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1
Response of the tropical Pacific to abrupt climate change 8,200 1
years ago 2
3
Alyssa R. Atwood1^* David S. Battisti2, C. M. Bitz2, Julian P. Sachs1 4
5 1 University of Washington, School of Oceanography, Seattle, WA 98195, USA 6 2 University of Washington, Dept. of Atmospheric Sciences, Seattle, WA 98195, USA 7
^ Now at: University of California, Berkeley, Dept. of Geography, Berkeley, CA 94720, 8
USA and Georgia Institute of Technology, School of Earth and Atmospheric Sciences, 9
Atlanta, GA 30332, USA 10
11
12
13
14
15
* Corresponding author. Tel: +1 206-694-3143; fax: +1 206-685-3351. 16
E-mail address: aatwood@berkeley.edu (Alyssa Atwood). 17
18
19
2
The relatively stable climate of the Holocene epoch (11,700 yr BP-present) was 20
punctuated by a period of large and abrupt climate change ca. 8,200 yr BP, when an 21
outburst of glacial meltwater into the Labrador Sea drove large and abrupt climate 22
changes across the globe. However, little is known about the response of the tropical 23
Pacific to this event. Here we present the first evidence for large perturbations to 24
the eastern tropical Pacific climate, based on sedimentary biomarker and hydrogen 25
isotopic records from a freshwater lake in the Galápagos Islands. We inform these 26
reconstructions with freshwater forcing simulations performed with the Community 27
Climate System Model version 4 in tandem with an intermediate complexity model 28
of the tropical Pacific. Together, the biomarker records and model simulations 29
provide evidence for a mechanistic link between (1) a southward shift of the 30
Intertropical Convergence Zone in the eastern equatorial Pacific and (2) decreased 31
frequency and/or intensity of Eastern Pacific El Niño events during the 8,200 BP 32
event. These results provide valuable insight into the controls of tropical Pacific 33
climate variability and the mechanisms behind the global response to abrupt climate 34
change. 35
36
The ability of the Earth’s climate system to undergo dramatic and rapid 37
reorganization on decadal timescales is exemplified by abrupt climate transitions, such as 38
those that occurred over the last glacial cycle in association with variations in the strength 39
of the Atlantic Meridional Overturning Circulation (AMOC). These events stretch our 40
understanding of the dynamical principles that govern the climate system, given the lack 41
of these events in the modern record and the inability of climate models to reproduce 42
such variability. The most recent event occurred ca. 8,200 yr BP during the relatively 43
stable climate of the Holocene epoch. Paleoclimate data and modeling studies suggest 44
that this event was caused by the catastrophic drainage of glacial lakes formed by the 45
retreating North American ice sheet into the Labrador Sea, which produced a sufficiently 46
large negative salinity anomaly to weaken the Atlantic Meridional Overturning 47
Circulation (AMOC) and cause dramatic cooling across the North Atlantic1-4. 48
Proxy records indicate that an estimated 163,000 km3 of glacial meltwater was 49
released through the Hudson Bay in one or two pulses during this event2,5-8. Polar ice and 50
3
marine records indicate that annual average surface temperatures dropped by 2-6 °C in 51
central Greenland (Fig. 1B)2,9 and by 1-3 °C in the North Atlantic Ocean and Europe 52
(Fig. 1C)6,7,10,11. The associated climate perturbations are generally thought to have 53
persisted for 100-150 years7,9,12, though some sedimentary records suggest that ocean 54
temperature and circulation anomalies in the North Atlantic may have followed a multi-55
step pattern over several centuries6. 56
The 8200 BP event is marked by reduced concentrations of atmospheric 57
methane13 (Fig. 1D), attributed to a decrease in tropical wetland area1,9 associated with a 58
southward shift of tropical continental rainfall13,14. Consistent with this interpretation, 59
proxy records from continental regions provide evidence for an intensified South 60
American summer monsoon15,16 (Fig. 1K), a weakened Indian (and possibly Asian) 61
summer monsoon15,17-20 (Fig. 1H,I), and arid conditions in the North Africa monsoon 62
region21-23. Taken together, this collection of continental hydroclimate records provide 63
evidence for a large-scale southward shift of tropical continental precipitation during the 64
8200 BP event. 65
However, little is known about the response of rainfall over the oceans, where 66
interactions between the tropical ocean and atmosphere produce low level wind 67
convergence and atmospheric convection that form organized rain bands known as 68
intertropical convergence zones (ITCZ). Robust ocean rainfall reconstructions during this 69
event are hindered by the low temporal resolution of marine sediments and the relatively 70
small changes in ocean chemistry and biology brought about by rainfall variations. In 71
particular, the behavior of tropical Pacific climate variability across the 8200 BP event is 72
poorly constrained. The tropical Pacific has a disproportionately large impact on global 73
