release ( S F 6 )

F=F~.~.6 "

dC~

dSF~

a Adapted from Denmead (1994). b Sampling rate for automated systems.

infrequent wind gusts can induce counter-gradient transport of gases, heat, and water vapor, and which can provide the greatest uncertainty in flux gradient / Bowen ratio measurements over cropland (Finnigan, 1979; Shaw et al., 1983; Meyers and Paw U, 1987) and forest (Denmead and Bradley, 1987) systems. Strong pulses in surface-air gas exchange due to episodic changes ( < l h ) in atmospheric conditions, such as a heavy downpour, may be neglected because of restricted measurements during the event. Nitric oxide (NO) is particularly susceptible, with emission rates typically increasing immediately following small rainfall events (Martin, 1996). On the other hand, CH4 uptake rates in soil are not strongly affected by small, short-term changes in temperature and soil water status (Mosier et al.,


35

1996). Because of these periodic short-term events, the question remains on the minimum sampling period to consider in time averaging of measurements for effectively estimating the actual magnitude of flux for the specific gas species being studied. On a larger temporal scale, appropriate time averaging has to separately consider daytime from nighttime measurements due to differential flux directions associated with different biological mechanisms governing gas (e.g., CO2) exchange (Denmead et al., 1996) and different daytime and nocturnal vertical mixing processes occurring in the atmospheric boundary layer (Merrill, 1985).

3. Recent developments in analytical methods Gas chromatography has been and still is the main analytical method commonly employed in measuring gas concentration. Its sensitivity (detection limit) depends on the type of detector installed characteristic of the gas being measured (Table 3). Gas chromatography and other commonly used analytical methods such as non-dispersive infrared analysis for measuring atmospheric gases were reviewed comprehensively by Crill et al. (1995). For methods, such as chambers and conditional sampling, that do not require fast response analytical devices for measuring instantaneous gas concentrations, gas chromatography is most appropriate for analyzing CO2, CH4 and N20. Sampling of gases using chambers can be automated for continuous monitoring of surface gas fluxes, including CH4 and N20 fluxes from rice paddies (Schtitz et al., 1989; Bronson et al., 1997). In conjunction with micrometeorological methods, however, fast-response analytical devices are required to measure gas concentrations. Fan et al. (1992) coupled a flame ionization detector (FID) detector with a tower- and an aircraft-based eddy correlation method for measuring CH4 fluxes, considering the small variance (< 5%) obtained between sensible heat fluxes taken at 20 Hz (sampling frequency of the sonic anemometer) and at 8 Hz (sampling frequency of the FID detector). They noted, however, potential underestimation of the actual magnitude of CH4 fluxes by 10% due to large noise in the signal and inadequate sensitivity of the detector to resolve < 0.1 ppbv CH4 concentrations. For CO2, open- and closed-path infrared (IR) analyzers provide adequate sensitivity and time constant for flux measurements using eddy correlation (Leuning and Moncrieff, 1990; Leuning and King, 1992), as well as flux gradient analysis (Denmead and Raupach, 1993; Wagner-Riddle et al., 1996a). For CH4 and N20, in ecosystems where surface-air exchange of these trace gases are low and instantaneous fluctuations of gas concentrations at very fine temporal resolution are required, tunable diode laser (TDL), and Fourier-transform infrared (FTIR) spectrometers offer high resolution and time constant to detect gas concentrations at pptv levels in a second or fraction of a second (Table 3). An overview of these advanced spectroscopic instruments can be found in the literature (Kolb et al., 1995). An example of a TDL currently being used for N20 measurements provides, at 10 Hz sampling rate, an instrument drift of 1 Hz).

56


60

'~,''i _ "

40

'7 t _

>,

E

-Q

E

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w'c'

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Net Webb

Q

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Figure 7. Illustration of the corrections necessary to determine the CO2 flux over the ocean by co-variance using open path IR measurements. The solid squares are the measured co-variance signal w 'c ', the open triangles is the correction for heat and water vapor (the Webb correction), while the solid circles are the resulting CO2 flux. The CO2 fluxes are at the upper range encountered over the ocean. The results are based on preliminary observations during the ASGAMAGE experiment in 1996. (C. Fairall, 1996; personal communication)

Air-sea fluxes of CO2 by covariance techniques to date have been done with open path CO2 sensors. The signal is sensitive to water vapor by overlap of the CO2 and H 2 0 IR-absorption peaks, pressure broadening effects, and air density effects of H and XE on the concentration of the CO2 gas. This so-called Webb effect is typically an order of magnitude greater than the actual CO2 flux over the ocean. Figure 7 shows an example of the raw signal, the Webb effect, and the corrected signal for a recent study in the North Sea. In this case the environment was optimal for covariance measurements with very high CO2 fluxes. As the figure shows, even the sign of the corrected and uncorrected flux signal differ. At this point the air side measurements of CO2 and most other trace gases are only possible in situations with extreme partial pressure perturbation. Results within the realm of conventional geochemical constraints have been obtained for an experiment in the Great Lakes and on a tower in the North Sea (Figure 8). However, the values are about a factor of two higher than those inferred from relationships between the gas exchange and wind speed obtained from water side measurements. The question is, does there continue to be a systematic bias in the air side results or are the oceanic constraints invalid for the environment in which the air-side measurements were performed ? A way to get around the large Webb correction is to dry the gas prior to analysis. The drying necessitates a closed path sensor and lines leading from the intake near the micrometeorological sensors through the dryer and into the analyzer. Lag corrections become an important issue for the covariance and eddy accumulation measurements where a high frequency of response is necessary. The gradient method does not suffer from this constraint but spatial inhomogeneity along with the different footprint of the measurements at different heights can make the interpretation of the gradient method ambiguous.

57


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.

.

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.

.

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Figure 8. Compilation of eddy covariance measurements and controlled flux (heat) measurements versus wind speed. The solid circles are the results using heat as a proxy for gas exchange of Haussecker et al. (1995) normalized to k,600. Note, heat exchange will not be enhanced by bubble entrainment. The squares are results from work on Lake Ontario (Donelan and Drennan, 1995), and the triangles are the preliminary results from the North Sea tower work during ASGAMAGE (C. Fairall, 1996; personal communication). The two gas exchangewind speed relationships described in Figure 6 are added as reference.

An alternate method of performing direct flux measurements is the controlled flux technique in which heat is used as a proxy for a gas (Haussecker et al., 1995). In this method, the water surface is illuminated with an infrared source (controlled flux) and the dissipation of the temperature signal is measured with a sensitive infrared camera. Heat can, for these purposes, be considered a highly soluble gas with a Prandtl number (or Schmidt number for heat) of about 6. Since heat transfer is retarded in the air boundary layer, heat dissipation will occur through the water boundary layer and making the rate of dissipation through the aqueous boundary proportional to the air-water gas transfer. A summary of the measurements is shown in Figure 8. The future of gas flux measurements across the air-water interface clearly lies in the direct air-side measurements of gas fluxes. The short response time will elucidate the mechanism controlling the gas fluxes which may in turn facilitate robust extrapolations. Of particular interest are the possibilities to use remote sensing by active and passive radiometry to extrapolate gas transfer velocities and gas fluxes over large space and time scales. For process oriented studies, water side measurements will continue to be utilized. The major issues to resolve are the influence of bubbles and surfactants on air-sea gas exchange. The first step towards reconciling the apparent mismatch between the lower k values obtained with water side measurements compared to air side measurements is to perform these measurements sideby-side. The first such study has been performed as part of the European ASGAMAGE experiment from a stationary platform in the North Sea. Shipboard comparisons between deliberate tracer results and direct air-side measurements are planned for 1998 under the auspices of NOAA.


58

Acknowledgments Salary support by NOAA/ERL for RW, from the U.S. Trace Gas Network (TRAGNET) for RL and USDA/ARS for AM during the preparation of this manuscript is gratefully acknowledged.

Nomenclature basal area of the chamber (m 2) proportionality coefficient specific heat of air at constant pressure (J g-~K-I) gas concentration in air immediately above the water surface concentration in the headspace (gm -3) Cah CBL Convective boundary layer C+ air sample reservoir in the 'up' position air sample reservoir in the 'down' position Cgas concentration (g m 3) Cg gas concentration measured in the C+reservoir/ Cg+ Sensor (g m "3) gas concentration measured in the C- reservoir/ Cg Sensor (g m -3) Cg(b) background gas concentration (g m -3) Cgo) gas concentration in the air going into the Chamber (gm -3) Cg(o) gas concentration in the air going out of the Chamber (gm -3) upwind gas concentration (g m 3) mean scalar gas concentration in the CBL (g m -3) Cc mean scalar gas concentration just above the CBL CL+ (g mE 3) gas concentration near the water surface Cw d zero-plane displacement (m) aqueous diffusion coefficient (m s-l) D water vapor pressure (gm -2) e latent heat flux (J m-2sl ) E EC eddy correlation method air flow rate (m sl ) f gas flux (g m ~ S"1) F FG flUX gradient method Fs~6 flux of SF 6 (g m "2 s"1) FTIR Fourier-transform infrared spectroscopy soil heat flux (J mZs "1) G h depth of the water in contact with the atmosphere (m) hH height of the helmet (m) mean depth of the ocean (m) ho sensible heat flux (J m-2s-l) H k gas transfer velocity for a non-reactive gas (m st ) Kg eddy diffusivity for gas (-) KH eddy diffusivity for heat (-) eddy diffusivity for momentum (-) Km solubility (g m -3) Ko eddy diffusivity for water vapor (-) Kv A b Cp Ca

C%u)

pXw pXa r Rnet Sc SF6 t T T+ TTDL X u U. w z zo Ze

partial pressures/fugacities of the gas in water partial pressures/fugacities of the gas in air enhancement factor by chemical reaction of the gas at the air-water interface (-) total net radiation (J m2s l ) Schmidt number (-) sulfur hexafluoride time (T) air temperature (K) air temperature measured by the top sensor (K) air temperature measured by the bottom sensor (K) tunable diode laser absorption spectroscopy upwind fetch (m) horizontal wind speed (m s -l) friction velocity (m s -l) vertical wind speed (m s-1) height of the sensor (m) surface roughness length (m) depth of the convective boundary layer (m)

bar indicates mean values prime indicates instantaneous deviation of the variable from the mean

Greek Symbols

c~ 13 8 c ), q ~: L d~h ~., Pa p, ~,~ x v

Ostwald solubility coefficient (-) ratio of the mean densities of dry and moist air (-) decay constant (s) pump rate per unit vertical wind speed (m 3 s-I) ratio of the mean densities of water vapor and air (-) ratio of the molecular weights of dry and moist air (-) yon Karman constant (-) latent heat of vaporization (J g-i K-i) atmospheric stability correction for heat (-) atmospheric stability correction for momentum (-) density of dry air (g m -3) density of dry air (g m -3) standard deviation of the vertical wind speed (m s-j) empirical coefficient (-) kinematic viscosity of the water (m 2 s-~)

Methodsfor stable gasflux determination in aquatic and terrestrial systems

59

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65

Sinclair, T.R., L.H. Allen, Jr. and E.R. Lemon (1975) An analysis of errors in the calculation of energy flux densities above vegetation by a Bowen-ratio profile method. Boundary-Layer Meteorology 8:129-140. Smethie W. M., T.T. Takahashi, D.W. Chipman and J.R. Ledwell (1985) Gas exchange and CO2 flux in the tropical Atlantic Ocean determined from 222Rn and pCO2 measurements. Journal of Geophysical Research 90: 7005-7022. Smith, K.A., A. Scott, B. Galle and L. Klemendtson (1994) Use of a long-path infra-red monitor for measurement of nitrous oxide flux from soil. Journal of Geophysical Research 99:16585-16592. Sommerfeld, R.A.A.R. Mosier and R.C. Musselman (1993) CO2, CH4 and N20 flux through a Wyoming snowpack and implications for global budgets. Nature 361:140-142. Steele, L.P., P.J. Fraser and R.A. Rasmussen (1987) The global distribution of methane in the troposphere. Journal Atmospheric Chemistry 5:125-171. Steudler, P.A., R.D. Bowden, J.M. Melilio and J.D. Aber (1989) Influence of nitrogen fertilization on methane uptake in temperate forest soils. Nature 341:314-316. Torgersen T., G. Mathieu, R. H. Hesslein and W. S. Broecker (1982) Gas exchange dependency on diffusion coefficient: direct 222Rn and 3He comparisons in a small lake. Journal of Geophysical Research 87: 546-556. Tsivoglou E.C. (1967) Tracer measurements of stream reaeration. Fed. Water Pollution Control Administration, U.S. Dept. of Interior, Washington D.C. Wagner-Riddle, C., G.W. Thurtell, K.M. Ling, G.E. Kidd and E.G. Beauchamp (1996a) Nitrous oxide and carbon dioxide fluxes from a bare soil using a micrometeorological approach. Journal Environmental Quality 25: 989-907. Wagner-Riddle, C., G.W. Thurtell, G.E. Kidd, G.C. Edwards and I.J. Simpson (1996b) Micrometeorological measurements of trace gas fluxes from agricultural and natural systems. Infrared Phys. Technology 37:51-58. Wanninkhof, R. (1992) Relationship between gas exchange and wind speed over the ocean. Journal of Geophysical Research 97: 7373-7381. Wanninkhof, R., W. Asher, R. Weppemig, H. Chen, P. Schlosser, C. Langdon and R. Sambrotto (1993) Gas transfer experiment on Georges Bank using two volatile deliberate tracers. Journal of Geophysical Research 98: 20237-20248. Wanninkhof, R., G. Hitchcock, W. Wiseman, G. Vargo, P. Ortner, W. Asher, D. Ho, P. Schlosser, M.L. Dickson, M. Anderson, R. Masserini, K. Fanning and J.-Z. Zhang (1997) Gas Exchange, Dispersion and Biological Productivity on the West Florida Shelf: Results from a Lagrangian Tracer Study. Geophysical Research Letters 24:1767-1770. Watson, A. J., R. C. Upstill-Goddard and P. S. Liss (1991) Air-sea exchange in rough and stormy seas, measured by a dual tracer technique. Nature 349: 145-147. Webb, E.K., G.I. Pearlman and R. Leuning (1980) Correction of flux measurements for density effects due to heat and water vapor transfer. Quarterly Journal Royal Meteorological Society 106:85-100. Wenthworth, W.E. and R.R. Freeman (1973) Measurement of atmospheric nitrous oxide using an electron capture detector in conjunction with gas chromatography. Journal Chromatography

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Wienhold, F.G., H. Frahm and G.W Harris (1994) Measurements of N20 fluxes from fertilized grassland using a fast response tunable diode laser spectrometer. Journal of Geophysical Research 99:16557-16567. Wienhold, F.G., M. Welling and G.W Harris (1995) Micrometeorological measurement and source region analysis of nitrous oxide fluxes from an agricultural soil. Atmospheric Environment 29:2219-2227. Wienhold, F.G., T. Zenker and G.W. Harris (1993) Dual-channel two-tone frequency modulation tunable diode laser spectrometer for ground-based and airborne trace gas measurements. In: A. Fried, D.K. Killinger and H.I. Schiff (Eds.) Tunable Diode Lase Spectroscopy, Lidar, and DIAL Techniques for Environmental and Industrial Measurement. Proceedings Society Photo-Optical Instrumentation Engineers, Vol. 2112, Washington, pp. 31-44. Wilson, J.D., V.R. Catchpole, O.T. Denmead and G.W. Yhurtell (1983) Verification of a simple micrometeorological method for estimating the rate of gaseous mass transfer from the ground to the atmosphere. Agricultural Meteorology 29:183-189. Wilson, J.D., G.W. Thurtell, G.E. Kidd and E.G. Beauchamp (1982) Estimation of the rate of gaseous mass transfer from a surface plot to the atmosphere. Atmospheric Environment 16:1861-1867. WMO (1995) Scientific assessment of ozone depletion." 1994. MWO Report No. 37, WMO, Geneva. Woolf D. K. (1997) Bubbles and their role in gas exchange. In: P. S. Liss and R. A. Duce (Ed.) The Sea Surface and Global Change. Cambridge University Press, Cambridge, pp. 173-206 Yamamoto, S., H. Kondo, M. Gamo, S. Murayama, N. Kaneyasu and M. Hayashi (1996) Airplane measurements of carbon dioxide distribution on Iriomote island in Japan. Atmospheric Environment 30:1091 - 1097. Yvon S.A., E.S. Saltzman, D.J. Cooper, T.S. Bates and A.M. Thompson (1996) Atmospheric dimethylsulfide cycling at a tropical South Pacific station (12~ 135~ A comparison of field data and model results. 1. Dimethylsulfide. Journal of Geophysical Research 101: 6899-6909.

Chapter 3

SOME RECENT DEVELOPMENTS IN TRACE GAS FLUX MEASUREMENT TECHNIQUES

O.T. Denmead, R. Leuning, D.W.T. Griffith and C.P. Meyer



SOME RECENT DEVELOPMENTS IN TRACE GAS FLUX MEASUREMENT TECHNIQUES

O.T.Denmead 1, R.Leuning 1, D.W.T.Griffith 2 and C.P.Meyer 3 CSIRO Land and Water, F.C.Pye Field Environment Laboratory., GPO Box 1666, Canberra, ACT 2601, Australia. 2Department of Chemistry, University. of Wollongong, Wollongong, NSW 2522, Australia. 3 CAIRO Division of Atmospheric Research, Private Bag 1, Aspendale, Vic 3195, Australia.

1. Introduction This contribution complements those of Lapitan et al. (1999) who deal in a general way with a wide range of methods for measuring surface gas fluxes in terrestrial systems, and Fowler (1999) who considers experimental designs for flux determination on various scales. It summarizes some recent developments in meteorological methods for inferring trace gas fluxes on scales not well catered for by conventional methods: in plant canopies, on small plots and at the regional scale. Chambers and most of the common micrometeorological flux techniques measure the net exchange of a gas between the land surface and the atmosphere. Interpretation and modelling omen necessitate more detailed information on the components of the flux: the canopy sources and sinks. Evapotranspiration and CO2 exchange are good examples of relevant exchange processes. The former has both soil and plant sources, each subject to different controls, while the latter has, by day at least, a soil source and a plant sink. Ammonia exchange is another example. The gas can be emitted and absorbed by both soil and foliage. Early attempts at flux measurement in plant canopies relied on gradient-diffusion approaches, but it is now apparent that this approach is inappropriate in the canopy space (Denmead and Bradley, 1987). The mean concentration profiles reflect more the distribution of sources and sinks than the direction of scalar transport. Alternative approaches to canopy transport have been developed in consequence, e.g., higher order closure models, large-eddy simulation, wavelet analysis and Lagrangian dispersion models. The first three approaches require rapid measurement of instantaneous scalar concentrations, which is not yet feasible for many trace gases. The inverse Lagrangian model developed by Raupach (1989b), however, permits the identification of sources and sinks of scalars in the canopy space from mean concentrations, which are usually much easier to measure. Lagrangian dispersion treats the canopy as an assembly of source elements, each releasing a plume of scalar material into the turbulent air flow within and above the canopy. The source strengths can be inferred from the mean concentration profiles and statistics of the turbulence. Asman et al. (1999) consider the use of isotopes as an alternative approach for identifying canopy sources and sinks. Fowler (1999) points out that the large point to point variability in soil gas emissions can make the scaling-up of chamber measurements very difficult. Alternative conventional micrometeorological methods integrate over large areas, smoothing out the small scale hetero-

70


geneity, but they require uniform land areas with lateral dimensions of hundreds of meters. Not only are such areas often difficult to find, but also many natural and man-made ecosystems are not so extensive. The different ecosystems that together make up wetlands, such as the peat wetlands illustrated in Figure 4 of Fowler t ~999), are an example of the former and landfills an example of the latter. There is scope for micrometeorological methods appropriate for smaller land areas, areas larger than the usual chamber size of, say, lm z, but with lateral dimensions of tens rather than hundreds of meters. Mass balance methods fill this gap. Mass balance approaches can also be used on a much wider scale to provide regional trace gas fluxes (Choularton et al., 1995; Yamamoto et al., 1996; Fowler, 1999). This application is discussed by Fowler (1999). Boundary layer budgeting techniques aim to provide estimates of the fluxes of scalars on regional scales. Two schemes will be described: one for the daytime convective boundary layer (CBL) and another for the nocturnal boundary layer (NBL). Both use time changes in atmospheric scalar concentrations to calculate spatially averaged surface fluxes. Fuller accounts are given in Raupach et al. (1992) and Denmead et al. (1996).

2. Inverse lagrangian dispersion methods for within canopy fluxes 2.1. Theoretical These methods, developed notably by Raupach (1989a, b) offer a relatively simple means for inferring fluxes of trace gases and their source-sink distributions within plant canopies. They provide a bridge between chamber or cuvette measurements on soil or individual foliage elements and whole canopy measurements on a field scale. They require measurements of the mean gas concentration profiles within the canopy and some knowledge of turbulence and Lagrangian time scales in that space. As for chambers, advantage is taken of the restricted air movement in the canopy, which makes for larger and more easily measurable concentration changes than in the unobstructed atmosphere above it. Within the canopy, the source or sink strength of the gas at levels z is designated by S(z). It is related to the vertical flux density F at any level by S ( z ) : dF(z)/dz,

(1)

F(h)= F(O)+ If S(z)dz

(2)

so that

where F(O) is the gas flux density at the ground surface and h is canopy height. The Lagrangian dispersion theory developed by Raupach (1989a) enables a prediction of the mean gas concentration profile, which Raupach designates by C(z), from knowledge of S(z). The inverse Lagrangian method described by Raupach (1989b) builds on that work to allow the inference of S(z) from C(z). The procedure uses a discretized, linear relationship between source and concentration profiles, in which the coefficients are determined by the statistics of the wind field. These statistics determine the contributions of gas emitted at particular heights to the concentrations developed at any height within the canopy. By summing the emissions from all source layers (denoted by the suffix j), the gas concentration at any height (denoted by suffix i) can be written as: c, - c . = Z D,jSAZJ

where CR is the concentration at a reference height above the canopy. The coefficients of the

(3)

Recent developments in trace gas flux measurement techniques

71

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Figure 1. Mean 30-min profiles of water vapour pressure and CO2 concentration in and above a wheat crop. Each profile shows differences from the top measuring height of 2.15m. The crop height h was 0.76m and the leaf area index was 3.6. (From Denmead and Raupach, 1993).

dispersion matrix, the D;j, are calculated from profiles of crw, the standard deviation of vertical wind speed, and TL, the integral Lagrangian time scale within the canopy, as parameterized, for example, by Raupach (1989a). Once the coefficients are known, equation (3) becomes a set of linear equations that can be solved for the source profile Sj. Raupach (1989b) recommends that to ensure a stable solution for the source profile, redundant concentration data should be included, so that source densities Sj are sought in m layers with n measured concentration values C;, with n > m. The computer program developed by Raupach for inferring Sj. runs on a personal computer in a matter of seconds.

2.2.

Applications

Examples of the use of inverse Lagrangian analysis for determining sources of heat, water vapour and CO2 in crops of wheat and sugar cane are given by Denmead and Raupach (1993) and Denmead (1995). In these cases, the strengths of four source layers were inferred from concentrations measured at six heights within and two above the canopy. It is difficult to test the fine detail of the procedure because of lack of independent information on the withincanopy sources and sinks, but generally in these studies, fluxes predicted by the analysis for the whole canopy have been within 10 to 20% of fluxes above the canopy measured by conventional micrometeorological methods. Figures 1, 2 and 3 from Denmead and Raupach (1993) show some illustrative examples of the operation of the inverse Lagrangian technique. In this case sources and sinks of water vapour and CO2 were inferred for a wheat crop. Figure 1 shows concentration profiles for both gases, Figure 2 shows cumulative fluxes of both through the canopy and Figure 3 compares some predictions of the analysis with observations. For water vapour, the analysis

72

O.T. Denmead, R. Leuning, D. W.T. Griffith and C.P. Meyer L_

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Figure 2. Flux densities of latent heat (XE) and COz (Fc) in the wheat crop of Figure 1, calculated by the inverse Lagrangian method. (From Denmead and Raupach, 1993).

predicts some evaporation at the soil surface and strong contributions from foliage in the top half of the canopy (Figure 2). For CO2, it predicts some soil respiration and strong assimilation in the top foliage. Figure 3a compares the predicted flux of water vapour at 0.19 m with mea-

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Figure 3. (a). Flux density, of latent heat at 0.19m in wheat crop of Figure 1, calculated by the inverse Lagrangian method (dots), and flux at the soil surface measured by minilysimeters; (b) Flux densities of latent heat and CO2 at the top of the wheat crop of Figure 1., calculated by the inverse Lagrangian method (dots) and by two conventional micrometeorological methods (lines). (From Denmead and Raupach, 1993).

73


surements of evaporation at the soil surface made with minilysimeters. It is to be expected that the water vapour flux at 0.19 m would be close to evaporation from the soil surface, but slightly higher because of evaporation from lower leaves, which is, in general, what the analysis predicts. Figures 3b and 3c compare predictions of the cumulative fluxes of both gases at the top of the canopy with independent measurements of those fluxes in the air layer above the canopy obtained with conventional micrometeorological techniques. Apart from some discrepancies early in the day, the agreement between Lagrangian and conventional methods is excellent, as good as the mutual agreement between the two conventional methods. 2.3. Conclusions This relatively simple computational and measurement scheme obviously has much promise for trace gas flux measurement in plant canopies. The above applications show how useful it can be for gases with sources and/or sinks in the foliage, but it should prove particularly useful for gases like N20 that have only a soil source. The concentration gradients will be much larger than in the air above the canopy and the within-canopy analysis should be uncomplicated by contributions from the foliage. Other problems to which it might be applied are pathways of CH4 emission in wetland plant communities, the fate of NH3 released from effluent applied to crops and the sources and sinks of CO, NOx, 03 and stable isotopes in canopies. The method is, however, based on extensive homogeneous canopies where the source and sink pattern is constant in the horizontal. It should not be expected to work very well in heterogeneous canopies such as native or regrowth forests.

3. Mass balance methods 3.1. Theoretical Based on the conservation of mass, the general method equates the horizontal flux of gas across a face of unit width on the downwind edge of a designated area with the surface emission or absorption of the gas along a strip of similar width upwind. The horizontal flux density at any height is the product of horizontal wind speed u and gas concentration Cg. The total horizontal flux is obtained by integrating that product over the depth of the modified layer Z which is about 1/10 of the fetch X in neutral conditions, but usually less than that in unstable conditions and more in stable conditions. The average surface flux density is then given by

F -O/X)I~u(C.,-Cb)dz

(4)

where Cb is the upwind, background concentration and the overbar denotes a time average.

3.2. Applications One difficulty in applying equation (20) is that the term

uCg is the

mean of instantaneous

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74

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Denmead, R. Leuning, D. W.T. Griffith and C.P. Meyer

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Figure 4 (a). Mean CI-I4concentrations on upwind (N and W) and downwind (S and E) boundaries of a 24m • 24m square on the surface of a landfill. (b) Horizontal flux densities of CI-I4 on upwind and downwind boundaries during run starting at 01:22 (stippled profiles in Figure 4a); (c) Surface flux densities of CH4 from landfill measured by mass balance method and a conventional aerodynamic method. The concentration profiles in Figure 4(a) were measured in the stippled interval. (From Denmead et al., 1 9 9 8 a ) .

where, as previously, the overbars denote time means and the primes, fluctuations about the means. The first term on the right of equation (5) represents the convective flux out of the treated area due to the mean flow of the wind. The second term represents a smaller turbulent diffusive flux. The first term, the product of the mean wind speed and the mean concentration, is the term that will be measured usually. Probably it will be enough to reduce it by an empirical correction of, say, 15% (Denmead, 1995). Another difficulty is that the effective gas concentration is the concentration in excess of background. Not only must the upwind concentration profile be measured as well as the downwind, but calculation of the flux through equation (4) involves the subtraction of experimentally determined data, an error-prone procedure. The technique is thus suited best to experimental situations where Cb is small and F large as in investigations of NH3 emissions from fertilizers, for which it has been much used (Denmead, 1995). Those conditions may not pertain in some natural ecosystems.


75

A third problem with this approach is that the fetch X should be known precisely, whereas it is likely to vary with wind direction if the designated area is a rectilinear plot. This complication can be overcome by working with a circular plot and measuring u and Cg at its centre. Regardless of compass direction, the wind will always blow towards the centre of the plot and X will always be equal to the plot radius. It will often be possible to approximate small ecosystems by equivalent circles. A more elaborate but less arguable approach is to form a mass budget for an area whose geometry is known precisely. A recent application has been to the measurement of CH4 emission from the surface of a landfill (Denmead et al., 1998a). In that case, the designated area was a square, 24m x 24m. Concentration measurements were made at 4 heights with sampling arms extending the length of all 4 boundaries. Measurements of wind direction were used to calculate the vector winds U and V normal to the test plot. The surface flux density F was given by

Io Io" [ where the subscripts 1 and 2 denote the upwind boundaries and 3 and 4 the downwind, and A is the area of the test plot. Figure 4 illustrates the application. It shows (a) concentration profiles on upwind and downwind boundaries during consecutive runs when the wind was NW, (b) corresponding horizontal fluxes for one of the runs and (c) the favourable comparison between the surface flux density of CH4 calculated by the mass balance approach, equation (6), and that calculated by a conventional aerodynamic micrometeorological technique with correction for advection caused by the smallness of the test site (Denmead et al., 1998a).

3.3. Conclusions As employed in the above manner, the mass balance method can suffer from errors arising from the large numbers of gas analyses required for a flux determination and becomes unreliable in light winds and variable wind directions. On the other hand, it is non-disturbing, has a simple theoretical basis, is independent of atmospheric stability or the shape of the wind profile and smooths over surface heterogeneity. The applications of mass balance methods described by Choularton et al. (1995), Yamamoto et al. (1996), and Fowler (1999) are on much larger scales than the small plot methods discussed here, with fetches of 20 to 200 km, and involve fitting models of mixing processes in the atmospheric boundary layer to the observed concentration data.

4. Convective boundary layer budgeting 4.1. Theoretical The CBL consists of a shallow surface layer about 100m deep, in which vertical gas fluxes are nearly constant with height and concentration gradients are relatively large, and an overlying mixed layer where fluxes vary only slowly with height and concentrations are uniform because of large-scale mixing. The CBL is capped by a sharp temperature inversion. The mixed layer grows during the day through the input of heat at the ground, entraining air from above the inversion as it does so, and eventually extends up to 1 - 2 km. Conventional micrometeorological flux measurements are appropriate for the constant-flux surface layer. However, CBL budgeting techniques are based on the rate of change of gas concentrations in the mixed

o.T. Denmead, R. Leuning, D. W.T. Griffith and C.P. Meyer

76

layer which acts like a giant mixing chamber moving over the countryside with the mean wind. The method aims to provide regionally averaged rather than local surface fluxes. Gas concentrations in the CBL change through growth of the mixed layer, through the entrainment of air with a different concentration from above, and most importantly, through a flux of gas to or from the surface. By following the buildup or drawdown of gas concentration in the CBL and its height, and allowing for entrainment, an estimate can be made of the average surface flux from the landscape over which an air column moving at the mean wind speed has passed during the day. The bulk properties of the mixed layer are independent of small-scale heterogeneities, so it acts as a natural integrator of surface fluxes over heterogeneous terrain. The distance over which the CBL carries some "memory" of conditions upwind, i.e. its instantaneous footprint, is usually 5 to 30 km. Typically CB[ budgets estimate average fluxes over an area of 103 to 104 km 2 extending about 100 km upwind. Fuller accounts of CBL development and CBL budgeting techniques are given by Raupach et al. (1992) and Denmead et al. (1996). Denmead et al. (1996) developed an expression for calculating instantaneous surface flux densities F from observations of CBL height h, CBL gas concentrations Cm and the gas concentration in the free atmosphere just above the CBL, C+:

F = hdCm/dt - (C+ -C,.Xdh/dt - W+)

(7)

where t is time and W+ is the subsidence velocity. This equation can be integrated over the day to yield the cumulated regional flux I:

I(t) = I~ F(t)dt

(s)

I (t ) : h(t )[Cm (t ) - C +(t )l - h(O )[Cm (O) - C +(0)]+ (y / 2 )[h~ - h 2 ]- I~ W+(t )[Cm (t ) - C. (t )]dt In equation (8), y = dC+/dz, i.e., y is the rate of change of concentration with height just above the mixed layer at h+. Because of the difficulties of measuring dh/dt and dCm/dt accurately, the integral form of the CBL budget equation, equation (8), is easier to apply. To apply equation(8), Cm, C+ and y must be determined by analyzing air samples from within and above the mixed layer obtained with the aid of aircraft, kites or balloons. Such measurements are not often available, so the analysis presented here is based on measurements of near-surface concentrations Cs and some simplifications concerning C+ and 7". Cm can be inferred from C~ measured at height z,. through an aerodynamic resistance ra calculated from conventional micrometeorological similarity theory: Cm = C,. - ra F,

(9)

and l]vl[(Z m - d ) / ( z

r -

s

-d)]-[//(z m ku,

d, z s - d)

(10)

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77

where L is the Monin-Obukhov length. For trace gases whose diurnal emissions are reasonably constant in time, equation (9) can be incorporated into equation (8) by recognizing that Ft I/t. Assuming that W§ is negligibly small and that 7"= 0 (i.e., there is a step change from Cm to a constant value of C+ at h), then

_ h(O[c (,)- c+

h(o)[c (o)- < (o)]

1 + [h(t)r (t)- h (O)G (O)l/ t

(12)

If production is controlled by the diurnal variation of light or temperature, as is the case for CO2 and N20, Denmead et al. (1996) assume that F(t) oc FA(t) where FA(t) is the available radiant energy. Then

I(t) -

h(t)[C~(t)- C+ (t)]- h(O)[C~(O)- C+(O)l 1 + hO)raO)FA O)/IA 0)-- h(O)G (O)FA(O)/IA (0)

(13)

where:

I A -~s

(13a)

is the integral flux of available energy at time t. A further simplification to the analysis is to assume that C+ has the current clean-air baseline value for the gas of interest.

