El Nino and the Earth's Climate: from Decades to Ice Ages

El Niño and the Earth’s climate : from decades to Ice Ages.

Julien Emile-Geay

Verlag Dr Müller

2008

Acknowledgements There are many people who generously contributed their time, ideas and encouragement to this work, and to whom I would like to extend my heartfelt thanks. First of all my advisor Mark Cane, for his support, patience, wisdom, thoughtful guidance, his love of science and his love for talking, sometimes, about everything else but science. If it were not for his invitation, I would not have come to the United States for my PhD - I would have stayed in Paris and my life would have taken a very, very different turn. Richard Seager, for being always available for sassy, ironic, and sometimes scientific comments, for his precious and inoxidizable enthusiasm, for his diligence at reading and editing my non-native prose, for his patience at correcting my mistakes and for (exceptionally) wearing pink socks with a pink hawaiian shirt. Peter deMenocal, for his support, breadth of knowledge and encouragement throughout my thesis. Ming-Fang Ting, for a patient appraisal of all my mistakes, and for her relentless kindness in answering my questions, which taught me the 2 or 3 things I know about atmospheric Rossby waves. Steve Zebiak , for being such an inspiring model to follow and for his willingness to share ZC black magic. Ed Cook, for being as much at ease with singular-spectrum analysis as with the history of Bhutan. Edwyn Schneider and Adam Sobel, for constructive reviews of the thesis manuscript. Gerald Haug, Bette Otto-Bliesner, Chris Hewitt, for kindly providing access to their data. C.Gao, E. Hendy, R.Stothers, M.Mann, R. Bay, J.Cole-Dai, T. Johnson, T. de Putter, W. Qian, J.Li, M.Evans, R.Villalba, A. Schilla, N. Dunbar, M. Lachniet, Andrew Wittenberg, for some inspiring advice. Everyone in the Lamont Climate Group for making it such a friendly environment to work in. In particular : Bruno Tremblay, for having found put me on the track of the one bug that bugged me most, for uncountable rides home, for his enthusiasm for science, his open mind, his joy and friendship. Alexey Kaplan, for his patience,

availability, interest and wit. Naomi Naik, for being such a tech wizard and always tweaking the knobs in the right direction. Gustavo Correa for his incredible patience, kindness and availability ; for educating me somewhat about the strange language of computers ; and giving me unique training in this universal language called Brazilian music. Larry Rosen for the IT, the Bush-bashing, the 800-pound gorilla, and above all for naming my hard drive ”Gonzo” after the death of Hunter S. Thompson. Jennie Velez for all the computer tricks, Ingrid bits, Matlab scripts and freebie candy. Virginia DiBlasi Morris, for fixing all the troubles. Yochanan Kushnir, Nili Harnik for educating me ever-so-slightly about our atmosphere. Doug Martinson for his profound insight on timeseries analysis and his inexhaustible sense of humor. Jason Smerdon, for his good will with Mathematica and his great enthusiasm. Irina Gorodetskaya, my office-mate, for putting up with my desktop sound system for close to 2 years. I am somewhat disappointed that after all that time, she never understood the essential distinction between Deep House and Minimal Techno, but thankfully we got along on Brad Mehldau, Keith Jarrett and brazilian tunes. More thanks go to : Alexander van Geen, for starting it all. My parents for all their support, though they were dreadfully close to asking the “When are you done with that thesis ?” question one time too many. Marc Spiegelman, for his great teachings and most important of all his good mood at all times. Edward Spiegel, for a very inspiring look at non-linear dynamics, and the memorable story of Voltaire’s sneering at Maupertuis, en français dans le texte. Alex Hall, Dan Schrag, for encouraging me to persevere in this field. The Boris Bakhmeteff Fellowship for supporting me over the 2004-2005 academic year. Martin Visbeck, for teaching me that a number without error bars is utterly worthless. Natalie Boelman, Felix Waldhauser, Chris Zappa, David Ho, Heather Griffith, Meredith Kelly, Trevor Williams, for making my life more enjoyable at Lamont in yoga and in traffic. Fellow students Celine Herweijer, Peter Almasi, Richard Katz, Allegra LeGrande, Sharon Stammerjohn, Jessie Cherry, for sharing my pains and smiles. Colin Stark, for his long-lasting scientific friendship, for being the only man who understood Mulholland Drive apart from David Lynch, and for an evening crunching numbers in Mathematica while I was cooking. Gary Snyder sensei, Ken Polotan sensei and all of Columbia Kokikai Aikido for helping me keep One Point in all circumstances (or trying to, at least). Stephanie, Dafna, Matthew and Fernando, for all the lunchtime escapes. Kashi, for reminding me of what I had come for.

Ori Heffetz and Dror Weitz, for convincing me by example that one’s scientific activity is only as great as one’s enjoyment of existence. Ross, Mike, Andrew for being my New York family and for religiously adhering to the Sunday night Simpsons gospel. Lara Eastburn, for diligent and generous review of this manuscript, which added a much-needed literary touch. Vincent Aurora, for a bit of the same. Amy Whitehouse, for her fantastic pictures of rock stars and scientists alike. Michael and Genevieve, my Atlanta family, for Elements of Style and healthy portions of Taste. Vanita, for sharing my life.

This thesis is dedicated to my family, who owns nothing but knowledge.

I would like to dedicate it especially to my grandfather, Maurice Geay, who made my New York adventure possible and died shortly after it had begun.

Foreword This book is the outcome of my PhD work at Columbia University, under the supervision of Prof. Mark A. Cane, Dr Richard Seager, and Prof. Peter B. deMenocal. It was originally published as my dissertation, albeit in a much more unpleasant form that seemed to the liking of the Columbia Library. When Verlag Dr Müller offered to publish it in a bona fide book form, I felt both honored and overwhelmed. Honored that it would be deemed readable by more than an academic thesis committee and overwhelmed by the amount of work required to make it (hopefully) a worthy read for a broader audience. This was a year ago. In the meantime, some adventures on the blogosphere have taught me that our field is currently under intense scrutiny for some alleged failures of the peer-review process. It therefore seemed important that the largest fraction of this book must pass through the rigorous review process of top journals of the American Geophysical Union and the American Meteorological society, which was only recently achieved. Hence, the first three chapters are essentially reprints of said articles, differing only in a few updated figures. The fourth chapter has only been reviewed by my thesis committee, and is admittedly less polished. It is given here as it was submitted in November 2006 and is the topic of ongoing research. Since the requirements of the production process meant that all figures herein would be printed in black and white, a PDF of this book is available in full colors at http://jeg.ocean3d.org/texts/JEG_book.pdf. The attentive reader will soon notice that the formal “We” is used throughout this work. Far from being a statement of royalty, it should be taken as a tribute to the collective nature of the scientific process that eventually resulted in those pages - as well as a certain Old World side of me that even New York, cultural capital of the New World, could never quite subdue. As I put the last touches on this book, I am amazed at how much I would like to change it. Perfectionism, however, is often the enemy of deadlines, and I must resolve to put down the pen at this point. This feeling of incompleteness is a testimony to the dynamic nature of science – never set in stone, growing and evolving as organically as a living creature. Thus, this work should merely be taken as a snapshot of our current (and fragmentary) knowledge of a few special topics in paleoclimatology, centered around the infamous El Niño. I have no doubt (and in fact actively hope) that new data will come to challenge some of these ideas in the near future. For the time being, I hope you find it an enjoyable and instructive read nonetheless. Julien Emile-Geay, Atlanta, GA. July 25th 2008.

Table of Contents Introduction: Of low-frequency tropical climate variability Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 9

1 Pacific Decadal Variability 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Linear Equatorial Wave Theory at Decadal Frequencies . . . . . . 1.2.1 The Problem . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 The Free Mode . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 The Forced Solution: Green’s Function . . . . . . . . . . . 1.2.4 Total Solution . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Low vs Mid-Latitudes . . . . . . . . . . . . . . . . . . . . 1.3.2 Modes Do Not Matter . . . . . . . . . . . . . . . . . . . 1.3.3 Response to Idealized Wind Patterns . . . . . . . . . . . . 1.4 Comparison with Previous Work . . . . . . . . . . . . . . . . . . 1.4.1 Comparison with Liu [2003] . . . . . . . . . . . . . . . 1.4.2 Comparison with Cessi and Louazel [2001] . . . . . . . . 1.4.3 Equivalence with the PGPV Solution. Scaling Arguments 1.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

17 17 21 21 22 26 28 30 30 31 31 35 36 36 36 38 41

2 El Niño and Volcanoes 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . 2.2 Explosive Volcanism and ENSO Regimes . . . . . . 2.2.1 Volcanic Forcing over the Past Millennium 2.2.2 Experimental Setup . . . . . . . . . . . . . 2.2.3 Results . . . . . . . . . . . . . . . . . . . . 2.2.4 A Phase Diagram for ENSO regimes . . . . 2.3 A Remarkable Case: the 1258 Eruption . . . . . . . 2.3.1 Forcing . . . . . . . . . . . . . . . . . . . . 2.3.2 Results . . . . . . . . . . . . . . . . . . . . 2.3.3 Comparison to the Proxy Record . . . . . . 2.4 Discussion . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . .

45 45 47 47 48 49 51 56 56 56 59 61

xi

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Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65

3 El Niño and the Sun 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Climate Forcing over the Holocene . . . . . . . . . . . . . . . . . . 3.2.1 Orbital Forcing . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Solar Irradiance Forcing . . . . . . . . . . . . . . . . . . . . 3.3 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Representation of Weather Noise . . . . . . . . . . . . . . . 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Solar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Orbital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Orbital & Solar . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Global implications . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Solar-induced ENSO and North America . . . . . . . . . . 3.5.2 Solar-induced ENSO and the North Atlantic . . . . . . . . 3.5.3 Solar-induced ENSO and the Monsoons . . . . . . . . . . . 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Limitations of the Model Arrangement . . . . . . . . . . . 3.6.3 Forcing Uncertainties . . . . . . . . . . . . . . . . . . . . . 3.6.4 Theoretical Implications of a Solar-Induced ENSO-like Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

73 73 75 76 76 79 79 79 80 80 83 83 86 86 88 89 91 91 91 92

4 El Niño in the Icehouse 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 The Climate of the Last Glacial Maximum . . . . . . . . . . . . . 4.2.1 The CCSM3 Simulations . . . . . . . . . . . . . . . . . . 4.2.2 The HadCM3 simulations . . . . . . . . . . . . . . . . . 4.2.3 Intercomparison of Simulated LGM Climates . . . . . . . 4.3 Characterizing Ice Age Teleconnections . . . . . . . . . . . . . . . 4.3.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Results: Glacial Teleconnections Patterns . . . . . . . . . 4.4 Modeling Ice Age Teleconnections . . . . . . . . . . . . . . . . . 4.4.1 A Nonlinear Model of Stationary Waves (NLIN) . . . . . 4.4.2 A Linear, Steady-State Model of Stationary Waves (ELM) . 4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

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92 93 94 101 101 103 104 104 105 108 108 113 119 120 127 135 137

Conclusion 143 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 A Pacific Decadal Variability: Boundary-Layer Correction

151

B El Niño and the North Atlantic

153

xiii

List of Figures 1.1

1.2

1.3

1.4

1.5

The Cold tongue index and its spectral properties (a) Cold Tongue index (CTI) and its 7-year lowpass filtered version. (b) Spectral estimate of the CTI using multi-taper method (MTM) and the robust noise estimation procedure of Mann and Lees [1996]. The black curve identifies harmonic components together with noise and broadband signals, while the “reshaped” spectrum only includes the latter two. Numbers above the curves correspond to the period of oscillation, in years. (c) Significance test of harmonic components, based upon an F -test [Mann and Lees, 1996]. A peak is deemed signicant if its F value is above the critical threshold imposed by a particular confidence level (95% or 99% here). . . . . . . . . . . . . . . . . . . . .

19

Eigenstructure of the free mode hM , for Ÿω = ≠0.029 to facilitate comparison with the gravest planetary basin mode of Yang and Liu [2003] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

Amplitude and phase of the functions E and Z , which characterize the response via hE . Along the functions are their asymptotic expansions for large and small ◊, denoted by the subscripts ∞ and 0, respectively. See text for details. . . . . . . . . . . . . . . . . . . . .

29

Effect of the latitudinal position of the wind forcing. a1) Varying extent of wind forcing in the F = 1 case ; a2) Thermocline response ; b1) Varying location in the F = 1 case ; b2) Thermocline response c1) Varying location in the F = sin(fi yyN ) case ; c2) Thermocline response. The numbers above the spectra on the right-hand side correspond to the period of oscillation (years). . . . . . . . . . . . . . .

33

y2

Response of the INC model to F = e≠µ 2 for varying values of the width parameter µ. The values of hE plotted here are the average over the last year of a 200-year integration of the shallow-water solver INC [Israeli et al., 2000], with a constant forcing applied. The model was run in a symmetric basin with yN = 60◦ N and Rayleigh friction r = 50 year≠1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

35

1.6

2.1 2.2

2.3 2.4

2.5

Reproducing Fig 5 of LIU03. The right-hand panels present the forc2 ing (a) F (y) = e≠µy /2 , b) F (y) = 1, c) F (y) = cos(fi yyN ), and d) F (y) = sin(fi yyN ), respectively. The left-hand panels present the corresponding thermocline response |hE („)| . . . . . . . . . . . . . . .

37

Response of the Zebiak-Cane model to volcanic forcing during the past millennium. Forcing and 200-member ensemble average. . . .

49

ENSO regimes as a function of the intensity of volcanic cooling . The abscissa is the intensity of volcanic forcing in a given year (k) and the ordinate is the fraction of the ensemble members that went into an El Niño event during the following year (k + 1). Colored dots correspond to remarkable eruptions of the past millennium . . . . .

52

ENSO regimes as a function of the intensity of volcanic forcing. Same as Fig 2.2 but with a forcing weakened by 30%. . . . . . . . .

54

ENSO regimes as a function of the intensity of volcanic forcing (2). As in Fig(2.2), except that the ordinate is the ensemble mean of the maximum of monthly NINO3 values reached by the model during the calendar year following the eruption. This gives insight into the impact of the forcing on the amplitude of events, as opposed to their frequency of occurrence. . . . . . . . . . . . . . . . . . . . . . . . .

55

Intra-ensemble distribution of the monthly NINO3 index in the period Jan 1259 - Dec 1259 (light gray curve), compared to the reference distribution computed over the rest of the millennium (black curve). We used a kernel density estimation with a Gaussian kernel and a width of 0.15◦ C. . . . . . . . . . . . . . . . . . . . . . . . . .