climate on interannual timescales resulting from the El Niño/Southern Oscillation 74
(ENSO) and it is believed to play a fundamental role in driving global climate variability 75
on decadal to orbital timescales. Hence, improved constraints are needed on these key 76
features of the climate system to fully understand the nature of the global climate 77
response to this abrupt climate event. 78
Here we present a novel set of hydroclimate proxy records, based on the 79
accumulation rate and hydrogen isotopic composition of sedimentary lipid biomarkers 80
from El Junco Lake in the Galápagos Islands to reconstruct changes in eastern equatorial 81
4
Pacific rainfall over the last 9,100 years. These records provide evidence for large 82
perturbations to tropical Pacific hydroclimate associated with the eastern Pacific ITCZ 83
and El Niño events around the time of the 8200 BP event. 84
To support the interpretation of these rainfall reconstructions, we invoke climate 85
theory and simulations with a state-of-the-art climate system model. While dynamical 86
theory supports the proxy evidence of a climatological southward shift of the ITCZ in 87
response to abrupt North Atlantic cooling, a theoretical basis to support a concomitant 88
change in ENSO variability is less well developed, though a substantial body of literature 89
indicates that ENSO is modulated by changes in the mean state of the tropical 90
Pacific24,e.g. 25,26-28. In order to evaluate the linkage between abrupt North Atlantic 91
cooling, a southward shift of the Pacific ITCZ and changes in ENSO variability (as 92
suggested by the El Junco Lake rainfall reconstructions), we performed simulations with 93
the Community Climate System Model version 4 (CCSM4) in tandem with an 94
intermediate complexity model of the tropical Pacific that explicitly evaluates the 95
influence of tropical Pacific mean state changes on the climate variability. Through 96
pairing such paleoclimate records with dynamical theory and numerical models, we shed 97
new light on the mechanisms behind the global response to abrupt climate change. 98
99
EL JUNCO LAKE BIOMARKER RECORDS 100
El Junco Lake is located on San Cristóbal Island, Galápagos (1°S, 89°W) in the 101
heart of the eastern equatorial Pacific cold tongue (Fig. 2). Rainfall in the modern climate 102
is governed by the annual migration of the ITCZ on seasonal timescales and by increased 103
convection during eastern Pacific El Niño events on interannual timescales29. On long 104
timescales, rainfall at El Junco Lake is thus likely sensitive to long-term changes in both 105
the position of the eastern Pacific ITCZ and the intensity and frequency of eastern Pacific 106
El Niño events. 107
Based on the findings presented in Atwood and Sachs (2014) and updated in Atwood 108 30, a novel set of proxies based on the hydrogen isotopic composition 109
(δ2H=((2H/1H)sample/(2H/1H)std-1)*1000) and accumulation rate of phytoplankton lipids in 110
sediments are used to infer changes in (1) climatological total mean annual rainfall at El 111
5
Junco Lake ∆ (Fig. 3A), and (2) climatological rainfall at El Junco Lake associated 112
with El Niño events∆ (Fig. 3B; i.e. the total rain that fell during El Niño events 113
divided by the total amount of time in the sedimentary interval). Changes in 114
climatological rainfall that are not associated with changes in El Niño rainfall (which we 115
term residual rainfall, ∆ ; Fig. 3C) can be inferred for periods in which the inferred 116
change in total rainfall opposes the change in rainfall associated with El Niño events, 117
where we limit our interpretation of ∆ to its sign during such periods (see 118
Supplementary Material). ∆ is based on δ2H values of dinosterol, a biomarker 119
produced by a dinoflagellate of the genus Peridinium in El Junco Lake 31, while ∆ is 120
based on the accumulation rate of C34 botryococcene ( ), a biomarker produced by 121
a single race of the green algae Botryococcus braunii that appears to bloom in response to 122
decreased nutrient concentrations associated with overflowing conditions in El Junco 123
Lake during eastern Pacific El Niño events32 (see the Supplementary Material for more 124
information). 125
126
RECONSTRUCTED TROPICAL PACIFIC CLIMATE CHANGES DURING THE 8200 127
BP EVENT 128
The El Junco Lake biomarker records indicate that total mean annual rainfall at El 129
Junco Lake (∆ ) varied substantially over timescales of centuries to millennia during the 130
past 9,100 years (Fig. 3B). Of the 9100-year record, the highest inferred values of 131 ∆ occur from ca. 8500-8200 yr BP and are preceded and followed by lower rainfall ca. 132
8700 yr BP and 8100-7100 yr BP, respectively. Concomitant with this inferred increase 133
in total climatological rainfall, the sedimentary flux of botryococcenes decreased to some 134
of the lowest values of the 9100-yr record ca. 8500-8000 yr BP (Fig. 3B). We interpret 135
this low botryococcene flux as indicative of reduced frequency and/or intensity of El 136
Niño-driven B. braunii blooms during this time. From these El Junco Lake biomarker 137