4.2. Applications Denmead et al. (1996; 1998b) have used equation (12) and (13) to estimate regional fluxes of water vapour, CO2, CH4 and N20 in southeast Australia from ground-based measurements. In the latter study, a tower was erected in a 30 ha field, half of which was planted to lucerne and half to a crop of Triticale. The field was in a larger region comprising pasture (70%) and cropland (30%). Continuous measurements of mean, 30-min mixing ratios for COz, CH4 and NzO were made on air drawn from 7 heights between 0.5 m and 22 m on the tower using Fourier transform infrared spectroscopy and non-dispersive infrared gas analysis. Aerodynamic parameters for calculating ra via equation (10) were obtained with an eddy correlation system mounted at 22 m. Eddy fluxes of CO2 were also measured at 22 m and at 2 m above the lucerne and the Triticale. These and the gas concentration measurements allowed calculation of the corresponding fluxes of CH4 and N20 using the gradient - diffusion approach (Leuning et al,. 1998). Radiosonde ascents were used to determine h at least twice per day. Occasional measurements of Cm, C+ and y were made for each gas using gas sampling flasks attached to balloons or in an aircrat~. To evaluate the integral flux through equations (12) and (13), the starting time (t = 0) was set at 08:30 when h was 300 to 500 m, and the final time at 15:30 when h was 1.5 to 2.5 km. Figure 5(a) compares CBL estimates of the average regional CO2 flux between 08:30 and 15:30 with the eddy fluxes of CO2 measured during the same period. It might be expected that the CBL fluxes would be close to those observed at 22 m where the daytime footprint was 2 to 10 km covering a landscape similar to that of the region at large. However, the CBL fluxes were generally larger than the 22 m fluxes, being intermediate between those observed over the more productive lucerne and Triticale. This points to a problem with the ground-based observation system. When surface concentrations are extrapolated to the mixed layer through equation (9), the estimated Cm reflects the initial surface concentration and so biases the CBL flux estimate towards the local surface flux. This was confirmed by the direct balloon and air-

78

c~. T. Denmead, R. Leuning, D. W.T. Griffith and C.P. Meyer 2.5 "~.

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craft observations of C,,, which for CO2 were several ppm higher than those predicted by equation (9). For CH4 and N20, with reasonably uniform animal sources in the observation region, equation (3) appeared to predict mixed layer contributions satisfactorily. A second difficulty with this observation system is that it is fixed in space (Eulerian coordinate system), whereas a system which moves with the mean wind is required (Lagrangian coordinate system). To reduce errors from this source, only days with steady wind directions are suitable for application of the CBL methodology, particularly in regions with heterogeneous land use. This requirement will usually restrict the potential data base significantly. Other restrictions arise from the lack of convective conditions, e.g. on cloudy days. Despite the many data points shown in Figure 5(a), only 7 days from a total of 19 in the study of Denmead et al. (1998b) met all requirements for strict application of the CBL analysis.


79

A third problem is the high precision required in the concentration measurements. In the example study, the typical drawdown in the CO2 concentration of the mixed layer during the course of a day, C+ - Cm, was 3 ppm, while for CH4, the mixed layer enrichment was typically 20 ppb and for N/O, 2 ppb. A precision significantly better than 1% in absolute measurement is thus required. The CBL flux estimates for CH4 are compared with the average of nocturnal micrometeorological flux determinations at 22 m and with chamber measurements in the lucerne and Triticale in Figure 5(b). The latter were made by Meyer et al. (1998). The nocturnal micrometeorological flux estimates were used because they are the most reliable (see section 5.2) and because ruminant animals appear to produce CH4 equally throughout the day and night.The chamber measurements indicated that the soil itself was a CH4 sink with an average strength of-2 ng CH4 m 2 s-1. Both the CBL and micrometeorological measurements, however, showed a net upward flux indicating that the animal sources in the region dominate the soil sinks. The net CH4 emissions are consistent with a stocking rate between 0.5 and 1 cattle equivalents ha 1, the same range indicated by regional surveys of animal numbers. The CBL estimates of N20 emissions appeared to be intermediate between micrometeorological and chamber fluxes for the same observation periods (08:30 to 15:30) on 3 of the 4 days of measurement (Figure 5c). On the fourth day, October 23, the CBL estimates were higher than the locally measured fluxes, but that day followed 2 days of heavy rain which promoted a sharp increase in N20 emissions. As for CH4, one might expect the regional flux to be greater than the local soil flux because of the expected large regional contribution from animal dung and urine patches.

4.3. Conclusions For the simplified CBL budgeting scheme described here, the use of near-surface concentrations instead of mixed layer concentrations can bias the estimates towards local flux values. Measurements at more than one location in regions of heterogeneous land use would be a desirable improvement resulting in greater accuracy of flux estimates. Changing wind directions, unsuitable weather and the high precision required in gas concentration measurements are also limitations of the method. Neverthelesss, even in its simplified form, CBL budgeting is potentially very useful as a survey tool for estimating regional gas fluxes and in broadly homogeneous regions, appears to provide better than order of magnitude estimates from a relatively simple observation scheme. More elaborate approaches employing aircraft for direct measurements of concentrations in and above the CBL and perhaps a Lagrangian observation system could be expected to yield more precise results. Examples of the former are given in Wofsy et al. (1988), Betts et al. (1992), Ritter et al. (1992), and Choularton et al. (1995).

5. Nocturnal boundary layer budgeting 5.1. Theoretical At night when convective heating ceases, the CBL is replaced by the NBL, a shallow weakly turbulent layer which often extends to heights of only tens of meters, and is bounded by a lowlevel radiative inversion. The inversion inhibits vertical mixing so that emissions of gases from the surface are contained in a shallow air layer whose concentration changes appreciably. The surface flux can be calculated from:

80


F

- fz (dC/dt)dz,

(14)

Z being the height of the air layer whose concentration is affected by the emission. Equation (14) applies when concentration measurements are available up to the top of the inversion layer.

5.2. A p p l i c a t i o n s

Figure 6 shows an example of NBL budgeting for COz and CH4. The data come from the same campaign in which the CBL experiments described in Section 4.2 were performed. A helium filled balloon was used to carry an airline aloft in a series of vertical traverses to a height of 100m. A non-dispersive infrared gas analyzer system was used to measure CO2 concentrations in air pumped from the balloon height and a gas chromatograph was used to measure CH4. On this occasion, a temperature inversion developed early in the evening around 18:00 and most of the CO2 emitted between then and 22:00 was contained between the ground surface and a height of 40m (Figure 6a). The CO2 enrichment of that layer corresponded to an average surface emission rate of 0.05 mgCO2 m 2 s1, in agreement with eddy flux measurements described in Section 4.2. Significant CH4 enrichment was observed up to 60m (Figure 6b) and the calculated flux was 250 ng CH4 mZs l, much the same as the CBL flux estimates made at the site. Other examples of NBL budgeting for the CH4 flux from wetlands are given by Choularton et al. (1995). A more general NBL budgeting approach is needed when concentration measurements do not extend to the top of the inversion layer. Then equation (8) must be modified to include the flux Fz at the top of the layer: r

(15

-

Figure 7 from Leuning et al. (1998) shows the 3 terms in equation (15) for CO2, CH4 and NzO on one night during the trace gas experiment described in Section 4.2. Fluxes at 22 m and

100 t 80

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Figure 7 (a), (b), (c). Nocturnal storage of CO2, N20 and CH4 between 0 and 22m on one night in a lucerne field; (d), (e), (f) . Rate of change of gas storage (0-22m), gas flux at 22m and the sum of both, representing the surface flux, for same period. (From Leuning et al., 1998).

changes in gas storage between the surface and 22 m were added to estimate the surface flux. In this instance, the storage term was ot~en very much larger than the flux at the top of the layer. Over 11 nights of measurement, it accounted for >60% of the surface flux of each gas. Fan et al. (1992) describe similar observations of CH4 and CO2 exchange over tundra made on a 12m tower. The storage term was 20 to 40% of the surface flux. The attraction of NBL budgeting is that the concentration gradients are larger than by day, hence more easily measurable. However, because the method is applicable only at night, inferences from it could lead to errors in assessing source and sink strengths. Examples include CO2 exchange with plant communities, where the day and night fluxes are of opposite sign, and N20 and NH3 emissions which are usually least at night. For trace gases whose emissions do not exhibit marked diurnal cycles, such as CH4 from animal sources, NBL budgets may give more reliable flux estimates than measurements in the day time.

5.3. Conclusions

The growth and height of the CBL are reasonably predictable, but not so for the NBL. Its shiffing height makes it difficult to work with a fixed sampling array and there will be times when the radiative inversion layer is impossibly deep or absent so that the method is not feasible at all. When a budget can be made, the requirement to measure concentration profiles rather than concentrations at a single point make it a more complicated procedure than CBL budgeting. Another weakness in comparison with CBL techniques is uncertainty about the extent of the surface that the budget represents. A rough estimate of its footprint is 1 to 5 km. On the other hand, the depth of the atmospheric mixing "chamber" is better defined, few

82

O.T. Denmead, R. Leuning, D. W.T. Griffith and C.P. Meyer

assumptions are required and the concentration changes usually will be larger and hence more easily detectable than in CBL budgeting. For trace gases whose emissions do not exhibit marked diurnal cycles, NBL budgets may be simpler alternatives than either CBL or conventional micrometeorological flux measurements by day. However, when there is a diurnal cycle in gas exchange, such as for CO2, errors could arise from assessing source and sink strengths from NBL budgets only. Of course, for CO2, the day and night fluxes represent the different processes of photosynthesis and respiration, so that these different approaches are not competitors, but supplement each other.

6. Summary Meteorological techniques for measuring trace gas fluxes on three important scales not well catered for by conventional methods have been discussed: an inverse Lagrangian dispersion method appropriate for the canopy scale, mass balance methods for small and heterogeneous ecosystems and boundary layer budgeting schemes for the regional scale. The inverse Lagrangian analysis offers a relatively simple measurement scheme for inferring fluxes of trace gases and their source-sink distributions within plant canopies. Inputs are the profiles of mean gas concentration and turbulence within and above the canopy. The analysis provides a bridge between chamber and cuvette measurements on soil and foliage elements and flux measurements on a field scale. Mass balance methods are appropriate for flux measurements in small ecosystems, tens of meters in lateral extent. Fluxes from areas of known geometry are calculated from the rate at which the wind transports gas across the upwind and downwind boundaries of the designated area. The method can fill the gap between chambers of, say, 1 m 2 in area and conventional micrometeorological methods representing, say, 104 m 2. It can suffer from errors arising from the large number of gas analyses required for a flux determination and may become unreliable when there are light winds and variable wind directions. On the other hand, it is nondisturbing, has a simple theoretical basis, smooths over surface heterogeneity and is independent of atmospheric stability or the shape of the wind profile. Convective and nocturnal boundary layer (CBL and NBL) budgeting techniques are discussed in the context of a recent experiment to estimate regional fluxes of carbon dioxide, methane, and nitrous oxide in a rural area of southeast Australia. CBL techniques estimate the average surface flux over regions of order 100 km 2 through the buildup or drawdown of gas concentration in the atmospheric mixed layer and its depth. An integral form of CBL budgeting was used to estimate daily fluxes. Input data were gas concentrations at 22 m and CBL heights obtained with radiosondes. The atmospheric gas concentrations above the CBL were assumed to be the current clean-air baseline values. It was concluded that even with this simplified observation scheme, CBL budgeting can be a very useful survey tool and in regions that are homogeneous in the large, can provide better than order of magnitude estimates of trace gas fluxes. NBL budgeting techniques follow the change of gas storage in the surface layer at night when low-level radiative inversions inhibit vertical mixing. The footprint is difficult to estimate, but is of order 1 to 5 km. On one occasion during tile experiment, balloon measurements were made up to a height of 100 m, but routinely, tower-based measurements were made to 22 m. It was concluded that for gases whose emissions do not exhibit marked diurnal cycles, NBL budgets may be simpler alternatives than either CBL budgets or conventional micrometeorological measureJnents made by day. When diurnal variation is large, both day and night measurements are needed to define the 24-hour flux.


83

References Asman, W.A.H., M.O. Andreae, R. Conrad, O.T. Denmead, L.N. Ganzeveld, W. Helder, T. Kaminski, M.A. Sofiev and S. Trumbore (1999) How can fluxes of trace gases be validated between different scales? In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 85-97. Betts, A.K., R.L.Desjardins and J.I. MacPherson (1992). Budget analysis of the boundary layer grid flights during FIFE 1987. Journal of Geophysical Research 97:18533-18546. Choularton, T.W., M.W. Gallagher, K.N. Bower, D. Fowler, M. Zahniser and A. Kaye (1995). Trace gas flux measurements at the landscape scale using boundary-layer budgets. Philosophical Transactions of the Royal Society of London A 351:357-369. Denmead, O.T. (1995). Novel meteorological methods for measuring trace gas fluxes. Philosophical Transactions of the Royal Society of London A 351:383-396. Denmead, O.T. and E.F. Bradley (1987). On scalar transport in plant canopies. Irrigation Science 8:131-149. Denmead, O.T. and M.R. Raupach (1993). Methods for measuring atmospheric gas tr~sport in agricultural and forest systems. In: L.A. Harper, A.R. Mosier, J.M. Duxbury and D.E. Rolston (eds.) Agricultural Ecosystem Effects on Trace Gases and Global Climate Change. American Society of Agronomy Special Publication 55, Madison, WI, pp. 19-43. Denmead, O.T., M.R. Raupach, F.X. Dunin, H.A. Cleugh, and R. Leuning, (1996). Boundary-layer budgets for regional estimates of scalar fluxes. Global Change Biology 2:255-264. Denmead, O.T., L.A. Harper, J.R. Freney, D.W.T. Griffith, R. Leuning and R.R. Sharpe (1998a). A mass balance method for non-intrusive measurements of surface-air trace gas exchange. Atmospheric Environment (in press). Denmead, O.T., R. Leuning, D.W.T. Griffith, I.M. Jamie, M. Esler, H.A. Cleugh and M.R. Raupach (1998b). Estimating regional fluxes of CO2, CH4 and N20 at OASIS through boundary-layer budgeting. In: R. Leuning, O.T. Denmead, D.W.T. Griffith, I.M. Jamie, P. Isaacs, J. Hacker, C.P. Meyer, I.E. Galbally, M.R. Raupach and M.B. Esler (eds.) Assessing biogenic sources and sinks of greenhouse gases at three interlinking scales. Consultancy Report 97-56, CSIRO Land and Water, Canberra. Fan, S.M., S.C. Wofsy, P.S. Bakwin, D.J. Jacob, S.M. Anderson, P.L. Kebabian, J.B. McManus, C.E. Kolb and D.R. Fitzjarrald (1992). Micrometeorological measurements of CH4 and CO2 exchange between the atmosphere and subarctic tundra. Journal of Geophysical Research 97:16627-16643. Fowler, D. (1998). Experimental designs appropriate for flux determination in terrestrial and aquatic systems. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier Science, Amsterdam, pp. 99-121. Lapitan, R.L., R. Wanninkhof and A.R. Mosier (1999) Methods for stable gas flux determination in aquatic and terrestrial systems. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 27-66. Leuning, R., D.W.T. Griffith, O.T. Denmead and I.M. Jamie (1998). Air-land exchanges of CO2, CH4 and N20 during OASIS 1994 and 1995 measured using FITR spectroscopy and micrometeorological techniques. In: R. Leuning O.T. Denmead, D.W.T. Griffith, I.M. Jamie, P. Isaacs, J. Hacker, C.P. Meyer, I.E. Galbally, M.R. Raupach and M.B. Esler (Eds.) Assessing biogenic sources and sinks of greenhouse gases at three interlinking scales. Consultancy Report 97-56, CSIRO Land and Water, Canberra. Meyer, C.P., I.E. Galbally, D.W.T. Griffith, I.A. Weeks. I.M. Jamie and Y.P. Wang (1998). Trace gas exchange between soil and atmosphere in southem N SW using flux chamber measurement techniques. In: R. Leuning, O.T. Denmead, D.W.T. Griffith, I.M. Jamie, P. Isaacs, J. Hacker, C.P. Meyer, I.E. Galbally, M.R. Raupach and M.B. Esler (Eds.) Assessing biogenic sources and sinks of greenhouse gases at three interlinking scales. Consultancy Report 97-56, CSIRO Land and Water, Canberra.

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Paulson, C.A. (1970). The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology 9:857-861. Raupach, M.R. (1989a). A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies. Quarterly Journal of the Royal Meteorological. Society 115:609632. Raupach, M.R. (1989b). Applying Lagrangian fluid mechanics to infer scalar source distributions from concentration profiles in plant canopies. Agricultural and Forest Meteorology 47:85-108. Raupach, M.R., O.T. Denmead, and F.X. Dunin, (1992). Challenges in linking atmospheric CO2 concentrations to fluxes at local and regional scales. Australian Journal of Botany 40:697-716. Ritter, J.A., J.D.W. Barrick, G.W. Sachse, G.L. Gregory, M.A. Woerner, C.E. Watson, G.F. Hill and J.E. Collins Jr. (1992). Airborne flux measurements of trace species in an Arctic boundary layer. Journal of Geophysical Research 97:16601-16625. Wofsy, S.C., R.C. Harriss and W.A. Kaplan (1988). Carbon dioxide in the atmosphere over the Amazon basin. Journal of Geophysical Research 93:1377-1387. Yamamoto, S., H. Koudo, M. Gamo, S. Murayama, N. Kaneyasu and M. Hayashi (1996). Airplane measurements of carbon dioxide distribution on Iriomote Island in Japan. Atmospheric Environment 30:1091-1097.

Chapter 4

H O W CAN FLUXES OF TRACE GASES BE VALIDATED B E T W E E N D I F F E R E N T SCALES?

W.A.H. Asman, M.O. Andreae, R. Conrad, O.T. Denmead, L.N. Ganzeveld, W. Helder, T. Kaminski, M.A. Sofiev and S. Trumbore



WORKING GROUP REPORT HOW CAN FLUXES OF TRACE GASES BE VALIDATED BETWEEN DIFFERENT SCALES?

W.A.H. Asman (Rapporteur), M.O. Andreae (Chairman), R. Conrad, O.T. Denmead, L.N. Ganzeveld, W. Helder, T. Kaminski, M.A. Sofiev and S. Trumbore

1. Introduction

Estimates of fluxes of trace gases and their spatial and temporal variability are needed for several reasons. Firstly, we need to know the magnitude and direction of the flux as well as the relative importance of the different source categories. Secondly, flux estimates or descriptions of the key processes regulating the fluxes at the scale considered are used to drive atmospheric models. Thirdly, flux estimates are used to validate the output of atmospheric models. The information on fluxes is needed at different spatial scales - ranging from the local (point) scale, to areas at the scale of a nature reserve (few km in diameter), provinces (up to about 100 km), to the country, continental and the global s c a l e - and temporal scale. In this report we focus mainly on the bottom-up approaches in scaling, where information on fluxes at lower scales is integrated to obtain information at a higher scale level. Firstly, we review selected flux measurement techniques at different scales. More detailed overviews are given by Lapitan et al. (1999), Denmead et al. (1999) and Fowler (1999). Secondly, methods used to derive fluxes using atmospheric models are discussed. This leads to a discussion on so-called "redundant' measurements. Finally, we present recommendations for future research on validation techniques applicable between different scale levels. The use of isotopes and tracers in scaling is extensively discussed by Trumbore (1999), and will not be reviewed here.

2. M e t h o d s to m e a s u r e fluxes at different scales

A variety of techniques are used to determine trace gas fluxes, including chamber, micrometeorological and airborne methods. In most cases these methods "see" different areas. For example, the chamber method represents only the area covered by the chamber. Alternatively, ground-based micrometeorological methods "see" a larger area, or footprint, depending on factors such as the height of the meteorological tower, surface roughness and atmospheric stability. Therefore, results of these methods, in fact, may only be compared for areas that do not show any spatial variation in the gas fluxes. As such homogeneous areas do not exist in practice, the comparison should be made for areas that are as homogeneous as possible, such as functional types (see Seitzinger et al., 1999). If the different methods used yield similar results, this indicates that they can be used successfully at their own, specific, scale levels. In theory, it is also possible to compare different methods for heterogeneous areas by stratifying landscapes on the basis of differences in e.g. land use and soils. Fluxes may then be measured

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according to a strategy based on the footprint of the selected technique. This procedure, however, may fail if non-linear processes regulate gas fluxes. Most methods applied at scales larger than 1 m 2 cannot be used continuously. For example, because the meteorological conditions are outside the range of conditions for which the technique was developed and tested. Estimates of fluxes over longer time periods (e.g., season, year) thus can only be obtained with tools (such as models) that integrate fluxes over time, using short-term measurements. This may cause measurement problems when the daily variation in fluxes is high. For example, the carbon dioxide (CO2) flux from terrestrial ecosystems is negative fluxes during daytime, and positive at night. Some methods can be used during a substantial fraction of the time, whereas others are restricted to very specific (meteorological) conditions that occur much less frequently. These issues are discussed in relation to long-term measurements of CO2 and water vapour fluxes by Baldocchi and Valentinti (1996). Irrespective of the techniques used, footprint analysis (e.g. Leclerc and Thurtell, 1990), and geostatistics (e.g. Ambus and Christensen, 1994) are critical steps in scaling up flux measurements. Micrometeorological measurements of oceanic fluxes pose additional problems. For many trace gases, the fluxes between the ocean and the atmosphere are very small compared to fluxes between terrestrial ecosystems and the atmosphere. This causes technical problems related to the detection limits of instruments.

2.1. Methods based on soil concentration profile measurements

Fluxes between soil and atmosphere car: be determined from the vertical gradient in gas concentration within the soil using a diffusion model. The effective diffusivity of the gas in the soil can be measured using tracers such as radon (Rn) (Born et al., 1990; Whalen et al., 1992; Koschorreck and Conrad, 1993). This method has been applied to measure soil uptake of methane (CH4) and could also be applied to determine fluxes of nitric oxide (NO) from upland soils to the atmosphere. Measurement of a concentration gradient, however, is difficult if the layer in which the major part of the concentration gradient occurs is thin. In the case of CI-I4 fluxes from aquatic sediments, for example, a marked CH4 consumption gradient may occur within 1-5 mm depth. The concentration gradient integrates diffusion, production and consumption processes. Hence, the net flux can be measured directly from these processes, as well as indirectly from the concentration gradient and the diffusivity (e.g. Galbally and Johansson, 1989; Remde et al., 1993; Rudolph et al., 1996). The recommended method to scale up is not the averaging of fluxes measured at different sites, but to establish the relation between gas fluxes and the processes controlling them, and the environmental conditions. This knowledge can then be combined with information on the spatiaJ and temporal variations in the controlling factors, allowing to estimate the flux at a larger scale (Matson et al., 1989). The footprint of the gas concentration profile method is 0.1-0.5 m. The method focuses on processes in soil, isolated from possible interference by the vegetation.

2.2. Chamber techniques

Under ideal circumstances (homogenous fetch, level and homogeneous terrain) chamber and micrometeorological methods give comparable results. Yet, the objectives of these two techniques differ. The chamber technique is often used to isolate the soil's contribution to the flux in systems containing tall vegetation. Conventional micrometeorological methods yield

How can fluxes of trace gases be validated between different scales ?

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information on the total flux to or from an ecosystem, containing both soil and vegetation. They do not give information on processes such as adsorption of gases within the canopy. The Inverse Langrangian dispersion method described below aims to fill such scale gaps. The chamber technique may not give reliable results when the temperature, radiation, energy balance and gas concentration inside the chamber differ from those in the field. In addition, there is no turbulence inside the chamber (or a different turbulence, if fans are used). This is usually not critical for soils where the flux is controlled by soil processes and diffusion in the soil and not by air turbulence. The conditions inside the chamber are more critical when there is a canopy (grass, agricultural crops) for which the exchange is a function of turbulence. The chamber technique cannot be applied in oceanic systems, since the gas exchange velocity depends on turbulence and wind speed, which both are perturbed by or interfere with the chamber operation. Perturbation effects of chambers can be avoided by, for example, limiting the time period of operation or by maintaining an air flow through the chamber. Finally, the typical footprint of a chamber is 0.5-5 m, although megachambers of 30 m long have also been used (Smith et al., 1994).

2.3. Lagrangian dispersion method Inverse Lagrangian methods are not only used to measure the flux over a canopy, but also to infer sources and sinks within the canopy (Denmead et al., 1999). They bridge the "gap" between enclosures (soil chambers, leaf cuvettes) and towers. The method has been used to identify sources and sinks of heat, water vapour, C02, CH4 and ammonia (NH3). It should also be useful for gases stemming from soils only, such as N20, and to determine fluxes of carbon monoxide (CO), NO, ozone (03) and stable isotopes in the canopy. The footprint of the Lagrangian dispersion method is smaller than that of conventional micrometeorological measurements. Typically, it will correspond with three canopy heights upwind. The Lagrangian dispersion method is, however, based on extensive homogeneous canopies where the source and sink patterns are constant horizontally. Therefore, it is not likely to be appropriate for heterogeneou~ forests. Neither is the method expected to work under calm conditions, such as at night when gas fluxes is determined by the wind field, temperature inversion and other factors.

2.4. Micrometeorological tower methods Tower measurements, with conventional micrometeorological techniques, yield an integrated flux estimate over the upwind fetch. They are applied to obtain ecosystem fluxes and to study canopy processes. Multi-layer eddy correlation measurements can improve the definition of flux divergence in the canopy. Micrometeorological techniques have, in principle, a larger footprint than the chamber technique. The footprint can be enlarged by using a higher tower (i.e. elevation above the canopy), but this creates a lower signal to noise ratio and problems with convection and advection interfering with turbulent transport. In addition, very high towers are needed for forests, which poses practical problems. The footprints of the micrometeorological methods vary from tens to hundreds of metres, depending on the height of the instruments above the surface of investigation and the meteorological conditions. At night, when low wind speeds and temperature inversion may inhibit vertical transport, there can be substantial storage of emitted gas in the air between the surface and the level of the sensor. In such cases, additional measurements of the concentration profile are needed to account for changes in this storage, which at times may exceed the vertical flux through the

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top of the layer (Denmead et al., 1999). l~.!icrometeorological methods, so far, have not been very useful for measuring gas fluxes over oceans.

2.5. Airborne micrometeorological techniques Airborne eddy correlation techniques address scales of tens to hundreds of kilometres. Limitations of these methods are related to the limited availability of high-frequency and highprecision sensors and to operational limitations imposed by aircraft. Airbome eddy correlation techniques can represent large spatial scales, but the temporal resolution is limited. The instantaneous footprint of the method ranges from 1 to 10 km, depending on the cruising height of the aircraft. The effective footprint size is -100 km, taking into consideration that signals must be averaged over 20 minutes or more.

2.6. Convective boundary layer budgeting method When there is enough insolation, the earth's surface is heated and convection develops, leading to a well mixed convective boundary layer (CBL), capped by a sharp temperature inversion. The fluxes are derived from changes in concentration in the CBL, corrected for the inflow from the free troposphere. The latter is estimated from the concentration in the free troposphere and the changes in mixing layer height with time. The concentration in the CBL can only be measured with the aid of aircraft, kites or balloons. Such measurements, however, are often not available. Concentration at the CBL height then has to be estimated by extrapolating near-surface concentrations and by assuming constant concentration just above the mixing layer. The convective boundary layer method has been applied to derive fluxes of CO2, CH4 and N20 in Australia (Denmead et al., 1999). Its application over the oceans poses problems because of a weak boundary layer development, low flux rates and small concentration changes. The footprint is usually 5 - 30 km, but during the day it can range from 50 to 150 km. Therefore, the method can be used to help bridging the gap in footprints between micrometeorological tower and airbome measurements. The convective boundary layer method cannot be applied under conditions with low wind speeds, variable wind directions and non-convective conditions, and it requires high-precision concentration measurements. The applicability of the convective boundary layer method may be extended if used in combination with a Lagrangian observation system moving with the wind.

2.7. Nocturnal boundary layer budgeting method At night there is often no convection as a result of which the nocturnal boundary layer is often bounded by a radiative inversion at heights of typically tens of metres. The flux can be determined from concentration changes, that can be considerable due to the shallowness of the boundary layer. Vertical concentration profiles up to the height of inversion have to be measured as a function of time to obtain a good estimate of the budget. The nocturnal boundary layer budgeting method can not always be applied, in particular when the inversion layer is extremely deep or absent. Another weakness of the method is that the extent of the surface for which the budget is computed, remains uncertain, with estimated footprints ranging from 1 to 5 km.


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2.8. Airborne mass balance method The vertical flux is derived from the measured difference between the outflow and inflow of the gas over a given area, by measuring the change in concentrations. Vertical concentration profiles should be measured at a reasonable number of sites to avoid interference from plumes. The method should particularly yield good results when the upwind and downwind parts of the flight are over the sea, where the vertical concentration profile shows less fine structures than over land. The airborne mass balance method is appropriate for the long-lived atmospheric trace gases (CH4 and N20) and to estimate fluxes of CH4, N20, CO, "wintertimeCO2" and some of the less reactive volatile organic carbon compounds (VOC) (see Fowler, 1999). Measurements should be performed under conditions with steady boundary layer winds (315 m sl), with a well defined temperature inversion that is capping the boundary layer and in the absence of deep convection. The area that can be investigated depends on the length of the measurement (depending on the above conditions) and on the type of carrier used. For balloons and small aircraft the footprint can be as small as 3-5 km. The maximum footprint of the method is about 1000 km. The airborne mass budget method has potential for obtaining information on the N20 flux for upwelling areas in the oceanic margin. If the N20 concentration in sea water and the wind speed are measured simultaneously and in the same area, the gas exchange parameterizations of Liss and Merlivat (1986) and Wanninkhof (1992) could be verified with the airborne mass balance method. In addition, the method could be applied to determine fluxes over "trade wind" islands. This method represents the largest scale for which fluxes can be derived directly from measurements. At larger scales, gas fluxes have to be simulated with atmospheric models.

3. Application of atmospheric models to derive flux estimates Two distinct methods exist to derive fluxes using atmospheric models, i.e. forward simulations and inverse methods. In forward atmospheric modelling, emissions are used to drive the atmospheric model to predict concentrations and fluxes. Inverse methods use observations of atmospheric concentrations to derive flux estimates (Heimann and Kaminski, 1999). Both methods can be applied at all scale levels. The necessary level of detail of the descriptions of controlling processes depends on the spatial and temporal resolution required. For both methods the inflow and outflow for the model area considered need to be known from measurements or modelling. Data on inflow and outflow become more critical as the spatial scale decreases, being most critical for gases with long lifetimes, and less important for highly reactive gases.

3.1. Forward modelling The concentration and deposition fields simulated with forward modeling can be validated by comparing the model results with measured concentrations and fluxes. After validation the model can be used to interpolate in space and time between measurement sites. For a sound validation the measurement data should be representative of the spatial scale and the time period considered (Sofiev, 1999). Measurements should be representative for an area having the same size as the model's

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grid elements. In practice the area represented by a measurement site depends on the particular situation around the site. If there are strong signals from sources nearby, the measurement data only represent a limited area. The variability of the concentration of a trace gas within a model grid cell and the standard deviation of the model results should be taken into account. This variability can be quantified as a standard deviation of a point measurement in a model grid cell. A first estimate of the spatial standard deviation at one site can be obtained from the standard deviation of a time series at that site, although the spatial standard deviation is often likely to be greater. An estimate of the standard deviation of the model results can be obtained from a sensitivity analysis with the model, where model parameters are varied within likely ranges. Another estimate of the uncertainties of the model results for longer averaging periods can be found from the model results that are used to calculate the average. The time period represented by the model simulations and that of the measurements should be identical. In general, results represent a larger area when the averaging time is longer.

3.2. Inverse models

Inverse models use observations of atmospheric concentrations to derive flux estimates. The inverse problem can be formulated as: Ac = T ( q )

(1)

where /x c is the temporal change in the vector of the observed concentrations, q is the vector of sources and sinks, and T is the transport model. The transport model can not be used to describe the backward transport, as it is not possible to model backward diffusion. The unknown sources and sinks can be found by minimizing the difference between measured and modelled concentrations by varying the source and sink strengths. Similar to forward modelling, modelled and observed concentrations are compared and, therefore, inverse modelling has the same requirements regarding the representativeness of measurement sites as forward modelling. Inverse methods can provide an estimate of the unknown flux vector and an error covariance matrix quantifying the uncertainty in this flux estimate. In contrast to forward modelling, both are determined by objective minimization algorithms (e.g. Menke, 1989). In principle, the method can be applied both to reactive and inert trace gases. However, solving the inverse problem can become very complicated for a reactive trace gas, depending on the number of its reaction partners. For a number of trace gases (e.g., CO2, CH4, or F11), however, transport and chemistry in equation (1) can be approximated by a linear relationship (T), which links sources and sinks to the concentrations. In that case, the source and sink estimate as well as the uncertainties are directly given by algebraic equations. The relation of the inverse problem to scaling issues for unknown fluxes will be illustrated with two examples for the inversion of CO2 on different spatial scales. In the first example, a globally uniform flux field is to be inferred from atmospheric observations at a monitoring network of 25 stations. This is an overdetermined system, since there are 25 constraints for 1 unknown variable. Then, the flux can be determined with a relatively small uncertainty. In case of spatial inhomogeneity of the atmospheric measurements, however, the flux estimate will be biased. Assuming that all stations are located in one hemisphere and that the flux from that hemisphere is larger than that from the other one, the implied increase in the concentration on that hemisphere will be higher than the increase in the global average concentration. Consequently the inversion, which is based on this inhomogeneous network, will overestimate the global flux. In the second example the earth's surface is divided into more than 25 unknown source


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regions. This is an under-determined system. In this case, information on the unknown fluxes has to be combined with the atmospheric measurements, such as flux estimates on the basis of an emission inventory, process models or other measurements. There are now two constraints, being the modelled concentrations which should not deviate too much from the measured ones, and the flux which should not deviate too much from the a p r i o r i flux estimate. For this relatively high resolution of the fluxes, the bias in the estimate is reduced compared to the first example. But the uncertainty for the various flux components is higher. The reason for this is that in the first example the coupling of all the flux components together is an extremely strong constraint on the solution, but it does not take the spatial variability of the fluxes into account. So far, inverse modelling has been mainly applied to obtain global emission fluxes. Recently, the method has also been applied to find the CH4 emissions of Northwestern European countries, using measurements at 200 m height at the Cabauw meteorological tower in the Netherlands and a simple atmospheric transport model using trajectories (Vermeulen et al., 1998). The results were in good agreement with existing emission inventories. Inverse runs are also used to optimally locate future observation sites based on specific hypotheses for the sources and sinks of the trace gas to be tested (e.g. Hartley and Prinn, 1993).