57

2.6

Multiproxy view of the 1258 eruption: (a) Volcanic forcing (black curve) in Wm≠2 and 200-member ensemble mean response of NINO3 in the Zebiak-Cane, after applying a 20-year low-pass filter (light blue curve). (b) year-to-year change in PDSI over the american West [Cook and Krusic , 2004] (c) Standardized tree-ring width at El Asiento, Chile [Luckman and Villalba, 2001] (d) Titanium percentage in core 1002 from the Cariaco basin [Haug et al., 2001] (standard deviation units). 60

2.7

A flood proxy from Peru: record of fine-grained lithics since 800 A.D. from Rein et al. [2004]. The thick red curve is lowpass filtered, and the shaded area corresponds to the Medieval Climate Anomaly. Note the sharp transition around 1260 A.D. . . . . . . . . . . . . . . . . xvi

62

3.1

EOF analysis of the top-of-the-atmosphere insolation over the Holocene. The leftmost column shows the EOF pattern as a function of calendar month (Jan =1, Feb =2, etc..), the center column shows the PC timeseries, and the rightmost column its spectral density, computed with the multitaper method [Thomson, 1982]. Numbers above the graph refer to the period in kyr. To obtain the contribution of a mode to the total insolation at any given time, each EOF pattern must be weighted by the value of the corresponding PC. . . . . . . . . . . . 74

3.2

Spectral analysis of the 14 C production rate record. a) 14 C timeseries from Bond et al. [2001], converted to Wm≠2 for the intermediate scaling (a Maunder Minimum solar dimming of 0.2%◊S◦ , see text for details). b) Multi-taper spectra and 99% confidence level for rejecting the null hypothesis that the series is pure ”red noise” (AR(1) process). This follows the methodology of Mann and Lees [1996]. . . . . . . .

78

Model response to solar forcing (∆F = 0.2%So , experiment Sol0.2 ). a) Solar forcing (grey) and response (TW ≠ TE ) (black). b) Wavelet spectral density (arbitrary units, with maxima in black, minima in white). The thick black line is the cone-of-influence, the region under which boundary effects can no longer be ignored [Torrence and Compo, 1998] . The Morlet wavelet was used here) c) Global Wavelet Spectrum and 95% confidence level (see text for details). d) Probability of a large El Niño event over a 200 year window. . . . . . . . . .

82

3.4

Same as Fig 3.3 but for orbital forcing (experiment Orb ). . . . . .

84

3.5

Same as Fig 3.3 but with orbital and solar forcing (∆F = 0.5%So , experiment Orb_Sol_0.5). . . . . . . . . . . . . . . . . . . . . . .

85

Linear prediction of ENSO variability from solar parameters. a) Lowpass-filtered zonal SST difference (EW), and predictor variables: PC1 and PC2 from Fig 3.1 and Fo from Fig 3.2, with ∆F = 0.5%So b) Comparison between predicand and predicted variable. See text for details. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87

ENSO influence over the North Atlantic. On the left are regression patterns of wind vectors from the specified product, smoothed by a 3-month running average, on the NINO3 index, normalized to unit variance. Hence, units of regression coefficients are given per standard deviation of the index. On the right are corresponding correlation patterns, shown for the meridional component only. (a) GFDL H1 surface wind-stress regression, (b) GFDL H1 meridional wind-stress correlation. (c) POGA-ML surface wind-stress regression (d) POGAML meridional wind-stress correlation (e) Analysis of ICOADS data, surface wind regression (f ) Analysis of ICOADS data, meridional wind correlation. . . . . . . . . . . . . . . . . . . . . . . . . . . .

90

3.3

3.6

3.7

xvii

4.1

Intercomparison of GCM Climatologies: Surface Air Temperature. (a) Present climate, taken from the NCEP/NCAR Reanalyses [Kalnay, 1996] ; (b) HadCM3 CTL ; (c) CCSM3 CTL ; (d) [b -a] ; (e) HadCM3 LGM (f ) CCSM3 LGM . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Intercomparison of GCM Climatologies: Upper Tropospheric Zonal Wind. (a) Present climate, taken from the NCEP/NCAR Reanalyses [Kalnay, 1996] ; (b) HadCM3 CTL ; (c) CCSM3CTL ; (d) [b -a] ; (e) HadCM3 LGM (f ) CCSM3 LGM. (The color scales and contour intervals are the same within a row). . . . . . . . . . . . . . . . . . 4.3 SVD Analysis of Present Day Teleconnections from the tropical Oceans. Mode 1 a) SST pattern (left singular vector) ; b) Geopotential height field (Z250) (right singular vector) c) Expansion coefficients (normalized) . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 SVD Analysis of Present Day Teleconnections from the Tropical Oceans. Mode 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 SVD Analysis of CCSM3 (CTL) Teleconnections from the Tropical Oceans. Mode 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 SVD Analysis of the CCSM3 (LGM) Teleconnections from the Tropical Oceans. Mode 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 SVD Analysis of the HadCM3 (CTL) Teleconnections from the Tropical Oceans. Mode 1 . . . . . . . . . . . . . . . . . . . . . . . 4.8 SVD Analysis of the HadCM3 (LGM) Teleconnections from the Tropical Oceans. Mode 1 . . . . . . . . . . . . . . . . . . . . . . . 4.9 Validation of the Non-Linear Model (NLIN) a) El Niño composite of diabatic Heating field in the NCEP Reanalyses, 1949-1999 ; b) Model streamfunction response to such heating at 250 mb, in 106 m2 s≠1 c) El Niño composite of 250mb streamfunction in the NCEP Reanalyses, 1949-1999 (blue contours are negative, red contours are positive values). Units are in 105 m2 s≠1 . . . . . . . . . . . 4.10 Response to El Niño and Idealized Heating in NLIN: NCEP Basic State: a) Realistic El Niño heating cut outside the tropical Pacific ; b) Streamfunction response (to be compared to Fig 4.9b) ; c) Gaussian heating centered at [190◦ E,0◦ N] ; d) Streamfunction response. . . . 4.11 Response to El Niño Heating in NLIN: CCSM3 Basic State: a) Precipitation-derived heating in CTL b) geopotential height response for the pre-industrial case c) Same for LGM d) geopotential height response for the LGM . . . . . . . . . . . . . . . . . . . . . . . . . 4.12 Response to El Niño Heating in NLIN: HadCM3 Basic State: a) Precipitation-derived heating in CTL b) geopotential height response for the pre-industrial case c) Same for LGM d) geopotential height response for the LGM . . . . . . . . . . . . . . . . . . . . . . . . . xviii

106

107

111 112 114 115 117 118

122

124

125

126

4.13 EOF Analysis of Pseudo-Green’s Function Forcing in ELM: CCSM3 CTL basic state. a) First EOF b) corresponding PC, indicated the favored location for forcing exciting such a mode c) Second EOF d) corresponding PC. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.14 EOF Analysis of Pseudo-Green’s Function Forcing in ELM: as in Fig 4.13, but with the CCSM3 LGM basic state. . . . . . . . . . . . 4.15 Difference in El Niño Heating in CCSM3: LGM minus CTL . . . 4.16 EOF Analysis of Pseudo-Green’s Function Forcing in ELM: as in as in Fig 4.13, but for the HadCM3 CTL basic state. . . . . . . . . . . 4.17 EOF Analysis of Pseudo-Green’s Function Forcing in ELM: as in as in Fig 4.16, but for the HadCM3 LGM basic state. . . . . . . . . .

xix

129 130 131 132 133

List of Tables 2.1

2.2

Count of warm ENSO events in the year following a volcanic eruption. Comparison to Table 1 of Adams et al. [2003], with chosen keydate lists over the period 1649-1979. IVI = Ice-Core Volcanic Index [Zielinski, 2000; Robock and Free, 1995]. VEI=Volcanic Explosivity u Index [Simkin and Siebert , 1994]. ‘ Crowley’= chronology adjusted to the forcing used for our numerical experiments. M/L =“medium to large” eruptions. Listed are the number of eruptions in each list, the number of eruptions followed by a El Niño event within a year, and the ratio of the two previous numbers. See text for details. . . . Probability of an El Niño event after the 1258 eruption. Shown here is the fraction of ensemble members that produced an El Niño event in a 12-month window following the eruption. We applied the 3 following criteria for El Niño occurrence: {NINO34 Ø 0.5} for 6 consecutive months, {NINO34 Ø 1} (“strong El Niño”) and {NINO34 Ø 2} (“very strong El Niño”) over identical intervals. . . .

51

58

3.1

Summary of the numerical experiments used in this study. . . . . .

4.1

Variance analysis of Northern Hemisphere winter geopotential height. The total variance is the integral of 250 millibar geopotential height ss Õ 2 variance over the Northern Hemisphere NH ÈZ250 ÍdA, in 106 m2 . Then shown, for each mode, are the squared covariance fraction (SCF), in percent of the total covariance between SST and Z250 , and the fraction of the total variance in the field explained by the projection onto a given mode (FOV) . Only modes 1 through 4 are shown for brevity. (See text for details.) . . . . . . . . . . . . . . . . . . . . . . . . . . 116 EOF analysis of the sliding tropical forcing experiments in ELM. Numbers shown here are the fraction of variance explained by each mode for geopotential height at the ‡ = 0.257 level (close to 250 mb). Only modes 1 to 4 are shown for brevity. See text for details. . 134

4.2

xxi

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Introduction: Of Low-Frequency Tropical Climate Variability “I am far too much in doubt about the present, far too perturbed about the future, to be otherwise than profoundly reverential about the past.” Augustine Birrell

Why climate? The fate of so many human societies has been so contingent upon atmospheric and oceanic conditions that it has become difficult to overstate climate’s importance in shaping History as we know it. Indeed, it is widely recognized that the relative climate stability of the Holocene (roughly, the past 10,000 years of the Earth’s history) has been the necessary condition for the growth of human populations, their sedentarization, the domestication of crops and large mammals, the development of large-scale food production and technology, and their spreading among different parts of the globe along similar biomes, chiefly defined by their climate [Diamond , 1999]. The backdrop for what we term Civilization, its origins, evolution and expansion across continents and overseas, seems to have been largely conditioned by climate. Yet, within the seemingly quiescent Holocene, intense droughts catalyzed the demise of highly organized human societies (Akkadian Empire, Tiwanaku and Classic Mayan civilizations), often in a matter of a few years [deMenocal , 2001; Haug et al., 2003]. It is the Trade winds and their northeasterly direction that pushed Columbus to the Caribbean to find “Indians”. The people of Japan partly owe the idiosyncrasy of their culture and language to a Mongol invasion reduced to nothingness by a tropical cyclone in 1281, thereafter named typhoon, or “divine wind” [Emanuel , 2005]. Closer to us, former commerce secretary William Daley estimates that “at least $1 trillion of [the U.S.] economy is weather-sensitive”* . Still, it would be excessive to claim that weather and climate are the sole determinants of human history and economics – to quote Gordon Manley, “the fall of Rome should not be attributed to a joggle of the * http://www.research.ibm.com/weather/DT.html

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Introduction

barometer”. Nonetheless, it is now clear that they have had a pervasive influence on human affairs, at times a critical one [Davis, 2001]. This growing awareness, and the necessity to better understand the dynamics of the Earth’s climate, have underpinned a tremendous research effort in the past 50 years. Prominent in such a development has been the increasing recognition that anthropogenic emissions of greenhouse gases (carbon dioxide and methane, chiefly), have been warming the global mean temperature by about 0.8◦ C since 1860 (http:// data.giss.nasa.gov/gistemp/2005/), a sizable change only expected to worsen as emissions grow [Houghton, 2001], and to which many human and biological systems will have trouble adapting [McCarthy, 2001]. It then becomes critical to understand how the climate of our planet functions and how it could change under external forcing. Why low frequencies? Just as history informs us about the nature of human beings and dynamics of the complex social entities they form, so do past climates inform us about the dynamics of Earth’s climate system. By teaching us about the vastness of its parameter space, they provide a crucial testbed for the numerical models used to forecast its future evolution. As Edward Gibbon put it, “we know of no way of judging the future but by the past”. One of the past’s most salient messages, coming from all manner of geological and instrumental sources, is the demonstration that climate displays most of its variability on the longest timescales – the lowest frequencies – as epitomized by the Ice Ages of the Quaternary. Vexingly enough, we still don’t know precisely why the latter occurred. Other sources of low-frequency variability remain at the forefront of modern climate research and forecasting. What do we mean by low-frequency? Here we face a problem of definition, as the lowest resolvable frequency a dataset may offer is inversely proportional to its length. In the 1970s, the term “low-frequency” described oceanic phenomena with a time-scale longer than a few seasons, and atmospheric scales longer than a few days. In the three subsequent decades, an ever-expanding stream of data has been gathered, showing almost ubiquitously that a climate record exhibits significant (if not dominant) variability on the longest timescale it resolves. As timeseries grew longer, spectra went redder. In our case, the lowest frequency we shall consider is determined by the availability of reliable data allowing a quantitative assessment of climate variability. This criterion is undoubtedly subjective, and here we have chosen to restrict our focus to timescales of 10 to 105 years: decades to Ice Ages. Why the Tropics? The Tropics are the Earth’s recipient of net annual mean radiation, and it is within cumulus towers of the deep Tropics that most of the energy that drives the atmospheric heat engine is transferred from the surface in the form of latent heat release [Peixoto and Oort , 1992]. Moreover, they cover about 50% of the surface of the Earth. Any theory of global climate change must therefore address changes in the Tropics in some way. El Niño and the Earth’s climate: from decades to Ice Ages.

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In addition, they are currently the stage of vigorous climate phenomena (chiefly, monsoons and the El Niño-Southern Oscillation, or ENSO) and there is ample evidence that these can organize atmospheric flow across the globe [e.g. Horel and Wallace, 1981; Ropelewski and Halpert , 1987; Trenberth et al., 1998]. Finally, low-frequency variability has been abundantly documented over the tropical oceans and continents ; from Pacific Decadal Variability [Trenberth and Hurrell , 1994; Zhang et al., 1997; Mantua et al., 1997; Guilderson and Schrag , 1998], to centennial changes in the tropical Pacific [Quinn, 1992; Hendy et al., 2002; Cobb et al., 2003] and Atlantic [Haug et al., 2003], to millennial cycles in the Asian monsoon [Neff et al., 2001; Fleitmann et al., 2003; Gupta et al., 2003; Wang et al., 2005] and glacial/interglacial changes in ENSO frequency and amplitude [Moy et al., 2002; Tudhope et al., 2001]. At present, there is little or at best incomplete physical understanding of the causes of this variability. The long-standing paradigm of paleoclimatology holds that variations in the amount of sunlight received at the surface, amplified by a number of feedbacks, give rise to important variations in global climate. The scientific debate bears on exactly which parts of the system matter for the amplification of changes seeded by natural forcing (orbital, volcanic or solar), or whether some subparts of the system (the carbon cycle or the ENSO system, for instance) can produce self-sustained variability without invoking external forcing. This is true of timescales ranging from a few decades to the entire Quaternary era. Historically, the vast majority of ice age theories has shown a one-sided view of global heat balance, focused almost exclusively on high latitudes, with a belief that ice-albedo feedbacks were the cornerstone of glacial-interglacial cycles. Foremost amongst them, Milankovitch’s theory contends that it is the amount of summer insolation received at 65◦ N over continents that determines whether or not the winter snows survive the summer season [Milankovitch, 1941], thereby determining the growth or decay of large continental ice sheets. Though there are problems with this view [e.g. Paillard , 2001; Cane et al., 2006], orbital variations in insolation are still considered to be the “pacemaker of Ice Ages” [Hays et al., 1976], despite discrepancies between the spectrum of the climate response (stacked ” 18 O , showing a dominant peak around periods of 100 kyr) and that of the forcing, which is dominated by precession (with a period close to 23 kyr). This difference implies a strongly non-linear response of the climate system to insolation forcing. What is the dominant source of this non-linearity? By far the most widely accepted explanation is that of Broecker et al. [1985] that the North Atlantic thermohaline circulation (THC) may switch rapidly between two quasi-stable modes of operation (“on” or “off”), an argument later refined for glacial-interglacial transitions by Broecker and Denton [1989]. In the current climate, this meridional overturning circulation (MOC) contributes significantly to the poleward ocean heat transport in the Atlantic [Gordon, 1986; Ganachaud and Wunsch, 2000], and there is ample indication that its intensity varies in concert with climate over the last few glacial cycles: reduced or suppressed during Greenland cold episodes, and near current levels during warm periods [Boyle and Keigwin, 1987; Adkins et al., 1997; McManus et al., 2004]. There are, however, numerous problems with a THC-centric view of climate change.