records, we thus infer increased total rainfall but reduced rainfall associated with El Niño 138
events around the time of the 8200 BP event. 139
6
Wet conditions in the Galápagos Islands around the time of the 8200 BP event 140
coincide within age uncertainty to reduced climatological rainfall in Costa Rica33 (Fig. 141
1J), and increased climatological wind-driven upwelling in the Cariaco Basin34 (Fig. 1L), 142
which was interpreted by the authors as strengthened trade winds off the coast of 143
Venezuela. Collectively, these paleoclimate records provide strong evidence for a 144
climatological southward shift of the ITCZ in the eastern Pacific and western Atlantic 145
Oceans during the 8200 BP event (Fig. 1; Fig. 2). Importantly, the El Junco Lake 146
biomarker records provide the first evidence of a southward shifted ITCZ from a 147
maritime region that is free of monsoonal influences. 148
Concomitant with the inferred increase in climatological rainfall at El Junco Lake 149
(∆ ) ca. 8500-8000 yr BP (Fig. 1M), the low sedimentary flux of botryococcenes 150
provides evidence for reduced frequency and/or intensity of eastern Pacific El Niño 151
rainfall during this time (Fig. 1E). Further evidence of reduced El Niño rainfall comes 152
from grainsize records in El Junco Lake sediment35 (Fig. 1G). Akin to the botryococcene 153
flux variations, sedimentary sand fraction (% by mass) was interpreted by the authors as a 154
proxy for changes in the frequency and/or intensity of heavy rainfall that occurs at El 155
Junco Lake during strong El Niño events. The covariance between sand content and 156
botryococcene flux supports the existence of such a shared driver of variations in these 157
two proxy records through the Holocene (Fig. 1E,G). Low sand content coincident with 158
reduced botryococcene flux thus provides an independent estimate of reduced El Niño 159
rainfall around the time of the 8200 BP event. 160
A final line of evidence of reduced ENSO variability during the 8200 BP event 161
comes from a high-resolution speleothem record in Malaysian Borneo36 (Fig. 1F and Fig. 162
S1). Located in the western equatorial Pacific (4°N, 114°E), interannual rainfall 163
variability at the Bukit Assam cave site is primarily driven by ENSO, with rainfall 164
deficits and positive rainfall δ18O anomalies occurring during El Niño years36,37. Variance 165
in the 2-7-year bandpass filtered δ18Ocalcite (i.e. ENSO band) is shown in Fig. 1F 166
(comprised of six sub-annually resolved records of length 60-210 years spanning 8320-167
8140, 6740-6580, 5710-5630, 5230-5160, 3380-3230, 2600-2390 yr BP). These three 168
highly disparate proxy records (botryococcene concentration and sand content from a 169
Galápagos lake and speleothem δ18O from Borneo) show good overall correspondence 170
7
through the Holocene, with variable activity during the early Holocene (6-9 kyr BP), low 171
variability during the mid-Holocene (4-6 kyr BP) and increasing variability during the 172
late Holocene (2-4 kyr BP). The correspondence between these three proxy records 173
bolsters our interpretation of a common ENSO signal in these records. In addition, the 174
variance in Borneo speleothem δ18O reaches a minimum from 8220-8170 yr BP, relative 175
to all other 50-year intervals in the Holocene record, which falls within the (broader) 176
local minima in the El Junco Lake botryococcene concentration and sand records (Fig. 177
1E-G; Fig. S1). Taken together, these three records provide strong evidence of weak 178
ENSO variability across the tropical Pacific during the 8200 BP event. 179
TROPICAL PACIFIC RESPONSE TO ABRUPT NORTH ATLANTIC COOLING FROM 180
THEORY AND MODELS 181
It has been well established from theory and idealized climate model simulations 182
that the atmosphere responds to extratropical heating and cooling through meridional 183
displacements of the zonal mean ITCZ towards the heated hemisphere, in order to 184
balance the imposed hemispherically-asymmetric energy input38,39. A similar response is 185
seen in GCMs under a prescribed freshwater input to the North Atlantic, where a 186
southward shift of the zonal mean climatological position of the ITCZ follows a 187
weakening of the AMOC and expansion of Arctic sea ice40-43. However, while the zonal 188
mean ITCZ response is robust across models of varying complexity, the spatial 189
heterogeneity of the ITCZ response can be large. In particular, while the southward shift 190
of the Atlantic ITCZ is large and robust, the Pacific ITCZ response is typically weaker 191
and less robust across models. Importantly, the position of the Pacific ITCZ represents a 192
key feature of the background state of the tropical Pacific that modulates the 193
characteristics of ENSO. 194
While theory and modeling studies support the proxy evidence for a southward 195
shifted ITCZ during the 8200 BP event, a theoretical basis for the concomitant reduction 196
in El Niño rainfall variability is less clear. In order to evaluate this aspect of the tropical 197
Pacific response to a freshwater perturbation in the North Atlantic and thereby provide a 198