4. Application of multiple measurements In many cases fluxes can be determined with different techniques, used simultaneously. If results of such multiple or "redundant" measurements are similar, the confidence in the results of the individual techniques is increased. In addition, instead of only measuring the net flux to or from an ecosystem, the components of the flux in the ecosystem (e.g. soil and canopy fluxes) can be measured. This reflects that the net flux depends on different processes and, therefore, is likely to give better possibilities for upscaling. The usefulness of multiple measurements can be illustrated for micrometeorological techniques in combination with other methods. Conventional micrometeorological methods measure only net exchange over a surface, but give no information regarding the underlying processes of trace gas production and consumption. For example, CO2 is taken up by plant canopies through photosynthesis, and it is released by root and heterotrophic respiration in soils and by respiration in above-ground components of plants. On time scales of hours to seasons, these components of the net C flux may be measured directly using a combination of techniques: (i) chamber measurements of soil respiration; (ii) below-canopy eddy covariance measurements of the CO2 flux inferring the respiration; (iii) measurement of leaf-level photosynthesis rates; and (iv) inference of the vertical distribution of sources and sinks within the canopy from CO2 profile measurements (Denmead, 1995). Comparison of the sum of below-canopy fluxes with the eddy flux measurement of the whole ecosystem flux requires some extrapolation. For example, the leaf-level photosynthesis rates depend on local light intensity, which will vary through the canopy. Estimating phototsynthesis for a whole canopy thus requires an estimate of total leaf area and variation of light intensity in the canopy. Similarly, CO2 emission rates from soils may will spatially (e.g., according to soil organic matter content, pH and drainage). For a gas that is both produced and consumed within an ecosystem, such as CO2, the net annual flux represents the difference of two very large numbers. Again, when measured for the whole ecosystem by eddy covariance, there is no information as to where, when and by which process C is being stored or released. Independent estimates of the rate of annual C flux (e.g. in a forest) may be obtained from investigation of the rate of tree growth (from tree ring

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widths or dendrometer band measurements plus allometry to relate the growth of the stem to the growth of the tree), litterfall, root growth and mortality, and soil C storage or release. Because annual increments of growth or C storage may be small and hard to detect (for example in soils), chronosequences (the substitution of space for time) or long-term observations (for rates of tree recruitment and mortality) are needed. Long-term estimates give rates averaged over several years to decades and therefore may not exactly represent the magnitude of the same flux during the specific year when eddy covariance measurements are made. Multiple approaches to determine the C budget for a single stand of black spruce boreal forest have been described in Goulden et al. (1998). Fire plays a dominant role in the C cycle of upland boreal ecosystems. C storage in trees and soil moss detrital layers was determined for sites with similar drainage conditions and vegetation but with differences in the time passed since the last fire. These measurements were used to estimate the rate of C sequestration in regrowing moss and trees recovering from fire. These estimates suggest the 120 year old black spruce stand in the flux tower footprint should be accumulating carbon at an average rate of 0.6+0.2 tons C ha-~yr~. The eddy covariance measurements showed that the ecosystem lost 0.5+0.3 tons C ha~yr ~, averaged over three years. To account for the difference between estimates, Goulden et al. (1998) had to infer a net loss of 0.8+0.5 tons C halyr l from humic materials stored deeper in the soil profile (below surface detritus but above the mineral soils). Three lines of independent evidence supported this conclusion. Automated chamber measurements showed that soil respiration increased through the summer/fall period as the deeper portion of the soil profile thawed and warmed (even as air temperatures were decreasing). The magnitude of increased respiration was consistent with a 0.8 ton C halyr l loss. Radiocarbon measurements of the respired CO2 showed that the source of soil respiration in the fall and winter was derived from decomposition of organic matter more than 30 and up to several hundred years old. Measurements of soil organic matter inventory and radiocarbon content showed that large amounts of carbon of that age were stored in humic materials at depths which thawed and warmed in the late summer and fall. Jar incubations showed that, when thawed and warmed, this deep soil organic matter decomposed rapidly. These multiple lines of supporting evidence build confidence that a net loss of C from deeper organic layers was occurring, which balanced the C gains in surface detritus and trees. Scaling of eddy covariance-based tower fluxes to larger regions is fraught with uncertainty, because of the heterogeneity of fluxes at the landscape scale. Using boreal forests as an example, a 120 yr old black spruce stand was not a strong annual source or sink of carbon based on eddy flux measurements. The average age of forest stands in the surrounding region was 40-50 yr, and these stands are accumulating carbon in growing trees and moss. Assuming losses of deep soil C do not vary much with stand age, the younger stands should be accumulating C. Over very large regions, however, burning of only 1% of the forested area (consistent with an average fire recurrence interval of 100 years) will offset the C stored in the other 99% of area that is regrowing forest or wetland (Rapalee et al., 1998). Thus over very large areas and very long time-scales (averaging over many fire cycles), the net storage of C in these ecosystems should be smaller than that measured in a single (unburned) forest stand. Since the area burned varies widely from year to year, the interannual variability in C storage for a region may reflect the occurrence of fire more than the response of vegetation and soils to shifts in regional climate.

5. Conclusions and recommendations Footprints in Figure 1 show that direct flux measurements can be made on space scales up to

95


le-01

le+O0

le+01

le+02

le+03

le+04

le+05

le+06

footprint (m)

Figure 1. Footprints of different measurementmethods to derive surface fluxes. Methods and their footprints are: (1) Soil gas concentration profile method, 0.1-0.5 m; (2) Chamber technique, 0.5-5 m; (3) Micrometeorological tower measurements, 10-500 m; (4) Nocturnal boundary layer budgeting method, 1-5 km; (5) Convective boundary layer budgeting method, 5-30 km; (6) Airborne micrometeorological techniques, 10-100 km; (7) Airborne mass balance method, 3-1000 km. Not all methods presented can be applied under all meteorological conditions or at sea (see text).

1000 km. This suggests that there are no large gaps between the scale levels for which the different methods can be applied. However, this assumption is not correct, as most techniques can only be applied under certain meteorological conditions. Therefore, different techniques should be used simultaneously to assess the flux at a particular level of scale. It also means that models should be applied to interpolate in time between the periods for which flux measurements are available. This is not always possible. The nocturnal boundary layer budgeting method, for example, infers nighttime fluxes on scales of 1-5 km, which could lead to serious errors in assessing source and sink strengths if the daytime fluxes are not known. Examples where the night and day fluxes differ, include CO2 exchange between plants and the atmosphere (where day and night fluxes are of opposite sign), and N20 and NH3 emission by soils for which fluxes are usually smallest at night. This observational gap can be filled by conventional, tower-based, micrometeorological flux measurements at, say 20 m or more above the surface, or by aircraft. Generally, such measurements are difficult to organize. Flux measurements on a scale of 10-1000 km are rare, but there are several methods to cover this scale level. Two distinct methods are used to derive fluxes using atmospheric models, forward simulations and inverse methods. In forward atmospheric modelling emissions are used to drive the atmospheric model to predict concentrations and fluxes. Predicted concentrations and fluxes can be validated against measurements. Inverse methods use observations of atmos-pheric concentrations to derive flux estimates. Major recommendations for future research on validation are that: - Inverse modelling techniques for the estimation of fluxes at continental scales should be developed further. This requires model investigation of the optimal location of continental sampling sites, and the development of techniques to derive grid scale concentration estimates from point measurements at a station. - Measurement programmes should include multiple, or "redundant" approaches to constrain important mechanisms or processes and to reduce uncertainties in flux estimates.

96

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- Validation of flux measurements for large (country or continental) spatial scales requires the appropriate combination of data from satellite surface sensors and satellite borne, airborne and ground-based concentration measurements. This requires co-ordination of the different research groups working on these problems, as discussed in detail by Burrows (1999).

References Ambus, P. and S. Christensen (1994) Measurement of N20 emission from a fertilized grassland: An analysis of spatial variability. Journal of Geophysical Research 99:16549-16555. Born, M., H. D6rr and I. Levin (1990) Methane consumption in aerated soils of the temperate zone. Tellus 42B:2-8. Baldocchi, D. and R. Valentinti (eds.) (1996) Strategies for monitoring and modelling CO2 and water vapour fluxes over terrestrial ecosystems. Thematic number. Global Change Biology 2(3). Burrows, J.P. (1999) Current and future passive remote sensing techniques used to determine atmospheric constituents. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric science 24. Elsevier, Amsterdam, pp. 315-347. Denmead, O.T. (1995) Novel meteorological methods for measuring trace gas fluxes. Philosophical Transactions of the Royal Society London A 351:383-396. Denmead, O.T., R. Leuning, D.W.T. Griffith and C.P. Meyer (1999) Some recent developments in trace gas flux measurement In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 67-84. Fowler, D. (1999) Experimental designs appropriate for flux determination in terrestrial and aquatic systems. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 99-121. Galbally, I.E. and C. Johansson (1989) A model relating laboratory measurements of rates of nitric oxide production and field measurements of nitric oxide emission from soils. Journal of Geophysical Research 94:6473-6480. Goulden, M.L., S.C. Wofsy, J.W. Harden, S.E. Trumbore, P.M. Crill, S.T. Gower, T. Fries, B.C. Daube, S.-M. Fan, D.J. Sutton, A. Bazzaz and J.W. Munger (1998) Sensitivity of boreal forest carbon balance to soil thaw. Science 279:214-217. Hartley, D. and R. Prinn (1993) Feasibility of determining surface emissions of trace gases using an inverse method in a three-dimensional chemical tracer transport model. Journal of Geophysical Research 98:5183-5197. Heimann, M. and T. Kaminski (1999) Inverse modelling approaches to infer surface trace gas fluxes from observed atmospheric mixing ratios. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gasfluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 275295. Koschorreck, M. and R. Conrad (1993) Oxidation of atmospheric methane in soil: Measurements in the field, in soil cores and in soil samples. Global Biogeochemical Cycles 7:109-121. Lapitan, R.L., R. Wanninkhof and A.R. Mosier (1999) Methods for stable gas flux determination in aquatic and terrestrial systems. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 27-66. Leclerc, M.Y. and G.W. Thurtell (1993) Footprint prediction of scalar fluxes using a Markovian analysis. Boundary-Layer Meteorology 52:247-258. Liss, P.S. and L. Merlivat (1986) Air-sea gas exchange rates: Introduction and synthesis, In: P. BuatMenard (ed.) The role of air-sea exchange in geochemical cycling. Reidel, Dordrecht, The Netherlands, pp. 113-129. Matson, P.A., P.M. Vitousek and D.S. Schimel (1989) Regional extrapolation of trace gas flux based on soils and ecosystems. In: M.O. Andreae and D.S. Schimel (eds.) Exchange of trace gases between terrestrial ecosystems and the atmosphere. Wiley and Sons, Chichester, pp. 97-108.

How canfluxes of trace gases be validated between different scales ?

97

Menke,W. (1989) Geophysical data analysis. Academic Press, San Diego, USA. Rapalee, G., S.E. Trumbore, E.A. Davidson, J W. Harden and H. Veldhuis (1998) Scaling soil carbon stocks and fluxes in a boreal forest landscape. Submitted to Global Biogeochemical Cycles. Remde, A., J. Ludwig, F.X. Meixner and R. Conrad (1993) A study to explain the emission of nitric oxide from a marsh soil. Journal of Atmospheric Chemistry 17:249-275. Rudolph, J., F. Rothfuss and R. Conrad (1996) Flux between soil and atmosphere, vertical concentration profiles in soil, and turnover of nitric oxide. 1. Measurements on a model soil core. Journal of Atmospheric Chemistry 23:253-273. Seitzinger, S., J.-P. Malingreau, N.H. Batjes, A.F. Bouwman, J. Burrows, J.E. Estes, J. Fowler, M. Frankignoulle and R.L. Lapitan (1999) How can we best define functional types and integrate state variables and properties in space and time ? In: A.F. Bouwman (Ed.) Approaches to scaling of trace gasfluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 151167. Smith, K.A., A. Scott, B. Galle and L. Klemedtsson (1994) Use of a long-path infrared gas monitor for measurement of nitrous oxide flux from soil. Journal of Geophysical Research 99:1658516592. Sofiev, M. (1999) Validation of model results on different scales. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 233-255. Trumbore, S. (1999) Role of isotopes and tracers in scaling trace gas fluxes. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 257-274. Vermeulen, A.T., R. Eisma, A. Hensen and J. Slanina (1998) Transport model calculations of NWEurope methane emissions. Submitted to Climatic change. Wanninkhof, R. (1992) Relationship between gas exchange and wind speed over the ocean. Journal of Geophysical Research 98:20237-20248. Whalen, S.C., W.S. Reeburgh and V.A. Barber (1992) Oxidation of methane in boreal forest soils - A comparison of 7 measures. Biogeochemistry 16:181-211.


Chapter 5

EXPERIMENTAL DESIGNS APPROPRIATE FOR FLUX DETERMINATION IN TERRESTRIAL AND AQUATIC ECOSYSTEMS

D. Fowler



EXPERIMENTAL DESIGNS APPROPRIATE FOR FLUX DETERMINATION IN TERRESTRIAL AND AQUATIC ECOSYSTEMS

D. Fowler Institute of Terrestrial Ecology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK

1. I n t r o d u c t i o n

Current interest in quantifying the exchange of trace gases and particles between terrestrial surfaces and the atmosphere has been stimulated primarily by observed increases in atmospheric concentrations in a range of trace atmospheric constituents. In some cases the chemical species involved have relatively short atmospheric lifetimes, for example gaseous ammonia (NH3) with a lifetime of a few hours. On the other hand nitrous oxide (N20) with slow atmospheric removal has an atmospheric lifetime in excess of 100 years. The search for the sources and sinks of each of the trace gases has shown with very few exceptions that sources or sinks of the trace gas in question in soils or vegetation play a major role in the atmospheric budget. Considering further the two examples, in the case of NH3 terrestrial surfaces are both sources and sinks depending on the relative concentrations at the surface (e.g. as apoplastic ammonium, NH4+) or in the atmosphere as NH3. Whereas for N20 terrestrial surfaces represent the major global source and a negligible sink, thus the methodology must be appropriate for bi-directional exchange. The techniques developed for measurement of trace gas fluxes are not unique to this scientific field, they are generally methods taken from the closely related fields of micrometeorology, environmental physics and plant ecology (Monteith, 1973; Woodward and Sheehy, 1983). The motives within these parallel areas of study were to understand the degree to which the exchanges of heat, water vapour and momentum were influenced by biological and physical processes and demonstrated at an early stage a major role of the vegetation in regulating the transpiration flux of water vapour above crop canopies. In most studies of land-atmosphere exchange processes to date the objectives have been to understand specific aspects of the underlying process for a particular surface. The up-scaling of the flux to much larger areas or for longer periods has generally been achieved through models. In developing some of the most elegant, yet simple models this approach has been highly successful, for example evapotranspiration of water from field crops calculated using the Penman-Monteith equation which is widely applied throughout the world. Complex treatments of the land-surface exchange of momentum, sensible and latent heat fluxes are included in a range of meteorological models. The land-atmosphere exchange processes are incorporated into models of long range transport and deposition of pollutants. Increasingly, these models are being adapted to include new developments in understanding land-atmosphere exchange of reactive trace gases (Sorteborg and Hov, 1996). An important difference between the studies of water vapour and energy fluxes and those of the trace gases, excepting the many orders of magnitude in the size of the fluxes, are the underlying links with the partitioning of energy at terrestrial surfaces. In the case of water and

102

D. Fowler

energy fluxes, a detailed study of the energy balance and the water fluxes show a strong dependence which is amenable to rigorous analysis of the underlying physical processes. In the case of the trace gases, while temperature and the energy balance at the surface may influence rates of exchange, the links are generally indirect, or conditional. For some of the reactive trace gases, chemical interaction with the surfaces of foliage and interaction with water films regulate rates of exchange (Flechard and F~'wler, 1998). The design of methods to measure fluxes of trace gases averaged over the range of scales 10 m 2 to 104 m 2 of terrestrial or aquatic surface include a well tried array of techniques from chambers to micrometeorology. For the second objective for this background paper, obtaining flux at a landscape scale or as a function of environmental characteristics requires more than simply obtaining fluxes at a flat, uniform and micrometeorologically ideal site. In this case, the objectives include identification of the quantitative influence of components of the landscape within the flux footprint on the observed rates of exchange. For this latter approach the measurement method is being used to investigate processes rather than simply obtain representative fluxes. The paper briefly outlines the main methods and some recent additions, drawing attention to the measurement strategies necessary for particular objectives. Lapitan et al. (1999) and Denmead et al. (1999) discuss the different measurement techniques used for determination of trace gas fluxes. The following section provides examples of the approaches available to quantify the influence of underlying processes on fluxes or contributions to the flux from components of the landscape.

2. Flux m e a s u r e m e n t at the 0.1-10 m 2 scale; enclosure methods

The measurement of trace gas fluxes at the 0.1 to 10 m 2 scale have been achieved primarily using enclosure methods. Such techniques include static (or closed) enclosures in which the rate of change of the trace gas concentration within the enclosure with time is monitored to determine the net exchange at the surface. Fc -

Ac V

t A

(1)

in which Fc is the net gas flux, Ac/t is the rate of change of trace gas concentration within the enclosure, V is the volume of enclosure, A is the area of source or sink (i.e. soil surface or leaf area). This method is particularly well suited to unreactive gases such as N20 and methane (CH4) for which the rates of reaction onte zhamber surfaces or with atmost-.heric gases within the chamber are small. The method has been widely applied to obtain estimates of fluxes of N20 (Smith et al., 1994; Skiba et aL, 1992) and CH4 in a wide range of soils (Sass et al., 1990; Schtitz et al., 1989; MacDonald et al., 1998). In particular, the static chamber method has been the method of choice for large numbers of individual measurements to quantify the spatial variability in trace gas fluxes over agricultural fields. For N20 flux measurement for example, estimates of the spatial variability in NzO flux within a 100 m 2 area of grassland has been shown to range over two orders of magnitude. Hence, determination of field-scale fluxes requires very large numbers of individual chambers to yield a statistically satisfactory flux estimate. Such application of chambers is not a practical method for the field scale. The alternative flux measurement method using enclosures is the dynamic method in which the difference in concentration between inlet and outlet (Co-C0 combined with the volume flow through the enclosure provides the flux which is then expressed per unit soil or leaf area within the enclosure:

Experimental designs appropriate f o r f l u x determination in terrestrial and aquatic ecosystems

103

Table 1. The loss of fertilizer N as NO for a range of N fertilizers and agricultural soils. Crop

N fertilizer

Bare soil, Germany

NaNO3 NH4NO3 NH4C1 NaNO3 NH4C1 Urea NaNO3 NH4C1 Urea NaNO3 NH4NO3 NH4C1 Urea 33% NaNO3, 67% limestone Manure NI-I4CI NaNO3 NH4C1 Urea NH4C1

Bare soil, Germany

Bare soil, Ge~many

Bare soil, Spain

Bare soil, Canada Bare soil Grass, Germany Grass, clover, dandelion, Germany

Plant incorporation experiment, plants as above, Germany Bare soil Soil mixed with plants Soil mixed Bare soil Perennial ryegrass, UK Perennial ryegrass, UK Grass ley, Sweden Bermuda grass, Texas, USA

Urine (NH4)2SO4 Ca(NO3)2 (NH4)2SO4

N application rate (kg ha -1)

% loss as NO

100

0.04 0.63 1.52 0.04 0.15 0.11 0 03 0.07 0.04 0.14 0.64 1.23 3.25 11 0.26 0.52 0.003 0.05 0.01

100

100

100

0-100 160-800 100 100

Reference

a

100

447 100 200 52

0.02 0.08 0.16 0.14 0.03 0.34 0.3 3.2

7 8 9 10

From Skiba et al. (1997). a 1, Slemr and Seiler (1984); 2, Slemr and Seiler (1991) (experiment 2, 15.7 to 3.8.1983); 3, Slemr and Seiler (1991) (experiment 4, 24.8 to 18.9.1983); 4, Sheperd et al. (1991); 5, Paul et al. (1993); 6, Slemr and Seiler (1991) (experiment 3, 4.8 to 14.8.1983); 7, Colboum et al. (1987); 8, Skiba et al. (1993); 9, Johansson and Granat (1984); 10, Hutchinson and Brams (1992).

r = (Co-C,)K A

(2)

where Co and C; are outlet and inlet concentrations respectively and 7,,. is the volume flow rate

of air through the chamber. In practice, both static and dynamic chamber methods are subject to a wide range of errors, including modification of environmental conditions within the chamber relative to those in the field. The modification includes: (i) the radiation budget in which almost all terms, in the short and long wavelength categories, are modified; (ii) the humidity (and if enclosure times are long, of CO2) and its feed back on stomatal function and the net exchange of CO2 and H20 with vegetation; (iii) modification of the turbulent structure of air and its profile through plant canopies; and, (iv) effect of pressure fluctuation on soilatmosphere exchange. The chamber methods have also been applied to measure nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), carbonyl sulphide (COS), dimethylsulphide (DMS), carbon disulphide (CS2), hydrogensulphide (H2S), sulphur dioxide (SO2) and a range of biogenic volatile organic compounds (VOC) (Slanina, 1997). In general the experimental design has been either: (i) process orientated - designed to investigate production, emission or mecha-

104

D. Fowler

nisms of exchange; (ii) designed for long-term flux measurement, to follow the sea-sonal or annual time course in fluxes of trace gases which show large temporal cycles; and, (iii) complementary flux measurements. Measurements of a particular trace gas flux to complement a much broader study, e.g. of a campaign to test methodologies of trace gas measurement, or the fate of applied fertilizer, or to define the net inputs or losses in ecosystem studies. Most measurements of trace gas fluxes to date were process oriented. In some studies a relatively short period of fluxes were measured and reported simply to identify a new source or sink for an atmospheric trace gas. For example, in the measurement of NO the detection of an emission flux from soil is relatively recent and early measurements simply identified the presence of this loss mechanism for fixed nitrogen from soil, its magnitude and likely importance to chemical processes in the atmosphere (Galbally and Roy, 1978). The use of enclosure methods was entirely appropriate for such studies, is much simpler than micrometeorological approaches and with an appropriate enclosure provides an unambiguous signal to interpret. The bulk of the published literature on emissions of NO from soil (a selection of which is provided in Table 1) have been observed using enclosure methods, but the range and complexity of chambers for the measurements is considerable. In general, dynamic chambers with substantial volume exchange rates and additional turbulent mixing where necessary, are preferred in the recent studies as these minimize the difference between ambient conditions and those within the enclosure (Figure 1). The use of chambers had focussed on the questions which can with minor modification be applied equally to most of the other trace gases: (i) what are the field emission rates of NO from soils subject to a range of fertilizer nitrogen inputs; (ii) which soil chemical and physical variables regulate NO fluxes, and can functional relationships between flux and the variables be established; and, (iii) can a set of readily obtained environmental variables be used to simulate long-term NO emission from soil to provide the basis for extrapolation to seasonal and field scale emission estimates, and validated against direct measurements. While progress with the fist two questions has been clear, few studies to date have been designed to address the third question. The major variables which regulate NO emission from soil have been identified from work listed in Table 1 and models of the process have been developed by e.g. Galbally and Johansson (1989). The extrapolation of NO fluxes to provide annual emissions over regions has however been based on a much smaller range of variables. For example, the approach of Williams et al. (1992) is based on a very simple exponential response of the NO emission rate to temperature: Fr

= A exp (B. Lo,,)

(3)

where F~vo~ is the flux in ng N m -2 s~, A is a factor to characterise soil properties and has units ofng N m 2 s1 and B is a temperature coefficient (0.071 + 0.007 ~ The values of A were obtained from land use maps and B was provided empirically from measured temperature responses of NO emission to soil temperature. While source strength estimates from up-scaling of this kind are necessary to compare the magnitude of this source term with other sources, the uncertainty introduced by the approach must be recognized. In particular, it is widely recognized that soil water, and in particular the water filled pore space strongly influences rates of gaseous exchange between soils and the atmosphere, has a major influence on NO fluxes. The measurements of Ludwig and Meixner (1994) over an arable soil showed that in very dry soils, the emissions of NO are small and that available soil nitrogen supply is also an important variable, whereas at intermediate soil water contents large emissions of NO were observed. The experimental design therefore needs to capture the range of physical, chemical and biological variability which can then be incorporated in subsequent models. The model extrapolation then remains within the boundaries of the parameter space validated by measurements.

105

Experimental designs appropriate forflux determination in terrestrial and aquatic ecosystems

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Figure 1. a) Dynamic chamber design; b) Schematic figure of the dynamic flow-through chamber. The walls and all internal surfaces are fluorinated ethylene propylene (FEP) Teflon (From Kim et al., 1994).

The spatial variability has been addressed at the 1 m 2 scale using chamber methods and has been one of the limitations in the methodology for provision of fluxes at the field (104 m 2 to 106 m 2) scale for trace gases which show large spatial variability. Among the best examples of the spatial variability are provided by the literature on emissions of N20 and methane (CH 0. In the case of N20 emission the spatial heterogeneity is large because the combination of key variables which regulate N20 flux also shows large spatial variability. The denitrification process requires anoxic conditions which may occur in microsites or over a large volume of soil, and available soil nitrogen. For exchange with the atmosphere a diffusion pathway to the soil surface is required. In addition, microbiological processes are temperature sensitive. Given these constraints, and temporal and spatial variability in the variables, the observed range of emission fluxes in a single field of 20 to 500 ng N20-N m 2 s1 is not surprising (Christensen et aL, 1996). The scale of the variability in mean fluxes using simple static chamber methods may be seen in the diagram (Figure 2). Such variability can be quantified directly and used to deduce the number of chambers necessary to obtain field scale fluxes (Christensen et al., 1996).

D. Fowler

106

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Distance (m) Figure 2. "Contour map" of N20 flux within 42 6 x 42.6 m grid sampling area, sho:~,ing three simulated positions of megachamber (values on contour lines in g N20-N ha .] d-~. (From Smith et al., 1994).

However, the fluxes over the field scale are more easily measured directly with current sensitive detectors including tunable diode laser absorption spectroscopy (TDLAS) which is ideal for micrometeorological flux measurements (Zahniser et al., 1995). Thus the chamber methods are limited in scale to 0.1 to 102 m2. The upper limit represents the megachamber approach such as that used by Smith et al. (1994) and also by Galle et al.

r

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120

144

168

192

216

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N=O flux (g N=O-N ha ~ d ~)

Figure 3. Frequency distribution of fluxes from 51 simulated random positions of megachamber (from Smith et aL, 1994).


107

(1994). The size of the large flexible wall chambers vary in the range 20 m e to 100 m 2 and provide a very useful means of integrating the very fine scale variations in emissions. The broader scale variations over a field, as illustrated in Figure 2, would limit the ability of even the large chambers to provide field scale flux estimates. An illustration of the spatial variability in N20 fluxes over agricultural grassland has been provided by Smith et al. (1994) from an experiment in which a range of different chamber sizes (0.008 m 2 to 62 m 2) were analyzed to map the fine scale variability in N20 flux over an area of 43 m x 43 m (as illustrated in Figure 2). A simulation of fluxes averaged over 62 m 2 at 51 random positions within the mapped area yielded a mean flux of 145 ng N20-N m 2 s-1 with a coefficient of variation of 25%. This mean flux may be compared with the measured mean value obtained using the megachamber at the 3 positions illustrated in Figure 2 that were possible during the study of 253 ng-N20-N m -2 s~ with a coefficient of variation of 67%. The spatial variability simulated over this area is illustrated in Figure 3. This experiment illustrates very clearly the difficulty in obtaining field scale fluxes using chambers for trace gases whose controlling variables show very large spatial variability. Other combinations of chamber measurements which reveal the quality of experimental design include CH4 fluxes over both wetlands (emission) and agricultural and semi-natural soils (deposition or oxidation). Methane emission from rice production has been extensively studied using chamber methods (Schiatz et al., 1989; Sass et al., 1990). While spatial variability in emission exists, the agronomic practices reduce the scale of variability and the more important focus for experimental design is the temporal variability in emission. An automated chamber technique designed for seasonal measurements but with sufficient time resolution to observe diurnal responses in CH4 flux to changes in temperature, water table and the net exchange of carbon dioxide (CO2) to the crop canopy has yielded key data to permit the up-scaling of CH4 emission from rice production (Schtitz et al., 1989). Variability in CH4 source strength at the 1 m 2 to 10 m 2 scale is illustrated by a horizontal transect of CH4 fluxes in peat wetlands (Figure 4). The data illustrated in Figure 4 are taken from peatlands in Scotland (MacDonald et al., 1998) but are very similar to data from other

Figure 4. Methane emission transect.

108

D. Fowler

Table 2. CI-h flux and mean soil physical and chemical characteristics from disturbed and undisturbed forest in Cameroon and Borneo. Wet lowland evergreen dipterocarp tbrest (Borneo)

Moist pre-montane semi-deciduous forest (Cameroon) Near primary forest

Old secondary tbrest

Primary forest

Old secondary forest

Young secondary forest

-27.2(18.6)

-17.5 (11.4)

-15.4 (6.4)

-13.9 (8.4)

-10.8 (9.5)

36.1 (6.3)

33.4 (3.7)

34.4 (5.1)

35.4 (4.9)

31.7 (7.0)

22.9 (0.4)

23.2 (0.6)

24.8

25.1

25.5

0.64 (0.12) 0.97 (0.06)

0.72 (0.12) 1.12 (0.17)

0.75 (0.06) 0.98 (0.09)

0.76 (0.07) 1.01 (0.06)

1.0 (0.14) 1.3 (0.12)

PH c (BaC12)

3.5 3.5

3.7 3.8

3.3

3.1

3.5

Organic carbon c (%)

10.3 3.5

4.5 1.4

3.6 (0.8)

3.3 (1.3)

3.0 (2.0)

Total N c (%)

0.27 0.20

0.21 0.11

0.33 _

0.25 _

0.22 _

CI~ flux a (ng m a s"l) Soil water content

(%) Soil temperature

(~ Bulk density b (g cm3)

From MacDonald (1998). a Median () standard deviation, all other figures are means. In Cameroon the number of CH4 flux measurements is 54, and in Borneo 20. b Upper figure is for 1-5 cm, lower figure tbr 6-10 cm. c Upper figure is tbr top 1-5 cm, lower figure tbr 5-50 cm.

northern peatlands (Bartlett et al., 1992). By contrast, rates of C H 4 oxidation in soils are confined within a narrow range as shown for a broad range of land uses in the UK, Cameroon and Borneo (Table 2). Thus for CH4, the spatial variability in fluxes depend primarily on whether the lanscape contains significant wetland areas (which are large potential sources), or whether wetlands areas (or saturated soils) are absent. In the latter case, CH4 oxidation rates in soil will determine the magnitude of the flux which will be small (1 to 20 ng CH4 m2 s-~), and -ve (i.e. deposition). In mixed landscapes with a small proportion (1% to 20%) occupied by saturated soils or wetland, predicting the net CH4 exchange and its temporal variability is a considerable challenge for experimental approaches or models. The detailed mechanistic studies of fluxes of reduced sulphur compounds in crop canopies and of VOC emissions from vegetation are necessarily limited in spatial scale. In the case of emissions of isoprene and other VOCs for example, it has been very important to establish the source strength of foliage as a function of the local irradiance on leaves and therefore the importance of position within the plant canopy (Steinbrecher et al., 1996). The data for both process study and extrapolation to annual and regional scale emissions has been based on chamber studies for VOCs. The field data using small enclosures over vegetation has provided mean emission fluxes per unit leaf dry weight and converted into standardized emission rates for the leaf temperature and irradiance (GOnther et al., 1991). An example of such studies from the work of Street et al. (1997) is presented in Table 3 for isoprene emission from 6 European tree species. The objectives of these chamber measurements include both improvements in understanding processes of trace gas production or consumption and the provision of key variables to model fluxes to canopies and the landscape scale. In many of the recent studies the response of the VOC emission flux to irradiance and leaf temperature are widely reported (GOnther et al., 1991; Pio et al., 1994).

109

Experimental designs appropriate for flux determination in terrestrial and aquatic ecosystems

Table 3. Summary of total summed, average emission rates of isoprene measured lbr various plant species during 19921994. Mean measured emission rates Standardized emission rates a ng h-I per gram dry.weight ng h -1 per gram dry.weight

Species / type / date Quercus petraea (n=4) (CO2 exp., 1992) Quereus petraea (n=l 1) (03 exp., 1992) Picea sitchensis (n=3) (Temp. exp., controlled env., 1993) Picea sitchensis (n=8-13) Field samples 1993

Picea sitchensis (n=4) (1 sample day, 1993) mean of 3 sample days June/July, 1992 and 1993 Hex europaeus (field samples)

Control Treated Control Treated 21.0 ~ 32.6 ~ Day (16 m) (13 m) Night (16 m) (13 m) Young Old Young (n=9) Old (n=12) Flowering (April 1993) Nonflowering (June 1993)

Quercus ilex (n-9) Field samples, Mediterranean, June 1993 Pinus pinea Field samples, Mediterranean, June 1993 Eucalyptus globulus Field samples, S. Europe, June 1994

Young (n=9) Old (n=-20) Young (n=9) Old (n=37)

2359 4758 9235 10193 10380 6769 10978 7611

2650 + 160 5080 + 1100 829 + 312 915 + 322 2391 6317 1083 + 956 145 + 120 135 :k 75 29 + 20 1562 + 418 218+ 98 1056 + 1090 904 + 559 71-705 b

5506 1082 7622 7396 281-668 b

1780

1866

29996 i 20402

18297

2898 + 1663 9301 + 4505 70509 + 46376 10435 + 9491

2258 7385 61398 17981

From Street et al. (1997) a Values presented for standard conditions of 30~ and 1000 gmol m2 s~ using current models. b Isoprene exceeded detector range, therelbre total estimated should be at least 1705 ng g-i h-l, and 1968 ng g~ h -1 (standardized), all gorse values + 25% on average.