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Clement and Cane [1999] point out a number of observations that the paradigm fails to explain when it comes to glacial cycles. In particular, state-of-the art climate models fail to produce a temperature response to THC shutdowns that is of the magnitude and extent implied by the geologic record. While absence of proof is not proof of absence, these shutdowns imply buoyancy perturbations of epic proportions, which raises suspicion as to their realism (see Clement and Peterson [2006]; Seager and Battisti [2007] for a review). Wunsch [2002] pointed out that the acronym THC is now often invoked as a “deus ex machina”, which has come to symbolize the cause of lowfrequency climate change on decadal [Sutton and Hodson, 2005] to millenial timescales [Alley et al., 1999], as well as extremely abrupt climate change [Clark et al., 2002] like the Younger Dryas cooling [Broecker , 1997], without always establishing how changes in the Atlantic MOC come about. As it is now clear that the ocean is a not a heat engine, but a mechanically-driven system [Munk and Wunsch, 1998; Gnanadesikan, 1999; Wunsch and Ferrari, 2004], there is every reason to believe that the most potent driver of THC changes is the global wind field [Wunsch, 2002], which is known to be significantly affected by tropical SSTs. This is not so say that the THC is irrelevant to low-frequency variability. Still, these elements provided a strong incentive to explore alternate mechanisms of climate change actively involving the tropics. The pioneering work of Amy Clement and collaborators [Clement and Cane, 1999; Cane and Clement , 1999; Clement et al., 1999, 2000] aimed at providing a physically-based alternative. They established the ENSO system as the potential source of global, low-frequency climate variability – precisely via its non-linear behavior. In many ways, this book can be seen as a continuation of their pioneering work, by investigating a subset of mechanisms whereby low-frequency variability is produced within the tropical Pacific and exported to the rest of the globe. Where are we in 2008? Some of these original ideas have survived, others have not. The proposition that ENSO would be repressed during mid-Holocene due to an increased precessional insolation contrast [Clement et al., 2000] has been qualitatively supported by various proxy records [Tudhope et al., 2001; Moy et al., 2002; Koutavas et al., 2006]. The idea that the tropical Pacific can account for some of the changes in proxy records all over the world is rapidly gaining currency On the other hand, the suggestion that the glacial world would be more La Niña-like [Cane, 1998; Cane and Clement , 1999] has not been confirmed by observation, as preliminary assessments of the zonal SST gradients from sediment cores hinted to a more El Niño-like state at the LGM [Koutavas et al., 2002; Stott et al., 2002] (though some recent evidence calls for a near-neutral change from present conditions [Mix , 2006; Lea et al., 2006]). Furthermore, Seager and Battisti [2007] suggest that the cold periods recorded in Greenland ice cores (“stadials”) correspond to an El Niño-like state, and conversely for interstadials, La Niña-like. This is partly borne out of a better theoretical understanding of ENSO teleconnections developed in the past few years [Seager et al., 2003, 2005a], and a much improved documentation of its behavior over the past millennium [Cobb et al., 2003; Mann et al., 2005], all of which have provided a strong motivation to El Niño and the Earth’s climate: from decades to Ice Ages.

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look at the intriguing changes of the Medieval Climate Anomaly (known to some as the Medieval Warm Period) and the Little Ice Age. Coupled model simulations of glacial climates have also tremendously improved, allowing for a perusal at how ENSO changed under glacial boundary conditions. Closer to us, much work has been done on the causes of the decadal modulation of ENSO, with the emerging consensus that it is unlikely to originate outside the tropics [Hazeleger et al., 2001; Schneider et al., 2002; Karspeck and Cane, 2002; Karspeck et al., 2004; Seager et al., 2004] Therefore, the objective of the present work is to further explore the origins and the consequences of low-frequency tropical climate variability, on timescales of decades to Ice Ages. This scope is ambitious indeed, and we shall not fool the reader into thinking that all scientific questions on the topic shall be answered in the following pages – only a few mechanisms could be researched in detail. The very disparity of these timescales and our limited ability to model or analyze them made it necessary to reduce these broad scientific questions to a set of manageable problems. As the tool is enslaved to the problem it aims at investigating, assumptions and simplifications had to be made, often tied to the physical timescale of interest. In this spirit, the following four chapters reflect the four spectral bands under investigation: 1. Chapter 1 , 101 ≠ 102 years: Pacific Decadal Variability 2. Chapter 2 , 102 ≠ 103 years: Volcanoes and ENSO over the past Millennium 3. Chapter 3 , 103 ≠104 years: Millennial-scale, solar-induced variability of ENSO over the Holocene 4. Chapter 4, 104 ≠ 105 years: Ice Age changes in ENSO teleconnections In Chapter 1 we revisit recent theories of decadal variability [Liu, 2003; Cessi and Louazel , 2001] that use a shallow-water formalism of the tropical Pacific and argue that tropical Pacific decadal variability (PDV) can be accounted for by basin modes with eigenperiods of 10 to 20 years, amplifying a mid-latitude wind forcing with an essentially white spectrum. We question this idea using a different formalism of linear equatorial wave theory. We obtain a general solution that allows us to explore more realistic wind forcings, and we find that the equatorial thermocline is inherently more sensitive to local than to remote wind forcing (Planetary Rossby modes only weakly alter the spectral characteristics of the response). This leads us to rule out basin modes as the cause for the “memory” of the system, and we discuss alternative mechanisms that can more plausibly account for it. Next we look at how the ENSO system behaved in the past millennia. Though we previously described internal mechanisms of variability, they seem insufficient to explain the tropical Pacific SST varability observed over the past millennium, as reconstructed from coral biogeochemistry [Cobb et al., 2003]. If variability was indeed not endogenous, then it is only natural to look for an external origin: namely, the radiative forcing due to solar and volcanic activity. This work directly follows that of Mann et al. [2005], who found that the SST anomalies of the Little Ice Age (≥ 1650 ≠ 1850) and

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Medieval Climate Anomaly (MCA, ≥ 1000≠1300) could be partially explained by the dynamic response of the ENSO system to solar and volcanic (thermodynamic) forcing, notwithstanding the system’s tremendous internal variability. The answer lies in the fact that the solar irradiance is thought to have varied on centennial timescales and that these changes coincided with periods of different volcanic activities. For instance, during the Little Ice Age, a dimmer Sun and more active volcanoes conspired to produce a colder global climate, but the tropical Pacific responded by a more El Niño-like state, a consequence of the “thermostat mechanism” [Clement et al., 1996]. The latter governed the model’s response to any change in imposed radiation. It should be noted that the current generation of coupled general circulation models [Vecchi et al., 2006, and Vecchi et al., in prep.] tends to exhibit the opposite reaction to a uniform change in radiation (i.e. increased radiation goes with a more El Niño-like state), a potential caveat which shall be discussed later. In Chapter 2 we carry out a detailed analysis of the response of ENSO to volcanic forcing over the past millennium. The work of Mann et al. [2005] had raised important questions, since it was the first to advance a dynamical explanation for the idea that volcanic eruptions may cause El Niño events [Handler , 1984]. We reassess this controversial claim, building on their work, by using estimates of volcanic forcing over the past millennium [Crowley, 2000] and a climate model of intermediate complexity [Zebiak and Cane, 1987] of similar design to theirs. We draw a diagram of El Niño likelihood as a function of the intensity of volcanic forcing, which shows that in the context of this model, only eruptions larger than that of Mt Pinatubo (1991) can shift the likelihood and amplitude of an El Niño event above the level of the model’s internal variability. Since the past 150 years have not seen any significantly larger eruption, this finding reconciles, on one hand, the demonstration by Adams et al. [2003] of a relationship between explosive volcanism and El Niño, and on the other hand, the ability to predict El Niño events of the last 148 years without knowledge of volcanic forcing [Chen et al., 2004]. The study is also partly motivated by the increasing amount of evidence that hydroclimates on continents adjacent to the Pacific are largely governed by tropical SST anomalies in modern times [Schubert et al., 2004; Seager et al., 2005b] for much of the past two millennia [Cook et al., 2004; Herweijer et al., 2006, 2007]. We therefore make use of such teleconnections to seek El Niño’s footprint in extratropical paleoclimatic records and suggest that the 1258 eruption briefly interrupted a solar-induced megadrought in the American west, via its influence on ENSO. In Chapter 3 we take a step back and refocus on the last 10,000 years, the Holocene. The motivation for this work came from the intriguing correlation, uncovered by the late Gerard Bond, that over the Holocene episodes of lower solar irradiance were accompanied by sizable surges of iceberg discharge into the North Atlantic, with a recurrence time of roughly 1500 years [Bond et al., 2001]. As often, the explanation involved the THC in some way, which enticed us to test the possibility that the record of ice rafted debris (IRD) of Bond et al. [2001] could instead be driven by the Sun via ENSO and its teleconnections. We use the same ENSO model as before, forced by El Niño and the Earth’s climate: from decades to Ice Ages.

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irradiance changes from cosmogenic isotopes and a range of radiometric scalings that bracket the current uncertainties of the reconstruction. It will be shown that for a moderate to strong scaling, the model responds with comparable sensitivity to orbital and solar forcing, despite the order of magnitude that separates them. As a result, our model ENSO produces persistent tropical SST anomalies that vary in concert with fluctuations detected by cosmogenic isotopes, of the order of magnitude required to trigger noticeable extratropical impacts. In the spirit of Chapter 2, we delve into the paleoclimate record to test the validity of this mechanism, which we find supported by available data. We also establish a link between tropical Pacific SSTs and North Atlantic winds, which shows that ENSO has the capacity to orchestrate the surface ocean circulation there, with the sign suggested by the IRD record. We thus propose that ENSO acted as a mediator of the solar influence on climate. An important caveat of Chapters 2 and 3 is that they assume stationary ENSO teleconnections. However, there is no reason to believe that teleconnections patterns are immutable. On the contrary, the odds are that as the mean climate state changes, so do teleconnections. The assumption of stationarity is not thought to be a major impediment at times when the atmospheric circulation was quite close to the present time’s, but this does not hold for glacial times when large swaths of northern hemisphere continents were covered by ice sheets 3 to 4 km thick. There is ample modeling evidence that this drastic orographic difference, along with the more moderate changes in tropical SSTs (typically 2 or 3◦ C), must have fundamentally reorganized the structure of the Jet Streams, the stationary and low-frequency Rossby waves, and therefore the transient eddies that interact with them along the “storm tracks” – so that teleconnection patterns must have been profoundly altered [Cook and Held , 1988; Yin and Battisti, 2001]. This we tackle in Chapter 4, by analyzing simulations of the Last Glacial Maximum (LGM, ≥21,000 ago) using two state-of-the-art climate models. The hope was that the models would concur to show sizable changes in teleconnections, with at least some common features that would give credence to their physical basis. It will be shown that the changes are sizable indeed, but that the two models show little consistency. This failure, however, provides us with an opportunity to examine the causes of discrepancies between coupled general circulation models (GCMs), which we do by using simplified models. Both are based on the primitive equations of atmospheric motion. One is a fully non-linear time-marching model [Ting and Yu, 1998] ; the other a linear, stationary wave model that is essentially the linearized version of the former [Ting and Held , 1990]. Use of these models, which ignore transient eddy feedbacks, allows for an exploration of the extent to which one can account for observed teleconnections only on the basis of planetary wave propagation. Experiments confirm the established fact that present-day ENSO teleconnections can largely be understood by stationary wave dynamics (see Held et al. [2002] and references therein). A similar attempt proves unsuccessful for LGM teleconnections, and herein we investigate why. The approach taken here is a minimalist one: how might we account for observed climate fluctuations on decadal to millennial timescales with as few physical processes

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Introduction

as necessary ? ENSO dynamics will be found able to explain a great number of them, but many puzzles remain, and a more holistic view of the system will be presented in the discussion’s closing.

El Niño and the Earth’s climate: from decades to Ice Ages.

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Milankovitch, M. (1941), Canon of insolation and the Ice Age problem, 484 pp., U.S. Department of Commerce, Israel program for Scientific Translations. Mix, A. C. (2006), Running hot and cold in the eastern equatorial pacific, Quatern. Sci. Rev., 25 (11-12), 1147–1149. Moy, C., G. Seltzer, D. Rodbell, and D. Anderson (2002), Variability of El Niño/Southern Oscillation activity at millennial timescales during the Holocene epoch, Nature, 420 (6912), 162–165. Munk, W., and C. Wunsch (1998), Abyssal recipes II : Energetics of tidal and wind mixing, Deep-Sea Res., 45, 1977–2010. Neff, U., S. Burns, A. Mangini, M. Mudelsee, D. Fleitmann, and A. Matter (2001), Strong coherence between solar variability and the monsoon in Oman between 9 and 6 kyr ago, Nature, 411(6835), 290–293. Paillard, D. (2001), Glacial cycles: Toward a new paradigm, Rev. Geophys., 39, 325–346, doi:10.1029/2000RG000091. Peixoto, J. P., and A. H. Oort (1992), Physics of climate, New York: American Institute of Physics (AIP), 1992. Quinn, W. H. (1992), Large - scale ENSO event, the El Niño and other important regional features, in Registro del fenómeno El Niño y de eventos ENSO en América del Sur, vol. 22, edited by L. Macharé, José; Ortlieb, pp. 13–22, Institut Fran cais d’Etudes Andines, Lima. Ropelewski, C., and M. Halpert (1987), Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation, Mon. Weather Rev., 115, 1606–1626. Schneider, N., A. J. Miller, and D. W. Pierce (2002), Anatomy of North Pacific Decadal Variability., J. Climate, 15, 586–605. Schubert, S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and J. T. Bacmeister (2004), On the Cause of the 1930s Dust Bowl, Science, 303(5665), 1855–1859, doi:10. 1126/science.1095048. Seager, R., and D. Battisti (2007), Challenges to our understanding of the general circulation: abrupt climate change, in The global circulation of the atmosphere: Phenomena, Theory, Challenges, edited by T. Schneider and A. H. Sobel, pp. 331–371, Princeton University Press. Seager, R., N. Harnik, Y. Kushnir, W. Robinson, and J. Miller (2003), Mechanisms of hemispherically symmetric climate variability, J. Climate, 16 (18), 2960–2978.