more robust interpretation of the El Junco Lake hydroclimate reconstructions, we 199
performed simulations with CCSM4 in tandem with the Linearized Ocean Atmosphere 200
8
Model44-46, an intermediate complexity model of the tropical Pacific that explicitly 201
evaluates the influence of tropical Pacific mean state changes on the coupled system. 202
In response to the large (1 Sv) prescribed freshwater perturbation in the North 203
Atlantic, the AMOC weakens in CCSM4 by 85% within 25 years after the hosing is 204
initiated and the Southern Ocean overturning cell extends into the North Atlantic (Fig. 205
S2). The AMOC weakening produces a large reduction in northward ocean heat transport 206
that causes the areal extent of sea ice to expand throughout the Arctic and sea surface 207
temperatures to decrease across the NH (and increase in the SH; Fig. S3A-F). The 208
resultant hemispherically asymmetric changes in surface energy fluxes that are initiated 209
by the weakening of the AMOC and intensified by Arctic sea ice growth produce a 210
strengthening (weakening) of the NH (SH) Hadley cell and a southward shift of the mean 211
annual position of the ITCZ. In association with these changes to the Hadley circulation, 212
an anomalous surface pressure gradient develops between the North and South Atlantic 213
that drives northerly wind anomalies in the tropical Atlantic (Fig. S3G-I). The evolution 214
of anomalous SSTs, surface pressure and winds is consistent with the mechanism of 215
equatorward cooling propagation proposed by Chiang and Bitz 47 and Cheng, et al. 41 216
whereby cooling is propagated from the North Atlantic high latitudes to the subtropics 217
through the ocean gyre circulation and further propagated to the tropical Atlantic through 218
the wind-evaporation-SST (WES) feedback.. 219
Large changes in the background state of the tropical Pacific are simulated in 220
CCSM4 in response to the freshwater perturbation. The WES feedback appears to 221
propagate the cooling into the tropical Pacific, whereby the increased surface pressure 222
gradient between the cooled tropical Atlantic and the tropical Pacific drives a 223
northeasterly wind anomaly on top of the background northeasterly trades, increasing the 224
surface wind speed and evaporative cooling across central America to the eastern and 225
central Pacific (Fig. 4A; Fig. S4). The amplitude of the annual cycle of SSTs in the Niño3 226
region is reduced by 40% under hosing. In the annual mean, surface easterlies increase by 227
20-70% near along the equator and in the tropical north Pacific, SST cools by ~1°C 228
across the tropical Pacific, the thermocline warms by up to 2°C along the equator in the 229
Pacific (Fig. 4), and the ITCZ shifts southward in the eastern and central Pacific (Fig. 5; 230
Fig. S5). In CCSM4, this southward ITCZ shift is the result of the weakening of the NH 231
9
ITCZ branch and strengthening and southward shift of the SH ITCZ branch in the central 232
and eastern Pacific. A southward shift of precipitation in the eastern Pacific and in 233
central-to-South America occurs in both the model simulations and the proxy records of 234
the 8200 BP event (Fig. 5). Although there is not perfect point-to-point correspondence 235
between the simulated and reconstructed rainfall changes, considering the strengths and 236
limitations of both the model and data sets, there is general agreement in their large-scale 237
dynamical context. 238
Associated with this southward ITCZ shift is an increase in easterly zonal wind 239
stress that maximizes under the northern branch of ITCZ and along the equator in the 240
eastern and central Pacific (Fig. 4B). This pattern of anomalous climatological zonal 241
wind stress is a key contributor to the ENSO response in CCSM4, as described in the 242
subsequent section. ENSO weakens under the freshwater release in the North Atlantic, as 243
demonstrated by a 23% reduction in the variance of Niño 3 SSTAs in CCSM4 hosed 244
simulations, which is significant at the 95% confidence interval using an F test of 245
variances (see Methods; Fig. S6). The reduction in variance is even more pronounced 246
(32%) when evaluated using the 2-7 year bandpass filtered time series (data not shown). 247
The southward shift of the Pacific ITCZ and weakened ENSO variability simulated by 248
CCSM4 under abrupt North Atlantic cooling agrees with the hydroclimate changes in 249
Galápagos Islands and Borneo during the 8200 BP event. 250
251
RESPONSE OF ENSO TO AN EQUATORWARD SHIFT OF THE PACIFIC ITCZ 252
In order to evaluate the impact of the simulated changes in the tropical Pacific 253
climatological annual cycle on ENSO, we performed simulations with the Linearized 254
Ocean Atmosphere Model (LOAM). Two simulations were performed in which the 255
tropical Pacific mean state variables were prescribed in LOAM based on the CCSM4 256
preindustrial control run and hosed run, respectively. The first EOF of tropical Pacific 257
SST variability in the CCSM4 and LOAM simulations is shown in Fig. S7. In agreement 258
with the fully coupled CCSM4, ENSO variability (as defined by variance of Niño 3 259