The limitation o f enclosure techniques is their spatial coverage. M o s t o f the artefacts introduced by the enclosure itself can be o v e r c o m e with sophistication o f the equipment or quantified by experiment. The temporal changes can likewise be o v e r c o m e by automating the equipment. Some o f the most significant developments in understanding the mechanisms and providing annual emission fluxes have been provided by long-term automated enclosure methods. The spatial limit o f the enclosure method has recently been extended by the use o f the enclosed catchment at Risdahlsheia (Jenkins, 1997) with a ground area o f 400 m 2 to determine net ecosystem rates o f NO, 03, SO2 and CO2 exchange. Such enclosures present an opportunity to test hypotheses and to develop models describing the major controlling variables. There are also difficulties including the attribution o f the measured fluxes to c o m p o n e n t s o f a complex ecosystem and the same concerns over modification o f the environment. E v e n with these measurements the practical limit for enclosure techniques remains substantially below 103 m z.

3. Measuring fluxes over the field scale (104-106 m 2) The most widely used field scale methods are those using micrometeorological methods which extend from 102 to typically 106 m 2. The upper limit is generally constrained by the availability o f suitable fetch for measurement and cost and convenience. Another the problem is that as the

110

D. Fowler

footprint of the studied area expands, then the requirement for information on properties of the surface which may influence the measured flux increases with the square of the linear extent of the fetch. At the lower limit, fluxes of emitted trace gases have been measured using mass balance methods, especially for NH3 soil emissions (Denmead, 1983; Wilson et al., 1983). The experimental design constraints are similar in principle to those for the chamber studies. In the case of field scale fluxes, the early work followed closely the approach of micrometeorological studies of heat water and momentum, the objective was to obtain sufficient field data to identify the major variables regulating the flux so that scaling could be achieved using models. Seldom did the micrometeorological approaches provide mean fluxes data which could be used directly to represent annual fluxes. At its simplest the field data were used to parameterize components of a resistance network which were then applied to concentration data, e.g. for estimating fluxes of reactive trace gases SO2, NO2 and NH3. These fluxes are now almost invariably simulated using models containing spatially disaggregated land use, meteorological and concentration fields and progress in refining the methods down to finer and finer grid scales is rapid (Erisman and Baldocchi, 1994). For the trace gases N20, CH4 and CO, the focus is primarily on emissions and the scaling problems are larger than those for SO2, NO2 and NH3 as the terrestrial areas over which representative fluxes are required are much larger. Taking emissions of CH4 from high latitude wetlands for example, the terrestrial area of these peat dominated wetlands is of the order 3 to 9 x 1012 m ~ (Sebacher et al., 1986; Mathews and Fung, 1987). The mechanistic basis for extrapolation of emissions of the radiatively active gases is weaker than the deposition of short lived, reactive gases. The reason for this is that at present there are no models which from first principles are able to simulate all of the mechanisms and achieve fluxes of the same order as those measured. Thus, as is the case for VOCs, the response of the measured flux to e.g. temperature, water table and soil NO3 is coupled with mean measured fluxes for particular land use categories in standardized conditions as input in emission models. Thus the measurement database for key ecosystems is essential.

3.1. Eddy co-variance The methods available include Eddy Co-variance (or eddy correlation) in which the vertical flux density, F,., of a trace gas may be written as: ~ =wp.,

(4)

where the bar denotes an average over an appropriate measuring period, w is the vertical velocity and p.s. is the density of the trace gas. This may be considered as the sum of two components, the product of mean vertical windspeed w and the trace gas density A. and fluctuations about the means of the same quantities w' and p's: F.,= w p. + w'p'.,

(5)

where ps. is the gas density and w' and p's are the instantaneous vertical wind velocity and the departure from the mean concentration of the trace gas, respectively. It has been assumed by some that there is no mean vertical flow of air to complicate the above, very simple relation, However, in practice, sensible and latent heat fluxes cause vertical gradients in air density which result in an apparent vertical flow of air. This effect, described by Webb et al. (1980), has important implications for fluxes of some trace gases. The mean w signal resulting from these effects is too small to be detected in field measurements. However, the effect on trace gas fluxes has been estimated by Denmead (1983) from the work of Webb et al. (1980).


111

Equation 4 now becomes: F,=W'ps+(p.,/p~)[/2/(l+py)]E+(p.,/p)H/cpT

(6)

In equation (6) H is the sensible heat flux, E the latent heat flux,/2 equals the ratio of molecular weight of dry air to that of water vapour, or= p~/p~; p~ is the density of water vapour; pa is air density; the total air density p = p~ + p~; c, is the specific heat of air at constant pressure; and, T is the air temperature. The corrections are large for any of the trace gas species whose vertical flux is small in relation to the ambient concentration. Vertical fluxes are frequently normalized for ambient concentrations at a reference height S(z) The resulting quantity F]S(~), which is identical to the deposition velocity (vd) (Chamberlain, 1975), has been widely applied in trace gas studies, and the error in determining va in conditions of moderate sensible and latent heat fluxes becomes significant for deposition velocities smaller than 5 mm s-~. These problems, however, may be overcome by the measurement system. For example, if the sample gases are dried and brought to the same temperature or if the mixing ratios are determined for each sampling point, density problems are eliminated.

3.2. Flux-gradient methods 3.2.1. The aerodynamic method

The vertical transport of an entity towards the surface can be described as follows: F,. = -Pa K,~.Ss/Bz

(7)

where Ks is the transfer (diffusion) coefficient for the trace gas s; s (=ps/pa) is the mixing ratio of the gas with respect to dry air; z is height; and 6 s ~ z is the vertical gradient in air concentration in the constant flux layer. If the concentration decreases towards the surface, fis/& is negative by convention and the flux is towards the surface, and vice versa. The flux density for momentum, Fm (more commonly denoted z), may be written as F = PaKm~z~(6u / 6z)

(8)

where u is the wind velocity. The heat flux is then: F h = c p to a

Kmz~(60~z)

(9)

where 0 is the potential temperature. In neutral atmospheric conditions the eddy diffusivities for heat, water vapour, trace gases, and momentum are equal (K,,cz~ = KH~zj, etc.). In these conditions the eddy diffusivity may be determined from the wind profile equation, where: U(z) = U* l n z - d k Zo

(10)

K,,(z~--kU,(z-d)

(11)

and

where U is the windspeed at height z, U, is the friction velocity, k is von Karman's constant, d is the zero place displacement, and zo the roughness length. The measurement of a concentration gradient (6s/fiz) then enables an estimate to be made of the trace gas flux from equation 7, provided the distribution of sources and sinks is such that the value of d is the same for momentum and for the trace gas in question. The zero plane displacement (d) is in principle

112

D. Fowler

an unknown quantity. Over low vegetation ( 0) when the ambient concentration is lower than the compensation concentration (m < mc), or a deposition (J < 0) when m > me. When the fluctuations in the ambient concentration overlap with the variations of compensation concentrations, the compensation concentration is a critical variable for the regulation of the flux. This is the case for many trace gases (Conrad, 1994). Knowledge of the compensation concentration allows parameterization of fluxes from simultaneous production and consumption processes, as me = P/k, thus providing information about the mechanisms that are ultimately responsible for the ensuing flux. If mc and either P or k are known, the resulting net flux can usually be calculated (provided that some additional parameters such as soil density and gas diffusivity are known). This has been shown for the

206

R. Conrad and F.J. Dentener

exchange of nitric oxide (NO) between s~';,1 and atmosphere (Galbally and Johansson, 1989; Remde et al., 1993; Rudolph et al., 1996b). Compensation points can be measured by combining appropriate micrometeorological techniques and concentration measurements. For example, aerodynamic gradient methods have been used to measure exchange fluxes of ammonia (NH3) between a plant canopy and the atmosphere. The atmospheric concentration at which there is no net exchange is then defined as the compensation concentration (e.g. Sutton et al., 1995). The compensation point concept provides a useful theoretical framework for measuring and modelling trace gas fluxes between the biosphere and the atmosphere. However, it must be carefully evaluated whether the mathematical formulations described above can be usefully applied, since production and consumption processes should be homogeneously distributed at the scale considered. For example, the concept has been successfully applied for dihydrogen (H2) exchange between legume fields and the atmosphere (Conrad and Seiler, 1980b), but the parameters measured are only valid at the scale of the field. They do not apply when only parts of the system (e.g. soil without plants) are considered (Conrad, 1996a), because production and consumption of H2 are spatially separated. For example, H2 is produced by N2-fixing bacteroids in plant root nodules, while H2 is consumed by H2-oxidizing bacteria and abiotic enzymes in the soil (Conrad and Seiler, 1981; Schuler and Conrad, 1991). Compensation points are found in soils, vegetation and water bodies (oceans/lakes). This paper addresses the use of compensation points in understanding measured atmosphere-biosphere exchange fluxes and describing those fluxes in models. We only consider compensation points for terrestrial systems.

2. Compensation concentrations in soil and vegetation 2.1. Occurrence of compensation concentrations The example of H2 exchange shows that the compensation concentration can be governed by a composite of soil and vegetation processes. For simplicity, we shall also consider those processes taking place in plant roots (such as H2 production by legumes) as soil processes. However, it is still necessary to differentiate between the pedosphere (soil) and the phyllosphere (active above ground biomass). The total mass of leaves and the leaf area index depend on the type of vegetation. Especially in forests with a dense canopy there is a stratified system in which a trace gas (e.g. NO) may undergo chemical reactions in the atmosphere below the canopy before it is emitted into the tropospheric boundary layer or deposited from the troposphere onto the canopy or soil (Duyzer et al., 1983; 1995; Kramm et al., 1991). In addition, the net flux between troposphere and biosphere may be affected by production and consumption processes occurring both in the soil and the plant canopy. The direction of the flux is determined by the compensation point, which may be different for soils and plants. Our current knowledge base of compensation points is compiled in Table 1 for different trace gases. In soil, compensation concentrations have been described for all the trace gases listed, except for methane (CH4) and ammonia (NH3). For NH3 we may assume that compensation concentrations do exist, since biogenic soil ammonium (NH4+) should readily equilibrate with soil hydroxyl anions (OH) to form NH3. Indeed measurements by Langford et al. (1992) suggest that at some locations soil NH4+ and soil pH are consistent with measured atmospheric NH3 concentrations. However, to our knowledge explicit measurements of soil NH3 compensation points are still lacking.

207

The a p p l i c a t i o n o f c o m p e n s a t i o n p o i n t c o n c e p t s in s c a l i n g o f f l u x e s

Table 1. Literature reports a demonstrating production and consumption processes in upland soils and plants (phyllosphere) and the existence of compensation concentrations. Trace gas

Soil Production

Hz

Consumption

Vegetation Compensation

Production

Consumption

Compensation

1,2

3,4

1,5

n.r.

n.r.

n.r.

CO CH4 OCS

6,7 14-16 20-22

3,8 17-19 23, 24

3,7 n.r. 24

9-11 n.r. 25

12,13 n.r. 26-28

n.r. n.r. 29

NzO NO

18,30,31 40,41

32-34 42-44

35 45-47

36-38 37.38,48-50

39 48-50

n.r. 50

NO2

51-53 58,59

51-53 60

52 n.r.

48,54,55 61,62

55-57 62,63

54,55,57 62,64

NH3

n.r. = not reported. a 1, Conrad and Seiler (1980b); 2, Schuler and Conrad (1991); 3, Liebl and Seiler (1976); 4, Conrad and Seiler (1981); 5, Conrad and Seiler (1979); 6, Conrad and Seiler (1982); 7, Conrad and Seiler (1985); 8, Conrad and Seiler (1980a); 9, Seiler et al. (1978); 10, Ltittge and Fischer (1980); 11, Tarr et al. (1995); 12, Bidwell and Bebee (1974); 13, Peiser et al. (1982); 14, Sexstone and Mains (1990); 15, De Groot et al. (1994); 16, Yavitt et al. (1995); 17, King (1992); 18, Conrad (1995); 19, Conrad (1996a); 20, Bremner and Steele (1978); 21, Aneja et al. (1979); 22, Adams et al. (1981); 23, Castro and Galloway (1991); 24, Lehmann and Conrad (1996); 25, Feng and Hartel (1996); 26, Taylor et al. (1983); 27, Brown and Bell (1986); 28, Protoschill et al. (1996); 29, Kesselmeier and Merk (1993); 30, Bouwman (1990); 31, Granli and Bockman (1994); 32, Ryden (1981); 33, Slemr et al. (1984); 34, Donoso et al. (1993); 35, Seiler and Conrad (1981); 36, Weathers (1984); 37, Dean and Harper (1986); 38, Klepper (1987); 39, Lensi and Chalamet (1981); 40, Davidson (1991); 41, Williams et al. (1992); 42, Conrad (1996b); 43, Rudolph et al. (1996a); 44, Dunfield and Knowles (1997); 45, Johansson and Galbally (1984); 46, Remde et al. (1989); 47, Rudolph et al. (1996b); 48, Johansson (1989); 49, Weber and Rennenberg (1996a); 50, Wildt et al. (1997); 51, Slemr and Seiler (1984); 52, Slemr and Seiler (1991); 53, Baumg~h'-tneret al. (1992); 54, Rondon et al. (1993); 55, Weber and Rennenberg (1996b); 56, Segschneider et al. (1995); 57, Thoene et al. (1996); 58, Fenn and Hossner (1983); 59, Milchunas et al. (1988); 60, Buijsman and Erisman (1988); 61, Sutton et al. (1995); 62, Mattsson and Schjoerring (1996); 63, Hutchinson et al. (1972); 64, Husted et al. (1996).

The situation is more complicated for CH4, since consumption processes occur in deeper soil layers, whereas production processes occur in the surface soil (see Conrad, 1996a). Due to differences in the vertical distribution of CH4 production and consumption, it is difficult to attribute a compensation concentration which would be useful at the field scale. More research is needed on this subject. In the phyllosphere, both production and consumption processes have been described for all the trace gases except for H2 and CH4 (Table 1). Hence, plants probably do not interfere with the fluxes of H2 and CH4 in upland sites with well aerated soils. This is different in wetlands, where the vascular system of aquatic plants may serve as a venting system for the emission of H2 and C H 4 ( a n d of other trace gases, such as nitrous oxide, N20) from the submerged soil into the atmosphere (Conrad, 1995; 1996a). Plant compensation concentrations for carbon monoxide (CO) have not yet been measured, but they should theoretically exist, since plants can both produce and consume CO (Table 1). The reason why, so far, detection of CO compensation points has failed is that CO exchange with plants has generally been measured in flushed chambers. The CO concentration observed at the outlet of the chamber was probably much less than the compensation concentration which would have been be reached in a closed system at steady state between production (a light-dependent reaction) and consumption (a dark reaction). The concentration increase of CO caused by the plants was always meadow > natural forest > man-disturbed environment. As a rule the fluxes of trace gases like CH4, N20 display log-normal spatial distributions, extremely patchy with "hot-spots" of intensive emission as compared with more-or-less even or normally distributed emission patterns for gases like CO2. However, the homogeneity of marine ecosystems should not be underestimated. The application of remote sensing has revealed strong patchiness of oceanic water related to upwelling phenomena and currents. This inhomogeneity has much bigger scale as compared to terrestrial systems, being displayed within 102-104 m scale as compared with 10 -3-10~ m in terrestrial habitats. Table 3 presents the relevant characteristics of the open ocean and a forest. The degree of patchiness of gases and 2.as fluxes in the ocean depends on the relative importance of turbulent mixing and the gas life time, i.e. reactivity. Nitrous oxide exhibits less patchiness than a more reactive gas such as dimethyl sulphide (DMS). Carbon dioxide shows even less spatial heterogeneity since it is well buffered. The diel cycle of carbon dioxide in the ocean is rather limited because of this buffering mechanism, whereas in terrestrial systems it may be very pronotmced. The net carbon dioxide fluxes in these ecosystems are, however, of similar magnitude.

Table 3. Comparison between the ocean and a forest in terms of trace gas and mixing dynamics. Physical property

Ocean

Forest

Mixing of horizontal layers and vertical convection

High

Low

Spatial gradients of dissolved trace gases and their precursors

Gradual

Steep

The main mechanisms of gas transfer

Diffusion Bubbles

Diffusion Ebulli'ion Vascular transport

The rate of diffusive gas transfer

Low

High

The relationship between the rates of production and consumption of trace gases

Almost complete compensation

Lack of compensation

Net gas fluxes

Low

High

Gas turnover time

High

Low

Spatial organization of biota

Unstructured dominance of free-swimming organisms; vertical stratification over tens of meters; position of chemocline is well defined

Structured dominance of rooted immobile organisms; vertical stratification over cm to meters; mosaic-type of chemoclines

Relations between scale, model approach and model parameters

231

6. Conclusions and recommendations There are a variety of different reasons why modellers chose to work at a certain scale. Ideally, the scale of modelling should coincide with the scale of the questions to be answered and the processes involved. However, the scale of the information available and that of the information required usually do not match because of data limitation, computer resources and environmental questions posed by policy makers. This requires development of modelling strategies which allow coupling of models working at different scales and originating from different scientific disciplines. To this end we should give more attention to aggregation problems, non-linear effects as a result of coupling, and advanced methodology for nesting, both on-line and off-line. Moreover, these coupled, scale-bridging, models should be tested at various scales so that the uncertainties will be constrained and the most important parameters at the scale of interest made apparent. As a general rule, for deriving flux estimates we should not resolve the processes on scales smaller than needed to answer the questions raised, because (i) this would be a waste of resources, and (ii) could result in an imbalance in the detail and the level of scale between different model components which does not necessarily improve the results. However, for predictions beyond present-day conditions it is mandatory to include detailed knowledge of the most important processes. Validation of such a model is, of course, rather limited, but deserves attention given our changing environment.

References Archer, D. (1999) Modelling carbon dioxide in the ocean: A review. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 169-183. Bouwman, A.F., J.G.J. Olivier and K.W. van der Hoek (1995). Uncertainties in the global source distribution of nitrous oxide. Journal of Geophysical Research 100:2785-2800. Bouwman, A.F. (1996). Direct emission of nitrous oxide from agricultural soils. Nutrient Cycling in Agroecosystems 46:53-70. Bouwman, A.F., R.G. Derwent and F.J. Dentener (1999) Towards reliable bottom-up estimates of temporal and spatial patterns of emissions of trace gases and aerosols from land-use related and natural sources. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 1-26. De Wilde, H. and W. Helder (1997). Nitrous oxide in the Somali Basin: the role of upwelling. Deep Sea Research H 44:1319-1340. Heimann, M. (1995) The TM2 tracer model, model description and user manual. DKRZ Report No. 10, Ger. Clim.Comput. Centr. Hamburg, 47 pp. IPCC (1997). The 1996 Revised IPCC Guidelines for National Greenhouse Gas Inventories. OECD, Paris. Lapitan, R.L., R. Wanninkhof and A.R. Mosier (1999) Methods for stable gas flux determination in aquatic and terrestrial systems In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 27-66. Mosier, A., C. Kroeze, C. Nevison, O. Oenema, S. Seitzinger and O. van Cleemput (1998). Closing the global atmospheric N20 budget: nitrous oxide emissions through the agricultural nitrogen cycle. Nutrient Cycling in Agroecosystems 52:225-248. Nov~ik, M., S.H. Bottrel, D. FoltovL F. Buzek., H. Groscheovfi., K. Zfik (1996) Sulfur isotope signals in forest soils of Central Europe along an air pollution gradient. Environmental Science and Technology 30:3473-3476.

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Soetaert, K. and P.M.J. Herman (1995). Nitrogen dynamics in the Westerschelde estuary (SW Netherlands) estimated by means of the ecosystem model MOSES. Hydrobiologia 311: 225-246. Von Schulthess, R., D. Wild and W. Gujer (1994) Nitric and nitrous oxide from denitrifying activated sludge at low oxygen concentration. Water Science and Technology 30:123-132.

Chapter 12

VALIDATION OF M O D E L RESULTS ON DIFFERENT SCALES

M.A. Sofiev



VALIDATION OF MODEL RESULTS ON DIFFERENT SCALES

M.A. Sofiev Institute of Program Systems, c/o. Sroiteley str. 4, bid. 1, app. 18, Moscow 117311, Russia


One of the most difficult problems in numerical simulations of atmospheric processes is the validation of calculated results against actual measurements. All models are based on sets of assumptions and simplifications of either complicated or poorly known processes. Therefore, it is necessary to carefully assess the accuracy of each model component to reach a balanced configuration. Since such a procedure still does not guarantee a proper reproduction of reality, validation of the total model is also important. The testing procedures used during model development differ from those used for model validation (Figure 1). During model development the quality of separate model components is examined; this requires specific measurement data, analytical solutions of test problems and peer review of scientific approaches. Model validation is the numerical evaluation of the modelled quality without consideration of such aspects as internal model structure, underlying principles and scientific approaches. For validation it is essential that measurement data are accompanied by a description of the data's precision and applicability for the model considered. There are three main objectives of model validation: (i) quantitative evaluation of the precision of the model results; (ii) improvement of the understanding of a particular phenomenon; and, (iii) assessment of the general factors regulating atmospheric transport and chemistry processes. A quantitative evaluation of the model's precision is especially important for further application of model results, for example, in the construction of scenarios on emission reduction and associated abatement costs on the basis of anthropogenic loads and ecosystem sensitivity, as calculated with mathematical models. Clearly, scenario calculations should be based on input data of known quality, as the answer has to be formulated in concrete figures which can be used in further assessments. Qualitative conclusions in terms of a "reasonable agreement" or "acceptable precision" are therefore not appropriate. The second set of objectives of model validation is related to understanding particular observations, such as trends, and maximum and minimum values. Model validation generally results in an expression of the reliability of the model simulations, the model's ability to reproduce a particular process and an indication of possible causes of disagreement. The output of the validation procedure is a key input to further model development, and a basis for the design of measurement strategies. The last set of objectives is related to the fundamental description of processes, which is particularly important in exploring new investigation areas, for example, in heavy metal or persistent organic pollution problems. In addition to the above approaches based on modelmeasurement comparison, such description implies a direct comparison between models.

236

M.A. Sofiev

of model r e s u ~ ,•.o.mparison withmeasurements and "state

1~(,

I/ V

Measurement data sets for development

Verification measurementdata sets State of the art knowledge

Analytical solutions Generally accepted scientific approaches

j MODEL DEVELOPMENT

K.

J MODEL VALIDATION

Figure 1. Simplified scheme of verification during the model development and validation. The results indicate the processes reproduced similarly by most of the models considered, which can be seen as the "current state of knowledge" on a particular process. Particularly in large-scale modelling there are many examples where the amount and quality of available measurements is insufficient for complete and reliable model evaluation. In such cases an additional artificial data set, reflecting what is referred to as "generally accepted" estimates, can partially fill the data gap. An example of such analysis is an estimation of the transport distance of a pollutant. This parameter cannot be easily measured but it can be inferred from the analysis of the output of different models. Where there is agreement of results from several independent models, it is possible to speak about the "generally accepted value" of the transport distance. This paper will discuss the current status of validation methods for atmospheric pollution models applied to different scales. The focus is on evaluating the complex models rather than on testing the separate model components. The scales and the corresponding types of models considered are locally, regionally and globally oriented. The differences between the type of observational data and their use in vaiidating models at different scales will also be emphasized. Short-range (local) models are validated against available monitoring data obtained from specially developed field experiments with large sample volumes appropriate for detailed analysis. The validation of long-range models requires careful consideration of the representativeness of the measurement for the appropriate scales.

2. Classes of atmospheric chemistry transport models There are different ways to classify models of air pollution distribution. Here, models will be classified on the basis of their spatial coverage, which is closely related to their spatial and temporal resolution. Three groups of models can be distinguished: (i) global and hemispheric models with a spatial coverage of more than 10000 x 10000 km, a spatial resolution of several degrees latitude x longitude and averages of the output data over a month or longer periods; (ii) region,q models, with a spatial coverage of about 1000 x 1000 km, a spatial resolution of about 100 Ion and averages over several days; and (iii) local models with a coverage of up to 100 x 100 km, a spatial resolution of several kilometers or less, and averages over one hour or less.

Validation of model results on different scales

237

Global models are generally developed for evaluation of mass balances of a substance, transformation of a set of compounds in the atmosphere; assessment of the main pathways and distribution of emissions, and deposition and interactions between the Northern and Southern hemispheres. Correspondingly, the methods for validation should consider those parameters appropriate for the temporal and spatial scale of the model. Global models generally operate with seasonally, annually or multi-annually averaged data. The spatial resolution of the results comprises several degrees. Therefore the availability of representative monitoring data is a major problem. In addition, global models often include complicated chemistry schemes for the analysis of multiple substances (e.g. NO, hydrocarbons, ozone). Regional models also simulate the distribution of pollution on a large scale and in the long term. They generally include complex no:,-linear chemical transformations of the substances considered. Hence, validation of regional models requires information on multiple substances from long-term measurements. Similar to global models, a problem in applying regional models is to select measurement data that represent large areas and long time periods. The validation of global and regional models is normally carried out at several measurement sites, which should represent all the main characteristics of the regions covered by the calculations, such as climatic zones, soils and water surfaces. The model is used for extrapolating to areas where no measurement data are available. Ideally, the period of the validation corresponds to that of the simulations. Contrary to the first two types of models, local models are mainly used for the evaluation of short-term concentrations averaged over time periods of minutes to hours. The major goal is to make a short-term forecast of the pollution distribution around the emission sources (both continuous and instantaneous). Local models are also used to assess the distribution of accidental pollutant release and emission of hazardous substances. Except for ozone models, the description of the chemistry of these models is of secondary importance. One of the main problems of local models is to correctly describe the pollution pattern :,,ear the emission source for different atmospheric conditions. As local models describe near-source processes, they are often constructed using a non-hydrostatic approach (contrary to hydrostatic approaches where air is considered as a liquid in quasi-equilibrium). For their validation, the model should produce a three-dimensional description of pollution plumes in the complete calculation domain. Validation is done for a number of typical conditions, for example, for several types of meteorological stability, so that the results can be extrapolated to other episodes. The classification of atmospheric models presented above is not strict. Current fast improvement of computer power results in increasing comprehensiveness of the models. The global models of today are now as comprehensive and detailed in their process descriptions as the regional models of some years ago. In some cases modem computers allow one to apply regional models to global or hemispheric calculations (Galperin et al., 1995). Another way of increasing the comprehensiveness without losing the spatial coverage is to use zooming models, which actually work at several different scales simultaneously (e.g. Moussiopoulos, 1994; Galperin et al., 1996). Currently, there are some efforts to compare models based on different approaches and on different scales. In the field of local modelling, the initiative, "Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes" (Olesen, 1995b), should be mentioned. Comparison of regional models was done within the ozone model comparison project (Hass et al., 1994), and acid models were compared by the EMEP / MSC-W (CoOperative Programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe / Meteorological Synthesising Centre West) and EUMAC (European Modelling of Atmospheric Consistence) model comparison (Hass et al., 1996). Heavy Metal

238

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Sofiev

Model Intercomparison was done in EMEP (Sofiev et al., 1996) and some others. Most of these projects concentrated on the development of the best available measurement data set and evaluation of the overall model behaviour.

3. Statistical techniques The first step in all model validation exercises is the calculation of the mean or median values of observations and model results. They are calculated in a general way as follows: -

1

a4

x - --~--~ x, m

(1)

t=l

if arithmetic averaging is chosen, and x = med(x,M)

(2)

for the median. Here M is the sample size and x either measured or modelled values. The use of median values is preferable if there is a possibility of values falling outside the main trend or far from the average values (outliers), because this method is robust to their existence and it yields results that are not sensitive to outliers. Contrarily, the arithmetic mean is more appropriate if the effectiveness of the estimation is more important than its robustness. The correlation coefficient between two sets is also taken as sample estimate: r

-

coy(x, y ) -

~

--

(x - x)(y - y )

(3)

The correlation coefficient is not a robust estimate and should always be applied with caution. The following example from the FMEP Heavy Metal model Intercomparison (Sofiev et al., 1996) illustrates this point. One of seven participating models produced values for 10 monitoring sites, as indicated in Figure 2. The extremely high value of the correlation coefficient (0.985) is mainly due to the monitoring site GB2. The correlation coefficient decreases to 0.958 by excluding this point. This value is high, but the small size of the sample (9 pairs) results in large uncertainties in its determination, which is reflected by the wide 67% confidence range (0.73 - 0.99) (see equation 7). The 67% confidence range means that the true value is inside this range with a probability of 0.67 (such a range for normal distributions equals 2 x the standard deviation). The regression analysis is generally based on linear assumptions of the model-measurement relationship considered to be distributed by the first-order polynomial additive, normally distributed, stochastic component ~.

y,=Ax,+B+g,,

i=l,M

(4)

On the basis of these assumptions, the method of least squares leads to the common estimates of the regression coefficients: AMLs = x y -

Xy --2

'

BMI,s = y - AMI.s x

(5)

x 2 -x

All estimates presented above are finite-size sample estimates of the parameters considered. Their standard deviations can be calculated as follows:

239


-o i2

dB2

:3 O

-~ io o

G'B1 ~ NL3 ,i,

N2 ,F1 9GB4

Mean obs.=3.52 Mean calc.=3.5 Corr.=0.985

,, GB3 DK1

2

4

6

8

i0

12

Observed

Figure 2. Measured and calculated lead concentration in ~g 1~ in precipitation. Abbreviations of the stations are the notation~ accepted in OSPARCOM monitorin? network (Oslo and Paris Commission, !994). Source: Sofiev et al. (1996).

1 o- E = - ~ o - x

(6)

for arithmetic mean E(x); 2 1 ~

O'r

rx

(7)

JM

for correlation coefficient rx; o~.

1

o~. =

~

-~ x-

(8)

x/Met,.

for regression slope A; and O"B

~

,/7 ,/7 +x-2

(9)

for the regression bias. The notations here are similar to those of equations (1)-(5). In some cases equation 4 is not correct, and application of the method of least squares might give uncertain results. Some reasons for that and possible solutions for a more robust methodology of linear regression are discussed by Sofiev (1994), Sofiev and Galperin (1994) and Sofiev et al. (1994). In addition to the above simple model evaluation procedures, the ability of a model to describe the measured time trends (both short-term and long-term) can be quantitatively evaluated by the mean square deviation, maximum absolute deviation and the maximum relative deviation between modelled and measured trends. These are calculated as follows: p _ ~1 N

N ~-~( y' - x , ) l=,

2

(10)

M.A. Sofiev

240 for the mean square deviation;

(11)

Q = max ( [ y , - x, ,i = 1,X) for maximum absolute deviation, and

IT,- x,I

R-max lY, +x,l'

(12)

i : l,N, y~ + x~ >0 'v'i)

for the maximum relative deviation. N is the number of elements in a set. There are other parameters which may be used to compare two data sets, but equations (10)-(12) are probably the most common with clear physical meaning. Equation (10) is closely related to the method of least squares and is applicable to the estimation of overall agreement of model results and observations. Maximum values of absolute deviation in equation (11) indicate the ability of a model to reflect peaks. This estimate may be normalised by the mean value over the measurement data set. The relative deviation in equation (12) is more sensitive to low values where small absolute errors result in high relative deviations, reflecting the model's quality in remote regions and for episodes with low concentrations. Kriging analysis can be used for interpolation of measurement data. The number of stations and their locations must be adequate to represent the heterogeneity of the region considered, to resolve major emission sources and characteristics of the terrain, such as relief and large water basins. After smoothed observed values have been obtained, they can be projected to a model grid and compared with the calculated results. Probably the best approach here would be the spatial analysis of the areas of differences, their location and final creation of a map showing areas of the model correspondence with measured data. This map can be useful both for model improvement and further use of the model data. An example of applied kriging analysis can be found in Berge et al. (1994).

4. Model-measurement comparison The comparison of model output with available measurements is the most widely used method for model validation. Practically all models are validated with some sets of observational data which are considered as an "ideal" reference. Nevertheless, there is a substantial difference in the approaches to model-measurement comparison used for regional and global models on the one hand and local models on the other. For each group of models, model validation against observations will be discussed first, with emphasis on the requirements of measurement data. After this, the different validation techniques will be summarized for each group of models. The precision of non-measurable model results for the group of regional and global models will be discussed briefly. For local models, a tracer experiment will be described as an example of a systematic method of model validation.

Table 1. Estimate of the representativeness (D) of monthly measurements for a large area (about

100 km linear size). Type of measurement

Minimum D

Maximum D

Mean value 23%

air concentrations

20%

28 %

NO2 air concentrations

27%

34%

31%

SO4=, NO3, NH4+ concentration in precipitation

14%

30%

25%

SO 2

Source: Galperin and Sofiev (1994).

241


4.1. Regional and global models 4.1.1. Measurement data

One of the characteristics of large-scale models is that they cannot reproduce high concentrations close to pollution sources. They cover large areas, including remote regions where concentrations are sometimes at the background level, close to the device detection limit. This raises the problem of calibrating the monitoring device and estimating its precision. Nowadays virtually all large-scale monitoring campaigns include quality assurance of the measurement technology and devices. For example, a regular quality assurance of the monitoring sites and their intercalibration are performed within EMEP each year (e.g. Fahnrich et al., 1993; Semb et al., 1994). For substances like sulphur and nitrogen oxides, the precision of measurement devices is high and corresponding errors are negligible. For other species such as zinc and cadmium, the standard deviation and detection limit is in the same range as the measured value itself. For some heavy metals and their compounds (e.g. mercury-containing species) and persistent organic pollutants, the measurements may only show the order of magnitude of their concentration or a mere detection of their presence. The problem of representativeness of point measurements for some of the surrounding areas and time periods was considered by several investigators. The representativeness of point measurements can be described on the basis of differences between data from closely located stations and the standard deviation of the sample mean. The deviation of the mean concentration in a grid cell depends on the size of the cell, the averaging period, and the type of substance (Ebel et al., 1994; Galperin and Sofiev 1993; Seilkop, 1994; Sirois and Vet, 1994). Table 1 presents the sum of squares of regular and chaotic differences between monthly values measured at two independent closely located monitoring sites, which is calculated as: D=

+or A-

(13)

where A is the mean value of relative differences between station data A, and era is its standard deviation. The distance between stations was about 100 km and several pairs of stations were considered for each compound. This allows for calculating the maximum, minimum and mean value of the representativeness, D. For daily measurements, the estimate of the representativeness taken from Seilkop (1994) shows the 95% confidence limits for individual summer air pollutant concentrations measured within 80-km grid cells and presented as a percentage of the grid-cell mean (Table 2). The uncertainty caused by limited representativeness of point measurements for a grid cell can be very important. Comparing daily measurements with model calculations carried out with a spatial resolution of about 100 km is not meaningful (Table 2). This uncertainty drastically decreases when measurements are averaged over longer time periods (Table 1).