14

Introduction

Seager, R., A. R. Karspeck, M. A. Cane, Y. Kushnir, A. Giannini, A. Kaplan, B. Kerman, and J. Velez (2004), Predicting pacific decadal variability, in Earth Climate: The ocean-atmosphere interaction, Geophys. monogr., vol. 147, edited by C. Wang, S.-P. Xie, and J. A. Carton, pp. 105–120, Amer. Geophys. Union, Washington, D. C. Seager, R., N. Harnik, W. A. Robinson, Y. Kushnir, M. Ting, H. Huang, and J. Velez (2005a), Mechanisms of ENSO-forcing of hemispherically symmetric precipitation variability, Quart. J. Royal Meteor. Soc., 131, 1501–1527, doi:10.1256/qj.04.n. Seager, R., Y. Kushnir, C. Herweijer, N. Naik, and J. Velez (2005b), Modeling of tropical forcing of persistent droughts and pluvials over western North America : 1856-2000, J. Climate, 18(19), 4068–4091. Stott, L., C. Poulsen, S. Lund, and R. Thunell (2002), Super ENSO and Global Climate Oscillations at Millennial Time Scales, Science, 297 (5579), 222–226, doi: 10.1126/science.1071627. Sutton, R. T., and D. L. R. Hodson (2005), Atlantic Ocean Forcing of North American and European Summer Climate, Science, 309 (5731), 115–118, doi: 10.1126/science.1109496. Ting, M., and I. M. Held (1990), The stationary wave response to a tropical SST anomaly in an idealized GCM, J. Atmos. Sci., 47, 254–2566. Ting, M., and L. Yu (1998), Steady response to tropical heating in wavy linear and nonlinear baroclinic models, J. Atmos. Sc., 55, 3565–3582. Trenberth, K., and J. Hurrell (1994), Decadal atmosphere-ocean variations in the Pacific, Clim. Dyn., 9, 303–319. Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski (1998), Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures, J. Geophys. Res., 103, 14,291–14,324, doi:10.1029/97JC01444. Tudhope, A. W., et al. (2001), Variability in the El Niño-Southern Oscillation through a Glacial-Interglacial Cycle, Science, 291, 1511–1516. Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison (2006), Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing, Nature, 441, 73–76, doi:10.1038/nature04744. Wang, Y., et al. (2005), The Holocene Asian Monsoon: Links to Solar Changes and North Atlantic Climate, Science, 308(5723), 854–857, doi:10.1126/science. 1106296. Wunsch, C. (2002), OCEANOGRAPHY: What Is the Thermohaline Circulation?, Science, 298(5596), 1179–1181, doi:10.1126/science.1079329. El Niño and the Earth’s climate: from decades to Ice Ages.

BIBLIOGRAPHY

15

Wunsch, C., and R. Ferrari (2004), Vertical Mixing, Energy, and the General Circulation of the Oceans, Annual Review of Fluid Mechanics, 36, 281–314. Yin, J. H., and D. S. Battisti (2001), The Importance of Tropical Sea Surface Temperature Patterns in Simulations of Last Glacial Maximum Climate., J. Climate, 14, 565–581. Zebiak, S. E., and M. A. Cane (1987), A model El Niño-Southern Oscillation, Mon. Weather Rev., 115 (10), 2262–2278. Zhang, Y., J. Wallace, and D. Battisti (1997), ENSO-like interdecadal variability: 1900-93, J. Climate, 10, 1004–1020.

17

Chapter 1 Pacific Decadal Variability in the View of Linear Equatorial Wave Theory * “The book of nature is written in the language of mathematics.” Galileo Galilei

1.1

Introduction

The existence of decadal-scale variability in the Pacific Ocean is now well documented, and affects the climate and fisheries of the neighboring regions to a significant extent [e.g. Trenberth and Hurrell , 1994; Zhang et al., 1997; Mantua et al., 1997]. This Pacific Decadal Variability (PDV) is in the North Pacific usually described by the Pacific Decadal Oscillation (PDO) index [Mantua and Hare, 2002], which is quite energetic in the interdecadal spectral range. There have been suggestions [e.g. Newman et al., 2003] that the PDV derives much of its characteristics from the decadal properties of El Niño-Southern Oscillation (ENSO), notwithstanding feedbacks with subtropical and midlatitudes winds, which may have a significant role in amplifying decadal variability over the whole basin [Sarachik and Vimont , 2003]. Hence, rather than asking ”why is there a PDV?”, it is worthwhile to ask the more fundamental question of why there are decadal SST variations in the Tropical Pacific. To describe such variations, one can look at the Cold Tongue Index (CTI) [Deser and Wallace , 1990], computed here from the historical SST analysis of Kaplan et al. [1998]. The index and its spectrum are shown in Fig(1.1). The spectrum was estimated via the multi-taper method [Thomson, 1982; Ghil et al., 2002] and the robust noise estimation procedure of Mann and Lees [1996]. At the 95% level, one can see a number * Published

in Journal of Physical Oceanography with co-author Mark Cane. Reprinted with permission from the American Meteorological Society.

18

Chapter 1. Pacific Decadal Variability

of significant broadband peaks between periods of 2 to 7 years, as well as narrowband peaks in the quasi-biennial and quasi-quadrennial range [Jiang et al., 1995], all of which compose ENSO. One also finds some peaks in the decadal to multi-decadal range, none of which seem to rise above the red noise background used for significance testing, perhaps due to the shortness of the record. Thus, for our purpose, the salient feature tropical Pacific SST variability is the overall warm color of its spectrum at low-frequencies. An intriguing mechanism for this variability has recently been proposed in the work of Cessi and collaborators (Cessi and Louazel [2001], hereafter CL01, Cessi and Primeau [2001]; Cessi and Paparella [2001]) and that of Liu [2003] (hereafter L03) in the framework of the linear shallow water equations. These authors made an attempt to explain decadal variability as a “reddening” of weather fluctuations by dynamical ocean processes, planetary basin modes in this case. They argue that in a meridionally bounded basin, such modes with decadal timescales can be preferentially excited by the appropriate wind forcing in midlatitudes, resulting in a large local response and a somewhat weaker – but still sizable – equatorial response. The wind need not be highly organized; the usual “white noise” variations in midlatitudes might be all that is needed. Moreover, this scenario does open the possibility of tropical anomalies forcing teleconnections to higher latitudes that excite favorable winds, reinforcing the decadal mode of variation [Cessi and Paparella, 2001]. Cessi and collaborators work with the linear planetary geostrophic equations, while Liu, following Jin [2001], uses a truncated series of equatorial waves. L03 shows that the two approaches yield the same results. However, the latter are highly dependent on a few functional forms chosen to exemplify the forcing, which prevents rigorous comparison between the influence of low- and high-latitude forcing. We therefore approach the problem in a different way, one which relies heavily on results obtained by Cane and Moore [1981] and Cane and Sarachik [1981] (herafter CM81 and CS81) to derive the Green’s function of the problem in a closed basin. We obtain answers that are mathematically equivalent to those of CL01 and L03, but our approach leads us to an interpretation at odds with theirs. Most importantly, we find that high latitude winds have no advantage in forcing tropical ocean motions. On the contrary, they are typically less effective than low latitude winds. Since it is all too easy for the reader to get lost in the mathematical details, it may be worthwhile to give a brief informal account of the approach we will take. We wish to find the ocean’s response to a periodic wind forcing. As in CS81 we write the solution as a sum of a forced part and a free part. Both are made up of forced or free long equatorial Kelvin waves and long Rossby waves, the only modes that exist in the interior of the basin at low frequencies. These modes suffice to satisfy the boundary condition of vanishing zonal velocity at the east, but cannot satisfy the same boundary condition at the west. That requires the short Rossby waves that make up the western boundary currents. It is known [Cane and Sarachik, 1977] that the appropriate boundary condition for the long, low-frequency waves alone is that the meridional integral of the zonal velocity vanish along the boundary. This means that El Niño and the Earth’s climate: from decades to Ice Ages.

1.1. Introduction

19

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0, ∀ ‹ ∈ C∗ ,

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(1.36)

through which the various integrals can be computed. In all the following, a white spectrum for the forcing is implicitly assumed, as in CL01 and L03. This should not be thought of as a naïve simplification of reality, since it is known that the spectrum of midlatitude surface winds is far from flat, with significant power in the ENSO band. Instead, the idea is to see whether this low-frequency variability can arise via the amplification of stochastic wind forcing by ocean dynamics alone. Since synoptic weather

32

Chapter 1. Pacific Decadal Variability

systems are known to occur spontaneously with approximately Gaussian statistics, this is meant to provide a null hypothesis for the redness of the CTI spectrum. Response to F = 1 Let us consider the simplest forcing:  1

F = 0

for y ∈ [≠L, L] otherwise.

(1.37)

The analytical response to such forcing is: 2 hE = 3

√ √ √ i◊L E(◊L ) + fi erf( i◊L ) Z(◊N )

(1.38)

where ◊N = „yN 2 and ◊L = „L2 . This case provides a useful cross-check because the zero-frequency is easy to compute without recourse to our theory: if such a steady forcing were to cover the whole basin, then the slope of the equatorial thermocline depth would be unity, by virtue of the Sverdrup balance; there would be a node in the center of the basin, and hE = 1/2. Using the previous expansions for E and Z in the limit of small ◊, and the results from Appendix A, one can verify that the response to a steady forcing („ = 0) is indeed hE = 1/2 for this solution. In Fig 1.4(a1 and a2) we show the spectrum of the response to a varying latitudinal extent of this forcing, with yLN = 13 , 23 , 1, respectively. For L = yN , the forcing is nonzero at the northern wall, so that the boundary layer correction needs to be applied, as outlined in Appendix A. It is interesting to note that a spectral peak does arise for periods between 10 and 20 years range, but only in the case of tropical forcing. The more poleward the forcing, the weaker the peak and the lower its central frequency, so that basin-wide forcing alone does not, in fact, produce a peak anymore. The subtropical case produces a peak in the 50-100 year range. As the extent of the forcing increases, it eventually comes close enough to the northern boundary that the boundary layer correction has to be applied. It is a peculiarity of this Heaviside forcing, with a sharp jump introducing an infinite wind-stress curl at the edge, that the interior and boundary layer contributions are of similar magnitude when L = yN , and tend to cancel each other (see Appendix A). This explains the decrease between the zerofrequency response to L = 2y3N (black solid line) and L = yN (black dashed line) on panel a2). However, the comparison between latitude bands is not very meaningful in this case where the area under the forcing keeps increasing with its extent. Instead, one can divide the domain in three equal chunks‡ , called for convenience “tropics ”, “subtropics ”, and “midlatitudes ”. This is done in b1) and b2), where it can be seen that the shift of the peak towards low frequencies is indeed a consequence of the location ‡ Recall

that the convergence of meridians is neglected in the — -plane approximation. El Niño and the Earth’s climate: from decades to Ice Ages.

1.3. Results

33

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Figure 1.4: Effect of the latitudinal position of the wind forcing. a1) Varying extent of wind forcing in the F = 1 case ; a2) Thermocline response ; b1) Varying location in the F = 1 case ; b2) Thermocline response c1) Varying location in the F = sin(fi yyN ) case ; c2) Thermocline response. The numbers above the spectra on the right-hand side correspond to the period of oscillation (years).

34

Chapter 1. Pacific Decadal Variability

of the forcing, not its extent (the more poleward it is, the lower the frequency). In this case, the midlatitude spectrum shows a peak around 50-100 year periods, like the subtropical case, only weaker in magnitude. One can verify that this result is not an artifact of the chosen functional form, by applying another one – say, a sinewave – similarly cut into latitude bands (panels c1) and c2): the amplitude of the thermocline response increases in proportion to the strength of the forcing over a given region, but the location of the peaks is the same as in the F = 1 case. In fact, in this case, the midlatitude forcing fails to produce a peak. As remarked above, a caveat of such forcings is that they possess a discontinuous first derivative, which introduce an infinite wind-stress curl and are therefore unphysical. For the purposes of comparison with earlier work and numerical testing, it is instructive to look at a smooth forcing. y2

Response to F (y) = e≠µ 2

In this case as in the L = yN case seen above, the forcing is non-zero at the northern wall, requiring the boundary layer correction. However, for µ large enough, this correction is evanescent. The solution is: i1/2 hE = Z(◊N )

where p =

2„ µ

C√

A

fi 1/2 y2 p + K(µ, „, yN ) exp ≠µ N 2 2

(1.39)

as in CS81, and:

2p≠1/2 K(µ, „, yN ) = 3

A

1 ≠ e≠i◊N i◊N

B

√ ◊N ≠ E(◊N ) 3 i ≠ √ 2 ◊N

Since K e≠µ

BD

y2 N 2

A

1 ≠ e≠i◊N 1≠ i◊N

B

(1.40)

→ 0 as yN → ∞, the solution reduces to: hE =

p1/2 ifi/4 e 2

(1.41)

which is identical to Eq(31) in CS81. For a bounded basin (yN < ∞) and „ π 1, the lowest order response simplifies to: lim hE =

„→0

A

2fi 2 µyN

B1 2

(1.42)

Note that according to Eq(1.42), hE is equal to the integral of F (y) divided by yN and thus is independent of the scale of the forcing with the total amplitude held fixed. Remarkably, it is independent of „. We find therefore an O(1) zero-frequency response, which contrasts with CS81, but is a consequence of their considering an El Niño and the Earth’s climate: from decades to Ice Ages.

1.4. Comparison with Previous Work

35

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unbounded basin, where mass within any finite region need not be conserved. We checked this behavior in a numerical model using the INC scheme [Israeli et al., 2000]. In Fig 1.5 we plot the equilibrium response of this model. The µ≠1/2 scaling law from is found to hold over a broad parametric range.

1.4

Comparison with Previous Work

Since our results are in stark contrast with the claims of CL01 and L03, we dedicate this section to understanding the origin of the difference.

36

Chapter 1. Pacific Decadal Variability

1.4.1

Comparison with Liu [2003]

It is instructive to see to which extent our solution resembles that of L03. In Fig(1.6) we reproduce the results from his figure 5, using the methods of this chapter. For this computation we used his ideal wind forcings and the Green’s function obtained from (1.28), complemented by the boundary layer correction (see Appendix A) when needed. Except for a few minor differences in amplitude, the two pictures are in good agreement, especially for case d (F (y) = sin(fi yyN )). When they are not, (case c, F (y) = cos(fi yyN )), it is likely to be related to the fact that L03’s HE and HP solutions diverge from each other, presumably due to a singularity in the forcing used at the equator (one that corresponds to an infinite Ekman pumping, as illustrated by the dashed lines in the right-hand side of his figure a, b and c). When the latter Ekman pumping is non-singular, his HP and our solution are in close agreement. For well-behaved cases, we agree with L03’s conclusion that equatorial wave theory and planetary geostrophy give identical results at very low frequencies. As we shall see in the section 1.5, it is our interpretation that differs.