SSTAs) in LOAM decreased when the prescribed mean states from the CCSM4 control 260
were replaced by the mean states from the hosed run (Table 1), although the simulated 261
reduction in Niño 3 variance is considerably larger in LOAM (58%) than that in CCSM4 262
10
(23%). It is possible that nonlinearities in CCSM4 (which are absent in LOAM) may act 263
to compensate for the large (mean state induced) changes in linear stability, thereby 264
tempering the sensitivity of ENSO to the mean state changes in CCSM4. A comparison 265
of the eigenmodes of the coupled system in the two LOAM simulations demonstrates that 266
the decreased ENSO variance under hosing is due to a decrease in the growth rate of the 267
ENSO mode (as opposed to changes in the lower-order modes of the system; Table 1). 268
Sensitivity tests performed in LOAM demonstrate that the reduced growth rate of 269
the ENSO mode under hosing is primarily due to a deeper thermocline and secondarily 270
due to a more diffuse thermocline (decreased ), in the equatorial Pacific (Table 1; Fig. 271
4C). The growth rate is further reduced by the large-scale surface cooling in the tropical 272
Pacific (Fig. 4A). Details of the sensitivity tests can be found in the Supplementary 273
Material. Physically, the influence of these mean state changes can be understood in the 274
following way. In the first case, the equatorial subsurface warming decreases the 275
efficiency with which wind stress anomalies produce SST anomalies (as a given 276
thermocline depth anomaly on top of a deeper and more diffuse thermocline yields a 277
smaller change in SST). In the second case, the colder tropical Pacific mean state SSTs 278
decrease the response of the atmosphere to a given SST anomaly28. The source of the 279
subsurface warming appears to be associated with a dipole pattern in the windstress curl 280
anomaly along the equator, with negative (positive) windstress curl anomalies north 281
(south) of the equator (Fig. 4B). Following the theory of Clarke, et al. 48, the anomalous 282
windstress curl integrated along the northern and southern boundaries of the equatorial 283
Pacific basin between 5ºS-5ºN modify the warm water volume in the equatorial upper 284
ocean through Sverdrup transport. The dipole pattern of anomalous windstress curl is 285
thus consistent with the meridional transport of (warm) water into the equatorial upper 286
ocean, deepening the equatorial thermocline. 287
In summary, simulations with CCSM4 and an intermediate complexity model 288
support the available reconstructions of tropical Pacific climate change during the 8200 289
BP event and demonstrate a mechanistic link between abrupt North Atlantic cooling, a 290
southward shift of the Pacific ITCZ, and reduced ENSO variability. The simulations 291
indicate that the global climate response to North Atlantic freshwater release includes 292
hemispherically asymmetric changes in surface energy fluxes (initiated by the weakening 293
11
of the AMOC and intensified by the positive feedback from Arctic sea ice growth) that 294
produce a southward shift of the ITCZ and drive increased easterly trade winds across 295
central America and into the eastern and central tropical Pacific. Equatorial subsurface 296
warming occurs under the southward shift of the Pacific ITCZ through the associated 297
changes in wind stress curl. The coupled ocean-atmosphere interactions that give rise to 298
ENSO are stabilized by both the equatorial Pacific subsurface warming and by the large-299
scale surface cooling. 300
These simulations outline a clear set of mechanisms by which abrupt North 301
Atlantic cooling gives rise to reduced ENSO variability in CCSM4. In contrast to the 302
proxy data and the results from CCSM4, a number of the models used to examine the 303
impact of hosing in the Paleoclimate Model Intercomparison Project Phase 2 (PMIP2) 304
simulated increased ENSO variability under hosing43,49. Importantly, many of these 305
PMIP2 models (CCSM2, CCSM3, HadCM3, ECHAM5-OM1, GFDL CM2.1) had very 306
large biases in the mean state, annual cycle and interannual variability of the tropical 307
Pacific43. The CMIP5/PMIP3 models generally demonstrate notable improvements in 308
their representation of the mean state of the tropical Pacific50. While tropical Pacific 309
mean state biases are still present in CCSM4 (Fig. S8; Supplementary Material) the 310
CCSM4 model used in the present study is recognized as a top-performing GCM with 311
regard to its simulation of tropical Pacific climate variability50,51. The overarching 312
agreement between the simulated response of the tropical Pacific in CCSM4 and that 313
found in proxy reconstructions represents a notable advancement in our understanding of 314
the response of the tropical Pacific to abrupt climate change. Further, the proposed 315
relationship between changes in the mean climate, including the Pacific ITCZ, and 316
ENSO, may help uncover new frameworks with which to constrain the response of 317
ENSO in a future of global warming. 318
319
ACKNOWLEDGEMENTS 320
This material is based upon work supported by the U.S. National Science Foundation 321