Table 2.95% percentile range for daily concentrations measured at points within the 80-km grid cell. Type of measurement SO 2

air concentration

Lower limit

Upper limit

-76%

+172%

SO4= air concentration

-40%

+56%

03 maximum daily concentrations

- 17%

+20%

Source: Seilkop (1994).

M.A. Sofiev

242

4.1.2. Validation methods Generally, the number of statistical tools currently used for model validation is rather limited. Expressions of the validity include the correlation coefficient describing the agreement between measurements and the model simulations, various types of confidence intervals and percentiles, and linear regression analysis commonly based on the least squares method. Apart from statistical techniques, a widely used method is qualitative analysis of various graphs showing temporal variations of the concentrations at the stations, with a further qualitative evaluation of the model's ability to reproduce the peaks and lowest values as well as the main observed trends. The main advantage of the above methods is their simplicity. They are also fairly useful for interpretation of the results. The main disadvantage is that they sometimes lead to inaccurate or subjective conclusions, as they are based on assumptions on the type of distribution function of the data considered. First of all, they are supposed to be normally distributed N(x, a, or) according to the Gaussian distribution function (Figure 3): l

(x-=?

N(x,.,o ) - 4 ~

e 2~

(14)

where a is the mathematical expectation of the stochastic variable x , and cr the standard deviation of x. This function permits negative values of x, which is incorrect in the case of atmospheric concentrations and many other physical parameters. However, this function is acceptable for some cases where a > > or. In many other situations essentially positive functions should be used, such as the widely used gamma distribution (Figure 4): 1

x-1 -x e x>o O,

(15)

x_ 10 000 km

Global

300

R.G. D e r w e n t et al.

T a b l e 2. Average annual global budgets and fluxes of CO2 for the period

1980 to 1989 based on Houghton et al. (1995). Source/flux

Pg C yr -!

CO2 sources

Fossil fuel and cement Changes in tropical land use Total emissions from human activities

5.5• 1.6• 7.1•

P a r t i t i o n i n g b e t w e e n the e n v i r o n m e n t a l reservoirs

Storage in the atmosphere Oceanic uptake Uptake by northern hemisphere regrowth Additional sinks from CO2 and nitrogen fertilization

3.2• 2.0• 0.5• 1.4•

but selected on the basis of real, physical or economic constraints. For some issues, a relative (0 to 1 scale) may provide a satisfactory methodology for handling uncertainty.

2. Scales a n d t r a c e gas b u d g e t s

Considerable uncertainties remain in the regional and global budgets for a number of atmospheric trace gases because of the enormous spatial heterogeneity and temporal variability of the factors controlling trace gas fluxes in both aquatic and terrestrial ecosystems. Indeed, oceanic and terrestrial phenomena are expressed over scales ranging across many orders of

ecological complexity

10 I

(number of state variables)

O

High complexity process models

\

O

-1

....):~i................:9 ..... .:.: ....................-

.......::( . ............

space Extrapolation

Regionalmodels

.~ 1 ~ ~.. 3 ~

........q ill .......... O

....... :...i

log (1 O0 k m )

time 5 " ~

Generalcirculation models

log ( y e a r s )

F i g u r e 1. Arrangement of different types of models according to their characteristic spatial and temporal scale, compared with the scales over which models are extrapolated. Note that space and time scales are log scales. Modified from Murphy et al. (1993).

How should the uncertainties in the results of scaling be investigated and decreased ?

301

Figure 2. Diagramshowingthe top-downscaling approach with its hierarchyof models (inverse models)and the bottom-up approachwith the various forward(direct) models. magnitude, from seconds to millennia, and from microns to thousands of kilometers (Table 1) and variability is observed on all these scales. Techniques used for extrapolating measurements or properties and constraining results between different temporal and spatial scales are referred to as "scaling". Two approaches to scaling are used, i.e., the bottom-up and top-down scaling. Models are widely used tools in bottom-up scaling approaches. The range of possible models that can be used and coupled together is large, and models may cover many different scales (Figure 1). There is a need for a hierarchical approach to modelling ecological (aquatic and terrestrial) systems for crossing scales. Process models of high ecological complexity, probably imply high resolution in time and space. Ecological complexity is defined here as the number of components included in ecological process models. Regional models tend to have a lower temporal and spatial resolution and hence a reduced ecological complexity. Highly aggregated or parameterized models are generally associated with atmospheric or oceanic general circulation models. Figure 1 shows the scales over which the models are built compared with the scales over which models are extrapolated. Figure 2 illustrates on a circular diagram the different steps in scaling, the top-down approach with its hierarchy of models (inverse models) and the bottom-up approach with the various forward (direct) models. At each given scale, it is crucial to validate any model used with an observational data set. Measurement data should also be accompanied with further information concerning precision and applicability to validation at a given scale. Three main reasons can be invoked for conducting model validation: (i) to obtain a quantitative evaluation of the precision of the model output results; (ii) to gain an understanding of particular trends, minima and maxima, and so on; and (iii) to assess existing effects and to find out some "state of the art" understanding of phenomena (Sofiev, 199q). Here, uncertainty is investigated at a given scale using a mass balance approach. Uncertainty at a given scale depends, in part, upon the scaling approach, top-down or bottom-up, which has been chosen to reach selected objectives. We will illustrate this strategy with examples of a long-lived radiatively active gas such as CO2, and a short-lived reactive gas such as NO.

302

R.G. Derwent et al.

3. Calculating trace gas budgets on different scales 3.1. Mass balances and budgets A trace gas budget for a particular environmental compartment or reservoir can be formulated by drawing an imaginary boundary around the compartment and constructing a mass balance for the exchanges across the boundaries and the intemal sources and sinks. The basic equation for the trace gas mass balance in the compartment is: U

M

dc

(1)

F,n-F~,u,+~E,-~LI-V-t=l

/=1

dt

where c is the mean trace gas concentration in mass or molecules per unit volume, V is the compartment or reservoir volume, Fin is the advection flux of the trace gas into the compartment, Four is the advection flux of the trace gas out of the compartment, ZE is the sum of the internal source terms for the trace gas, EL is the sum of the internal sinks. At each scale above point-scale, the uncertainty can be reduced by constraining the Fin-Fout of that particular scale. The most obvious way to apply that constraint is by using trace gas measurements that encompass the complete scale size and another option is inverse modelling. Micrometeorological techniques allow measurement of field-scale fluxes which can constrain the fluxes derived from scaling up from the point measurement, while aircraft measurements may do the same for the local- or regional-scales. Inverse modelling using surface trace gas measurements from an appropriately-sited baseline station or network with meteorological data such as wind trajectories may constrain the local- or regional-scale trace gas flux. Such constraints are generally easier to apply at the local-scale rather than at the regional-scale but there is no fundamental difference. Such techniques, from the global-scale perspective, may give constraints to the trace gas budget from the regional-scale. An example of putting constraints on the regional-scale trace gas fluxes is given by Fowler (1999), using aircraft measurements around the United Kingdom. However, it is generally harder to put constraints at the regional-scale. Studies at the regional-scale call for creativity. For Europe, baseline trace gas monitoring stations and meteorological analyses are available and have been useful in defining European source strengths for a range of trace gases (Simmonds et al., 1993; Veltkamp et al., 1995; Hensen et al., 1995; Simmonds et al., 1996; Vermeulen et al., 1997). However, it does not follow that other regional-scale trace gas fluxes could be likewise constrained since it might not necessarily be feasible. Other options to reduce uncertainties include: (i) the use of multiple trace gases and their correlations with time and space; and (ii) isotopic signatures (Trumbore, 1999). On the global-scale, where the environmental compartment or reservoir contains the entire global atmosphere, the inflow and outflow are both by definition zero. For long-lived, wellmixed gases such as CO2 the annual growth in the atmospheric burden can be determined accurately from the network of measurements of atmospheric concentrations and there is no need for scaling-up when estimating that quantity. Consequently, the balance on the sinks and sources must equal the change in mass deduced from the concentration changes. This "constraint" limits the degree of uncertainty in the mass balance estimates. On the point-scale, that is at one single point in the atmosphere, the number of sources and sinks at tbat point is limited and can be accessed accurately by measurements or estimation methods and so there is no need for scaling. On intermediate-scales, depending on the scale and the character of a region, there are often a large number of sources and sinks which have to be assessed and which are not


303

accessible to direct measurement (because of their spatial extent or because of their number). Trace gas fluxes thus have inherently the largest uncertainty at the intermediate-scale. It is possible that the uncertainty increases with the number of source and sink "units" that have to be assessed and summed. For trace gases with relatively short atmospheric residence time, the situation is different. Atmospheric concentrations will be heterogeneous and global inventories can no longer be accurately assessed readily from a small number of atmospheric concentration measurements. However, it is conceivable that there are regions for which the inflow and outflow terms could be neglected, if the short-lived trace gas has a distinct, spatially limited source region, for example. A mass balance encompassing this source region and its immediate surroundings, including the regions to which the gas can be transported from that source, could be constructed in such a way that the advective terms of the mass balance would not have to be quantified.

3.2. Up-scaling and aggregation At the point (process) level we have a trace gas emission from a process, i, of which the observed intensity, E, is given by: E, : A, e ,(*)

(2)

where A; is the elemental size, c; is the emission factor for the considered process. The emission may depend on several continuous environmental parameters, such as temperature, pressure and humidity, and these are indicated by * in the above equation. Let the accuracy of the point-scale measurements of the emission flux be ere and assume that for point-scale measurements, the size of an element is known exactly. Then, for the larger scales we have:

El~ents E F,e,a / ,o~ / ,.~ / ~,,,h =

elements ~ ,( * ) ~

t=l

A~

(3)

j=l

and several conclusions may be noted: (i) the uncertainty of the sizes of elements becomes non-zero starting from the field-scale and so the uncertainty of any mapping increases whilst the spatial scale increases; (ii) qE also increases with spatial scale since the ranges of the continuous environmental parameters, or indeed any new parameters which may influence emissions, also increases whilst the spatial scale increases. Other processes (such as losses due to sinks and chemical transformations) can be considered in the same manner. Hence uncertainty in bottom-up scaling increases as the spatial scale increases. The only exclusion is the global-scale, where we have an additional constraint since Fm and Fo,t are zero. This constraint may simplify the processes of performing validation and reducing the uncertainties.

3.3. Down-scaling and disaggregation Let us consider the inverse modelling as the process of downscaling. E = Te

(4)

where E is the emission flux vector to be calculated, e is the measured concentration vector,

304

R.G. Derwent et al.

T is the inverted transport matrix. Let Re and 1~ correspond to the covariance matrices of the emission fluxes and concentrations, respectively, then they will be connected by the equation: RE = TRcT T

(5)

where T T is the transposed matrix T. The uncertainties in the transmission matrix also result in an additional variability of the emission fluxes. These two effects result in increasing uncertainty from top to bottom. The sequential application of scaling "bottom-up" (or "top-down") results in increasing uncertainties whilst going along the chain. Only the inclusion of additional information at each scale reduces uncertainties for a particular scale and gives an improved basis for the next steps in scaling.

3.4. Identifying and estimating uncertainties in scaling 3.4.1. Point scale

At the point-scale, there is no uncertainty in the elemental size since it is defined as the size of the measurement (e.g., a leaf, the enclosed m 2 of rice paddy). The uncertainty in the emission measurement is derived from replicate measurements and typically in the order of 1-10%. Depending on the continuity of the measurements, this can have a time resolution of the order of minutes to a year. At the point-scale, uncertainties in flux estimates are usually at their least and are governed by the experimental design and the accuracy of the trace gas observations required. A processbased model is constructed which explains much of the variance in the observed trace gas fluxes and, if the explanatory variables have been thoughtfully chosen, is suitable to extrapolate the fluxes from the point to the field-scale. 3.4.2. Field scale

The uncertainty in the elemental size now appears but may still be minimal (e.g. the size of an agricultural field is still well defined, the number of leaves in a stand of trees will already be based on an estimate with a standard deviation). So, the uncertainty at the point-scale is passed on to the field-scale, enlarged by the uncertainty in the elemental pool size and by spatial variability at the field-scale. The uncertainty is therefore higher at the field-scale compared with the point-scale. At these scales, the scaling of the trace gas fluxes is a relatively straight-forward process and will involve sub-dividing the field up into a number of elements. The process model would then be applied within each element and the field-scale flux would be obtained by summing over all the elements. Since the elements are not obviously dissimilar to the experimental sites, the model would be applied without change but employing the particular input data for the explanatory variables appropriate to each element. The uncertainties involved in scaling are usually at their least in moving from the point to the field-scale. They may be investigated by: (i) field-scale model evaluation campaigns; (ii) field-scale budget studies; (iii) isotopic methods can be used to increase the sensitivity of field-scale budget studies by introducing a unique label for the exchange of the trace gas with the ecosystem in question; and, (iv) using multiple trace gases to provide a cross check on the relative fluxes of the gases. By increasing the density of point-scale measurements within a field-scale study on a campaign basis, it should be possible to check the validity of the process-based model


305

employed, together with the aggregation procedures used to scale up to the field-scale. By making observations upwind and downwind of the studied field, it is possible to infer fieldscale trace gas fluxes which can be compared with the results of the aggregation and scaling procedures from the lower scale. 3.4.3. Local scale

In principle, the same procedures can be used to investigate uncertainties in scaling from the field- to local-scales as from the point- to field-scales, involving model evaluation campaigns and local-scale budget studies. The uncertainty in the element distribution increases (based on e.g. mapping, census), together with the spatial variation in the parameters (such as soil types and vegetation composition). In the case of the ecosystem trace gas flux at the local-scale, no other uncertainties are involved. However, when trying to constrain the flux at the local scale by fixing the Four, other sources and sinks may also contribute to the budget of the trace gas being studied. Uncertain-ties in these sources and sinks are largely independent and are transferred to the uncertainty of the trace gas flux at the local-scale. Scaling from the field scale to the local scale will involve using the process-based models for each of the fields within a locality. Since biological species composition and meteorological conditions are homogeneous over the locality by definition, the process-based models can be used at the local scale without significant adjustment. Data for the explanatory variables in the process-based models have to be obtained for each field and the aggregation process may have to be handled within a geographical information system. It may be that not all data on the explanatory variables are available, in which case new variables or surrogate data would have to be used. Depending on the definition of the locality, there might be up to hundreds of fields within the locality. For such techniques to be effective, the chosen ecosystem would necessarily need to be the dominant source or sink of the trace gas within the locality. 3.4. 4. Regional scale

Generally speaking the regional scale is not much different from the local scale, it is larger and therefore uncertainties grow accordingly. Uncertainty increases because it is most likely that the number of sources and sinks involved in the total flux (budget) increases, uncertainty in the element distribution increases (mapping, census) and spatial variation within the source and sink elements increases because soil composition, agricultural management will vary over the study region much more than at the local scale. Alternatively, the regional scale could be defined as that scale over which parameters may (but not necessarily) exceed the range covered by the field- and point-scale measurements (climate, temperature, light, etc.). At the local-scale this range is not exceeded. Uncertainties are generally the largest and confidence is the least in the scaling up of trace gas fluxes from the local- to the regional-scale (Figure 2), despite the approach being, in principle, much the same. Depending on the definition of the region, there might be up to thousands of localities within the region. Uncertainties enter into the scaling because coverages are not always available within the geographical information system for all of the explanatory variables in the process-based model. Surrogate variables may have to be constructed to fill in gaps in coverages. The process-based model may have to be simplified and perhaps recast using an entirely different and inferior set of variables. To investigate the uncertainties in the scaling up from the local- to the regional-scale, we

306

R.G. Derwent et al.

rely on the same validation procedures as employed at the smaller scales. This validation is, however, much more difficult than at the smaller scales and makes a significant contribution to the level of confidence in regional-scale ecosystem trace gas flux estimates. There are a number of difficulties associated with the validation of ecosystem fluxes for the trace gases at the regional-scale, as follows: (i) other sources and sinks for the trace gas may be operating on the regional-scale in addition to the chosen ecosystem source; there may be, for example, an animal enteric fermentation source of methane associated with the use of organic fertiliser in rice paddies; (ii) there may be significant changes in meteorology within the time-scale of the transit time across the region; (iii) it is difficult to establish accurately the upwind boundary conditions for the trace gas for each wind direction; (iv) large sources within the region may disguise the presence of smaller sources further upwind; (v) the large spatial scales involved with regional-scale validation makes experiments more onerous, difficult to replicate and expensive to maintain over extended periods; and, (vi) cross-checks with other pollutants may become unsatisfactory because of divergent fates and behaviour between the individual trace gases on the regional scale. 3.4.5. Global scale

From the bottom-up approach, uncertainty would be anticipated to be highest at the globalscale. This is not always the case because for some trace gases the global-scale ecosystem flux is tightly constrained by the global trace gas budget. Under these conditions, scaling up the uncertainties from the regional-scale to the global-scale is not relevant. However, in situations where the global budgets cannot be constructed with any confidence, the global constraint on total ecosystem flux does not apply and uncertainty is indeed highest at the most aggregated scale (Figure 2).

4. Investigating and reducing uncertainties in ecosystem fluxes The conceptual framework of uncertainties in results of scaling can be illustrated using examples of different environmentally important trace gases, with different atmospheric lifetimes. We use CO2 as an example of a long-lived trace gas and NO as a short-lived gas.

4.1. Carbon dioxide Here we u s e CO2 as an example of a trace gas which is long-lived and well-mixed throughout the lower atmosphere. Assessment of the uncertainties in the scaling of net ecosystem exchange fluxes of CO2 is of paramount importance for the implementation of possible mitigation strategies. Because of the rich data set of atmospheric CO2 observations, it is possible to construct a global budget which constrains the global net ecosystem exchange flux. For convenience the oceanic and terrestrial reservoir are discussed separately because of the differences in heterogeneity and net source and sink strengths. CO2 fluxes between the ocean and the atmosphere are investigated by either mass balance constraints in the ocean, or by determining the flux from air-water partial pressure differences (ApCO2) (see Archer, 1999). Mass balances can either be performed in the surface mixed layer (20-100 m), basin wide, or for the whole ocean. Fluxes are determined from the ApCO2 multiplied by the solubility, Ko and the gas transfer velocity, k: F = k Ko ApCO2

(6)


307

At the poim-scale, fluxes are generally determined by measuring the ApCO2 and estimating the k from a parameterization with wind speed which has an uncertainty of a factor of two based on the range of derived relationships (Liss and Merlivat, 1986). The partial pressure difference ApCO2 can be measured with an accuracy of 1-2 ].tatm while the range of ApCO2 over the open ocean ranges from roughly -80 ~atm for sinks during biological blooms to 150 ~atm for sources in the Equatorial upwelling region. In coastal areas and inland waters the range can increase t o - 2 5 0 j.tatm in intense bloom situations to 350 j.tatm during coastal upwelling. On average, the global disequilibrium has to be -7 to -10 ~tatm to satisfy the global constraint of 2 • 1015 g C yr -1 uptake by the ocean. The error in CO2 measurement is absolute such that the relative error in ApCO2 will range from 3% to over 100%. The greatest errors correspond to situations with low fluxes such that error estimates on point-scale fluxes from ApCO2 alone is about 3-10% with little possibility in the near future of reducing this uncertainty. Scaling from the point-scale to the local-scale and field-scales is performed by taking ApCO2 measurements over an extended area and over several seasons. Lack of temporal coverage contributes significantly to the uncertainty in the scaling. Accurate wind speed records can now be obtained on these scales from satellite observations and numerical weather prediction models. In addition to the average wind speed, the variability in the winds has to be known since the relationship between gas exchange and wind speed is thought to be nonlinear. Independent verification of uncertainty is difficult and can be best estimated from the variability in the parameters that influence pCO2 such as temperature and salinity. A promising technique to determine fluxes over the ocean, and thereby reduce uncertainties, are micrometeorological measurements such as eddy correlation, relaxed eddy accumulation, and gradient measurements. Because of the small magnitude of the fluxes, these direct flux measurements (see Fowler, 1999; and Lapitan et al., 1999) are singularly difficult to perform over the ocean. Depending on the height of measurement, these trace gas flux measurements can cover ranges from 1 to 10s of km. Scaling up CO2 fluxes from the local- and field-scales to regional-scales is frequently done by interpolating data using parameterizations with temperature and more recently ocean colour. Global maps of these parameters on short (day-month) time scales are available. Uncertainties are determined by the robustness of the parameterizations that are empirical rather than obtained from first principles. Verification on the regional-scale is commonly performed using simple process models embedded in basin-wide ocean circulation models. Global-scale estimates are scaled up from regional measurements in a similar way as regional extrapolations. The uncertainty of global oceanic exchange is reduced because of independent observational and modelling constraints. The global observational constraints are based on uptake and partitioning of carbon isot%es 13C and ]4C between ocean and atmosphere (Trumbore, 1999). Ocean circulation models (OCMs) tuned to mimic the observed 14C distributions or column burdens can be used to downscale observations to the regional-scale although this work is still at a rudimentary stage. Recently, independent global CO2 constraints using changes in O2/N2 ratios in the atmosphere have been implemented decreasing the uncertainty on the global scale (Bender et al., 1996; Keeling et al., 1996). Scaling terrestrial CO2 exchange is in many ways more difficult than for ocean exchange because of the extreme heterogeneity and small net fluxes compared to the large variable gross exchanges. On the regional-scale even the direction of fluxes is uncertain. For instance, it is not clear whether the rain-forests are a net source or sink for CO2, and the magnitude of the sink strength of the Northern Boreal forests is uncertain. The large uncertainty in trace gas exchange fluxes at the point-scale is to a large extent caused by temporal variability that

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ranges from diumal changes in photosynthetic uptake to interannual changes in climate. Pointscale measurements are seldom performed continuously and flux measurements have to be interpolated with significant uncertainty, even at this level. Local-scale constraints on uncertainty can be imposed by micrometeorological measurements but frequently this upscaling step cannot be done with sufficient temporal resolution to offer a strong constraint. At larger scales even fewer validation checks can be performed for terrestrial systems. Detailed process-oriented modelling approaches using functional types (e.g. the CASA model) incorporated into three-dimensional atmospheric transport models, provide a new way to increase the scale of observations through to the regional- and globalscales. On the global-scale the same constraints are in place as for the ocean. The combined net uptake by the ocean and terrestrial sinks is well known from the response of the global burden to the perturbation of the CO2 system by fossil fuel input; it is the partitioning between the oceanic and terrestrial sinks that is uncertain. As for the ocean, the global constraints are improving through the application of carbon isotopic ratios and determination of 02/N 2 ratios.

4.2. Nitric oxide

Nitrogen oxides are produced in several ecosystem processes mainly as nitric oxide (NO). In the atmospheric boundary layer NO is involved in many reactions. In this chapter, the sources and sinks of NO are discussed as an example of a highly reactive trace gas. Firstly, the most important sources of NO on a global scale are discussed (Table 3), then the atmospheric fates of the nitrogen oxides are described briefly. Finally, the processes by which these gases are removed from the atmosphere are described.

4.2.1. Sources of NO On the global scale, human activities as well as natural biogenic processes are important sources of NO. Some natural sources may even be linked to human activities. Table 3 starts with lightning, an important natural source, although not much information is available on the production of NO during lightning events. The production on the global scale is, however, relatively small although in background areas and in the absence of human activity, lightning can be important. Microbial denitrification and nitrification processes cause emissions from soils, with usually these processes taking place in the uppermost soil layer at depths of only a few centimeters. The production rate of NO has shown to be dependent on several variables. Strong correlations are observed with soil moisture content and temperature. The process seems to be understood relatively well on an ecosystem scale although the number of ecosys-

Table 3. Global budgets and their uncertainties lbr the nitrogen oxide (NO.,,) species. Source type

Global emissions (1012 g N yrl )

(1012 g N yr-l)

Uncertainty

Lightning

6

3

Soils

10

5

Biomass burning

8

2

Fossil fuel combustion

21

1

Total

45

14

Sinks

55

30


309

tems that have been studied is limited. Consequently there is an urgent need to carry out measurements in different ecosystems rather than long-term, detailed, studies on one site. The soil NO emission rate is also dependent on parameters such as ecosystem type, nutrient availability, and especially soil texture. In some areas the input from the atmosphere through dry and wet deposition is quite important in the soil nitrogen budget (Davidson and Kingerlee 1997; Delmas et al., 1997; Veldkamp and Keller, 1997). Through this mechanism, soil emissions are related to human activities. Global estimates of NO emissions from soil are rather crude and rely upon a limited number of measurements in a small number of ecosystems. Although the detailed geographical information on soil types and environmental parameters is lacking, more information about NO and soils is needed. As with the burning of fossil fuels, NO is also formed in the burning of biomass. A limited number of studies have been performed and little is known about the influence of biomass composition. Another important uncertainty is the number of fires that take place in a certain region, though increasingly, remote sensing methods are now being used to obtain better information on fires. Much more information is, however, available on the most important global source, fossil fuel burning. Emission factors are usually available as a function of fuel type and statistical information is available on fuel use. Therefore the uncertainty in the estimate of the emission from this source category is relatively small (Table 3). Summarizing, we can conclude that the largest uncertainties in estimates of global emissions of NO lie with the emissions of NO from soils and from lightning. 4.2.2. Atmospheric processes o f NO

NO may be rapidly converted by ozone (03) to nitrogen dioxide (N02). In the n0ctumal boundary layer NO will deplete 03 and participate in a series of other reactions such as reactions with NO2. During the day nitrogen oxides play a dominant role as a catalyst in ozone formation. In this process NO and NO2 are converted into one another rapidly: NO + 0 3 ~ NO2 In principle, this interconversion does not produce any ozone until hydrocarbons become involved. Through reactions of hydroxyl (OH) radicals, hydrocarbons are destroyed and radicals are formed that may shift, through reaction with NO, the above photostationary state towards ozone production. Note that no NO• (- NO + NO2 ) is lost in this process. It is therefore often more important to discuss the fate of NO• rather than NO or NO2 individually. NO and NO2 participate in several more reactions in which more stable products such as peroxyacetylnitrate (PAN), nitric acid and nitrate aerosol are formed and NOx is lost. As a result of these reactions, the atmospheric lifetime of NOx is typically less than one day. Usually, the term NOy is introduced in which apart from NO and NO2 also the secondary nitrogen oxides such as PAN, nitric acid and nitrate aerosols are represented. It is through these components that nitrogen oxides are finally removed from the atmospheric circulation. The atmospheric residence time of NOy is nearly 10 days. 4.2.3. Loss processes and the budget on a global scale

As outlined above, NO is hardly dry-deposited before being converted into stable products. Its products starting with NO2 are subject to several processes. Two major loss routes are important; dry and wet deposition. Dry deposition, the uptake of gases and aerosols at the surface in dry conditions is important for NO2 and for the secondary products such as PAN,

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nitric acid and the nitrates. Wet deposition is equally important for nitrate aerosols. The uncertainty in estimates of the deposition fluxes on a global scale is high. Conversion rates of NO and NO2 can be estimated quite well but the uncertainty in dry deposition rates is large. Wet deposition rates can be accurately assessed and the representativity of point measurements is reasonable in large regions such as Europe or North America. In other regions, knowledge is limited because of the lack of measurements. Another sink is the free troposphere and estimates of this sink are up to 10% of the NOx budget. The estimated total of all loss processes of nitrogen oxides on the global-scale amounts to 55 Tg yr-1. The uncertainty in this number however is large and could amount to + 50%. Consequently a gap exists between the estimated global emissions and the estimated sum of all loss processes. So in contrast to CO2, there is no constraint available that can be derived from the global budget for oxidized nitrogen. 4.2. 4. B u d g e t s on other scales

The first scale to consider would be the point-scale measurements. The problems and drawbacks of point-scale measurements are considered elsewhere in Lapitan et al. (1999) and Asman et al. (1999). On the lowest scale, enclosure measurements can be carried out well. In setting up the experiment attention has to be paid to the specific reactivity of NO. Especially the reaction with 03 can easily cause significant bias. Provided that residence times in the enclosure and sampling lines are small, suitable corrections can be made. With a good set up, estimates of the deposition velocity of NO2 and the emissions of NO can made on various locations. These methods can be used to study the influence of, for example, vegetation type or state and soil texture. In addition the influence of environmental parameters such as soilmoisture content or temperature can be studied. The results of such studies will need to be generalized to extrapolate to a larger scale. As a first approximation it could be assumed that, for example, the influence of soil temperature has a similar effect on soil emission rates in other ecosystems. On the field scale the use of micrometeorological methods may provide flux estimates of NO and NO2. With suitable selection of measurement sites, experimental conditions, etc. problems associated with fetch, instationarity (conversions), advection, etc., can be avoided. Conversion of NO and NO2 between the earth and the observation height may bias flux estimates. Using a detailed model simulation, Duyzer (1992) showed that in most conditions with low measurement heights these problems might be small, although experimental evidence is not available at this stage. Measurements can be carried out on different temporal scales, from hourly to annual averages. Flux footprint techniques are currently used to link point measurements carried out in the fetch area to field-scale measurements (Fowler, 1999). On larger scales, the reactive nature of NOx gases becomes a problem as on these scales, atmospheric conversion processes are important. Assessing budgets on these scales requires knowledge of concentrations of several gases including nitrates in aerosols and PAN. These concentrations are required to calculate the exchange of gases with other regions. In the absence of these measurements, the uncertainty of NOx budgets is large. On this scale the dry deposition rates of all secondary products of NO• gases is also a major source of uncertainty (see for example, Conrad and Dentener [1999] for a discussion on compensation concentrations in gas exchange between soils and canopies and the atmosphere).


311

5. Conclusions

For a relatively well-mixed and long-lived trace gas, uncertainties in trace gas fluxes to ecosystems appear to be greatest at the regional scale (Figure 2). This is certainly true of the main greenhouse gases: CO2, methane (CH4)and nitrous oxide (N20). That the global-scale is not the most uncertain, is a consequence of the constraints which can be applied through accurate trace gas observations, coupled with a thorough and complete understanding of trace gas life cycles. For a relatively short-lifetime trace gas, a different picture emerges (Figure 2). Such trace gases have markedly heterogeneous spatial distributions and it is often not possible to constrain global budgets with enough accuracy to reduce the uncertainty in trace gas budgets. Uncertainty in trace gas fluxes therefore continues to grow with increasing spatial scale, from the point-scale to the global-scale.

6. Recommendations

For the long-lived and well-mixed trace gases, the priority for future research should be given to reducing uncertainties in regional-scale trace gas fluxes. This can be done by: -

Comparison of regional-scale models with observations; Direct measurements of regional scale fluxes by mass balance; Studies of multiple trace gases; Application of isotopes; Applying estimation methods for uncertainties based on Monte-Carlo and boot-strapping techniques; Giving greater emphasis to the estimation of uncertainties.

-

-

For the short-lived trace gases, priorities for research, by necessity, have to be directed both towards the improvement of spatial and temporal resolution in trace gas ecosystem fluxes. This will involve: -

Increasing the accuracy and coverage of baseline monitoring of trace gas concentrations; Extending atmospheric process studies to include a wider range of conditions and seasons; Studies of multiple trace gases; Developing more detailed and reliable parameterizations of ecosystem exchange pro-cesses such as dry and wet deposition and air-sea exchange; - Improving the accuracy and completeness of trace gas emissions, particularly for human activities such as fuel combustion and biomass burning.

Few short-lived trace gases have adequate enough global budgets so that it is still not yet possible to assess reliably the relative importance of ecosystem exchange processes.

References

Archer , D. (1999) Modelling carbon dioxide in the ocean: A review. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp 169-183.