1.4.2

Comparison with Cessi and Louazel [2001]

This comparison is complicated by the fact that the wind patterns prescribed as a forcing of the PGPV equation (their Eq(19)) are specified in terms of the function g ≡ (F /y)y (in our notation). As this involves a differential, an integration constant is needed to fully determine F . Furthermore, one can show that all of the wind patterns used in their study imply that F scales at least as y 2 , as one approaches the equator. Any lesser-order polynomial would render the solution singular. These wind patterns have far greater amplitude in high latitudes so this part of the wind forcing naturally dominates. However, if a more realistic wind pattern were chosen, one characterized by a comparable amplitude in midlatitudes and the equator, then the PGPV solution would not show enhanced sensitivity to extratropical winds, since it behaves similarly to the equatorial wave solution at low frequencies, as explained by Liu [2003]. The reason for this similarity deserves a formal explanation.

1.4.3

Equivalence with the PGPV Solution. Scaling Arguments

Why does the PGPV equation seem to capture the thermocline motion of the equatorial wave solution at very low frequencies? A formal way to see this is to show that it is a special case of the low-frequency, long-wave approximation. Recall that the latter is obtained by using the following scaling [Cane and Sarachik, 1976]: ∂ ≥ O(‘), ∂t

∂ ≥ O(‘), ∂x

∂ ≥ O(1); ∂y

u, h ≥ O(1),

v ≥ O(‘)

(1.43)

El Niño and the Earth’s climate: from decades to Ice Ages.

1.4. Comparison with Previous Work

37

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!" 2 Wm≠2 ) tropical eruptions in the icecore based chronology of Crowley [2000], and the fact that in these 200 realizations of the experiment, the unforced probability of a warm event never reached above 40% (versus 42% in the previous case). Nonetheless, the qualitative behavior is identical, and the quantitative result quite close. Do strong volcanic eruptions also produce noticeably stronger El Niño events? One way of seeing this is to consider the maximum value of NINO3 in the same time window as before, presented in Fig 2.4. To limit intra-ensemble fluctuations, we limit ourselves to the ensemble average of this statistic for each year (1000 data points). It is clear that the average maximum size of ENSO events does increase sharply with volcanic forcing in the transition and forced regimes. However, the model physics constrain the index between about ≠2.5 and 3.5 ◦ C (vs ≠1.8 to 3.8◦ C in the Kaplan SST dataset‡ ), and even the strongest eruption (1258) does not alter this absolute (intra-ensemble) maximum reached by the index. So while very strong eruptions make an event significantly more likely, its amplitude will not fall outside the model’s range of internal variability. Hence, a simple way of understanding the effect of volcanic forcing is that for sufficiently high values, it adds to the likelihood of a warm event, which is normally nonzero because of the model’s self-sustained oscillatory behavior. Explosive volcanism does not trigger El Niño events per se, but rather “loads the dice” in favor of El Niño, also favoring higher amplitudes. Following eruptions associated with a cooling larger than 1 Wm≠2 , ENSO likelihood increases by about 50% on average. The most spectacular example of such behavior coincides with the largest eruption of the past millennium (1258 A.D.), upon which we shall now focus. ‡ http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/.NINO3/

54

Chapter 2. El Niño and Volcanoes

4>27/.31/452-/13?4@)!C4;0-93?0DB4.3E468FG4-9?1 Figure 2.3: ENSO regimes as a function of the intensity of volcanic forcing. Same as Fig 2.2 but with a forcing weakened by 30%.

El Niño and the Earth’s climate: from decades to Ice Ages.

2.2. Explosive Volcanism and ENSO Regimes

55

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Figure 3.7: ENSO influence over the North Atlantic. On the left are regression patterns of wind vectors from the specified product, smoothed by a 3-month running average, on the NINO3 index, normalized to unit variance. Hence, units of regression coefficients are given per standard deviation of the index. On the right are corresponding correlation patterns, shown for the meridional component only. (a) GFDL H1 surface wind-stress regression, (b) GFDL H1 meridional wind-stress correlation. (c) POGA-ML surface wind-stress regression (d) POGA-ML meridional wind-stress correlation (e) Analysis of ICOADS data, surface wind regression (f ) Analysis of ICOADS data, meridional wind correlation.

El Niño and the Earth’s climate: from decades to Ice Ages.

3.6. Discussion

91

in the North Atlantic [Bond et al., 2001]. This is consistent with periods of lower irradiance inducing an El Niño-like response.

3.6 3.6.1

Discussion Summary

We have found that solar and orbital forcing combine in a way that produces ENSOlike variance at centennial-to-millennial timescales, well above the model’s level of internal variability. The physics of the ocean-atmosphere system embodied in the model are able to pick out solar irradiance pertubations of intermediate amplitude (∆F = 0.2%So ), in the presence of orbital forcing and remarkably, in all cases, in the presence of a realistic amount of weather noise. For weak scalings of the solar irradiance (∆F = 0.05%So ), however, the response is indistinguishable from the variability of the unforced system. The results confirm the importance of orbital forcing in creating conditions favorable to the growth of ENSO variance over the Holocene, and suggest that solar irradiance variability may add centennial- to millennial-scale ENSO variance. We find qualitative agreement with high-resolution paleoclimate data and propose that ENSO mediated the response to solar irradiance discovered in climate proxies around the world.

3.6.2

Limitations of the Model Arrangement

The simplicity of the model is what allows the study of the coupled system over such long timescales, yet it creates caveats that are inherent to the model’s formulation [Clement et al., 1999]. While the chain of physical reasoning linking solar forcing to equatorial SSTs (the ”thermostat” mechanism) is certainly correct as far as it goes, the climate system is complex and processes not considered in this argument might be important. Perhaps cloud feedbacks play a substantial role, although it is still unknown whether the feedbacks associated with solar forcing would be positive or negative [Rind , 2002]. In a time of enhanced solar heating, the oceans should generally warm everywhere, including the subduction zones of the waters which ultimately make up the equatorial thermocline [McCreary and Lu, 1994]. This mechanism would complete a loop from equatorial SSTs through the atmosphere to midlatitude SSTs and then back through the ocean to equatorial SSTs. However, careful studies of Pacific SST variations in recent decades have shown that the oceanic pathway is ineffective because the midlatitude anomalies are diluted by mixing, especially as they move along the western boundary on their way to the equator [Schneider et al., 1999]. Still, since subduction and advection of midlatitude waters are the ultimate source of the equatorial thermocline, this oceanic mechanism must become effective at some longer timescale, and alter the operation of the thermostat [Hazeleger et al., 2001].

92

Chapter 3. El Niño and the Sun

Further, this study does not preclude the response of other components of the climate system independently capable of responding to external forcing (e.g. stratospheric ozone, the Atlantic meridional overturning circulation, monsoons). Indeed, ENSO could have been just part of a global response. Whether these subsystems acted in synergy will be left for future work. It is hoped that more complete models will soon be able to clarify their respective roles on such timescales.

3.6.3

Forcing Uncertainties

Since the SST response to the moderate forcing (∆F = 0.2%So ) is just at the magnitude of the drought patterns of recent times [Seager et al., 2005b], any reduction in the estimate of irradiance forcing makes the Sun an implausible cause of tropical Pacific climate change, let alone global climate change. So while there is a reasonably convincing empirical correspondence between proxies for solar output and tropical Pacific SSTs, the great uncertainties in solar irradiance forcing raise doubts about explanations of these SST variations as responses to solar forcing. Further, the hypothesis of a solar origin of millennial climate fluctuations is incumbent on the assumption that the corresponding signal in cosmogenic isotope records is indeed due to the Sun. We note that the most recent radiocarbon calibration effort, INTCAL04, displays weaker millennial cycles [Reimer and & Coauthors, 2004], though the production curve is still quite similar to INTCAL98, and such cycles are also present in a 10 Be record from Greenland [Yiou et al., 1997]. In any event, their irradiance scaling may not be known with satisfactory accuracy for a long time. Amidst such an array of uncertainties, a useful inference can still be made: for moderate to strong scalings of solar variability, it is physically plausible that ocean-atmosphere feedbacks amplified those changes above the level of internal ENSO variability, but weak scalings are unable to produce the necessary changes. A major caveat, as stated before, is the absence of volcanic aerosol forcing in the present study. The results need to be reassessed once such a timeseries becomes available.

3.6.4

Theoretical Implications of a Solar-Induced ENSO-like Variability

Our theory implies other predictions that should be testable with existing or future data: • Tropical Pacific SSTs: Periods of increased solar irradiance should be more La Niña-like, in the absence of other radiative perturbations (e.g. volcanoes). The development of better reconstructions of solar irradiance, as well as high-resolution coral and sedimentary proxy records, will prove crucial for testing this hypothesis, during the past millennium and beyond. El Niño and the Earth’s climate: from decades to Ice Ages.

3.7. Conclusions

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• Hemispheric Symmetry: Tropical SSTs can influence mid and high-latitudes via Rossby-wave teleconnections [Hoskins and Karoly, 1981; Horel and Wallace, 1981] or the transient eddy response to SST-induced variations of subtropical jets [Seager et al., 2003, 2005a]. Both cause climate anomalies with clear hemispheric symmetry. Consequently, if solar-induced ENSO variability did actually occur, it should appear in Southern Hemisphere climate records, e.g. tree-ring records of droughtsensitive regions of South America. This is in contrast to the predictions of waterhosing experiments in the North Atlantic [Zhang and Delworth, 2005], which have asymmetric responses about the equator in the Pacific. It is hoped that high-resolution proxy data from the Southern Hemisphere will soon enable us to distinguish between these competing paradigms of global climate change.

3.7

Conclusions

We propose that, given a mid-to-high-range amplitude of Holocene solar irradiance variations, ENSO may have acted as one of the mediators between the Sun and the Earth’s climate. The reasoning goes as follows: air/sea feedbacks amplified solar forcing to produce persistent, El Niño-like SST anomalies at times of decreased irradiance – the thermostat mechanism. In so doing, the ENSO system weakened the intensity of the Indian and Asian monsoons , and triggered IRD discharge events in the North Atlantic, generating global climate variability on centennial to millennial timescales. It is likely that other feedbacks were involved in this process, such as the wind-driven and thermohaline circulation of the ocean, and cloud feedbacks. So far, data from the past millennium and the longer Holocene seem to support our view. As more complete – and presumably more accurate – climate records become available, especially from the Southern Hemisphere, we hope that our mechanism can be tested in greater detail and on longer periods.

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Chapter 4 El Niño in the Icehouse: ENSO Teleconnections During the Last Glacial Maximum “Time is the longest distance between two places.” Tennessee Williams

4.1

Introduction

As mentioned in the opening chapter of this book, there is now increasing appeal in theories that address the tropical origin of global climate change. This stems from recent observations of the interhemispheric near synchroneity and symmetry of glacial /interglacial changes, and from the inability of the “THC” theory of climate change to satisfactorily explain observations of abrupt and millennial climate change [Cane and Clement , 1999; Clement and Peterson, 2006]. In contrast, tropical theories of global climate change have traditionally received less attention, which motivates the current effort. We know that the tropical oceans are currently the stage of important SST variability and that they can export it to higher latitudes via atmospheric wave propagation. This concept of “teleconnections,” dates back to Angstroem [1935] and its refinements are discussed, for instance, in Trenberth et al. [1998]; Alexander et al. [2002]. A complete theory of climate change must address how both aspects changed under different boundary conditions. If one wants to understand the last deglaciation, for instance, it is crucial to understand how different teleconnections were at that time, and why. A period of particular interest is the Last Glacial Maximum (LGM), for 3 reasons [Hewitt et al., 2003]:

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1. The changes are known to have been large (high signal to noise ratio) 2. The forcings are believed to be well known 3. There is an abundance of data with which to test the theories, growing and improving every year. Indeed, the LGM is a period of the Earth’s history that has been the target of considerable modeling efforts. It provides a critical test of our understanding of current climate dynamics, which is embodied in general circulation models. Our inability to sample the atmosphere of the past condemns us to look at this problem in the context of numerical simulations of the Earth’s climate. The exercise, however, is still of great interest because internal variability is often an overlooked aspect of the complex behavior of said models. Yet it is crucial to understand their response to external forcing. A second motivation stems from the prediction by Yin and Battisti [2001] that even SST changes on the small end of available LGM reconstruction [CLIMAP Project Members, 1981] were sufficient to fundamentally reorganize the structure of jets and storm tracks, thereby inducing a very different atmospheric circulation at the Last Glacial Maximum. The study, however, used a forced atmospheric general circulation model (AGCM) coupled to a slab ocean, which raises the question of how their mechanism would fare in a coupled ocean-atmosphere context. Thirdly, much of the interpretation of paleoclimate proxies relies on the principle of actualism: “The present is the key to the past”. As most proxies are used to describe non-local processes, paleoclimatologists rest heavily on the assumption that the statistical correlation between a climate signal and its expression in a particular geological object has remained approximately constant over time. In other words, we make the assumption of constant teleconnections. The climate modeling community is only beginning to address the potential consequences of altered teleconnections in the confrontation of their model results to paleoclimate data (see, for instance, Otto-Bliesner et al. [2003]), for which a good physical understanding is required. This chapter is structured as follows. In section 2 we describe the climatology of the two GCMs, comparing their simulation of the LGM, and their performance at reproducing the current climatology. Then we proceed to analyze teleconnections in the two GCMs and current observations (section 3). It will be shown that there is little consensus between models and that the sources of discrepancies are multiple. But both show interesting behavior at LGM, which begs for a physical interpretation. In section 4 we turn to a simpler class of dynamical models to understand key aspects of these changed teleconnections. It will be shown that the altered mean state and tropical forcing profoundly influence stationary wave propagation, but that these features are unable to fully explain the observed discrepancies. Discussion follows in section 5. El Niño and the Earth’s climate: from decades to Ice Ages.