under Grants ESH-0639640, EAR-0823503 and the U.S. National Oceanic and 322
Atmospheric Administration under Grant No. NOAA-NA08OAR4310685 to J. Sachs. A. 323
Atwood was supported by the National Science Foundation Graduate Research 324
12
Fellowship Program (DGE-1256082) and the Department of Energy Global Change 325
Education Program. D.S. Battisti was supported by a grant from the Tamaki Foundation. 326
We would like to acknowledge high-performance computing support from Yellowstone 327
(ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems 328
Laboratory, sponsored by the National Science Foundation. The authors would like to 329
thank Z. Zhang for the botryococcene data and for sample preparation and J. Conroy as 330
well as Z. Zhang for the age model. We thank the following for assistance in the field and 331
logistical support: P. Colinvaux, J. Overpeck, M. Steinitz-Kannan, J. Conroy, R. 332
Smittenberg, the Galápagos National Park, and the Charles Darwin Research Station. 333
Finally, we thank O. Kawka, D. Nelson, J. Gregersen, S. Wakeham, and J. Volkman for 334
laboratory support and useful discussions that improved this manuscript. 335
336
AUTHOR CONTRIBUTIONS 337
A.R.A. and J.P.S. developed the concept and design for the geochemical measurements. 338
A.R.A. carried out the geochemical analyses. A.R.A, C.M.B and D.S.B designed and 339
performed the model simulations. 340
341
METHODS 342
343
Data availability 344
The El Junco Lake biomarker data are available online at NOAA National Centers for 345
Environmental Information (https://www.ncdc.noaa.gov/data-access/paleoclimatology-346
data/datasets). 347
348
Field methods 349
Overlapping sediment cores to 372 cm depth were collected from El Junco Lake in 350
September 2004 from 6 m water-depth using a Nesje piston corer (9 cm dia.). An adapted 351
Livingston-type corer (7 cm dia.) was used to recover a 75 cm-long core with an undisturbed 352
sediment-water interface. Unconsolidated near-surface sediment was sectioned at 1 cm intervals 353
on-site and was stored frozen prior to analysis. Whole cores were transported to J. Overpeck’s 354
lab at the University of Arizona where they were split, imaged, and placed on a common depth 355
13
scale. The resulting composite sediment sequence was subsampled in 0.5 or 1 cm increments at 5 356
cm intervals. 357
358
Sediment age model 359
Details of the age model can be found in Zhang, et al. 32. Briefly, the upper 15 cm of the 360
interface core was dated with 210Pb, using a constant rate of supply model. 17 samples from the 361
Nesje cores and 4 samples from the interface core were selected for radiocarbon measurements. 362
Due to lack of sufficient macrofossils, bulk sediment (treated with acid and base to remove 363
carbonates and humics, resulting in “humin”) was used. Overlapping radiocarbon dates were used 364
to build the composite sedimentary sequence. Age model uncertainty was calculated following 365
the method of Anchukaitis and Tierney 52, in which a Monte Carlo approach was used to re-366
sample the probability distributions of the dated samples 10,000 times to create an ensemble of 367
age models. The probability distributions of the 210Pb-dated samples were taken to be Gaussian 368
functions while those of the 14C-dated samples were obtained from OxCal 4.2 calibrations (Bronk 369
Ramsey, 2009), using the ShCal04 curve. The 68% confidence interval from the 10,000-member 370
ensemble was used as the age uncertainty in Fig. 3. This age error ranged from a few years in the 371
upper layers of the sediment to 150 years in the deepest layers. 372
373
Lipid extraction and purification 374
Details of the lipid extraction and purification procedures can be found in the 375
Supplementary Material of Atwood and Sachs 29. Sediments were freeze-dried and heneicosanol 376
(n-C21 alcohol) was added as a recovery standard prior to accelerated solvent extraction (Dionex 377
ASE 200) using three cycles of dichloromethane (DCM) and methanol (MeOH) (9:1) at 100 °C 378
and 1500 psi. Neutral and polar lipids were separated from the total lipid extract on aminopropyl 379
SPE cartridges (Burdick & Jackson, 500 mg/4 mL) with DCM and isopropyl alcohol (IPA) (3:1 380
v/v), followed by 4% acetic acid (HOAc) in diethyl ether (Et2O). The neutral fraction was applied 381
to a column of 5% water-deactivated Si gel and separated into hydrocarbon, wax ester, 382
sterol/alcohol, and polar fractions with hexane, 10% ethyl acetate (EtOAc) in hexane, DCM, and 383
MeOH, respectively. Botryococcenes were further separated from the hydrocarbon fraction by 384
column chromatography as described in Zhang, et al. 32. Dinosterol was purified from the 385
sterol/alcohol fractions via normal phase (NP)- and/or reverse phase (RP)- high performance 386
liquid chromatography (HPLC) based on the methods presented in Atwood and Sachs (2012). 387
Biomarker identification and quantification methods can be found in the Supplementary Material 388
of Atwood and Sachs 29 and in Zhang, et al. 32. 389
14
390
δ2H measurements 391