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Asman, W.A.H., M.O. Andreae, R. Conrad, O.T. Denmead, L.N. Ganzeveld, W. Helder, T. Kaminski, M.A. Sofiev, S. Trumbore (1999) How can fluxes of trace gases be validated between different scales? In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 85-97. Bender, M., T. Ellis, P. Tans, R. Francey and D. Lowe (1996) Variability in the O2/N2 ratio of southern hemispheric air, 1991-t 994: Implications for the carbon cycle. Global Biogeochemical Cycles 10:9-21. Conrad, R. and F.J. Dentener (1999) The application of the compensation point concepts in scaling of fluxes. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 203-216. Davidson, E.A. and W. Kingerlee (I997) A global inventory of nitric oxide emissions from soil. Nutrient Cycling in Agroecosystems 48:37-50. Delmas, R., D. Serca and C. Jambert (1997) Global inventory of NOx sources. Nutrient Cycling in Agroecosystems 48:51-60. Duyzer, J.H. (1992) The influence of chemical reactions on surface exchange of NO, NO2 and 03: results of experiments and model calculations. In: X. Schwartz and Y. Slinn (Eds.) Proceedings of the Fifth International Conference on "Precipitation scavenging and atmosphere-surface exchange processes", Richland, Washington, pp 1105-1114. Fowler, D. (1999) Experimental designs appropriate for flux determination in terrestrial and aquatic systems. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 99-121. Hensen, A., W.M. Kieskamp, A.T. Vermeulen, W.C.M. Van Den Bulk, D.F. Bakker, B. Beemsterboer, J.J. Mols, A.C. Veltkamp and G.P.Wyers (1995) Determination of the relative importance of sources and sinks of carbon dioxide. Report ECN-C-95-035, Netherlands Energy Research Foundation Petten, The Netherlands. Houghton, J.T., L.G. Meira Filho, J. Bruce, H. Lee, B.A. Callander, E. Haites, N. Harris and K. Maskell (1995) Climate Change 1994." Radiative forcing of climate change and an evaluation of the IPCC IS92 emission scenarios, Cambridge University Press, Cambridge, 339 pp. Keeling, R.F., S.C. Pipier and M. Heimann (1996) Global and hemispheric CO2 sinks deduced from changes in atmospheric 02 concentrations. Yature 381:218. Lapitan, R., R. Wanninkhof and A.R. Mosier (1999) Methods for stable gas flux determination in aquatic and terrestrial systems. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 27-66. Liss, P.S. and L. Merlivat (1986) Air-sea exchange rates: Introduction and Synthesis. In: P. BuatMernard (Ed.) Role of air-sea exchange in geochemical cycling, Reidel, Boston, pp. 113-129. Murphy, E.J., J. Field, B. Kagan, C. Lin, V. Ryabchenko, J. Sarmiento and J. Steele (1993) Global extrapolation. In: G.T. Evans and M.J.R. Fasham (Eds.) Towards a model of ocean biogeochemicalprocesses. Springer Verlag, Heidelberg, pp. 21-46. Simmonds, P.G., D.M. Cunnold, G.J. Dollard, T.J. Davies, A. McCulloch and R.G. Derwent (1993) Evidence for the phase-out of CFC use in Europe over the period 1987-1990. Atmospheric Environment 27A: 1397-1407. Simmonds, P.G., R.G. Derwent, A. McCulloch, S O'Doherty and A. Gaudry (1996) Long-term trends in concentrations of halocarbons and radiatively active trace gases in Atlantic and European air masses at Mace Head, Ireland from 1987 to 1994. Atmospheric Environment 30:4041-4063. Sofiev, M. (1999) Validation of model results at different scales. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 233-255. Trumbore, S. (1999) Role of isotopes and tracers in scaling trace gas fluxes. In: A.F. Bouwman (Ed.) Approaches to scaling of trace gas fluxes in ecosystems. Developments in Atmospheric Science 24. Elsevier, Amsterdam, pp. 257-274. Veldkamp, E. and M. Keller (1997) Fertiliser induced nitric oxide emissions from agricultural soils. Nutrient Cycling in Agroecosystems 48:69-77.


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Veldkamp, A.C., R. Eisma, A.T.Vermeulen, W.M. Kieskamp, W.C.M. Van Den Bulk, B. Beemsterboer, O. Zwaagstra, J.J. Mols, A. Hensen and G.P. Wyers (1995) Validation of methane source strengths. Report ECN-C-95-034, Netherlands Energy Research Foundation, Petten, The Netherlands. Vermeulen, A.T., B. Beemsterboer, W.C.M. Van Den Bulk, R. Eisma, A. Hensen, W.M. Kieskamp, J.J. Mols, J. Slanina, A.C. Veltkamp, G.P.Wyers and O. Zwaagstra (1997) Validation of methane emission inventories for NW-Europe. Report ECN-C-96-088, Netherlands Energy Research Foundation, Petten, The Netherlands. Wanninkhof, R. (1992) Relationship between gas exchange and wind speed over the ocean. Journal of Geophysical Research 97:7373-7381.


Chapter 16

CURRENT AND FUTURE PASSIVE REMOTE SENSING TECHNIQUES USED TO DETERMINE ATMOSPHERIC CONSTITUENTS

J.P. Burrows



CURRENT AND FUTURE PASSIVE REMOTE SENSING TECHNIQUES USED TO DETERMINE ATMOSPHERIC CONSTITUENTS

J.P. Burrows Institute of Environmental Physics and Remote Sensing, University of Bremen, Postfach 330440 28334 Bremen, Germany

I. Introduction Atmospheric pollution is unfortunately not new, for example in the 17 th century Evelyn discussed in his work Fumifugium, "the inconvenience of the aer and smoake of London" (Finlayson-Pitts and Pitts, 1986, and references therein). The word smog is derived from the words smoke and fog and was originally used to describe the conditions often experienced in London in winter. Emissions from the buming of coal containing sulphur resulted in a smog having adverse health effects and being chemically reducing. Summer smog was first observed in Los Angeles, Califomia (Haagen-Smit, 1952; Haagen-Smit et al., 1952). It is also detrimental for health but is oxidizing in character. Since the late 1940' s, it is recognized that atmospheric pollution has not only local but also global impacts, and as a consequence environmental issues at both regional and global scales have become matters of scientific debate and public concern. The scientific community has responded to the need to identify and assess potential environmental hazards in a variety of ways. For example, much effort has gone into the study of the physical and chemical processes determining the behaviour of the atmosphere. Similarly a hierarchy of atmospheric models have been developed to simulate the current state of the atmosphere, to predict its future behaviour and to estimate response to both natural and anthropogenically induced change. The vast majority of the constituents considered as pollutants are present in trace amounts in the unpolluted atmosphere, the most notable exceptions being the chlorofluorocarbon compounds (CFCs) and halons, which have no known natural sources. The assessment of the impact and consequences of increasing emissions of constituents into the atmosphere is not trivial because of the inherent non-linear and complex nature of the atmosphere. Therefore, detailed knowledge about the elementary atmospheric processes is required. The measurement of the composition and trends in the mixing ratios of atmospheric constituents (gases, aerosols and clouds) enables to test our understanding of the biogeochemical cycles within the atmosphere. Such measurements may also be used as an early warning signal: of the potential negative consequences resulting from a specific anthropogenic activity. For long-lived atmospheric species a limited number of measurement stations around the globe may provide an adequate monitoring network. However, for short-lived species and species having sources that are variable in time and space, the global measurement of concentrations can best be made from remote sounding instrumentation aboard orbitting space-based platforms. The development of remote sensing techniques for atmospheric constituents (gases,

318

J.P. Burrows

aerosols and clouds) and parameters is one of the most exciting developments in the environmental sciences during the past 25 years. Using these techniques it is possible to monitor the composition and behaviour of the global atmosphere on both short and long time scales. Remote sensing data will improve both the prediction of weather patterns and establish the importance of changing atmospheric composition for global climate. The number of applications of remote sensing data is growing rapidly. Current and future generations of instrumentation will provide data of great importance for global change issues. In this study a brief overview is given of our current understanding of the atmosphere and environmental processes causing atmospheric changes. The relevance and use of passive remote sounding of the atmosphere from space is then discussed. Finally, some recent measurements by remote sensing techniques of some important tropospheric constituents are described.

2. The earth's atmosphere and environmental concerns The composition of the earth's atmosphere is different from that of neighboring planets such as Mars and Venus, which are apparently lifeless. Fossil records indicate that the atmosphere evolved to its present composition as a result of life. The atmospheric increase of the concentration of oxygen (02) and ozone ((a:) since 4600 million years before present indicates that the build up of oxygen resulted from photosynthesis after the appearance of life (Figure 1). The amount of oxygen as shown in the Figure 1, is estimated from the analysis of the geological records on the basis of the chemical composition of fossils. Life on Earth could not have existed on land until sufficient ozone was there to protect the biosphere from harmful short-wave radiation. The amount of ozone, shown in Figure 1, is calculated using a simple photochemical model (Wayne, 1992). According to the Gaia hypothesis the biosphere has played an important role in determining the composition of the atmosphere since life on Earth began (Lovelock, 1979). This hypothesis also suggests that the biosphere maintains favourable conditions for life on Earth. On the geological time scale the impact of anthropogenic activities on the atmosphere has been insignificant. However, since the industrial revolution the energy and food requirements for the increasing human world population have risen dramatically leading to increasing injection of a number of trace gases into the atmosphere, the most significant being carbon dioxide (C02) and methane (CH4). In following the earth's atmosphere is described, thereafter a discussi6n is presented of the environmental processes which drive atmospheric change. 2.1. The earth's atmosphere The earth's atmosphere is a complex system. It consists of a set of layers which differ in their temperature gradient with respect to altitude. Figure 2 shows typical temperature and pressure profiles for mid-latitudes. The sign rate of temperature change in the atmosphere as a function of height enables regions of positive and negative gradient or lapse rate to be defined. Starting at the earth's surface, the temperature decreases up to the region known as the tropopause. The latter separates the troposphere, which is vertically well mixed, from the stratosphere, which is characterized by slow vertical mixing. In the stratosphere the temperature increases from the tropopause to the mesopause, which separates the stratosphere from the mesosphere. Above the mesosphere, the temperature increases again in the thermosphere.

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higher during periods of solar activity. As a result the temperature of the thermosphere is regulated by the solar cycle. The pressure of the atmosphere is highest at the earth's surface and decreases with height according to the barometric formula. The height of the tropopause varies between about 8 km at the poles and 16 km in the tropics. The mesopause occurs at typically about 45 km. Between 80 and 90% of the atmospheric mass are contained within the troposphere. The mesopause has typically a pressure of a few millibar. The troposphere and stratosphere therefore contain over 95% of the mass of the atmosphere. The behaviour of the stratosphere and troposphere are coupled through gas and particle exchange. Overall, the conditions experienced by the biosphere at the earth's surface are determined in a complex manner by the physical and chemical processes occurring in these regions. 2.2. Relevant environmental issues

The physics and chemistry of the atmosphere are not in themselves environmental issues but rather matters of scientific curiosity. However, knowledge of these systems gained from measurements of the distributions of trace atmospheric constituents has contributed significantly to the public recognition of several important contemporary environmental issues, for example: (i) stratospheric ozone depletion; (ii) global warming and climate change; (iii) global increase of tropospheric ozone; (iv) air quality; (v) biomass burning; and, (vi) acidic deposition. Possibly the most dramatic example of the role of science in the understanding and prediction of an environmental issue was the discovery that stratospheric ozone is depleted by the tropospheric release of chlorofluorocarbon compounds (CFCs). This story had many twists and turns. The first measurements of CFCs in the troposphere were made by Lovelock et al. (1973). Subsequently it was proposed that their presence might lead to a depletion of the stratospheric ozone layer (Molina and Rowland, 1974), followed about a decade later by the discovery of the so-called ozone hole over Antarctica and the recognition that this resulted from chlorine chemistry (Farman et al., 1985). The consequences of a reduction of the stratospheric ozone layer affect both the stratosphere and the troposphere. The assessment of our understanding of the role of global ozone has become a significant international scientific activity (e.g. WMO, ! 995, 1998). After much scientific debate and public discussion, international agreements have resulted (the Montreal protocol and its amendments) and controls on the production of CFCs have been introduced. Only recently it has been recognized that global warming can influence both the intensity and duration of the stratospheric ozone loss (Shindell et al., 1998; Dameris et al., 1998). Although already discussed over a century ago by Arrhenius and others (Arrhenius, 1896), the issue of global warming caused by the injection of the so-called greenhouse gases such as CO2 into the atmosphere, has become prominent in recent years. This is because of the rapid increase in atmospheric CO2, associated with the combustion of fossil fuels in the second half of the twentieth century. The recognition that other species can behave in a similar manner but often more effectively than CO2 has resulted in the definition of the "global warming potential" of trace gases. The list of greenhouse gases now comprises many species, including CO2, CH4,nitrous oxide (N20), CFCs and tropospheric ozone. The governments of the world, concerned with the potential negative consequences of global warming, have mandated that evalu.~tions be made to provide national and intemational policymakers with an accurate assessment of our current understanding of climate change (e.g.


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Houghton et al., 1995). At the recent earth summit at Kyoto, one of the most important topics on the agenda was concerned with this issue. A number of international agreements resulted which intend to decrease the atmospheric loading of the greenhouse gases. In the troposphere ozone plays a number of important roles: (i) the photolysis of ozone initiates the production of the most important tropospheric oxidizing agent, hydroxyl radicals (OH), and thereby determines to a large extent the oxidative capacity of the troposphere; (ii) ozone acts as a greenhouse gas; and, (iii) et elevated levels ozone is a health hazard. A little over 30 years ago the troposphere was perceived to be relatively chemically inactive. Although ozone was known to play an important role in local air pollution (HaagenSmit, 1952), it was believed that tropospheric ozone originated from the stratosphere and that most of it was destroyed by contact with the earth's surface (i.e. dry deposition). In the mean time, it has been recognized that the troposphere has a large global photochemical source (Crutzen, 1973) and measurements indicate a global increase in the amount of tropospheric ozone (Volz and Kley, 1988). Consequently, the study of the physics and chemistry of tropospheric ozone and its precursors has become an important research area. The abundance of tropospheric ozone has a major impact on air quality. After its discovery in Los Angeles, summer smog has become an issue for many urban and rural locations in the northern and southern hemispheres. Anthropogenic emissions from industrialized (fossil fuel combustion) and rural areas (biomass burning) are considered to be the main causes of the poor air quality. Under typically anticyclonic conditions ozone, aerosols and other toxic compounds can be generated in amounts which are biologically hazardous. Recently air quality laws have been introduced in many countries to limit the amounts of aerosol and ozone in the urban and rural atmosphere. Biomass burning has now been identified as a significant source of many atmospheric trace gases and aerosols (e.g. Watson et al., 1990, and references therein). The practice of biomass burning is increasing with growing human population, particularly in the tropics, and it has become a major global environmental issue in its own right (Levine, 1991). In the unpolluted planetary boundary layer, dimethyl sulphide (DMS) and other sulphur containing compounds are emitted by a variety of sources including oceans and volcanoes. These sulphur compounds are oxidized to form sulphur dioxide (SO2), which reacts in the gas and liquid phases with the hydroxyl radical (OH) and hydrogen peroxide (H202), respectively, to produce sulphuric acid (H2SO4). The latter compound has a low vapour pressure and forms cloud condensation nuclei (CCN). In a similar manner nitrogen dioxide (NO2) reacts with OH to form nitric acid (HNO3) in the gas phase. The acid anhydride dinitrogen pentoxide (N205) formed by the reaction of NO2 with the nitrate radical (NO3) readily produces HNO3 on reaction with water (H20) in the liquid phase. HNO3 has a high solubility in water and, therefore, accumulates in tropospheric aerosols and cloud drops. Hence, in an unpolluted atmosphere the natural sources of SO2 and NO2 provide a mechanism by which the pH of aerosol and rain is expected to be slightly acidic. In tropical rain forests organic acids produced as a result of biogenic emissions can provide a further natural source of acid. The combustion of fossil fuels results in the release of large amounts of NO and NO2 (together denoted as NOx) and SO2 into the planetary boundary layer and the free troposphere. With increasing use of fossil fuels since the industrial revolution there has been a corresponding increase of the amount of acid deposition. Acidification was first recognized as an important ecological issue in the 1970's. Public interest in the consequences of acid deposition peaked in the middle of the 1980' s with the concern over forest die-back caused by

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acidification. However this remains an important environmental issue and research area (e.g. Heij and Erisman, 1995). Although our understanding of the chemistry of the troposphere has advanced significantly in the last 25 years, there have been continual surprises and discoveries indicating that our knowledge and predictive capability remains at an elementary stage of development. Examples are the observations of the springtime loss of ozone at high latitudes (Barrie et al. (1988), the satellite-borne observations of the large-scale production of tropical tropospheric ozone (Fishman et al., 1990; 1992) and recent discoveries of large clouds of bromine oxide in the troposphere both at the ground and from space (Richter et al., 1998a, and references therein). Common to all the environmental issues described above is the fact that they are of international rather than national significance. They impact on the regional and global rather than the local scale. To a large extent these environmental issues are the consequence of the higher standards of living and the increase in the world population over the last two centuries, which resulted in dramatic changes in the nature of the earth's surface and increasing and different emissions of trace gases to the atmosphere. Management of the anthropogenic influence on global change is a challenging task for the future. Accurate global knowledge of the amounts, trends, transport and chemistry of atmospheric constituents is essential information required for this task.

3. Assessment and prediction needs for the earth-atmosphere system The assessment of a given environmental issue relates the current scientific information and understanding of this issue to those who need it as a basis for policy and decision making. In this sense it bridges the research and the decision making communities. In order to assess accurately the impact of anthropogenic activity on atmospheric ciemistry and potential climate change, a detailed understanding is required of: (i)the physical and chemical processes determining the behaviour of the atmosphere; and, (ii) the biological, physical and chemical processes controlling the emission and deposition of trace gases at the earth's surface. Knowledge about these processes is gained through field measurements, laboratory studies, and modelling of trace gas fluxes and atmospheric processes. Of particular importance for assessment purposes are: (i) the accurate measurement of trace atmospheric constituents; and, (ii) the continuity of the measurements to generate data products which are readily comparable over several decades. The number and type of measurements of a species required to provide a true global representation depends on the atmospheric lifetimes of the species involved. Table 1 presents a list of atmospheric trace constituents and parameters, their atmospheric lifetimes or cycling time, and their relevance for a number of global environmental issues. The time scales of atmospheric processes range from hours to years (Table 1). For example, a pollution episode may only last for a week or less, but the consequences of such episodes may last much longer. This reflects the fact that processes such as the emission rate to the pianetary boundary layer, homogenous (e.g. radical-molecule, radical-radical, photolysis) and heterogeneous reactions, including both wet and dry deposition, determine the production and removal rates of species within the atmosphere. For climate research long-term series of trace gas, cloud and aerosol measurements are required. CO2 is only one of several gas species known to be increasing in the troposphere. For example, the amount of CH4 has almost doubled since pre-industrial times. For long-lived species such as CO2, CH4 or N20 a reasonable estimate of the global tropospheric amount can

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Table 1. List of atmospheric trace constituents and parameters and major relevant environmental issues.

Species/parameter Ozone (03) Nitrogen oxides (NOx)b Water vapour (H20) Carbon monoxide (CO) Methane (CH4) Nonmethane hydrocarbons (NMHC) Chlorofluorcarbons (CFCs) Chlorofluorhydrocarbons (HCFCs) Carbon dioxide (COz) Nitrous oxide (N20) Sulphur dioxide (SO1) Ammonia (NH3) Aerosol Clouds Polar stratospheric cloud Surface spectral reflectance UV radiation

Environmental issuea

Approximate lifetimes or cycling time

Well mixed in the atmosphere

TO, GW TO, AD TO, GW TO TO, GW, SO GW, SO GW, SO GW GW GW, SO AD AD GW GW, TO SO GW, TO. SO TO, SO, GW

1 week 1 day 1 week 2 months 10 years Hours-weeks Many years Many years > 100 years > 100 years Hours Hours Hours-weeks Hours to days Days to weeks Months Highly variable

No No No No Yes No Yes Yes Yes Yes No No No No No n.a. n.a.

a AD, atmospheric deposition; GW, global warming; SO, stratospheric ozone chemistry; TO, tropospheric ozone chemistry. b NOx refers to the sum of NO and NO2. These species are rapidly interconverted during the day by photolysis of NOE and the reaction of NO with O3.

be obtained from a network of ground-based measurement stations. The data from these networks indicate that the rate of increase of atmospheric CH4 has decreased recently (e.g. Dlugokencky et al., 1998). The origin of such changes remains unclear and a matter of much debate due to the lack of correlative global measurements. For short-lived, highly reactive atmospheric species the only feasible approach for obtaining their global distributions is to make regular measurements from space-based platforms. Provided they have sufficient accuracy, global space-based measurements of species with relatively long atmospheric lifetimes can be used to determine source and sink regions and possibly to infer trends. In order to meet the need for global observations of atmospheric species, an optimized and integrated measurement system is required. Ground-based, shipboard, balloon- and aircraftbome measurements often have much higher spatial resolution than satellite-bome measurements. However, the appropriate combination of measurements at different scales leads to well-calibrated and validated data sets. These are required for the next phase in developing our understanding of atmospheric and climate change.

4. Passive remote sensing of atmospheric constituents One of the first examples of the modem application of remote sensing of the composition of the atmosphere was the assertion by Hartley in 1880 that the UV absorption in the solar absorption spectrum was attributable to ozone. In this manner the earth's stratospheric layer was discovered. Since this pioneering work and especially in the past 30 years, there has been rapid progress in the development of atmospheric remote sensing techniques. The emissions from the aurora borealis and other night and day glow emissions in the visible wavelength range intrigued the philosophers of science since the middle ages (e.g. The Kings mirror, Konigsspielet, a chronicle of the middle ages; Roach and Gordon, 1973; Bone, 1991; Brekke and Egland, 1994, and references therein). However, it was the development of

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atomic and molecular physics in this century, which has enabled air glows to be attributed to transitions from excited atoms, molecules and ions in the upper atmosphere. Excitation occurs for example by the absorption of short wavelength radiation, as a result of the photodecomposkion of molecules and also from chemiluminescent chemical reactions. The identification of such features enabled "the composition of the upper atmosphere to be investigated much earlier than the lower atmosphere. There are two categories of remote sensing, active and passive. Passive techniques utilize electromagnetic radiation emitted from or transmitted through the atmosphere, the radiation source being for example the black body emission from the earth's surface, or solar and stellar irradiances. The most critical part of a passive remote sensing instrument is its detector. In contrast, active remote sensing systems have their own radiation source and a detector, for example, radar and lidar techniques. Satellite sounding instruments generally employ one of two types of viewing geometry, i.e. nadir viewing or limb viewing. Nadir viewing instruments observe a selected solid angle centered about a given spot on the Earth. Spatial coverage is maintained by a scanning system (generally across-track or conical) as the satellite moves. Limb viewing instruments scan vertically the earth's atmosphere, observing large horizontal paths at different altitudes. Limb viewing generally yields high vertical resolution and ability to observe higher in the atmosphere than nadir sounding instruments. At low altitudes, the horizontal resolution of the limb observation is often limited by multiple scattering and other effects. In addition, the likelihood of clouds interfering with the observation restricts measurements below 10 km. Therefore, limb observations have been primarily used for sounding the stratosphere and lower mesosphere.

4.1. Remote sensing of gases Whether active or passive, the retrieval of information about atmospheric trace gases relies on the knowledge of the absorption, emission and scattering of electromagnetic radiation in the atmosphere. Gases exhibit characteristic fingerprint spectra in emission or absorption: (i) rotational transitions are observed primarily in the far infrared and microwave spectral regions; (ii) vibrational rotational transitions may be observed in the infrared; and, (iii) electronic transitions are mainly in the UV, visible and near infrared spectral regions. The advantages and disadvantages of each spectral region are discussed briefly below. Various instruments utilizing remote sensing techniques to determine atmospheric composition are discussed in the sections. The characteristics of the different instruments and their objectives are summarized in Table 2. 4.1.1. Remote sensing in the far infrared and microwave spectral regions All gases having a dipole moment exhibit active rotational transitions in absorption and emission in the microwave (mm) and far infrared (sub-mm) spectral regions. The doppler line widths of rotational spectra are relatively narrow and the observed atmospheric emissions or absorptions are pressure-broadened by collisions with air molecules. On the one hand this enables the amount of gas at different pressures to be retrieved from an understanding of the line shape of the spectral features. On the other hand this results in the broadening of the absorption or emission line as the pressure in the atmosphere increases (Figure 2) leading to a loss of specificity in the lower atmosphere. This limitation is compounded by strong atmospheric absorption by water vapor and carbon dioxide and results in saturation in many parts of the spectral region.

Current and future passive remote sensing techniques used to determine atmospheric constttuents

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Therefore, ground-based measurements are restricted to atmospheric windows where the water vapour absorptions are relatively small. For limb space-based atmospheric sounding, this is less of a problem but retrieval of trace gases in the upper troposphere are restricted to a limited number of candidates. For many gases such measurements are optimal for sounding the stratosphere and mesosphere. For the lower stratosphere and upper troposphere, some interesting possibilities exist, but the range of application is more more restricted. Passive remote sounding instruments for both the mm-wave and sub-mm or far-infrared spectral regions have been developed for atmospheric remote sounding. Some examples are briefly described below. - Microwave. Millimeter and sub-millimeter wave radiometry have been very successfully used from a variety of platforms to measure stratospheric gases. By using microwave local oscillators (LOs) and filter banks, or appropriate spectrometers, the observed brightness temperatures can be measured at various frequencies or across an emission line from a particular molecule. The sensitivity of this technique has improved significantly during the last two decades, as the detector noise has been reduced. The first retrievals exploiting this technique were made from the ground based systems: vertical profiles being inverted from the pressure broadening of the observed emission line. However observations from platforms higher in or above the atmosphere have many advantages. Aircraft measurements typically use a fixed viewing angle, while balloon and satellite experiments scan the limb of the atmosphere. A significant American and European effort has gone into the development of microwave and sub-millimeter sounding of the stratosphere. Some highlights of these programs have been measurements of elevated C10 amounts, including ground-based measurements (deZafra et al., 1987), balloon-borne measurements (Waters et al., 1981, 1984; Stachnik et al., 1992) and aircraft-borne measurements (Crewell et al., 1993; Urban et al., 1998). Recently OH has been observed from an aircraft (Titz et al., 1995). The Jet Propulsion Laboratory (JPL) research team have successfully flown the Microwave Limb Sounder (MLS) aboard the Upper Atmospheric Research Satellite (UARS), which has been measuring stratospheric profiles of C10, 03, H20 and HNO3 since 1992 (Waters et al., 1993) (Table 2). In total some 140 publications have been published on the basis of MLS data. The Microwave Atmospheric Sounder (MAS) flew three times aboard the Shuttle also measuring C10, 03 and H20 and has also provided a unique set of observations (Hartmann et al., 1996). - Far-infrared. The spectral region from 7 to 200 cm l has been used to study molecular species in the stratosphere and mesosphere. In the far infrared spectral region vibrations having weak force constants and rotations of light molecules are active. For these stratospheric measurements, Fourier Transform Spectrometers (FTS) with relatively high spectral resolutions have been used. Two balloon-borne instruments have been flown a number of times (e.g. Carli et al., 1984; Chance et al., 1989). More recently an FTS instrument has been developed for flight on a high flying aircraft SAFIRE. (Table 2). In summary, with the possible exceptions of H20 and 03, trace gas measurements made thus far have exploited these long wavelength ranges exclusively for stratospheric measurements. 4.1.2. Remote sensing in the mid-infrared

In the mid-infrared, molecules with a dipole moment have active vibrational rotational transitions, which can be observed both in emission and absorption. For the observation of tropospheric species, pressure broadening of the spectral features is considerably less proble-

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Table 2. The different instruments discussed in this paper, coverage of their measurements, species measured and the

satellite. The list is not intended to be complete, but merely to illustrate the currently available instrumentation. Instrument

Name

Height of measurement a

Target Species

ATMOS

Atmospheric Trace Molecule Spectroscopy

ST and upper Tr

03, NO• N205

ATSR

Along Track Scanning Radiometer

AVHRR

Advanced Very High Resolution Radiometer. Four channels on the first 4 platforms listed, five channels on the last 5 platforms Backscatter Ultraviolet Ozone Experiment Cryogenic Limb Array Etalon Spectrometer

TR, sea surface TR

BUV CLAES

ST, TR, profiles ST

Space Shuttle Spacelab-3 CIONO2, HCI, HF, (1985), ATLAS-I,2 and 3 CH4, CFCs, e t c . (1992,1993 and 1994) Aerosols, clouds, sea ESA-ERS1,2 (1991-present) surface temperature Smoke, fire, clouds TIROS-N, aerosols, vegetation NOAA-6,-8, - 10; -7, -9, -11, -12, -13 (1978- present) 03 Nimbus-4 (1970-1974)

Temperature, N20, NO, NO2, HNO3, CF2C12, CFCI3, HCI, 03, CIONO2, CO> H20, and CH4 03, OH, HO2, HCI, HF, HNO3, CIONO2, HOCI,, etc. 03, NO2, H20 BrO, OCIO, SO> HCHO and clouds and aerosols 03, NO2

FTS

Fourier Transform Spetrometer

ST

GOME

Global Ozone Monitoring Experiment

TR and ST

GOMOS

Global Ozone Monitoring by Occultation of Stars Halogen Occultation Experiment

Upper TR, ST and ME ST CO 2, H20, 03, NO2, HF, HCI, CH4,NO ST 03, NO2, N20, H20, CF3CI, CH4, CIONO2, T and P ST and TR 03, N20, H20, CH4, CO and CO2 ST, ME CO2, H20, CO, N20, CH4, NO, NO2, N205, HNO3, 03 ST CO2, HNO3, 03, H20, NO2 ST CO2, 03 TR CO

HALOE ILAS I, II

Improved Limb Atmospheric Spectrometer

IMG

Interferometric Monitor for Greenhouse Gases Improved Stratospheric and Mesospheric Sounder

ISAMS

LIMS LRIR MAPS MAS MERIS

MIPAS

MLS MOPITT OMI POLDER

Limb Infrared Monitor of the Stratosphere Limb Radiance Inversion Radiometer Measurement of Air Pollution from satelllites Microwave Atmospheric Sounder Medium Resolution Imaging Spectrometer for Passive Atmospheric Sounding Michelson Inferometer for Passive Atmospheric Sounding Microwave Limb Sounder Measurement of Pollution in the Troposphere Ozone Monitoring Instrument Polarization and Directionality of the Earth's Radiance

ST

CIO, 03, H20

TR

H20, clouds and aerosol

Upper TR

Platform

03, NO,,, N205

UARS (1991-1993)

Geophysica - a high-flying aircraft ESA-ERS- 1 (1995-present)

ESA ENVISAT (2000) UARS (1991-present) ADEOS (1996-97) ADEOS II (1999) ADEOS (1996-97) UARS (1991-1992)

Nimbus 7 (1978-79) Nimbus 6 (1975) STS-2 (1981); Space Shuttle (1984 and 1994) Space shuttle ATLAS 1, 2 and 3 (1992, 1993, 1994) ESA-ENVISAT (2000)

ESA ENVISAT (2000)

CIONO2, CH4, CFCs, etc.; temperature ST CIO, 03, H20, HNO3 TR, profiles Total column of CO, CH4 + CO profiles TR 03, SO2,N O 2 , TR Polarization, aerosols, clouds

UARS (1991-present) NASA AM-1 (1999) EOS-CHEM (2003) ADEOS- 1 (1996-97)


Table 2. Continued. Instrument Name

SAGE I SAGE II

SAGE III

Stratospheric Aerosol and Gas Experiment I Stratospheric Aerosol and Gas Experiment II

Height of measurementa

Species measured

Platform

Upper TR and ST profiles

03, NO2, aerosols

NASA Atmospheric Explorer Mission (1979-81) NASA Earth Radiation Budget Satellite (1984 present) Meteor 3M ( 1999); International Space Station (2002)

Stratospheric Aerosol and Gas Experiment III Stratospheric Aerosol Measurement II Stratospheric and Mesospheric Sounder

ST ST, ME

SBUV

Solar Backscatter Ultraviolet Ozone Experiment

ST, TR, profiles

SBUV-2

Solar Backscatter Ultraviolet Ozone Experiment 2

ST. TR, profiles

SAM II SAMS

SCIAMACHY Scanning Imaging Absorption Spectrometer for Atmospheric Cartography

SCR SME

Selective Chopper Radiometer Solar Mesospheric Experiment

TES

Tropospheric Emission Spectrometer

TOMS

Total Ozone Monitoring Spectrometer

327

03, NO2, H20, aerosols 03, OclO, BrO. NO2, NO 3 aerosols Aerosols CO2, H20, CO, N20, CH4, NO 03 03

TR, ST, and 03, 02, O2(IA), 04, ME total NO, NO2, N20, columns BrO, OclO CO, and profiles H20, S02, HCHO, CO, CO2 and CH4, cloud, aerosols, pressure, temperature ST CO2, T ST, ME 03, O2(IA), NO2 Profiles TR total Various incl. columns HNO3, O3, NO, and profiles H20 ST, TR, 03 profiles

Nimbus-7 (1979-90) Nimbus-7 (1979-90) Nimbus-7 (1979-90) NOAA -9 (1985-present) 11(1989-95)-14 (1995present) ESA-ENVISAT (2000)

Nimbus 4-5 (1970-75) NASA (1983) NASA-EOS-CHEM (2003)

Nimbus 7 (1979-92) ADEOS (1996-97) Earth Probe (1996-) Meteor (1992-94)

aME, mesosphere; ST, stratosphere; TR, troposphere.

matical than at longer wavelengths. This is because the Doppler line width is proportional to the frequency of the light and is therefore larger in the mid-infrared than at longer wavelengths. Nevertheless, broadening of lines results in some loss of specificity of the measurements. In addition, in the mid infrared strong absorptions by H 2 0 and CO2 restrict the available spectral windows. Passive remote sensing by mid-infrared spectroscopy has been successfully applied to the measurement of a large number of stratospheric trace constituents and some upper tropospheric constituents. Initially measurements were made from mountain tops (e.g. Zander, 1981), and balloon and aircraft experiments were subsequently developed (Fischer et al., 1980; Murcray et al., 1975; 1979; Coffey et al., 1981; Brasunas et al 1988; Kunde et al., 1988). Both absorption and emission experiments have been made using a number of different instruments. The Limb Radiance Inversion Radiometer (LRIR) and Limb Infrared Monitor of the Stratosphere (LIMS) are both infrared radiometers which were flown aboard Nimbus 6 and 7, respectively, and recorded data in 1978 and 1979 (Gille et al., 1980; Gille and Russell,