4.2. The Climate of the Last Glacial Maximum

4.2

103

The Climate of the Last Glacial Maximum

The salient aspects of the LGM climate known at present are [Kohfeld and Harrison, 2000; Cane et al., 2006]: • a strong polar cooling (generally > 10◦ C, ≥ 20◦ C over Greenland) , and a moderarate tropical cooling (< 3◦ C over the ocean, < 5 ≠ 6◦ C over land). It is generally believed that these two effects combined to enhance the baroclinicity of the midlatitude atmosphere, which is central to the dynamics of teleconnections. • the presence of 3 to 4 km ice sheets over northern hemispheric continents [Peltier , 2004], believed to have drastically reorganized the subtropical jets and planetary wave patterns through the combined influence of orographic and thermal forcing (increased albedo over land). • A tendency for a drier climate overall, especially in the Tropics, though some areas did get wetter [Kohfeld and Harrison, 2000]. Combined with the surface cooling, this decrease in tropospheric water vapor is likely to have decreased the static stability of the glacial atmosphere, though almost certainly not uniformly. There exists a wealth of other pertinent information [see Broecker , 2002], which we shall not delve into here, as it is not immediately relevant to describe the atmospheric mean state. Although considerable progress has recently been made in simulating those features in climate models [Cane et al., 2006], the LGM still presents GCMs with outstanding challenges, like accurately simulating tropical cooling and changes in the thermohaline circulation. Some coupled AOGCMs do tend to produce a stronger (more realistic) tropical cooling than AGCMs coupled to a slab ocean, an indication that ocean dynamics are a crucial element of the climate response [Hewitt et al., 2003]. However, there is no agreement between models as to many important aspects of this response, including the sign of the change in Atlantic meridional overturning circulation (cf Hewitt et al. [2003] vs Shin et al. [2003]), or whether tropical Pacific SSTs were more El Niño or La Niña-like [Pinot et al., 1999]. This will prove an important point, as this change in the tropical mean state does affect the simulated ENSO and therefore its teleconnections. It has long been known that confidence in climate simulations can only arise through consensus. It is the driving force behind endeavors such as the Paleoclimate Modeling Intercomparison Project (PMIP, http://www-lsce.cea.fr/pmip2/. While a description of LGM teleconnections in all available GCM simulations is beyond the scope of this work, we deemed it important not restrict ourselves to a unique view of the LGM climate. Thus we consider two LGM simulations, using state-of-the-art land-ocean-ice-atmosphere GCMs: the Climate System Model (NCAR-CSM) * and the Hadley Center Climate Model (HadCM3) † , which we briefly describe below. * http://www.ccsm.ucar.edu/models/ccsm3.0/ † http://www.metoffice.com/research/hadleycentre/models/HadCM3.html

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Chapter 4. El Niño in the Icehouse

The CCSM3 Simulations

The NCAR CCSM3 is a global, fully coupled climate model. A detailed description of the simulations is given in Otto-Bliesner et al. [2006]. The atmospheric model is the NCAR CAM3, a three-dimensional primitive equation model solved with the spectral method in the horizontal and with 26 hybrid-coordinate levels in the vertical [Collins and Coauthors, 2006]. For these paleoclimate simulations, the atmospheric resolution is T42 (an equivalent grid spacing of approximately 2.8◦ in latitude and longitude). The land model includes a river routing scheme and specified but multiple land cover and plant functional types within a grid cell [Dickinson et al., 2006]. The ocean model is the NCAR implementation of POP (Parallel Ocean Program), a three-dimensional primitive equation model in spherical polar coordinates with vertical z -coordinate [Gent et al., 2006]. For these paleoclimate simulations, the ocean grid is 320x384 points, and 40 levels extending to 5.5 km depth. The ocean horizontal resolution corresponds to a nominal grid spacing of approximately 1◦ in latitude and longitude, with greater resolution in the tropics and North Atlantic. The sea ice model is a dynamic-thermodynamic formulation, which includes a subgrid-scale ice thickness distribution and elastic-viscous-plastic rheology [Briegleb et al., 2004]. Two simulations are considered here: 1. The control simulation, with pre-Industrial values for greenhouse gases and current land-sea-ice configuration (hereafter referred to as CTL). 2. The Last Glacial Maximum (LGM), with lowered greenhouse gases, altered orbital configuration, and glacial topography (ICE-5G reconstruction as in Peltier [2004]) and bathymetry (lowering of sea-level by 120m). The glacial boundary condition for those simulations was prescribed in compliance with the most recent specifications of PMIP (http://www-lsce.cea.fr/pmip2/).

4.2.2

The HadCM3 simulations

The simulations are described by Hewitt et al. [2003]. Succinctly: the AGCM, HadAM3, has a horizontal resolution of 2.5◦ by 2.75◦ and 19 vertical levels and is described in detail in Pope et al. [2000]. The OGCM, HadOM3, is based on the GFDL “Cox” ocean model [Cox , 1984]. Several modifications have been made to the original GFDL ocean model [see Gordon et al., 2000]. HadOM3 has a horizontal resolution of 1.25◦ by 1.25◦ and there are 20 depth levels. River runoff is included in the model using predefined river catchments, and the runoff enters the ocean at coastal outflow points. The sea ice model includes a simple thermodynamic budget and the ice thickness, concentration and snow depth are advected using the surface ocean current. Ice rheology is only crudely represented by preventing convergence of ice once the ice thickness reaches 4 m. This is a more dated coupled GCM, but it is widely believed to have less climatological biases than CCSM3. El Niño and the Earth’s climate: from decades to Ice Ages.

4.2. The Climate of the Last Glacial Maximum

105

The LGM boundary conditions are sensibly the same as for CCSM3, but for a much older version of Peltier’s ice-age topography [Peltier , 1994], with a slightly weaker sea-level lowering of 105 meters. The control case (CTL) refers to pre-industrial conditions, directly comparable to their CCSM3 counterpart.

4.2.3

Intercomparison of Simulated LGM Climates

Understanding differences between LGM simulations is not a simple matter (see, for instance, Braconnot [2004]). In this case, causes for discrepancies lie in the slightly different boundary conditions, notwithstanding the usual factors of resolution, model physics and spin-up procedure. Nevertheless, it will be seen that both models do concur, at least qualitatively, on several aspects of LGM climate – which gives credence to their reliability. In the following, we will focus exclusively on the Northern Hemisphere winter season (December-January-February, DJF), since it is currently the one where ENSO and its teleconnections reach their peak. An analysis and modeling of the Northern Hemisphere summer will be left for future work. The most notable differences are in surface temperature, which alters the structure of the jets and absolute vorticity contours. In Fig 4.1 we show the surface temperature distribution in: (a) the present climate, taken from the NCEP/NCAR Reanalyses [Kalnay, 1996] (b) HadCM3 CTL (c) CCSM3 CTL. Though the general features of the observed temperature field are well reproduced by the models, each shows important differences, in particular over the tropical oceans. The eastern Pacific “cold tongue” extends too far west in those models, and both have a double maximum on either side of the equator associated in some seasons with a tendency for a double intertropical convergence zone (ITCZ). These flaws strongly affect the simulation of ENSO, as we shall see. However, these differences pale in comparison to the anomalously warm temperatures over northern Eurasia and the Arctic (up to 12◦ C) in HadCM3 CTL compared to NCEP (d), and similarly in CCSM3 CTL (not shown). The glacial/interglacial changes (Panels E and F) are large – about twice this highlatitude bias. Both models do show a strong high latitude cooling, of up to 24◦ C over and downstream of the continental ice sheets in CCSM3. This change is overall much larger than in HadCM3, probably because of the higher glacial topography used in CCSM3 (Peltier [2004] vs Peltier [1994]). Both models agree on a weak tropical cooling (≥ 2 ◦ C on zonal average), and therefore a much increased equator-to-pole gradient (by 12 ◦ C in HadCM3, 13 ◦ C in CCSM3). By virtue of the thermal wind balance, this greatly enhances the baroclinic wind shear in midlatitudes. We choose to only show the upper tropospheric zonal wind in Fig 4.2, taken at ‡ = 0.257 (close to the 250 mb pressure level). Again, the two GCM simulations produce a realistic pattern to first order. However Panel D reveals that the HadCM3 subtropical jets are shifted too far south at almost every longitude, and are imperfectly separated. The CCSM3 control run is much more realistic in this respect. The situation changes markedly at the LGM: in HadCM3 the Asian Jet shifts north, while a jet appears downstream of the Laurentide ice sheet (with velocities up

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4.3. Characterizing Ice Age Teleconnections 117

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El Niño and the Earth’s climate: from decades to Ice Ages.

4.4. Modeling Ice Age Teleconnections

119

for plausibility. We are therefore condemned to explain both teleconnection changes. • However, both GCMs agree in producing a longer period ENSO at the LGM: the main period switches from 2 years to 3-4 years. Even though the models disagree on the pattern of teleconnection and the fraction of variance it explains, it is important to understand the cause of both changes. The question we wish to ask is the following: do LGM teleconnections differ because of 1. the structure of tropical heating (changing ENSO pattern)? 2. the medium of propagation (a changed basic state)? 3. the interaction with transient eddies in the subtropics and midlatitudes? In order to answer these questions, we now turn to simpler models of the Earth’s atmosphere, aimed at simulating the stationary wave response of the atmosphere to diabatic heating perturbations. Because these questions involve various degrees of non-linearity, two models will be considered – a non-linear (NLIN) and a linear one (ELM).

4.4

Modeling Ice Age Teleconnections

A review of ENSO teleconnections is beyond the scope of this book. The observational evidence is outlined in Bjerknes [1969]; Horel and Wallace [1981], while the theoretical foundations were laid out by Hoskins and Karoly [1981]; Simmons [1982]; Simmons et al. [1983]; Schneider and Watterson [1984]; Sardeshmukh and Hoskins [1988]. For recent reviews, see Alexander et al. [2002]; Hoerling and Kumar [2002]; Liu and Alexander [2007]. An instructive history of the evolution of this important subfield of climate dynamics is given in Trenberth et al. [1998], starting from the illuminating barotropic model of Hoskins and Karoly [1981] to a much more quantitative understanding involving ensemble GCM integrations. In spite of the numerous subtleties involved, the upshot is that the “protomodel” of Hoskins and Karoly [1981] still gives a useful qualitative understanding of the dynamics. While the quantitative refinements can be obtained with more complete stationary wave models (see also Held et al. [2002] and articles in the same issue), these are still much less complex than full GCMs. The subject of glacial stationary wave patterns has been previously explored by Cook and Held [1988], albeit with a much less convincing simulation of the LGM climate. Also, the study used a steady-state model linearized about the zonal average of the flow, whereas it has since been established that zonal asymmetries of the basic state play a paramount role in shaping the response [Ting and Held , 1990; Ting and Sardeshmukh, 1993]. It is therefore worthwhile to take a similar look at the problem with the basic states described in Section 4.2 and a non-linear stationary wave model.

120

4.4.1

Chapter 4. El Niño in the Icehouse

A Nonlinear Model of Stationary Waves (NLIN)

This model is made up of the governing dynamical equations of atmospheric flow (the so-called primitive equations) in ‡ coordinates: it is essentially the dynamical core of a GCM, devised to compute the response of the global circulation to prescribed heating and momentum fluxes given a background state. The latter is defined by five variables: the wind field in ‡ -coordinates (u, v, ‡) ˙ , temperature (T ) and surface pressure (Ps ). The model computes perturbations to such quantities, by producing divergent motion at upper levels, which excite Rossby waves that generate rotational motion across the globe, propagating primarily along great circles. The model is described in detail by Ting and Yu [1998], but we hereby recall a few of its properties. A semi-implicit time integration scheme is used for time marching [Hoskins and Simmons, 1975] with a halfhour time step, and a meaningful stationary wave solution is ensured by suppressing the growth of baroclinic eddies through a high Rayleigh damping and Newtonian cooling concentrated near the boundary layer‡ . The model is spectral in the horizontal, using an R30 (rhomboidal) truncation, and has 14 vertical levels located at ‡ = 0.015 , 0.050 0.100, 0.170 , 0.257, 0.355 , 0.460 , 0.568, 0.675, 0.777, 0.866, 0.935, 0.979, 0.9970. A rigid-lid boundary condition is applied at model top (‡ = 0) and the surface (‡ = 1). The advantage of this model is that it retains the full nonlinearity of the equations of motion (interactions between different wavenumbers), but allows disentangling of results, because the background state and diabatic heating can be specified independently of each other, unlike a full GCM. However, the suppression of baroclinic instabiliy means that it cannot simulate interactions between stationary and transient eddies. Also, its formulation prohibits wave-mean flow interactions. Nevertheless, is has proven a useful tool in analyzing ENSO teleconnections, by isolating the essential forcing agents of certain extratropical circulation anomalies [Ting and Yu, 1998], which is what we purport to do here. Usually this forcing is the diabatic heating field due to SST anomalies, together with the indirect effect of transient eddy heat fluxes. For the present application, we start from a specified background state composed of the three-dimensional climatological flow field, temperature and surface pressure of the CCSM3 or HadCM3 simulations in Northern Hemisphere winter (DJF). We then prescribe a three-dimensional heating perturbation approximating the anomalous diabatic heating Q due to ENSO SST anomalies (It is approximate as the full modeled diabatic heating was not available for this analysis, and had to be indirectly obtained through the precipitation field). This typically takes the form: Q = AV (‡)H(⁄, „)

(4.8)

Here ⁄ and „ are the geographical longitude and latitude, and A some amplitude. Unless the full tridimensional heating was available (see Sec 4.4.1), it is estimated as follows: since the horizontal heating distribution is so akin to the precipitation ‡ more

precisely, the damping timescale decreases from 15 days in the free troposphere to 0.3 days at the surface El Niño and the Earth’s climate: from decades to Ice Ages.

4.4. Modeling Ice Age Teleconnections

121

anomalies associated with El Niño, a good proxy for H(⁄, „) is to regress the model’s precipitation field onto some ENSO index – in this case, the left expansion coefficient a1 (t) of the SVD analysis seen earlier (Section 4.3.2). As is common practice (see, e.g. Gill [1980]), we approximate the vertical distribution of the heating by a sinewave: V (‡) = s 1 0

sin(fi‡) sin(fi‡)d‡

(4.9)

We start by evaluating the model’s performance in the current climate state, again for the DJF season. NCEP Basic State Typically, the model is run for fifty days, and the average of the output over the last twenty days reproduces stationary features quite well. As an example, we show in Fig 4.9 the response of the NLIN model to a three-dimensional diabatic heating perturbation associated with El Niño. The latter is diagnosed from the NCEP/NCAR Reanalyses Kalnay [1996] and displays a strong heating anomaly (≥ 1 Kday≠1 ) in the central equatorial Pacific east of the dateline, and a horseshoe-shaped cooling of similar amplitude around it. Both are direct consequences of the shift in convective precipitation that accompanies El Niño events (see, for instance, Seager et al. [2005], their Fig 1). Anomalies are present over the storm track regions and represent the anomalous latent heating due to the transient eddy response to altered stationary wave patterns during El Niño, though the compositing method remains an imperfect way of isolating those. Overall, the composite heating is an order of magnitude larger in the Tropics as in the midlatitudes. Similarly diagnosed was the stationary wave streamfunction at 250 mb anomalies for El Niño years (Ting, personal communication), shown in Panel C. The model (Fig 4.9b) satisfyingly reproduces the Rossby wavetrains emanating from the western equatorial Pacific in both hemispheres. However, its amplitude over the PNA route worsens downstream, and the pattern over North America ends up bearing only slight resemblance to the observations (Fig 4.9c). This is a sign that the transient eddy momentum fluxes (not included here) play a significant role in those areas. Which geographical locations matter most in creating the response? The question can be investigated with idealized heating patterns. In Fig 4.10 we gauge how much of this response can be obtained by: 1. Restricting this heating to the tropical Pacific 2. Specifying a bivariate Gaussian heating centered at the equator and 170◦ W. Comparison of Panels A and B with the previous figure will reveal that much of the simulated (and observed) streamfunction anomalies can be attributed to the tropical Pacific heating – in particular, the Pacific basin and its surroundings, and even much of

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130

Chapter 4. El Niño in the Icehouse

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El Niño and the Earth’s climate: from decades to Ice Ages.