Hydrogen isotope measurements of dinosterol were performed on a Finnigan Delta V 392
Plus Isotope Ratio Mass Spectrometer (IRMS) coupled to a Thermo Trace GC Ultra with a 393
Varian VF-17ms FactorFour capillary column (60 m x 0.32 mm x 0.25 μm) and a pyrolysis 394
reactor according to the methods described in Atwood and Sachs 31. Isotope values, expressed as 395
δ2H values, were calculated in Isodat software relative to VSMOW using a co-injection standard 396
containing n-C32, n-C40, and n-C44 of known δ2H values (obtained from A. Schimmelmann, 397
Indiana University, Bloomington, IN, USA). The measured isotope values of dinosterol were 398
corrected for the addition of hydrogen atoms (of known δ2H value) that occurred during 399
acetylation. The mean values and standard errors are reported from 3-6 analyses that were 400
typically performed on each sample (up to 11 analyses on samples that received different HPLC 401
purification procedures; see Atwood and Sachs 31). Instrument performance was evaluated using 402
H2 reference gas and a prepared standard of n-alkanes of known δ2H values. 403
404
CCSM4 configuration 405
We perform simulations with version 1 of the CCSM4 global atmosphere/ocean/land/ice 406
model, which employs the CAM4 atmospheric model (resolution of 0.9° latitude and 407
1.25°�longitude) and the POP2 ocean model (resolution is uniform in longitude at 1.13°�and 408
varies in latitude from 0.27°-0.65°). The model configuration matches the 1,000-year CESM1 409
(also known as CCSM4) preindustrial control run, which has been well studied and existing 410
biases of the tropical Pacific climatology and variability are well known51. While some ENSO 411
characteristics in CCSM4 compare well with observations, substantial ENSO biases in CCSM4 412
exist, including overly strong variance and periodicity in CCSM4 relative to observations. 413
However, mean state biases and ENSO biases are substantially smaller in CCSM4 at 1°� 414
resolution (employed here) as compared to 2°�resolution51. 415
The hosing runs are branched from the 1,000-year pre-industrial control run of CCSM4. 416
In these runs, a prescribed freshwater flux of 1.0 Sv was applied for a period of 100 years across 417
the surface of the northern North Atlantic (50°-70°N). This freshwater flux was used in the 418
PMIP2 hosing simulations (https://pmip2.lsce.ipsl.fr/). Although this method likely overestimates 419
the total freshwater flux to the North Atlantic as compared to that during the 8200 BP event 420
(recent estimates suggest 5.2 Sv over approximately 1 year5), our target is a large reduction in the 421
AMOC over a long enough period of time to obtain robust ENSO statistics. All other boundary 422
15
conditions are fixed at 1850 AD values. An ensemble of four hosing experiments were 423
performed, each integrated for 100 years in order to evaluate the robustness of the ENSO 424
response to changes in initial conditions and allow for a robust representation of the ensemble 425
mean and spread. 426
427
Description of the Linearized Ocean Atmosphere Model 428
The Linearized Ocean Atmosphere Model (LOAM)44,45 is a linearized variant of the 429
Zebiak and Cane 24 intermediate complexity model of the tropical Pacific, updated to include 430
observationally constrained parameter values and observed climatological mean fields. LOAM is 431
comprised of a linearized two-layer atmosphere model (assumed to be in equilibrium with respect 432
to underlying ocean) coupled to a 1.5-layer ocean model governed by the linear shallow water 433
equations on an equatorial β-plane with an active upper layer that governs the evolution of SST. 434
The heating of the atmosphere is a function of SST and surface wind convergence53; the forcing 435
of the ocean is by the wind stress. 436
The dynamics are governed by: 437
/ = ( ) + (1) 438
where x is the state of the system (consisting of the dependent variables for the ocean: meridional 439
and zonal current, upwelling, thermocline depth, and SST perturbations), M(t) is the monthly 440
(and spatially) varying dynamical system matrix, and F is a stochastic forcing. The equations that 441
define the dynamical system matrix are linearized around the seasonally-varying climatological 442
mean state of the atmosphere and ocean. The coupled modes of the system are found by 443
decomposing the annual propagator matrix (R = eMt) into its Floquet modes (eigenmodes of the 444
cyclo-stationary annual propagator matrix). Each mode has an associated period and decay rate 445
that is derived from the eigenvalues of the mode. Thompson and Battisti 54 and Roberts and 446
Battisti 45 demonstrated that LOAM with observed background states supports a leading mode of 447
the coupled system that has a similar spatial structure, decay rate, and period to that observed. 448
The leading (slowest-decaying) Floquet mode in LOAM is thus referred to as the ENSO mode. 449
Further details on LOAM can be found in Roberts 55, Roberts and Battisti 45, and Atwood, et al. 46. 450
A summary of the constants and tuning parameters used in LOAM are provided in the 451
Supplementary material (Table S1; Fig. S9). 452