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1984). The six channels of LIMS observed emission by CO2, HNO3, 03, H20 and NO2 from 15-65 km. Starting with the selective chopper radiometers (SCR) on Nimbus 4 and 5, the Department of Physics at the University of Oxford developed a series of instruments observing infrared emission from the atmosphere. A pressure modulated instrument flew on Nimbus 6 and the Stratospheric and Mesospheric Sounder (SAMS) flew on Nimbus 7 (Table 2). SAMS measured in limb viewing geometry the gases CO2, H20, CO, N20, CH4 and NO in the stratosphere and mesosphere. An improved version ISAMS flew aboard the UARS and added channels for NO2, N205, HNO3, 03, and H20 (e.g. Barnet et al., 1992) (Table 2). During the last 15 years Fourier transform spectrometers have been used successfully to sound the stratosphere and upper troposphere. One of the most important successes has been the Atmospheric Trace MOlecule Spectroscopy (ATMOS) project (e.g. Farmer et al., 1987; Gunson et al., 1996). The ATMOS instrument flew aboard Spacelab 3 and the Atmospheric Laboratory for Applications and Science (ATLAS) Space Shuttle missions (Table 2). ATMOS performed solar occultation measurements and a variety of trace gases in the upper troposphere and stratosphere have been retrieved. Since its launch in 1991 the Upper Atmospheric Research Satellite (UARS) has circled the Earth in a low earth non sun-synchronous orbit. The UARS flew three infrared experiments. In addition to ISAMS (described above) the Cryogenic Limb Array Etalon Spectrometer (CLAES) and the Halogen Occultation Experiment (HALOE) make infrared measurements designed to yield information about stratospheric and tropospheric trace constituents. The CLAES used a high resolution etalon to measure the limb emission in the infrared (Roche et al., 1982; Roche and Kumer, 1989). The target gases and parameters were N20, NO, NO2, HNO3, CF2C12, CFC13, HC1, 03, C1ONO2, CO2, H20, CH4 and temperature (UARS, 1987). HALOE used broad band filter radiometry to measure CO2, H20, 03 and NO2 and gas filter correlation radiometry to measure HF, HC1, CH4 and NO (Baker et al., 1986) (Table 2). All four stratospheric remote sensing missions (MLS, ISAMS, CLAES and HALOE) aboard the UARS have achieved their goals. Currently the HALOE and MLS are still measuring after eight years and have produced a unique record about the stratosphere. The Japanese space agency NASDA launched its ADEOS (Advanced Earth Observing Satellite) in 1996. The payload included ILAS (Improved Limb Atmospheric Spectrometer), IMG (Interferometric Monitor for Greenhouse gases) and a TOMS for atmospheric sensing. IMG and ILAS are nadir and limb sounding infrared instruments (Table 2). Early measurements show very promising results. The retrieval algorithms are currently being optimized. The only experiment flown up to the present, which specifically uses infrared information to probe the lower troposphere is the Measurement of Air Pollution from Satellites (MAPS) experiment. The MAPS instrument is a nadir sounding gas correlation, which makes global measurements of CO in the middle and upper troposphere. It flew three times between 1981 and 1994 on the NASA Space Shuttle (Reichle et al., 1986; 1990; Connors et al., 1991). Validation of MAPS was made using ground-based passive remote sensing instruments (Pougatchev et al., 1998). 4.1.3. Remote sensing in the UVvisible and near-IR

In contrast to the longer wavelengths, the source of radiation for passive remote sounding of the atmosphere in the ultraviolet, visible and near and short-wave infrared regions is the sun. The sun's maximum emission is around 580 nm. Beyond 300 nm the sun corresponds fairly well to a black body having a temperate around 5800 K. Absorption by atoms in the sun produce the well known Fraunhofer structure. This has many strong features in the ultraviolet


329

and visible but is less significant at longer wavelengths. Below 300 nm the solar emission deviates from black body behaviour. Moving further into the ultraviolet, the solar output is determined to a large extent by processes occurring at the edge of the sun. These processes are often correlates with solar activity. In 1957 it was proposed that satellite measurements of back scattered ultraviolet (BUV) radiation from the terrestrial atmosphere could be used to deduce ozone profiles on a global basis (Singer and Wentworth, 1957). The method relies on two effects, i.e. the scattering of light at short wavelengths and the absorption of ozone. Rayleigh scattering of light by air molecules has a strong dependence on wavelength, whereby the intensity of scattered light is a function of the inverse of the fourth power of the wavelength. Similarly, ozone absorption is strongly wavelength-dependent. These effects combine and as a result the penetration depth of light in the atmosphere varies strongly between the ozone maximum absorption in the Hartley band around 250 nm and its minimum beyond 380 nm in the Huggins bands. The numerical technique for the determination of vertical profile information was also studied for the determination of total ozone in the atmosphere by NASA (Dave and Mateer, 1967; Mateer et al., 1971). The development of this retrieval techniques has continued up to the present (Bhartia et al., 1996). The earliest measurement utilizing the BUV technique was undertaken by Rawcliffe and Eliot (1966) using a photometer observing at 284 nm. Ozone distributions were determined utilizing measurements from the USSR COSMOS satellites, which flew a double monochromator in 1965 and 1966 (Krasnopol'skiy, 1966; Iozenas et al., 1969a,b). The Backscatter Ultraviolet atmospheric ozone experiment (BUV) was the first of a series of instruments made by NASA and later NOAA, which has successfully made long-term measurements of the BUV for the vertical profile and total amount of ozone (Heath et al., 1973) (Table 2). BUV was launched aboard the Nimbus 4 satellite into a circular polar orbit at an altitude of 1100 km. This orbit is sun-synchronous and the satellite crosses the equator in an ascending mode every 107 minutes close to local noon. This instrument concept was developed and resulted in the SBUV (Solar Backscatter Ultraviolet) and TOMS (Total Ozone Mapping Spectrometer) being launched aboard Nimbus 7 (Heath et al., 1975). The SBUV instrument was further improved to the SBUV-2 and has been flown by NOAA on a series of satellites (Frederick et al., 1986) (Table 2). After Nimbus 7 (1979-1992) TOMS has also been flown on the Russian Meteor Platform (1992-1994), as part of the Japanese ADEOS satellite (1996-1997) and aboard Earth Probe (1996-present) (Table 2).These measurements have been used to derive a unique ozone data set. Readers are referred to the literature about the T O M S data record for more details. Recognizing the importance of long term calibration and validation of space based instrumentation, NASA developed the SSBUV (Shuttle SBUV), which flew 8 times on the shuttle to calibrate radiometrically the BUV instruments (Hilsenrath et al., 1988; 1996). The attention paid to the detail of the calibration of the NASA and NOAA BUV instruments has established the quality of these data sets (Hilsenrath et al., 1995). The Stratopsheric Aerosol and Gas Experiment I (SAGE I) instrument flew from 1979 to 1981 on the NASA Atmospheric Explorer Mission (Table 2). It is a satellite-borne spectrometer that measures the absorption of the sunlight by ozone with four channels centred at 0.385, 0.45 0.6 and 1.0 ~tm (McCormick et al., 1979; Chu and McCormick, 1979). SAGE II is a seven channel instrument from the same team (Maudlin et al., 1985), which was launched on NASA Earth Radiation Budget Satellite (ERBS) and is still working today. A third generation SAGE has been developed, which will be launched on board the Russian Meteor3M in 1999 and the international space station in 2002 (Table 2). SAGE determines atmospheric absorption in occultation and measures at sunrise and sunset. So far the SAGE

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series has provided reliable data about a number of atmospheric constituents. An interesting set of measurements were made by the Solar Mesospheric Explorer (SME), which was flown by NASA in 1983. It contained limb scanning ultraviolet, visible and near IR channels for measuring stratospheric and mesospheric 03, O2(lA) and NO2 (Rusch et al., 1984; Thomas et al., 1984; Mount et al., 1984) (Table 2). The Global Ozone Monitoring Experiment (GOME) represents the entry of the European Space Agency (ESA) into the measurement of global distributions of atmospheric constituents (Burrows et al., 1991; 1993; 1998b, and references therein). GOME is a small scale version of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and was originally named SCIA-mini. Both GOME and SCIAMACHY were proposed in 1988. GOME flies aboard the ESA's second earth research satellite (ERS-2), which was launched in April 1995. SCIAMACHY is a joint German, Dutch and Belgian national contribution to the ESA-ENVISAT payload to be launched in 2000 (Burrows et al., 1990; Burrows et al., 1991; 1995; Bovesmann et al., 1998) (Table 2). GOME is a nadir sounding spectrometer which observes simultaneously either the upwelling radiance from the top of the atmosphere or the extra-terrestrial solar irradiance between 240 and 790 nm. The resolution of the measurements is chosen to be suitable for the application of differential optical absorption spectroscopy (DOAS) technique, which was developed for long-path measurements and zenith sky observations (e.g. Platt and Perner, 1980; Mount et al., 1987; Eisinger et al., 1997). 4.2. Remote sensing techniques for aerosols and clouds

Molecular or Rayleigh scattering of electromagnetic radiation is strongly dependent on its wavelength (~-4), whereas scattering by aerosols and clouds obeys approximately Mie theory and has a relatively weak dependence on wavelength (~-l). Aerosols have been identified by their scattering effects both actively and passively. The first passive remote sensing experiment to measure successfully the abundance of atmospheric aerosols from space was the Stratospheric Aerosol Measurement (SAM II) aboard Nimbus 7 (McCormick et al. 1979). This experiment was a single channel radiometer observing in solar occultation and was the forerunner of SAGE. Stratospheric aerosols have also been measured by their infrared absorptions (e.g. HALOE). Tropospheric aerosol and smoke have been retrieved from several different types of remote sensing data. For example, algorithms to retrieve tropospheric aerosol have been developed for data from the Advanced Very High Resolution Radiometer (AVHRR) data over the ocean (Ignatov et al., 1995). Biomass burning smoke has not only been observed by AVHRR but also from TOMS data (Hsu et al., 1996). This algorithm has also been successfully applied to GOME data (Gleason et al., 1998). In the ultraviolet, visible and near-infrared spectral regions, radiation is strongly scattered by clouds, enabling their presence to be detected, provided that the spatial resolution is sufficiently high and the difference between the effective cloud albedo and the spectral reflectance of the earth's surface is suffici~,ntly large. Several algorithms have been developed, which aim at utilizing the oxygen absorption for the determination of cloud top height (Guzzi et al., 1996; 1998 and references therein). Clouds emit long-wavelength infrared radiation, the spectrum of which depends on their temperature. Observations of the infrared emissions by clouds with instruments on the METEOSAT platform and related meteorological satellites are routinely used to estimate the cloud top height and cover.


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The main objective of the ATSR (Along Track Scanning Radiometer) aboard ERS-1 and ERS-2 is the measurement of sea surface temperature. In addition, it has been used to determine aerosol and cloud (Table 2). The French POLDER (Polarization and Directionality of the Earth's Radiance) instrument aboard the NASDA platform ADEOS-1 measured polarization parameters (Table 2), which are being used to study polarization, aerosols and clouds in the atmosphere (Deschamps et al., 1990). 4.3. Validation of retrieved parameters from space based remote sensing measurements One of the most important aspects of any remote sensing experiment is the validation of retrieved parameters. Without a rigorous validation, the use of remote sensing data is of minimal value. Validation usually involves the measurement of the same parameter by an independent method, for example an in-situ measurement or an alternative remote sensing measurement from ground, ship or aircraft platforms, as required. Validation necessitates in-flight calibration of instruments in space. As the performance of instruments often degrades during their life in space, validation of data products is required systematically throughout the lifetime of a mission. This may be achieved by utilizing data from networks and organizing validation campaigns. A good example of the former is the NDSC (Network for the Detection of Stratospheric Change). This has been established to provide long-term measurements of the stratospheric composition at a selected set of locations. A similar network is needed for the troposphere, but requires more measurement sites than in the stratosphere because of the high variability of the troposphere. Unfortunately the value and importance of validation is not always recognized by the relevant governmental agencies. Often the majority of the funds designated for a particular mission have been used up during the industrial fabrication and launch of an instrument. The consequences of poorly calibrated or validated data is a less than optimal exploitation of data products. In summary, validation of the parameters retrieved from remote sensing measurements is an essential part of a mission. Validation measurements are required throughout the lifetime of a mission. Validation is best achieved by the comparison of the retrieved parameter with independent measurements. The latter may be in situ measurements or independent remote sensing measurements.

5. Remote sensing measurements of trace gases in the troposphere The retrieval of trace constituents in the trt, posphere is more difficult than in the stratosphere or mesosphere, because (i) pressure broadening and strong tropospheric absorptions makes the application of microwave, sub-millimeter and infrared techniques difficult if not impossible to invert; (ii) for nadir sounding, multiple scattering in the ultraviolet and visible wavelengths smears the information about absorption from the lower atmosphere; and, (iii) limb sounding has a horizontal resolution of the order of 400 km, implying that tropospheric clouds are nearly always present in the field of view, effectively restricting the limb measurements to above the cloud top. The most reliable and self-consistent approach for passive remote sensing of the troposphere is, therefore, the simultaneous use of limb and nadir sounding of the atmosphere (e.g. as pioneered by SCIAMACHY and related instruments).

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5.1. Tropospheric ozone

For tropospheric ozone, the residual technique was pioneered by Fishman et al. (1990), who used total 03 column, estimated from TOMS, combined with the co-located stratospheric ozone profiles from SAGE and SBUV in a series of studies to demonstrate the presence of elevated tropospheric ozone for large-scale pollution events (Fishman e t a l . , 1991; 1992; 1996). Figure 3 shows results of one of the studies done by Fishman and coworkers. These studies stimulated the investigation of the use of TOMS data for the determination of the tropospheric ozone column amount by related techniques. Hudson, Thompson and coworkers have developed and refined a technique called the tropical tropospheric ozone (TTO) method (Hudson etal., 1995; Kim et al., 1996; Hudson and Thompson, 1998). This technique utilizes a Fourier analysis to identify the range of latitudes for which the method is applicable by using the recognition of a planetary wave pattern to estimate stratospheric and background tropospheric ozone. Ziemke et aL (1998) have developed two different residual techniques. The first combines TOMS and stratospheric ozone information from the UARS instruments HALOE and MLS. The second approach identifies high clouds in the tropical region and assumes that such columns contain only information on stratospheric ozone. The use of such condensation cloud differential information (CCD) has given the technique its name.

Figure 3. Tropospheric ozone determined by the Tropospheric Ozone Residual technique on October 3 and 6, 1992. D.U., Dobson unit; 1 D.U. = 2.69 x 1016molecules per cmz. Source: Fishman et al. (1996).


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The GOME and SCIAMACHY ultraviolet measurements in nadir viewing geometry are similar to those of SBUV and SBUV-2. However, in contrast to the NASA instruments, GOME and SCIAMACHY observe the entire spectrum at a spectral sampling of 0.1 nm and a spectral resolution of 0.23 nm, whereas SBUV and SBUV-2 instruments measure consecutively 12 selected wavelengths. Each set of 12 SBUV wavelength measurements takes about 30 s, and the spectral resolution of each individual measurement is 1 nm. During the development of the SCIAMACHY proposal the idea of utilizing additional information in the temperature dependence of the ozone Huggins bands was proposed. The potential use of this technique for the retrieval of tropospheric information about ozone is described by Chance et al. (1991), Rozanov et al. (1992; 1993), Munro et al. (1993), Burrows et al (1994), Chance et al. (1997), Rozanov et al (1998) and DeBeek et al. (1998). Currently one issue, limiting the exploitation of the retrieval of ozone profiles from the GOME data set is the presence of systematic radiometric calibration errors, which have been identified but not yet eliminated from the operational geophysical irradiance and radiance data products. Some schemes have been developed, which remove these errors (Bramstedt et al., 1998; Hoogen et al., 1998; Eichmann et al., 1998) but these have not yet been implemented operationally. Above the ozone maximum the vertical resolution of GOME data for the ozone profile is of the order of 6 km. In the lower stratosphere and troposphere, accurate profiles with an effective vertical resolution of around 10 km have been retrieved from GOME data (Munro et al., 1998; Burrows et al., 1998b; Hoogen et al., 1999). However the removal of systematic errors in the GOME data set is critical with respect to the accuracy of tropospheric ozone retrievals from this data. The higher information content of the GOME profile information about ozone, as compared to the SBUV, arises from a variety of reasons. For example the higher effective signal to noise ratio of the measurements, the measurement of additional spectral features, such as the temperature dependent Huggins bands and the relatively temperature independent Chappuis bands, all play a role. Using a priori information about the temperature and height of the tropopause combined with the GOME measurements enables the tropospheric and lower stratospheric columns of ozone to be derived. Accurate knowledge about the scattering characteristic of tropospheric clouds and the earth's albedo is important in this respect. As a result of primarily multiple scattering in the lower atmosphere, the number of pieces of independent information retrievable in the troposphere and lower stratosphere from nadir viewing is limited. A simple way to obtain the tropospheric excess amount of a gas in the tropics is by assuming that locally the stratosphere is longitudinally homogeneous. This is similar to the residual approaches mentioned above. This has been used to identify the amount of excess tropospheric ozone produced over and in the plume downwind of Indonesia from the forest fires in September 1997. This excess column amount of ozone is attributed to the troposphere and is shown in Figure 4.

5.2. Nitrogen Dioxide At the earth's surface and in the lower atmosphere large amount of oxides of nitrogen are released by a variety of natural phenomena, chemical processes and anthropogenic activities. The major source of stratospheric NO and NO2 is nitrous oxide (N20). N20 is released into the troposphere by the biological reduction of NO3 and the oxidation of NH4 + in soils. Due to its long tropospheric lifetime, significant quantities of N20 are transported to the stratosphere, where it is destroyed by photolysis and by reaction with excited oxygen atoms to produce NO,

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Figure 4. Excess tropospheric columns of ozone (upper panel), nitrogen dioxide (middle panel) and formaldehyae (lower panel), derived from GOMF data. Source: A. Ladst~tter-WeiBenma)'er and J.P. Burrows, 1998, personal communication.


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Figure 5. Global total column density of nitrogen dioxide derived from GOME data. The white colours indicate areas where no measurements were made. Source: A. Richter, L. Hild and J. P. Burrows, 1998, personal communication. which in turn reacts with 03 to produce NO2. The second source of stratospheric oxides of nitrogen is downward transport from the mesosphere. The stratospheric column of NO2 has a strong seasonal cycle (see for example Eisinger et al., 1996; Richter et al., 1998b,c; Wittrock et al., 1998). In the polar vortex in winter and spring, NO2 amounts are low because of the enhanced formation rate of N205 at the low temperatures and its subsequent heterogeneous removal to form aerosols containing nitric acid and polar stratospheric clouds. In summer the polar latitudes have high stratospheric NO2 values. This is because of the thermal instability of N205 and the increased daytime photolysis of NO2 precursors such as N205 and C1ONO2. In remote and unpolluted regions of the planetary boundary layer, natural sources of NO• (NO and NO2) such as lightning, result in relatively small mixing ratios, typically being less than 20 pptv. In contrast, the amount of NO• in downtown city air is often above 100 ppbv. Thus NO2 has large tropospheric variability. Both the distribution of sources and the lifetime of NO2 are very different in the troposphere compared to the stratosphere. Figure 5 presents a composite picture of the total column density of NO2 retrieved from GOME observations on 15, 16 and 17 September 1997. The total column is clearly showing the differences in both stratospheric and tropospheric NO2 patterns. The low stratospheric amounts of NO2 in the polar vortex above Antarctica and the high values above the Arctic are readily observable. Elevated tropospheric NO2 can be seen over Europe, the United States, the Middle East oil fields and other industrial regions. In addition, the production of NO2 from biomass burning in the southern hemisphere and its transport around the globe is clearly visible. The retrieval of the tropospheric c~'!umn of NO2 requires the subtraction of the stratospheric column. This can be achieved by assuming local longitudinal homogeneity of the stratosphere. Figure 4 shows the excess tropospheric NO2 over Indonesia during the fires of September 1997 observed using GOME data (Burrows et al., 1998b).

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5.3. Formaldehyde Similar to several other trace species, formaldehyde (HCHO) occurs in elevated concentrations as a result of pollution in the lower troposphere. Formaldehyde is generated during the oxidation of hydrocabons in copious amounts. However, as it is photolyzed and reacts with OH, it has a relatively short tropospheric lifetime. The observation of formaldehyde from GOME over industrial and biomass burning regions has been discussed by Perner et al. (1998) and Burrows et al. (1998b). Thomas et al. (1998) have also observed formaldehyde over Borneo using GOME data. An example of the plume of excess tropopsheric HCHO from Indonesian fires in 1997 is shown in Figure 4.

5.4. Sulphur dioxide Sulphur dioxide is both emitted into thetroposphere and also formed during oxidation of dimethyl sulphide (DMS) and other sulphur containing species produced in the biosphere. Important sources of atmospheric SO2 are volcanoes. However, the major single global source is probably the combustion of sulphur-containing fossil fuels. In the stratosphere there are two important sources of sulphur dioxide, i.e. injection by volcanic eruptions and oxidation of carbonyl sulphide (COS), which is transported from the troposphere. SO2 has a short lifetime in the troposphere where it is oxidized in both the gas and liquid phases to form H2SO4. Its gas phase oxidation by OH leads to the formation of cloud condensation nuclei. In rain and aerosols it is oxidized by H202 to sulphuric acid. In the dry lower stratosphere the lifetime of SO2 is expected to be longer compared to that in the troposphere.

Figure 6. Total column density of sulphur dioxide over Eastern Europe averaged over the period 15-29 February 1996 derived from GOME data. D.U., Dobson unit; 1 D.U. = 2.69 • 1016moleculesper cm2. Source: M. Eisinger and J.P. Burrows, 1998,personal communication.


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SO2 was first retrieved from space-based measurements using TOMS data (Krueger, 1983; Krueger et al., 1995; 1996). Successful observations have been made on volcanic eruptions such as E1 Chichon and Pinatubo which injected SO2 not only into the stratosphere, but also into the troposphere. Recently GOME measurements have been used to observe tropospheric SO2 from both volcanic plumes and fossil fuel burning (Eisinger et al., 1998; Eisinger and Burrows, 1998a,b). Figure 6 shows the retrievals of the total column of SO2 observed over Eastern Europe in the first half of February 1996.

5.5. Halogen oxides GOME is the first space-borne remote sensing instrument, whose measurements are suitable for halogen oxides in the lower atmosphere. Its primary halogen targets are BrO and OC10, and under specific circumstances C10 may be retrievable. This is very complementary to the microwave instruments used to measure stratospheric C10. As these halogen radicals play an important role in the catalytic removal of stratospheric 03, one of the original aims of GOME was to measure their stratospheric abundance. While OC10 has only been observed by GOME mainly in the stratospheric polar vortex, BrO has been observed globally from GOME observations (Eisinger et al., 1996; Hegels et al., 1998) as large clouds of BrO occur in the troposphere both in the southern hemisphere (T. Wagner and U. Platt, 1998, personal communication) and in the northern hemisphere (Richter et al., 1998a). Examples of observations of northern hemispheric clouds of BrO are presented in Figure 7, showing a polar projection of the average total column amount of BrO in the months of March and April 1997. The large BrO cloud observed by GOME does not correlate with the stratospheric polar vortex or other dynamical behaviour. This cloud is attributed to tropospheric production of BrO in the boundary layer. The BrO above the Hudson Bay is clearly visible in late winter and early spring. The region of BrO production appears to move northwards from spring to summer but in late summer it is no longer visible (Richter et al. 1998a). The mechanism for the production of BrO is not yet well explained but is probably a natural process. The BrO is considered to be in large part responsible for the spring time low ozone episodes first reported some 10 years ago (Barrie et al., 1988, and references therein).

6. Planned future tropospheric measurements The need to study the change of atmospheric composition over long time intervals requires the continuity of measurements. This argues strongly for long-duration missions, which make the same well-calibrated measurements over many years. Passive remote sensing experiments utilizing absorption spectroscopy such as TOMS, SBUV, SAGE, GOME and SCIAMACHY (Table 2) are well suited for this task. In the next few years a number of important atmospheric remote sensing missions are planned by NASDA, NASA and ESA. NASDA has constructed a second ADEOS satellite which will have POLDER and ILAS-II (Improved Limb Atmospheric Sounder) on board. The NASA-AM-1 platform will have the MOPITT instrument (Measurement of Pollution in the Troposphere), which aims to measure vertical profiles of carbon monoxide and methane in the troposphere. MOPITT is a Canadian instrument supported by an international science team. NASA-AM is due for launch in the middle of 1999.

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Figure 7. Polar projection of the GOME retrieval of tropospheric BrO in March and April 1997. Source: A. Richter and J. P. Burrows, 1997, personal communication.


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The ESA-ENVISAT is planned for launch in 2000. It has a large payload, including SCIAMACHY, MIPAS (Michelson Interferometer for passive atmospheric Sounding), GOMOS (Global Ozone Monitoring by Occultation of Stars) and MERIS (MEdium Resolution Imaging Spectrometer). MIPAS, MERIS and GOMOS are ESA-developed instruments, whereas SCIAMACHY is a joint contribution of Germany, the Netherlands and Belgium to ENVISAT. GOMOS uses UV visible spectroscopy primarily to measure 03, NO2 and NO3 in the stratosphere. As indicated by its name, it uses the stellar occultation technique and makes measurements mainly at night. GOMOS is similar to SAGE or HALOE, but it uses a variety of stars as light sources instead of the sun. This enables many more star rise and star sets to be observed, when observing from a sun synchronous platform such as ENVISAT. Daytime measurements by GOMOS are influenced by solar radiation scattered from the atmosphere. MIPAS observes the infrared atmospheric emission both by day and night. GOMOS and MIPAS will both measure in the upper troposphere. MERIS observes during the day in a selected set of visible and near-infrared channels at a relatively high spatial resolution. MERIS data will also be used to retrieve albedo, aerosols and water vapor. The SCIAMACHY utilizes near simultaneous limb and nadir measurements of the scattered light in the atmosphere between 240 and 2400 nm to determine the amounts and distributions of tropospheric constituents. The target species and parameters are 03, NOR, N20, BrO~ CO, H20, SO2, CO, CO2, CH4, aerosols temperature and pressure. For the longlived gases such as N20, CH4 and CO2 the scientific objective is to measure the small gradients, which define source and sink regions. The NASA EOS-CHEM mission to be flown in 2003 will include the instruments TES (Tropospheric Emission Spectrometer, a nadir sounding Michelsen interferometer) for measurement of a variety of tropospheric gases such as 03, HNO3, and NO, and the OMI (Ozone Monitoring Instrument, a Dutch contribution to EOS-CHEM) with objectives similar to those of GOME.

7. Conclusions In the last 25 years remote sensing of atmospheric constituents has established itself as an important research field. Global remote sensing observations are essential to understand the natural processes which determine the global behaviour of the atmosphere and to assess the impact of human activity on the atmosphere. In addition, remote sensing of the atmosphere provides data needed to assess the impact of international agreements designed to limit the environmental impact of industrial activity. Following the great success in developing an adequate measurement strategy for stratospheric constituents, the challenge is to accurately measure the tropospheric composition. This is technically much more difficult than measurements in the upper atmosphere. The use of assimilation techniques to maximize the information retrieved from tropospheric sounding is foreseen as essential. A number of experiments have been or will be developed which are designed to determine the concentration of constituents in the troposphere. Well validated global data about the distributions of atmospheric constituents, obtained from remote sensing instrumentation, is essential for testing our knowledge and understanding of the atmosphere. High vertical, and horizontal spatial resolution as well as an appropriate temporal resolution is required. The parameterizations used in chemical and transport models (CTMs) of the atmosphere can only be validated by using such global data. One of the most critical parameterizations for CTMs in this respect is the fluxes of species into or out of

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atmosphere from the earth's surface. As the new generation of advanced data products become available, it is to be expected that the current generation of CTMs will be improved dramatically. This is a challenging task for the years ahead.

Acknowledgements This study has in part been supported by the University and State of Bremen. I would like to thank my close scientific colleagues P.J. Crutzen and D. Perner, without whom the GOME and SCIAMACHY projects would not have been started or realized. I would like to express my gratitude to all the scientists and engineers, who have worked on GOME and SCIAMACHY, in particular the ESA scientists and engineers, who supported and participated in the development of the GOME instrument (Drs C.J. Readings, Dr. P. Dubock Dr. A. Hahne, Dr. J. Callies) as well as the German, Dutch and Belgian governments and ESA for supporting the development of SCIAMACHY. I take this opportunity to express my thanks to all the scientists in my research group at the University of Bremen, who have generated many of the figures shown and provided much stimulating scientific discussion. Finally I would like to dedicate this study to my late father-in-law, K. Holtkotte, who died during the writing of this manuscript.

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349

PARTICIPANTS AND CONTRIBUTING AUTHORS 1 WITH FIELDS OF RESEARCH

Andreae, M.O. Max Plack Institute for Chemistry Biochemistry Department Postfach 3060 D-55020 Mainz, Germany Email: [email protected]

Bouwman, A.F. National Institute of Public Health and the Environment P.O. Box 1 3720 BA Bilthoven, Netherlands Email: [email protected]

Biogeochemistry, atmospheric chemistry

Soil science, biogeochemistry, global emission inventories

Archer, D. Department of Geophysical Sciences University of Chicago Chicago I11 60637 USA Email: [email protected]

Burrows, J.P. Universit~it Bremen Institut fur Umweltphysik/Fernerkundung Fachbereich 1, Postfach 330440 D-28334 Bremen, Germany Email: [email protected]

Oceanography, C cycle modelling in oceans

Atmospheric physics and chemistry, remote sensing

Asman, W.A.H. Assenvej 399 4000 Roskilde Denmark Email: [email protected]

Conrad, R. Max Planck Institute for Terrestrial Microbiology Karl von Frisch Strasse D-35043 Marburg/Lahn Germany Email: [email protected]

Atmospheric ammonia (chemistry, meteorology)

Microbiology

Batjes, N.H. International Soil Reference and Information Centre P.O. Box 353, 6700 AJ Wageningen Netherlands Email: [email protected]

Denier van der Gon, I-I.A.C. Department of Soil Science and Geology Wageningen Agricultural University P.O. Box 37, 6700 AA Wageningen Netherlands Email:[email protected]. wau.nl Methane fluxes from rice fields/tropical wetlands, measurements

Soil science, regional~global soil databases and applications Bogdanov, S. National Institute of Meteorology and Hydrology 66 Tsarigradsko Chaussee Sofia 1784 Bulgaria Email: [email protected]

Denmead, O.T. CSIRO Land and Water GPO Box 1666 Canberra ACT 2601 Australia Email: [email protected]

Trace gas emissions from agriculture and other human activities

Micrometeorology, flux measurement at local and regional scales

t Authors who were not at the workshop are indicated with *

350 Dentener, F.J. Institute for Marine and Atmospheric Research Utrecht University Princetonplein 5 3584 CC Utrecht, Netherlands Email: [email protected]

Ganzeveld, L. Institute for marine and Atmospheric Research University Utrecht Princetonplein 5 3584 CC Utrecht, Netherlands Email: [email protected]

Global tropospheric modelling, atmospheric chemistry

Atmosphere-biosphere exchange of trace gases at the global scale

Derwent, R.G. Meteological Office London Road, Bracknell RG 12 2SZ Berkshire U.K. Email: [email protected]

Garqon, V. LEGOS, UMR 5566 CNRS/CNES/UPS, GRGS 18 av. E. Belin 31400 Toulouse, France Email: [email protected], fr

Regional and global scale modelling acid rain and ozone formation, validation

Local~regional ocean C cycle modelling, oceanographic monitoring instrument development

Duyzer, J.H. TNO Institute of Environmental Sciences, Energy Research and Process Innovation P.O. Box 342, 7300 AH Apeldoorn Netherlands Email: [email protected]

Griffith, D.W.T.* Department of Chemistry University of Wollongong Wollongong NSW 2522 Australia Email: [email protected]

Photochemical ozone production, modelling, monitoring of gas concentrations

Atmospheric chemistry, FTIR techniques, surface exchange of trace gases

Estes, J.E. Remote Sensing Research Unit Department of Geography University of California, Santa Barbara CA 93106-4060, USA Email: [email protected]

Heimann, M.* Max Planck Institute for Meteorology Bundesstrasse 55 D-20146 Hamburg Germany Email: [email protected]

Remote sensing, GIS, land use~cover mapping at local to global scales

Atmospheric modelling, inverse modelling, atmospheric chemistry

Fowler, D. Institute of Terrestrial Ecology Bush Estate, Penicuik EH26 0QB Midlothian United Kingdom Email: [email protected]

Helder, W. Department of Marine Chemistry and Geology Netherlands Institute for Sea Research P.O. Box 59 1790 AB Den Burg (Texel), Netherlands Email: [email protected]

Environmental physics, micrometeorology, field measurements of trace gases

Marine biogeochemistry, N-cycling

Frankignoulle, M. Universit6 de Li6ge Mecanique des Fluides Geophysiques Unit6 d'Oceanographie Chimique B 4000 Sart Tilman, Belgium Email: [email protected]

Houweling, S. Institute for Marine and Atmospheric Research University Utrecht Princetonplein 5 3583 CC Utrecht, Netherlands Email: [email protected]

Marine chemistry, fieM measurements, coastal ocean

Global modelling of tropospheric chemistry, non-methane hydrocarbons, inverse modelling

List of participants and contributing authors withfields of research

3 51

Kaminski, T. Max Planck Institute for Meteorology Bundesstrasse 55 D-20146 Hamburg Germany Email: [email protected]

Meyer, C.P.* CSIRO Division of Atmospheric Research Private Bag 1 Aspendale Vic 3195 Australia Email:

Inversion of atmospheric transport, global scale, adjoint modelling

Exchange of trace gases, atmospheric chemistry, emission inventories

Kroeze, C. LUW-WIMEK P.O. Box 9101 6700 HB Wageningen Netherlands Email: [email protected]

Middelburg, J. Netherlands Institute of Ecology Centre for Estuarine and Coastal Ecology Korringaweg 7 4401 NT Yerseke, Netherlands Email: [email protected]

Biogeochemistry, environmental systems analysis modelling

Marine biogeochemistry, modelling and measurements in sediments

Lapitan, R.L. USDA-ARS-NPA 301 S. Howes St., Fort Collins P.O. Box E CO 80522 U.S.A. Email: [email protected]

Mosier, A.R. USDA-ARS 301 S. Howes St., Fort Collins P.O. Box E. CO 80523 U.S.A. Email: [email protected]

Micrometeorology, soil physics

Soil science, N and C cycle, nitrification/denitrification, elevated C02 and soil processes

Leuning, R.* CSIRO Land and Water GPO Box 1666 Canberra ACT 2601 Australia Email: [email protected],au

Nov~ik, M. Czech Geological Survey Geologicka 6 15200 Prague 5 Czech Republic Email: [email protected]

Land-atmosphere exchanges of radiation, heat and water, local and regional trace gas fluxes