4.4. Modeling Ice Age Teleconnections

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HadCM3 basic state An identical analysis for HadCM3 is displayed in Fig 4.16 and Fig 4.17. The CTL case is characterized by a similar Rossby wave pattern, though closer to a latitude circle, which is reminiscent of the pattern in Fig 4.12b. The modal behavior is very strong (74% of the variance) and is expected to dominate the total response, since the prescribed ENSO heating (Fig 4.12a) projects strongly onto the first PC. Surprisingly, the first mode radically changes character at the LGM (Fig 4.17a), switching to a dipole with a center of action over the Siberian peninsula and one of opposite sign over the Norwegian Sea. Now comparing Fig 4.17a to Fig 4.12d – and recalling that the sign of the EOF pattern is arbitrary – we can see a striking resemblance. This means that the response of the non-linear model was dominated by a modal behavior (SWB). If true, this would imply that the same pattern could be obtained by using solely the tropical part of the heating, and this is indeed the case (not shown). Though the study of Simmons et al. [1983] only considered a barotropic model on the sphere, similar results have been obtained in more complete ones – including this

134

Chapter 4. El Niño in the Icehouse

baroclinic model. A physical explanation is that, for a reasonable upper-level tropical divergence, the upper-tropospheric contours of absolute vorticity determine the Rossby wave source to leading order, thus inducing a similar Rossby wave response even for rather different near-equatorial SST anomalies [Sardeshmukh and Hoskins, 1988]. It is an item of experience that barotropic models do a surprisingly good job at describing this propagation, provided the linearization is done about the ambient flow field close to the equivalent barotropic level [Ting , 1996]. Since the strongest vorticity gradients are found in the upper troposphere near the Asian Jet exit region, any forcing in its vicinity will tend to excite such a mode. The corollary is that it is necessary for the GCMs to accurately simulate the jet structure (and its change at LGM) in order to produce realistic teleconnections. Summary In Table 4.4.2 we summarize the variance explained by the various modes previously described. In all cases but HadCM3 LGM, the first mode is a Rossby wave train of the SWB type. In both models, the fraction of variance explained by the first mode decreases significantly at the LGM, which means that the location of the heating gains more importance. This may partially explain why NLIN does better at reproducing the current teleconnection patterns – even with a deeply unrealistic heating field – than it does at the LGM. The analysis confirms the great sensitivity of the stationary wave response to the mean state, consistent with Ting and Sardeshmukh [1993]. In some instances, this sensitivity seems to dominate the forcing, to the point that realistic teleconnection patterns can be excited by erroneous forcings provided the basic state favors a realistic SWB mode. Case \ EOF rank NCEP CCSM3 CTL CCSM3 LGM HadCM3 CTL HadCM3 LGM

1 39% 48% 34% 74% 66%

2 18% 15 % 21% 7% 10%

3 11% 9% 10% 5% 5%

4 9% 8% 9% 4% 5%

Table 4.2: EOF analysis of the sliding tropical forcing experiments in ELM. Numbers shown here are the fraction of variance explained by each mode for geopotential height at the ‡ = 0.257 level (close to 250 mb). Only modes 1 to 4 are shown for brevity. See text for details.

Overall, this linear analysis sheds light on the behavior of NLIN by disentangling the influence of the forcing and the basic state in the nonlinear response. It also confirms Ting and Yu [1998]’s observation that for realistic amplitudes of forcing, say O(1 Kday≠1 ), the essence of the non-linear response is captured by the linear model. However, both models are a far cry from the GCM’s LGM teleconnection patterns, a topic to which we now turn our attention. El Niño and the Earth’s climate: from decades to Ice Ages.

4.5. Discussion

4.5

135

Discussion

We have used two simplified, stationary-wave models to understand the causes for different ENSO teleconnection patterns in glacial times, as simulated by two coupled GCMs. We were motivated by the importance of ENSO teleconnections in the current climate, and the possibility that their reorganizations at the LGM might have important implications for ice sheet mass balance. Given the basic state and some estimate of ENSO-related diabatic heating, the models simulate the large-scale dynamical response of the atmosphere without transient eddy feedbacks. The non-linear model (NLIN) proves successful at simulating current stationary wave patterns associated with ENSO at present (NCEP basic state), and the simulated geopotential height field bears some resemblance to the teleconnection patterns observed in both control simulations (CCSM3 and HadCM3). This resemblance, however, is virtually non-existent when it comes to reproducing glacial teleconnections. A linear, steadystate model (ELM) is then employed to understand this behavior. It is found that the relative success of NLIN at CTL hinges on the modal character of the response (given a realistic background flow field). At LGM, both GCMs simulate a rather different ambient flow – different from their CTL counterpart and different from each other. The linear analysis informs us that the modal character of the response is diminished at the LGM, and thus the sensitivity to the forcing is enhanced. This is the first main difficulty in simulating the glacial teleconnections. One should note, however, the diabatic heating had to be indirectly backed out from the precipitation field§ , assuming a vertical structure peaking in the mid - troposphere. This proves to be a rather good approximation in the control case and within the tropics, but the heating may have been peaked at higher altitudes by virtue of the decreased static stability of the Glacial tropical atmosphere. Also, this vertical structure is appropriate for latent heat release associated with deep convection – not the shallow heat release associated with precipitation in the storm track regions, where baroclinic instability (“slanted convection”) prevails. Thus we propose that the relative failures of NLIN are to be blamed on the incorrect specification of the heating over the storm track regions. More work is therefore needed to extract this information from the GCMs and quantify its importance. Nonetheless, we expect this limitation to pale in comparison to the absence of transient eddy feedbacks, lacking from either model. It is known that El Niño-induced changes in the latitudinal positions of the jets trigger changes to the transient eddy momentum fluxes in midlatitudes, which induce equatorward low-level flow at high latitudes, with a noticeable zonally-symmetric component [Seager et al., 2003, 2005]. Clearly, the neglected transient eddy fluxes of heat and momentum must play a key role in establishing the response to tropical forcing described in Section 4.3.2. How might one asses their role? To this end, it would be instructive to use a nonlinear model of Northern Hemisphere winter storm tracks, such as devised by Chang [2006]. The latter study presents § monthly

values for this field in both GCMs were not available for this work

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an impressively realistic simulation of transient eddy statistics over the Atlantic and Pacific storm track regions. It has not been used in the present work, because it remains to be established whether such a model can accurately reproduce the interannual variability of the storm tracks in response to tropical heating. If this can be achieved, we propose to force this model with ENSO-related heating anomalies and quantify how stationary waves, jet streams and transient eddies conspire to bring about vastly different teleconnection patterns at the LGM. It will undoubtedly deepen our understanding of the dynamics at work in the current generation of climate models and hopefully shed light on how these elements interacted in nature, bringing us closer to solving Ice Age puzzles. Another aspect of ENSO teleconnections neglected in this study is their potential change over the Southern Hemisphere. Traditionally overlooked, there is now increasing evidence that the latter is the stage of processes governing many aspects of the global ocean circulation. Most notably, it has been proposed that the Atlantic meridional overturning circulation (MOC) is driven by the upwelling of North Atlantic Deep Water with the recirculation cells of the Southern Ocean, thought to be primarily controlled by the wind field over the Drake Passage [Toggweiler and Samuels, 1993, 1995, 1998; Gnanadesikan, 1999]. Therefore, any sizable change in surface winds over the area could affect the Atlantic MOC to some extent. The impacts of ENSO over the Southern Ocean are important in the current climate, in particular in terms of sea-ice concentration and extent [Yuan et al., 1996; Yuan and Martinson, 2000]. Their change in a glacial world can conceivably participate in reorganizing the sea-ice field, thereby strongly affecting the local winds and the Southern Ocean’s density structure. Another noteworthy impact of ENSO over the Southern Hemisphere is its ability to shift westerly wind belts equatorward over the ocean during El Niño years [Seager et al., 2003]. Seager et al. [2007] point out that, were such changes to persist, these would markedly reorganize the salt export associated with the Agulhas retroflection, crucial to the salinity balance of the Atlantic basin [Gordon, 1985]. Large glacial-interglacial fluctuations in this export have been documented over the past 5 cycles [Peeters et al., 2004] and are hypothesized to have played a significant role in the fluctuations of NADW production over those timescales. Seager et al. [2007] propose that ENSO might remotely drive this salt export, thereby partially controlling the fluctuations of the Atlantic MOC on centennial timescales. Currently, most climate models used at the LGM have too crude a grid size to adequately resolve such a retroflection. It would thus be worthwhile to explore if they can simulate ENSO-related changes in the surface wind field that support such a tropical mechanism for the resumption of the Atlantic MOC after the LGM. Hence, once the changes in ENSO teleconnections have been understood over the Northern Hemisphere, there will remain a wealth of interesting problems to tackle over the Southern Ocean, which we shall leave for future work.

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Conclusion “We are not certain, we are never certain. If we were, we could reach some conclusions and we could, at last, make others take us seriously.” Albert Camus

Summary The goal of this book has been the exploration of some mechanisms of low-frequency climate change in the tropical Pacific and of their impacts on the rest of the globe. Its approach was reductionist in essence, aiming at isolating the relevant physical processes through simple models of the ocean and atmosphere. The problem was broken up into four chapters, which correspond to the four spectral bands under investigation. Chapter 1 focused on decadal variability. We built upon the rich tradition of reduced-gravity ocean models and extended linear equatorial wave theory by computing the Green’s function of the problem. This allowed us to rigorously gauge the relative importance of tropical and extratropical wind forcing in generating thermocline motion at the equator. It was found that the sharp decrease of the Green’s function with latitude meant that tropical winds always had a relative advantage over extratropical winds, in spite of their lesser variance. We also pointed out the relative inefficiency of the oceanic ”modes” to pick out power from a white spectrum of the wind field, since they all are damped. Tropical winds are able to generate a strong equatorial response with periods of 10 to 20 years, while midlatitude winds can only do so for periods longer than about 50 years. We concluded on these theoretical grounds that the latter are unlikely to be responsible for the observed decadal variability in sea-surface temperature. In addition, we discussed other impediments to a modal behavior of the Pacific basin on those timescales – coastline geometry, dissipation and most importantly the energy loss to the barotropic eddy field – all of which weighed strongly against the possibility that baroclinic eigenmodes could be responsible for the decadal component of tropical Pacific SST records. In contrast, we exhibited alternative mechanisms relying on ocean-atmosphere coupling (whether local or not) that

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could more plausibly account for it. Beyond the mathematical contribution of formally unifying different solutions to the low-frequency equatorial wave problem, this work makes a contribution to the field of climate dynamics, in that it helps narrow down the substantial set of mechanisms of Pacific decadal variability that have been offered so far. We then turned to the behavior of ENSO over centennial to millennial timescales. Chapter 2 explored specifically the sensitivity of ENSO to volcanic forcing and its interaction with solar forcing over the past millennium (1000-1999 A.D.). We used a model of intermediate complexity with a simplified parameterization of the effect of atmospheric radiation on SST. This allowed us to repeat simulations of the millennium up to 200 times, building a solid statistical base for assessing the impact of tropical volcanic eruptions on the likelihood of El Niño events. In accordance with previous studies [Mann et al., 2005], the model responded to increased radiation with a La Niña-like pattern, as predicted by the thermostat mechanism of Clement et al. [1996]. It was found that only forcings greater than about 4 Wm≠2 (i.e., larger than the two largest tropical eruptions of the past 150 years, Krakatau in 1883 and Mt Pinatubo in 1991) can push the likelihood and amplitude of an El Niño event above the model’s level of internal variability. This explained why Adams et al. [2003] could detect a statistical relationship between explosive volcanism and El Niño over the past 350 years, while on the other hand Chen et al. [2004] were able to predict El Niño events of the last 148 years without knowledge of volcanic forcing. This result an important step in the resolution of what was hitherto a paradox. If explosive volcanism can tip the dynamical balances towards an El Niño, then it was reasonable to expect that the strongest eruption of the millennium (1258 A.D.) may have done so. We peered into the array of high-resolution paleoclimate data to show that indeed, it is likely to have triggered a moderate-to-strong El Niño event in the midst of prevailing La Niña-like conditions induced by increased solar activity during the Medieval Climate Anomaly. In so doing, we developed a multiproxy method that is the first building block of a more comprehensive paleo-ENSO index over the past millennia, which is needed to more thoroughly test the thermostat mechanism. In Chapter 3, we explored the possibility that ENSO may have acted as a mediator of the much debated solar influence on climate. By using a range of scaling estimates between the amount of cosmogenic isotopes and solar irradiance, we bracketed uncertainties relative to the sensitivity of the ENSO system to solar forcing over the Holocene. The model proved remarkably sensitive to even modest changes in irradiance, which were able to generate a response in the east-west SST gradient that surpassed the model’s considerable level of internal variability, even in the presence of realistic amounts of weather noise. Combined with orbital changes in insolation, we found solar irradiance able to drive millennial oscillations in the tropical Pacific SSTs. Its subtle amplitude, accumulated over these longs timescales, could plausibly account for a multitude of climate anomalies detected across the globe, concomitantly with excursions in the concentration of cosmogenic isotopes. The influence of the Sun, albeit small, was non-zero when averaged over the seasonal cycle, while precessional forcing El Niño and the Earth’s climate: from decades to Ice Ages.

145 did average to zero. Remarkably, the corresponding SST response was of similar magnitude (a fraction of a degree) in both cases, despite the order of magnitude difference in peak-to-peak variations. As in Chapter 2, this Chapter assumed modern ENSO teleconnections and used the rich array of high resolution paleoclimate records (IRD, speleothems, corals, tree-rings, lacustrine sediments) to test our hypothesis, which we found supported by the available data under this scenario. If confirmed by subsequent studies, this would have important implications for understanding the climate of the past millennium. Indeed, it lended support to the notion that the Little Ice Age and the Medieval Climate Anomaly were just two of the most recent swings induced by solar activity over the Holocene, and possibly over more ancient epochs as well [Bond et al., 2001]. Nonetheless, the current tests were far from definite, so we detailed other predictions of the theory as specific targets of tests to come, based on data that we hope available to the climate community within a reasonable timeframe. With the recognition that these empirical tests all rely heavily on modern teleconnections patterns, we dedicated Chapter 4 to understanding how these may have varied in the past, especially at the Last Glacial Maximum (LGM). To this end we analyzed the LGM simulations of two state-of-the-art climate models, HadCM3 and CCSM3 and investigated how their teleconnections differed from the present-day ones. While both models did a credible job of reproducing modern ENSO teleconnections, they showed very different patterns at the LGM – different from the preindustrial case and different from one another. This must be partly due to the reduction in ENSO activity observed at LGM in CCSM3, not in HadCM3. While unhelpful in trying to understand natural climate change, this failure provided us with an opportunity to peer into the causes of discrepancies between coupled general circulation models (GCMs) via simplified models. We used primitive equation models with idealized damping to compute the atmospheric response to anomalies in tropical Pacific diabatic heating anomalies. Use of these models, which ignore transient eddy feedbacks, allowed for an exploration of the extent to which one can account for GCM teleconnections only on the basis of planetary wave propagation. Experiments confirmed the established fact (see Held et al. [2002] and references therein) that present-day ENSO teleconnections can largely be understood in terms of linear, stationary wave dynamics. A similar attempt proved unsuccessful for LGM teleconnections, leading us to suggest that the non-linear trilogy of jets, planetary waves and transient eddies must be invoked to explain the changes that occurred in the GCM simulations of the LGM.