Stable isotope geochemistry

Liss, P.S. University of East Anglia School of Environmental Sciences NR4 7TJ Norwich England Email: [email protected]

Panikov, N.S. Institute of Microbiology Russian Academy of Sciences Prosp. 60-1etija Octjabrja 7 Moscow 117811 Russia Email: [email protected]

Environmental chemistry, air-sea exchange

Soil microbiology, wetland, trace gas modelling

Malingreau, J.P. CCR Unit- SDME 10/85 European Commission 200 rue de la Loi 1049 Brussels, Belgium Email: [email protected]

Plant, R.A.J. Department of Soil Science and Geology Wageningen Agricultural University P.O. Box 37 6700 AA Wageningen, Netherlands Email: [email protected]

Remote sensing, biomass burning

Physical geography, land use effects on N20 emissions

352 Schimel, D.S.* NCAR, UCAR Climate System Modeling Program, Climate and Global Dynamics Division CO 80303 USA Email: [email protected]

Wanninkhof, R. NOAA/AOML Ocean Chemistry Division 4301 Rickenbacker Causeway Miami, FL 33149 USA Email: [email protected]

Biogeochemistry, trace gas production in ecosystems

Air-sea exchange, CO: uptake by oceans

Seitzinger, S.P. Inst. of Marine & Coastal Sciences Rutgers/NOAA CMER Program NJ 08901-g521 USA Email: [email protected]

Coastal marine biogeochemistry ~, P, C), water-shed land use effects on N/P input to coastal zone Sofiev, M.A. Institute of Program Systems c/o. Stroiteley str. 4 Bid. 1, App. 18 Moscow 117311 Russia Email: [email protected]

Long-range atmospheric transport modelling, model validation and intercomparison Starink, M. Netherlands Institute of Ecology Centre for Estuarine and Coastal Ecology Korringaweg 7 4401 NT Yerseke Netherlands Email: [email protected]

Microbiology Trumbore, S. Earth System Science UC Irvine 19172 Jamboree Road Irvine CA 92717-3100 U.S.A. Email: [email protected]

Isotope geochemistry Wania, F. Wania Environmental Chemsits Corp. 280 Simcoe Street, Suite 404 Toronto, Ontario M5T 2Y5 Canada Email: [email protected]

Global~regional modelling of persistant organic pollutants and of their air-surface exchange

INDEX

a posteriori estimates 284, 288, 289-291 a priori est'.'mates 284, 288-291 ADEOS, see remote sensing-Advanced Earth Observation Satellite adjoint model 285,289-292 Advanced Very High Resolution Radiometer 137, 138, 140, 142, 164, 326, 330 aerodynamic gradient method, s e e flux measurement aerosols, emission inventory 17 aggregation 5, 7, 12-14, 15, 16, 22, 197-200, 224-225, 303 -modelling 197-200 aircraft-based measurement 42, 43 aircraft-based sensors 32-33 ammonia, -compensation concentration 208 -emission inventory 17 -environmental controls 161-162 -processes 161-162 analytical devices 35-36 animal, -categories 15 -housings 7 -manure 15 -production 15 -waste 7, 15 animal waste management 7 aquatic systems, -bomb 14C 50, 51 -carbon dioxide fluxes 32-33 -chamber method 52 -eddy accumulation 51, 55, 56 -eddy correlation 51 -eddy covariance 57 -flux measurement methods 47-57 -fugacity 47 -gas transfer velocity 9, 47, 48, 50, 54, 174 -gradient method 51 -kinematic viscosity 48 -langrnuir cells 49 -mass balance 53 -mesoscale eddies 49 -remote sensing 57 -saturation level 49 -Schmidt number 48, 54, 55

aquatic systems, continued -surface roughness 48 -tracers, 51, 53 -waves 48, 49, 55 -Webb correction 56 arithmetic mean 239 ATMOS, see remote sensing- Atmospheric Trace Molecule Spectroscopy atmosphere, -memory 76, 283 -mesopause 318 -oxygen 318-319 -ozone 318-319 -pressure 318-319 -remote sensing 317-347 -stratosphere 318-319 -temperature 318-319 -thermosphere 319 -tropopause 318 -troposphere 318-319, 321 atmospheric measurements, -monitoring networks 19, 278, 286-287, 292 -outliers 285 -representativeness 240-241,278, 287 atmospheric model 4-6, 18-21, 91,235-237, 275295 - s e e also chemistry transport model atmospheric transport, -memory 283 -rectifier effect 264, 266, 269, 282 ATSR, see remote sensing- Along Track Scanning Radiometer autocorrelation 249 AVHRR, s e e Advanced Very High Resolution Radiometer biomass burning 7, 159, 321 biome, emission factors 7, 8 black box model 227-228 black carbon, emission inventory 17 bomb ~4C 50, 51 Bowen ratio method, s e e flux measurement Bowen ratio, -latent heat flux 41 -net radiation 41 -sensible heat flux 41

354 Bowen ratio, continued -soil heat flux 41 BUV, see remote sensing- Backscatter Ultraviolet Ozone Experiment canopy flux 69, 70-73 carbon dioxide -convective boundary layer budgeting 78 -emission inventory 17 -environmental controls 161-162 -global budget 300 -infrared analyzer 42, 43, 56 -isotopes 261-266 -modelling 171-183 -nocturnal boundary layer budgeting 80, 81 -oceans 171-183 -processes 161 - 162 -profiles 42, 43 -uncertainty 306 carbon dioxide cycle, uncertainty 265 carbon dioxide flux, -ecosystems 32-33 -geologic scale 179 -gyre 178 -mesoscale 177 -microscale 172-175 -mixed layer 175-177 -molecular scale 171-172 carbon monoxide, -compensation concentration 208 -emission inventory 17 -isotopes 267-268 -variability 268 carbonate buffer 171, 174, 179-180 carbonyl sulphide, 208, 336 -compensation concentration 208 CBL, s e e convective boundary layer CFC, s e e chlorofluorocarbons CH4, s e e methane chamber method, s e e flux measurement chemistry transport model 3, 4-6, 18-21, 91, 275295 -advection 280, 281 -bayesian approach 284, 288 -chemical mechanisms 4 -comparison - method of least squares 251 -comparison - ranking procedure 251-252 -continuity equation 279 -coverage 236 -global 236, 237 -inverse approaches 275-295 -local 236, 237 -off line 4, 280 -on line 4, 280

chemistry transport model, continued -precision 244-245 -regional 236, 237 -reliability 235 -resolution 236 -spatial scale 4, 18-21,236-238, 278 -subgrid transport 280, 282 -temporal scale 4, 18-21 -validation 235-255,282 -verification 236 -wind fields 284 -chlorofluorocarbons, 4 CLAES, see remote sensing - Cryogenic Limb Array Etalon Spectrometer climate data, uncertainty 12 climate model 130-131 closed-path infrared analyzers 35-36 CO, s e e carbon monoxide C 0 2 , s e e carbon dioxide compensation concentration 205-216 -ambient concentration 205 -ammonia 208 -carbon monoxide 208 -carbonyl sulphide 208 -dihydrogen, 208 -environmental controls 210 -nitrogen oxides (NOx) 208 -nitrous oxide 208 -plant canopy 208-209 -soil 208-209 computer assisted classification 133, 135 concentration data, redundant 71 conditional sampling 39, 40-41, 45 -nitrous oxide 39, 45 continuity equation 279 convective boundary layer budgeting, s e e flux measurement correlation coefficient 8, 239 crop production 15 cross-correlation 250 CTM, s e e chemistry-transport model data, -animal production 15 -crop production 15 -environmental 22 -forestry 15 -geographical 11-14 -georeferenced data 127, 134 -land cover 127-129 -land use 13-14, 15 -livestock production 15 -oceans 12-13 -projection 134

Index

data, continued -quality 14, 134 -soils 13 -surrogate 305, 306 -vegetation 13-14 database, -mask files 13 -projection 134 daytime boundary layer budgeting, s e e convective boundary layer budgeting deposition 3, 20, 205 -parametrization 20 detection limit, measurement methods 36 diffusion 194 dihydrogen, compensation concentration 208 dimethylsulphide 4, 321,336 DMS, s e e dimethylsulphide DOAS, see remote sensing- Differential Optical Absorption Spectroscopy dry deposition 20 ebullition, s e e gas exchange ECD, s e e electron capture detector economic data, -animal production 15 -crop production 15 -forestry 15 -livestock production 15 ecoregions 136 electron capture detector 36, 45 emission estimates 6-16 -databases 11-14 -uncertainty 6-16 emission factor 6, 7, 8, 17, 21 -biomes 7 emission inventory 3, 16-18 -aerosols 17 -ammonia 17 -black carbon 17 -carbon dioxide 17 -carbon monoxide 17 -global 16-18 -methane 17 -nitrogen oxides (NOx) 17 -nitrous oxide 17 -regional 18 -spatial resolution 16-18 -sulphur dioxide 17 -temporal resolution 16-18 -validation 20-21 -volatile organic compounds 17 emission -measurement data 7 -scenario 3

3 55

emission, continued -spatial patterns 6 -temporal patterns 6, 15-16 -uncertainty 158-159 -variability 22 enclosure, s e e flux measurement - chamber method energy balance method 34, 41, 112-113 environmental controls 195-197, 198 environmental data 22 Eulerian coordinate system 78 experimental design 101-121 farm model 11 fertilizer use 15 fertilizers, nitric oxide flux 103 fetch 39, 75, 89, 110 FID, s e e flame ionization detector fires, flaming 7 fires, smouldering 7 flame ionization detector 35-36 flux measurement, -aerodynamic gradient method 111-112, 206 -airborne 90 -aquatic systems 50-57 -Bowen ratio method 34, 41, 112-113 -campaigns 113-114 -canopy height 38 -chamber - experimental design 101-109 -chamber method 29, 32-33, 34, 37, 46, 52, 87, 88, 89, 101-109 -chamber method - aquatic systems 52 -chamber method - design 105 -chamber method - errors 103 -chamber method- footprint 88, 89 -chamber method - spatial coverage 109 -chamber method 29 -chemiluminescence 36 -closed chamber method 29, 32-33, 34, 37, 46, 102 -convective boundary layer budgeting 32, 33, 34, 43, 44, 75-79, 82, 83, 90, 115-116 -convective boundary layer budgeting Eulerian coordinate system 78 -convective boundary layer budgeting footprint 76, 90 -convective boundary laye," budgetingLagrangian coordinate system 78 -convective boundary layer budgeting methane 77, 78, 115-116 -convective boundary layer budgeting nitrous oxide 77, 78 -detection limit 36 -dynamic chamber method, s e e open chamber

356 flux measurement, continued -eddy accumulation- aquatic systems 51, 55, 56 -eddy accumulation method 34, 36, 40-41, 51, 55, 56 -eddy correlation - aquatic systems 51 -eddy correlation method 29, 34, 36, 39-40, 51, 89, 90 -eddy covariance - aquatic systems 57 -eddy co-variance method 39-40, 57, 94, 110 -eddy diffusivity- tracers 44 -eddy diffusivity 37, 38, 39,44 -eddy relaxation method 34 -energy balance method 34, 41, 112-113 -experimental design 101-121 -fetch 3 9 , 75, 89, 110 -field scale 109 -flux gradient method 34, 36, 38-39, 51, 111113 -footprint 31, 44-45, 46, 76, 87, 88, 89, 95, 109, 117, 229 -inverse Lagrangian dispersion method 70-73 -inverse Lagrangian dispersion method carbon dioxide 72 -inverse Lagrangian dispersion method latent heat 72 -Lagrangian dispersion method 89 -mass balance - aquatic systems 53 -mass balance - turbulent diffusive flux 74 -mass balance method 34, 41, 53, 73-75, 91, 113 -mass balance method - convective flux 74 -mass balance method - fetch 41, 75 -mass balance method - footprint 91 -mass budget 115,250 -megachamber 44, 89, 105-106 -methane 38 -micrometeorological method 29, 32-33, 34, 37-44, 87, 89, 109-114 -monitoring 114 -nitrous oxide 38, 39 -nocturnal boundary layer budgeting 43, 8082, 90, 115 -nocturnal boundary layer budgeting - carbon dioxide 80, 81 -nocturnal boundary layer budgetingfootprint 90 -nocturnal boundary layer budgetingmethane 80, 81 -nocturnal boundary layer budgeting - nitrous oxide 81 -open chamber method 32-33, 34, 37, 46, 102103, 105 -regional scale 114-116

flux measurement, continued -relaxed eddy accumulation 40 -soil concentration profile 88 -static chamber, s e e closed chamber -strategy 101 - 121 -surface roughness length 37 -tracer method 34 -tunable diode laser 106 -water-side 229 -wind speed 38 -zero plane displacement 38 footprint 31, 44-45, 46, 76, 87, 88, 89, 95, 109, 117, 229 -airborne mass balance 95 -airborne micrometeorological method 95 -atmospheric stability 31 -chamber method 95 -convective boundary layer budget 43, 90, 95 -micrometeorlogical method 95 -nocturnal boundary layer budgeting 90, 95 -roughness length 31 -soil gas concentration profile 95 -vegetation 31 -wind speed 31 forestry 15 formaldehyde, remote sensing 334, 336 fourier transform infrared spectrometer, nitrous oxide 39, 45, 46 fractional bias 246 friction velocity 76 FTIR, s e e Fourier-transform infrared spectrometer FTS, s e e remote sensing - Fou:ier Transform Spectrometer fugacity 47 functional strata 128 functional type 7, 16, 22, 128, 153-168,224 -environmental controls 155-157, 164 -methane 157-161 -remote sensing 155, 163 gamma distribution 242, 243 gas chromatography 35-36, 45 gas diffusivity 205 gas exchange, ebullition 55, 174, 194 gas filter correlation infrared absorption analyzer 36 gas flux, s e e trace gas flux gas transfer, modelling 175 gas transfer velocity, -aquatic systems 9, 47, 48, 50, 54, 174 -wind speed 9 gaussian distribution 242, 243 GC, s e e gas chromatography

357

Index

GCM, s e e global climate models generalization 5, 15, 16 geographic data 11-14, 125-150 -land use 13-14 -oceans 12-13 -soils 13 -vegetation 13-14 georeferenced data 127 -projection 134 GFCIR, s e e gas filter correlation infrared absorption analyzer global area coverage 142 global emission inventories 16-18 global models, validation method 242-244 global vegetation index 142 GOME, s e e remote sensing - Global Ozone Monitoring experiment GOMOS, s e e remote sensing - Global Ozone Monitoring by Occultation of Stars gradient method, aquatic systems 51 H2, s e e dihydrogen H202, s e e hydrogen peroxide half-life 262-263 HALOE, s e e remote sensing - Halogen Occultation Experiment halogen oxides, remote sensing 337, 338 Henry's law 171 heterogeneity, s e e variability histogram 250 hot spots 11,230 HNO3, s e e nitric acid hydrogen peroxide 321 hydroxyl radical 321 ILAS, s e e remote sensing - Improved Limb Atmospheric Spectrometer IMG, s e e remote sensing - Interferometric Monitor for Greenhouse gases infrared absorption spectroscopy 35-36 infrared analyzer 42, 43, 56 infrared analyzer, carbon dioxide 42, 43 interannual variability 22, 200 inverse Lagrangian dispersion method, s e e flux measurement inverse modelling 277-295, 91, 92-93 - a p o s t e r i o r i estimates 284, 288, 289-291 - a p r i o r i estimates 284, 288-291 -adjoint model 285,289-292 -advection 280, 281 -brute force 283 -isotopes 292 -rectifier effects 282 -subgrid transport 280, 28I

inverse modelling, continued -synthesis 283 -uncertainty 287, 290-292 IR, s e e infrared absorption spectroscopy ISAMS, s e e remote sensing - Improved Stratospheric and Mesospheric Sounder isoprene, flux 109 isotopes 50, 259-272 -abundancies 260 -carbon 50 -carbon dioxide 261-266 -carbon monoxide 267-268 -common isotopes 260 -fossil fuel 266 -global methane budget 266 -global nitrous oxide budget 268 -half life 262-263 -kinetic isotope effect 266, 268, 271 -mass spectrometer 260, 2"70, 271 -methane 266-267 -nitrous oxide 268-269 -radioactive decay 262 -rare isotopes 260 kinematic viscosity 48 kinetic isotope effect 266, 268, 271 Lagrangian coordinate system 78 Lagrangian dispersion method 89 land cover, -aerial photography 141 -changes 125 -classification 135-137, 141 -classification schemes 129, 131 -data 127-129 -functional type 128 -geographically referenced data 127 -legends 137, 138-141 -mapping 133-144 -mapping- regionalization 137 -mosaics 141 -seasonal dynamics 128 -types 7 land use data 13-14, 15 landfills 159 laser spectroscopy 45 life form 137 LIMS, s e e remote sensing - Limb Infrared Monitor of the Stratosphere livestock production 15, 158 local models, measurement data 245 -validation method 245-248 log-normal distribution 242, 243

358 LRIR,

remote sensing - Limb radiance Inversion Radiometer

see

mapping, -dominant vegetation 136 -land cover 134-144 maps, science quality 134 MAPS, s e e remote sensing - Measurement of Air Pollution from Satellites MAS, s e e remote sensing - Microwave Atmospheric Sounder mass balance method, s e e flux measurement mass spectrometer 260, 270 maximum absolute deviation 240 mean 238 mean square deviation 239-240 measurement data, outliers 285 measurement data, representativeness 7, 240241,278,287 mechanistic model 187-202 median 238 megachamber 44, 89, 105-106 MERIS, s e e remote sensing - Medium Resolution Imaging Spectrometer for Passive Atmospheric Sounding mesopause, s e e atmosphere mesosphere, s e e atmosphere methane flux 32, 107, 108 -conditional sampling 39 -eddy correlation 39 -flux gradient 39 -forests 108 -mass budget 115, 116 -spatial variability 108 -transect 107 methane, -aquatic systems 158 -aquatic systems 158 -biomass burning 159 -convective boundary layer budgeting 77, 78 -emission inventory 17 -environmental controls 161-162 -forests 108 -functional type 157-161 -isotopes 266-267 -landfills 159 -livestock production 158 -mass budget 115, 116 -nocturnal boundary layer budgeting 80, 81 -oxidation 159 -processes 161-162, 190 -spatial variability 108 -transect 107 -uncertainty 266-267

method of least squares 240, 251 micrometeorological method, s e e flux measurement MIPAS, s e e remote sensing - Michelson Inferometer for Passive Atmospheric Sounding MLS, s e e remote sensing - Microwave Limb Sounder model, - s e e also chemistry transport models, trace gas flux model -validation, s e e validation -climate 130-131 -functional type 224-225 -hierarchy 301 monitoring networks, atmosphere 19, 278, 286287, 292 monitoring stations, sulphur dioxide 19 MOPITT, s e e remote sensing - Measurement of Pollution in the Troposphere multiple measurement 71, 87, 93-94, 95 N20, s e e nitrous oxide natural 14C 50 NBL, s e e nocturnal boundary layer NDIR, s e e non-dispersive infrared absorption NDVI, s e e Normalized Difference Vegetation Index near-infrared diode laser 35-36 net primary production 133 NH3, s e e ammonia NIRDL, s e e near-infrared diode laser nitrate radical 321 nitric acid 321 nitric oxide, -emission inventory 17 -environmental controls 161-162 -field scale 310 -global sources 308 -point scale 310 -processes 161 - 162 -temporal scale 12 nitric oxide flux, -agricultural soils 103 -extrapolation 104 -soil 7, 103 nitrogen oxides, -atmospheric processes 309, 333-335 -compensation concentration 208 -remote sensing 333-335 -uncertainty 307 nitrous oxide flux, -agricultural soils 103,220-223 -conditional sampling 39

359

Index

nitrous oxide flux, continued -eddy correlation 39 -flux gradient 39 -modelling 222-224 -soils 8 -variability 106 nitrous oxide, -compensation concentration 208 -convective boundary layer budgeting 77, 78 -emission inventory 17 -environmental controls 161-162 -fourier transform infrared spectrometer 46 -isotopes 268-269 -nocturnal boundary layer budgeting 81 -processes 161-162 -profiles 42, 43 -tunable diode laser 42,43 -uncertainty 268-269 NO, s e e nitric oxide, nitrogen oxides Non-dispersive infrared absorption, 36 nonmethane volatile organic compounds 18 Normalized Difference Vegetation Index 138 NOx, s e e nitrogen oxides, nitric oxide NPP, s e e net primary production nutrient balance models 11 03, s e e ozone ocean data 12-13 -uncertainty 12-13 ocean flux, micrometeorological method 229 ocean, -calcium carbonate 179 -carbonate buffer 171, 174 -carbonate buffer 179-180 -gas transfer 172, 175 -geologic scale 179 -gyre 178 -mesoscale 177 -microscale 172-175 -mixed layer 175-177 -molecular scale 171-172 -processes 173 -trace gas flux modelling 8 -modelling 171-183 OCS, s e e carbonyl sulphide OH, see hydroxyl radical OMI, s e e remote sensing - Ozone Monitoring Instrument open chamber method, s e e flux measurement Ostwald solubility coefficient 47, 48, 49 outliers 238, 285 ozone 4, s e e also atmosphere -layer 320 -remote sensing 332-333,334

POLDER, s e e remote sensing - Polarization and Directionality of the Earth's Radiance quantile 246, 247 quartile 246, 247 r2, s e e correlation coefficient radiative forcing 3 radioactive decay 262 radon 51 rectifier effect 264, 266, 269, 282 redundant concentration data 71 redundant measurement 71, 87, 93-94, 95 regional emission inventories 18 regional model 197-200 regional model, validation method 242-244 regression, -analysis 238 -bias 239 -coefficients 238 -models 8 -slope 239 relaxed eddy accumulation, s e e flux measurement remote sensing 13-14, 125-150, 164-165,317347 -Advanced Very High Resolution Radiometer 137, 138, 140, 142, 164 -aquatic systems 57 -atmospheric composition 165 -calibration 135 -land cover 125-150 -land cover mapping 133-134 -Landsat 143 -Normalized Difference Vegetation Index 138 -Pathfinder 143 -SPOT 143, 164 -validation 96, 331 -vegetation data 13-14 remote sensing of atmosphere, -Advanced Earth Observation Satellite 326, 328, 329 -Advanced Very High Resolution Radiometer 326, 330 -Along Track Scanning Radiometer 326, 331 -atmosphere 317-347 -Atmospheric Trace Molecule Spectroscopy 326,328 -Backscatter Ultraviolet Ozone Experiment 326, 329 -Cryogenic Limb Array Etalon Spectrometer 326, 328

360 remote sensing of atmosphere, continued -Differential Optical Absorption Spectroscopy 328 -Dobson unit 332, 336 -ENVISAT, 326-327, 339 -far infrared 324-325 -formaldehyde 334, 336 -Fourier Transform Spectrometer, 325 -Global Ozone Monitoring by Occultation of Stars 326, 339 -Global Ozone Monitoring Experiment 326, 329, 330, 333,334, 335, 336, 337, 338 -Halogen Occultation Experiment 326, 328, 329,332 -halogen oxides 337, 338 -Improved Limb Atmospheric Spectrometer 326, 328, 337 -Improved Stratospheric and Mesospheric Sounder 326, 328 -Interferometric Monitor for Greenhouse gases 326 -Limb Infrared Monitor of the Stratosphere 326 -Limb Radiance Inversion Radiometer 326 -Measurement of Air Pollution from Satellites 326,328 -Measurement of Pollution in the Troposphere 326, 337 -Medium Resolution Imaging Spectrometer for Passive Atmospheric Sounding 326, 339 -Michelson Inferometer for Passive Atmospheric Sounding 326 -microwave 324-325 -Microwave Atmospheric Sounder 326 -Microwave Limb Sounder 325,326, 332 -mid-infrared 325-328 -Nimbus 326, 327, 328 -nitrogen dioxide 323 -ozone 332-333 -Ozone Monitoring Instrument 326, 339 -Polarization and Directionality of the Eartll's Radiance 326, 331,337 -SAFIRE 325 -satellites 326-327 -Scanning Imaging Absorption Spectrometer for Atmospheric Cartography 327, 329, 333,337,339 -Selective Chopper Radiometer 327, 328 -Solar Backscatter Ultraviolet Ozone Experiment 327, 328, 329, 333,337 -Solar Mesospheric Experiment 327 -Space shuttle 326, 328

remote sensing of atmosphere, continued -Stratospheric Aerosols and Gas Experiment 327, 329, 330, 337 -Stratospheric Aerosols Measurement 327, 328 -Total Ozone Monitoring Spectometer 327, 328, 329, 330, 332, 337 -trace gases 324-339 -Tropospheric Emission Spectrometer 327 -tropospheric ozone residual technique 332 -Upper atmospheric Research Satellite 325, 326, 327, 328, 332 representativeness 7, 240-241,278, 287 robust statistical method 8, 238 SAGE, s e e remote sensing - Stratospheric Aerosols and Gas Experiment SAM, s e e remote sensing - Stratospheric Aerosols Measurement SBUV, s e e remote sensing - Solar Backscatter Ultraviolet Ozone experiment scale, -flux models 10, 11 -model approach 219-232 -model parameters 219-232 -modelling 187-202, 219-232 -trace gas budget 300, 302 -trace gas flux 299 scaling, -bottom-up 5,303 -top-down 5,275-295,301,304 Schmidt number 48, 54, 55 SCIAMACHY, s e e remote sensing - Scanning Imaging Absorption Spectrometer for Atmospheric Cartography science quality maps 134 SCR, s e e remote sensing - Selective Chopper Radiometer SF6 44, 53 SME, s e e remote sensing - Solar Mesospheric Experiment SO2, sulphur dioxide soil, -data 13 -fertility 8 -moisture 8 -organic matter 8 -oxygen 8 -processes 190 -profile data 13 -temperature 8 spatial distribution of controlling factors, -surrogate 11, 14 -uncertainty 11

Index

spatial resolution 16-18 spatial variability, gas flux 10, 102, 105, 106 stable isotopes, 228 standard deviation 238, 239 statistical average model 249, 252 statistical methods 238-240 -robustness 8, 238 stratification 5, 8, 16, 151-167 stratosphere, s e e atmosphere sub-grid heterogeneity 131,132 substrate production 189-191 sulphur dioxide 4, 17, 19, 321 -emission inventory 17 -monitoring stations 19 -remote sensing 336-337 sulphur, stable isotopes 228 sulphuric acid 321 summary model 10, 11,306 surface concentration anomalies 9 surrogate spatial distribution 14 TDL, s e e tunable diode laser temporal distribution of emissions 15-16 temporal resolution 16-18 temporal variability, trace gas flux 10, 107 terrain units 136 terrestrial ecosystems, -carbon dioxide fluxes 32-33 -environmental controls 195-197, 198 -modelling 187-202 -regional modelling 197-200 TES, s e e remote sensing- Tropospheric Emission Spectrometer thermosphere, s e e atmosphere TOMS, s e e remote sensing - Total Ozone Monitoring Spectometer TOR, s e e remote sensing - tropospheric ozone residual technique trace gas exchange, s e e gas exchange trace gas flux model 5, 6, 10, 11, 21,219-232, 171-202, 219-232 -aggregation 301 -black box 227-228 -coupling of models 224-226 -development 221 -empirical 187-188 -environmental simulation 129, 130-133 -gas transfer 175 -mechanistic 187-202 -microbial population 188, 193 -non-linear processes 225-226 -ocean 8, 171 - 183 -parameters 219-232 -process 301

3 61

trace gas flux model, continued -process 8 -regional scale 197-200 -regional scale 301 -regression 8, 187, 199 -scale 300 -soil 9 -spatial scale 211, 219-231 -stability-dependent method 9 -substrate production 189-191 -summary model 10, 11,306 -terrestrial systems 187-202 -types 187-189 -validation 301 trace gas flux, -aggregation 225 -canopy 69, 70-73 -carbon dioxide 32 -compensation concentration 205-216 -deposition 205 -ecosystems, 30-31 -environmental controls 155-157, 164 -environmental controls 161-162 -environmental controls 195-197, 198 -exchange 20 -functional type 153-168 -functional type 153-168 -heterogeneity s e e variability; trace gas fluxvariability -hot spots 11,230 -integration 224-225 -isoprene 109 -mass budget 115, 116 -methane 107, 108 -methane 32 -model 6, 21, 171-202, 187-202, 219-232 -model approach 219-232 -model development 221 -model, s e e model -net flux 205 -nitrous oxide 32 -processes 161 -pulses 16 -regional modelling 197-200 -regulating factors 8 -spatial variability 10, 102, 105, 106, 230 -temporal variability 10, 107 -variability 10, 11, 30-35, 102, 105, 106, 107, 108,230 trace gas, substrate 192 -transport 194-195 tracer 51, 53, 54, 174, 269-270 -aquatic systems 51, 53

362 tracer, continued -deuterium 270 -eddy diffusivity 44 -lead 270 -method 34 -oxygen 270 -radon 270 -release 43-44 -SF6 44, 53, 54 transport models, s e e chemistry transport models tropopause, s e e atmosphere troposphere, s e e atmosphere tunable diode laser 35-36, 39, 40, 42, 43, 44, J06 -nitrous oxide 39, 42, 43 UARS, s e e Upper atmospheric Research Satellite uncertainty, -aggregation 303 -carbon dioxide 306 -carbon dioxide partitioning 265 -climate data 12 -disaggregation 304 -economic data 15 -emission factor 7-8 -emissions 6-16 -field scale 304 -fluxes 31, 34, 44 -global budgets 300 -global scale 306 -inverse modelling 287, 290-292 -local scale 305 -methane budget 266-267 -methane fluxes 158-159 -models 235-255 -nitrogen oxides 307 -nitrous oxide budget 268-269 -ocean data 12-13 -point scale 304 -process models 8-11 -reduction of uncertainty 306 -regional scale 305 -regression model 8 -soil data 13 -spatial distribution 11-14 -statistical analysis 238-240 -temporal distribution 15-16 -trace gas fluxes 158-159 -vegetation data 13-14 validation 19, 20-21, 95,257-274, 282 -arithmetic mean 239 -autocorrelation 249 -correlation coefficient 239 -cross-correlation 250

validation, continued -ecosystem flux 306 -fractional bias 246 -histogram 250 -mass budget 250 -maximum absolute deviation 240 -mean square deviation 239 -method 242-244 -method of least squares 240 -model comparison 248-252 -quantile 246, 247 -quartile 246, 247 -ranking procedure 251-252 -regression analysis 238 -regression bias 239 -regression coefficients 238 -regression slope 239 -remote sensing 96, 331 -standard deviations 238, 239 -trace gas flux model 301 -tracers 247 variability, -carbon monoxide 268 -emissions 22 -interannual 22, 200 -methane flux 108 -nitrous oxide flux 106 -trace gas flux 10, 11, 30-35, 102, 105, 106, 107,108, 230 vascular transport 194 vegetation, -classification schemes 129, 131,133, 135137, 141 -data 13-14 -dominant 136 -functional type 128 -heterogeneity 131 -landscape variability 136 -legends 137, 138-141 -mapping 133-144 -mapping - regionalization 137 -net primary production 133 -remote sensing 13-14, 125-150 -sub-grid heterogeneity 131, 132 -types 125 -uncertainty 13-14 VOC, s e e volatile organic compounds volatile organic compounds 7, 17, 18 -emission inventory 17 Von Karman constant 76, 111 waves, see aquatic systems Webb correction 56 wet deposition 20

Approaches to Scaling of Trace Gas Fluxes in Ecosystems (Developments in Atmospheric Science)

Trace Elements in Soils (Developments in Soil Science)

Microclimate for Cultural Heritage Developments in Atmospheric Science

Atmospheric Science (Advances in Geosciences)

Physiological Limnology: Approach to the Physiology of Lake Ecosystems (Developments in Water Science)

Scaling in Hydrodinamic flows

Formal Approaches to Poetry: Recent Developments in Metrics

Trace Elements in Fuel

Trace Evidence in Tarrant

Trace Inorganics In Water

Trace Elements in Soils

Approaches to Discourse in Dementia

Approaches to Meaning in Music

Petroleum Geology (Developments in Petroleum Science)

Contemporary hydrogeology (Developments in Water Science)

Approaches to Meaning in Music

Geology and Geochemistry of Oil and Gas (Developments in Petroleum Science)

Groundwater Hydraulics (Developments in Water Science)

Abnormal Formation Pressures (Developments in Petroleum Science)

Paraffin Products (Developments in Petroleum Science)

Polymer Flooding (Developments in Petroleum Science)

Streamflow Characteristics (Developments in Water Science)

Integrated Flow Modeling (Developments in Petroleum Science)

Developments in Block Copolymer Science and Technology

Geology and Geochemistry of Oil and Gas (Developments in Petroleum Science)

Dynamics in Atmospheric Physics

Periodic Nanostructures (Developments in Fullerene Science)

Fractals and scaling in finance

Fractals and scaling in finance

Scaling concepts in polymer physics

Scaling Methods in Soil Physics

Approaches to Scaling of Trace Gas Fluxes in Ecosystems (Developments in Atmospheric Science)

Trace Elements in Soils (Developments in Soil Science)

Microclimate for Cultural Heritage Developments in Atmospheric Science

Atmospheric Science (Advances in Geosciences)

Physiological Limnology: Approach to the Physiology of Lake Ecosystems (Developments in Water Science)

Scaling in Hydrodinamic flows

Formal Approaches to Poetry: Recent Developments in Metrics

Trace Elements in Fuel

Trace Evidence in Tarrant

Trace Inorganics In Water

Trace Elements in Soils

Approaches to Discourse in Dementia

Approaches to Meaning in Music

Petroleum Geology (Developments in Petroleum Science)

Contemporary hydrogeology (Developments in Water Science)

Approaches to Meaning in Music

Geology and Geochemistry of Oil and Gas (Developments in Petroleum Science)

Groundwater Hydraulics (Developments in Water Science)

Abnormal Formation Pressures (Developments in Petroleum Science)

Paraffin Products (Developments in Petroleum Science)

Polymer Flooding (Developments in Petroleum Science)

Streamflow Characteristics (Developments in Water Science)

Integrated Flow Modeling (Developments in Petroleum Science)

Developments in Block Copolymer Science and Technology

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