Caveats and Future Work Although much insight can be gleaned through simple models, this simplicity comes at the expense of completeness. The model used in Chapter 1 is as idealized as they come: a single baroclinic mode, linear dynamics, straight coastlines, in the — -plane approximation, supplemented by the low-frequency long-wave approximation. It is

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only meant to give a qualitative description of the Pacific basin. While this adiabatic model has proven surprisingly successful at describing observed interannual anomalies in tropical circulations [e.g. Cane, 1984] as to warrant operational forecasts of El Niño (T.Barnston, personal communication), its applicability to decadal studies fades as the thermal structure is assumed fixed and no dissipation was introduced. Furthermore, it is not coupled to the atmosphere, while we argued that coupling was most likely the key to this Pacific Decadal Variability. The immutability of stratification is an even more serious caveat as we consider the evolution of ENSO on timescales of centuries to millennia. It is known that the properties of ENSO are sensitive to the background state upon which the anomalies develop [Zebiak and Cane, 1991; Wittenberg , 2002]. Since the physics familiar to the interannual ENSO (the Bjerknes [1969] feedback) operate on all timescales, and also seem crucial in establishing the climatology [Dijkstra and Neelin, 1995], the definition of this background state is unclear: what is ENSO and what is not? To some extent, the model modifies its own background state, should the latter be defined on a 50 year average, say. Indeed, Wittenberg [2002] reduced the problem to understanding the response of ENSO to an El Niño- or La Niña-like climatology. It is clear, however, that the structure of the equatorial thermocline is not entirely determined within the Tropics and that the full tridimensional circulation of the upper ocean is involved in its establishment [Boccaletti et al., 2004]. While it now appears that decadal temperature anomalies subducted in the subtropics do not reach the equator with any appreciable amplitude [Schneider et al., 1999], thermocline waters are ultimately ventilated from higher latitudes where solar forcing would inevitably affect their formation. Pycnocline waters are made up from subtropical mode waters with neutral densities between 25 and 26.5 kgm≠3 , which outcrop in the subtropics and midlatitudes [Johnson and McPhaden, 1999]. The formation regions of these mode waters tend to be localized in the western part of the subtropical gyre, while lower pycnocline waters originate from beyond the gyre boundary with more zonally-elongated outcrops [Hanawa and Talley, 2000]. It is therefore unclear, even in the absence of atmospheric feedbacks, how a uniform change in incoming solar radiation would modify the equatorial thermocline, not least because of the mixing and stirring of subducted waters by the eddy field below the mixed layer. It is an outstanding problem in oceanographic research, one that will undoubtedly mobilize the full array of observational, theoretical and modeling capabilities for its resolution. Even greater simplifications were made in the treatment of atmospheric radiation in our coupled model. It comprises no radiation scheme whatsoever, while the difference between top-of-the-atmosphere and surface downwelling shortwave radiation was only accounted for by a fixed cloud fraction. One might also find surprising that the simple model of Gill [1980] would even approximately simulate near equatorial surface winds despite its tremendously crude physics. Its success is an item of experience [Neelin et al., 1998], but for this problem the primary matter is the incoming radiation into the ocean mixed layer, which is obviously affected by clouds – absent in this simple model. Unfortunately, cloud physics are known to be the Achilles’ heel of El Niño and the Earth’s climate: from decades to Ice Ages.

147 the most comprehensive GCMs. Marine stratus clouds, especially, are crucial to the sign (let alone the magnitude)of the climate response to radiative perturbations [e.g. Bony et al., 2006]. Moreover, there is considerable controversy as to how they would respond to solar forcing, as some have suggested that their formation may be influenced by galactic cosmic rays [Marsh and Svensmark, 2000; Carslaw et al., 2002]. The absence of an explicit moisture equations also means that no water vapor feedback can occur, while Pierrehumbert [1999] has argued that it should be instrumental in producing global climate change on millennial timescales. In short, while the model emphasizes the dynamical adjustment of the ocean via the Bjerknes feedback, it is still unknown how these effects would fare in comparison to the change that solar irradiance would impose on the atmosphere and the basin-wide ocean circulation, either by direct radiative forcing or indirect forcing via air/sea fluxes. Only a model with a realistic representation of all these elements can inform us about these interactions, which constrains us to use coupled GCMs. Yet, the abundant evidence of their flaws in simulating the present-day tropical climatology [Delecluse et al., 1998] or LGM climate (Chapter 4) does not bode well for such an exercise. At present, there is still no consensus on the ENSO response to greenhouse gas forcing amongst GCMs [Collins, 2005]. Hence the rationale for using our simpler model, whose dynamical thermostat behavior seems supported by the observation of an increased SST gradient along the equator over the twentieth century [Cane et al., 1997]. At this point, it is also premature to expect consensus amongst GCMs in their response to solar perturbations. Further, it seems that some GCMs respond differently to solar and greenhouse gas forcing, a puzzling fact which has yet to be explained (A. Clement, personal communication). This issue partly motivated our Chapter 4, since the past provides a critical testbed for coupled GCMs and their simulation of the tropical Pacific. The disagreement between the 2 GCMs proved substantial, albeit not unexpected. We saw that both models produced an ENSO extending too far west into the warm pool, which contributed to the difference between simulated and observed present-day teleconnections. Why did the latter differ at LGM? While the divergence in ENSO behavior between models undoubtedly played a role, we also had to make an assumption regarding the vertical distribution of heating and how it scaled with precipitation. This assumption was arguably acceptable in the Tropics, not over the storm tracks. Unfortunately, the full three-dimensional diabatic heating fields were not available for this study, which suggests an obvious area for improvement. Equally important are the transient eddy heat and momentum fluxes, which are typically not saved in long GCM integrations. It is hoped that those will be made available in future integrations of coupled GCMs for the purpose of such analyses. More fundamentally, it was shown that transient eddies are instrumental in shaping the ENSO teleconnection pattern at the LGM, and this is the most pressing point to address. In addition, we should point out that stationary models are tuned to the current climate (inasmuch as the specified damping is meant to account for mixing by transient eddies), which could partly explain their poor performance in the LGM climate.

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Finally, a word should be said about uncertainties in the forcing: while these are believed to be relatively small for decadal variability and the LGM, this is not the case over the Holocene. Specifically, we are tracking either minute changes in solar irradiance over long timescales – which some argue are insignificant [Foukal et al., 2006] – or large, abrupt changes in stratospheric aerosol loading, which are only indirectly recorded in ice cores and often with large error bars. Furthermore, no dataset of Holocene volcanic forcing was available for the study in Chapter 3, though the upshot of Mann et al. [2005] is that it is at least as important as solar forcing, even over centennial timescales. These limitations leave ample room for progress.

Outlook With some appreciation gained from these successes and failures, we can now formulate questions for future research: • How do coupled GCMs with a realistic ENSO cycle react to volcanic perturbations? Is the thermostat mechanism operating in them and with which amplitude? • How do such models react to centennial perturbations in solar irradiance? Is that response qualitatively different from that to greenhouse gases? If so, why? • How might one construct an objective multiproxy index of ENSO intensity over the past millennium? • If ENSO is indeed a mediator of the solar influence on climate, and if the mechanism of Shindell et al. [2001] also contributes, what is the relative importance of the two? Could they reinforce one another? • What is the role of the tridimensional ocean circulation in determining the equatorial Pacific thermocline? In particular, how does the Atlantic MOC influence it, and how? Conversely, how do tropical Pacific SSTs influence the North Atlantic? Can a positive feedback exist between the two systems, as suggested in Chapter 3? • If at least one of these two positive feedbacks operates, might this non-linearity lead to bifurcations that could explain some aspects of abrupt climate change? • Why do LGM simulations of ENSO and its teleconnections differ so much? Can idealized models of the storm tracks help explain this difference? How might this knowledge be used to improve long-range climate forecasts? It is hoped that answers to these questions will be found within a few years and that they will empower us with a greater ability to predict the future of our climate.

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151

Appendix A Pacific Decadal Variability: Boundary-Layer Correction When the forcing reaches the poleward boundaries of the domain, the forced solution (1.32) has to be amended. Our solution holds within a distance ‘ of the northern wall, so that we can write the ”interior” western boundary mass flux : UI = 2i„

⁄

yN ≠‘

0

F (y)E(„y 2 )dy

(A.1)

Far from the equator, the PGPV solution holds to lowest order : h=

A

F y

B C y

1 ≠ ei„y i„

2›

D

(A.2)

and the boundary layer zonal mass flux can be computed using geostrophy : 1 u = ≠ hy y

so that :

C

1 U‘ = ≠ hy y

DyN

+

yN ≠‘

(A.3)

⁄

yN

yN ≠‘

h dy y2

(A.4)

Taking F = 0 at y = yN ≠ ‘ since it is already included in the interior integral, performing another integration by parts, and using previous formula for UI , the boundary-layer return flow then writes : C

D

⁄ yN hN FN 1 ≠ e≠i◊N U‘ = ≠ + ≠ 2i„ F (y)E(„y 2 )dy yN yN i◊N yN ≠‘

(A.5)

where FN = F (yN ). We can ignore the integral, which is approximately ≠2i„ ‘ FN E(◊N ) ≈ O(‘) . Again using (A.2) to compute the compute the meridional mass flux into the

152

Appendix A. Pacific Decadal Variability: Boundary-Layer Correction

northern wall : ⁄

1 VN = v d› = yN ≠1 0

5⁄

0

≠1

h› d› ≠

⁄

0

≠1

6

F d› =

hN ≠ FN yN

(A.6)

Therefore the total boundary layer correction is : C

FN 1 ≠ e≠i◊N U‘ + VN = ≠ 1≠ yN i◊N

D

(A.7)

So that for a symmetric basin, the total mass flux is : I

IF = ≠ 2i„

⁄

0

yN

C

FN 1 ≠ e≠i◊N F (y)E(„y )dy + 2 1≠ yN i◊N 2

DJ

(A.8)

which reduces to (1.32) if the forcing vanishes at the boundary (FN = 0). For ◊N Ø 1, one can obtain a crude estimate of the importance of the boundary forcing goes as follows : FB ≥ FyNN , FI = 2i„yN F (using E = O(1)), so that : FB 1 ≥ Æ1 FI ◊N

(A.9)

which confirms the intuitive result that for basins sufficiently big, the effect of the northern and southern boundary mass fluxes is lesser than that of the interior mass flux.

El Niño and the Earth’s climate: from decades to Ice Ages.

153

Appendix B ENSO Impacts on North Atlantic Winds We show evidence of a link between ENSO and surface winds over the northern North Atlantic. We use wind field data from three sources : surface observations, a coupled general circulation model (GCM), and a forced atmospheric GCM. The idea is to progressively strip down the physics to isolate the mechanism responsible for the linkage. The data sources are : • Our best observational estimate of surface winds over the North Atlantic. Since wind stress estimates are unfortunately unavailable before 1949, the wind velocity field was taken from the analysis of Evans and Kaplan [2004], which uses an optimal interpolation (OI) of ICOADS winds [http://icoads.noaa.gov/, Worley et al. [2005]]. This has the effect of retaining the large-scale features of the field, which are most relevant for our study. • the GDFL coupled model (version 2.1) simulation H1 (http://nomads.gfdl. noaa.gov/CM2.X/CM2.1/data/cm2.1_data.html). This is a state-of-the-art ocean-atmosphere general circulation model in the configuration used for the Fourth Assessment report of the Intergovernmental Panel on Climate Change. The forcing is a reconstruction of natural and anthropogenic radiative perturbations over the period 1860-2000. This particular simulation has a vigorous, self-sustained ENSO with variance comparable to that observed. but very similar results were obtained with other ensemble members H2 and H3. • POGA-ML simulations with the NCAR CCM3 model, as used in Seager et al. [2005b]. The AGCM is coupled to a two-layer, entraining, mixed-layer ocean model, with historical SSTs [Kaplan et al., 1998] prescribed only in the tropical Pacific (computed elsewhere). The results analyzed are the average of a 16member ensemble, which isolates the influence of the boundary conditions - in this case, tropical Pacific SSTs over the period 1860-2000. The data can be found

154

Appendix B. El Niño and the North Atlantic at http://kage.ldeo.columbia.edu:81/expert/SOURCES/.LDEO/.ClimateGroup/. MODELS/.CCM3/.PROJECTS/.poga-ML/.poga-ML-mean/. In both models we analyzed the wind stress, since it is the most relevant to surface ocean dynamics.

The period of analysis was the longest common to all datasets, 1860-2000. All fields were smoothed by a 3-month running average. They were then regressed onto the corresponding NINO3 index : for historical SSTs, we used the extended SST analysis of Kaplan et al. [1998], while the model-generated NINO3 index was computed in the second example (GFDL H1). Results are shown in Fig 3.7 : the left-hand panels show the regression patterns of surface wind-stress (or velocity when analyses of stress were not available) on the normalized NINO3 index, and the right-hand ones show the linear correlation maps of the meridional component with NINO3. All datasets are in broad agreement that northeasterly winds tend to occur over the area of interest during periods of high NINO3. Nonetheless, the amplitudes are weak and it is necessary to establish whether any of these correlations are statistically significant. For the period 1860-2000, with monthly data smoothed over 3-months intervals, N ≥ 500, so the significance 95% threshold is |fl| ≥ 0.1. We found that correlations are significant at the 95% level everywhere in the POGA-ML ensemble mean (Fig 3.7, 2b), which very effectively isolates the response to tropical SST variability. The correlation is consistently high in this case, because of a dynamical linkage between the two basins : El Niño-induced changes in the latitudinal positions of the jets trigger changes to the transient eddy momentum fluxes in midlatitudes, which induce equatorward low-level flow at high latitudes, with a noticeable zonally-symmetric component [Seager et al., 2003, 2005a]. In nature, however, this signal is potentially swamped by atmospheric dynamics independent of ENSO. Indeed, we find in the surface wind analyses (3b) that the ENSO/North Atlantic connection is very weak north of ≥ 48◦ N . Repeating this analysis for five 50-year periods between 1860 and 2000 (sliding the window by 18 years each time), we found that this was due to a strong non-stationarity of the correlation in the northern parts of the basin : well above the 95% level in some decades, well below in some others. This result was also obtained for geostrophic wind fields derived from the sea-level pressure (SLP) data of Kaplan et al. [2000]. This could be due either to observational error (in SST, winds, as well as SLP) or to noise. However, we found that a similar non-stationarity occurred in the GFDL simulations H1, H2 and H3, which have no measurement error. Therefore local variability is to blame in lowering the observed correlation to NINO3. This therefore suggests the following interpretation : a link between the tropical Pacific and the North Atlantic is at work in nature as in the two GCMs, but it is of modest amplitude compared to the natural climate variability of the North Atlantic, which is quite energetic in the multidecadal spectral range. The consequence is that the statistical link only emerges on long timescales. The simulated and instrumental SLP data are consistent with this idea, albeit too short to be conclusive, and perhaps El Niño and the Earth’s climate: from decades to Ice Ages.

BIBLIOGRAPHY

155

veiled by the confounding influence of anthropogenic greenhouse gas increase.

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