In the present study, the tropical Pacific mean states from 100 years of the CCSM4 pre-453
industrial control and all 100 years of one of the four hosed simulations are prescribed in LOAM 454
to evaluate the influence of the mean state changes on ENSO dynamics. Further, sensitivity tests 455
are performed to determine which of the mean state changes are responsible for the changes in 456
16
ENSO, by comparing ENSO properties in LOAM run with climatological mean states from the 457
control run to those in LOAM run with individually substituted mean states from the hosed runs. 458
459
Statistical Information 460
Following Russon, et al. 56 and von Storch and Zwiers 57, the significance of the 461
relative change in the variance of Niño3 SSTAs under North Atlantic hosing was 462
assessed by comparing the ratio of the sample variances (2 2) to the F distribution 463
with degrees of freedom given by k1/τ1 -1 and k2/τ2 -1, where k1 and k2 are the number of 464
time steps in 320 years of the CESM preindustrial control run and 320 years of the 465
compiled hosed run, respectively, and τ1 and τ2 represent the characteristic time scales, 466
which were obtained from the empirical estimate of the autocorrelation function of each 467
time series ( ), summed over the number of lag intervals equivalent to the first two 468
changes in sign (L). 469
= ; = − 1, − 1 ; = 1 + 2 (1) Using this framework: k1 = k2 = 3840 mo, τ1 = 15 mo, τ2 = 13 mo, F =
2 2 470
1.11° 0.87° = 1.27 (Fig. S6), and ( − 1, − 1) = 1.22at the 95% quantile. 471
Thus, the null hypothesis that the two sample variances are equal is rejected at the 95% 472
confidence level. 473
474
475
17
Tables and Figures 476
477 Table 1. Period and growth rate of the ENSO mode, and variance of Niño 3 SSTAs (3-478
month running mean) from the LOAM simulations with prescribed mean states from 479
100 years of the CESM preindustrial control run (Control) and a 100-year CESM 480
hosed run (Hosed). Control + X indicates LOAM run with all mean state fields from 481
the CESM preindustrial control run except X, and instead X is taken from the CESM 482
hosed run. u,v-vel represent the mean horizontal ocean currents averaged over the top 483
50m of the ocean, w-vel represents the mean upwelling at 50m, and U,V-winds 484
represent the zonal and meridional near-surface wind fields (i.e. lowest vertical level in 485
CAM). “subsfc temp” denotes a suite of mean state fields and parameter values that 486
affect the rate of surface warming due to entrainment of water below the surface mixed 487
layer; these include the depth of the climatological thermocline (ℎ), the mean vertical 488
temperature gradient at the base of the surface mixed layer ( ), and a set of parameter 489
values (Γ) that relate variations in temperature at the base of the mixed layer to 490
variations in thermocline depth (see Eqn. 12 of Roberts and Battisti 45). The variance 491
of Niño 3 SSTAs in 320 years of the CESM preindustrial control run and the last 80 492
years of the hosed CESM run (the ensemble member from which mean states were 493
prescribed in LOAM) is indicated in the top two rows. 494
Model Mean States Mode Period (yr)
Mode Growth (yr-1)
Variance (°C2)
CESM Control -- -- -- 1.11 CESM Hosed -- -- -- 0.72
LOAM
CESM Control 3.97 0.57 1.11 CESM Hosed 3.35 0.41 0.46 Control + SST
(atm only) (ocn only)
3.70 (3.87) (3.78)
0.52 (0.51) (0.58)
0.80 (0.84) (1.07)
Control + u,v,w-vel 4.69 0.57 1.54 Control + U,V-winds 4.00 0.71 2.20
Control + subsfc temp ( only)
(ℎ + Γ only)
3.43 (4.05) (3.35)
0.34 (0.52) (0.37)
0.32 (0.91) (0.37)
19
Figure 1. El Junco Lake biomarker records in comparison to North Atlantic and 496
tropical hydroclimate records encompassing the 8200 BP event. The highlighted 497
color bar and the diamond with error bars represent the timing of the terminal 498
Lake Agassiz outburst at 8.33 kyr with error ranges from 8.04 – 8.49 kyr 8. A) 499
Mean sortable silt size from a deep-sea sediment core in the subpolar North 500
Atlantic (core MD99-2251) indicating flow speed of the Iceland Scotland 501
Overflow Water (ISOW)6; B) bidecadal δ18O values of ice from the GISP2 ice 502
core58; C) subpolar North Atlantic SST reconstruction based on the relative 503
abundance of the polar foraminifer Neogloboquadrina pachyderma sinistral 504
coiling from core MD99-22516; D) mean atmospheric CH4 concentrations from 505
the Greenland ice core GRIP59; E) botryococcene accumulation rate from this 506
study (plotted on a log scale; modified from Zhang, et al. 32); F) variance of 2-7 yr 507
bandpass filtered speleothem δ18O (sub-annually resolved) in 60-yr sliding 508
windows from Bukit Assam Cave in Malaysian Borneo36 (red). The yellow circles 509
represent the variance across each entire high-resolution window and the yellow 510
square represents the variance across the 60-yr period following the large δ18O 511
enrichment at 8230 yr BP (see Fig. S1 for an enlarged view of these data); (G) 512
percent sand in El Junco Lake sediments 35; H) speleothem δ18O from Dongge 513
Cave, China20; I) speleothem δ18O from Qunf Cave, Oman60; J) speleothem δ18O 514
from Venado Cave, Costa Rica33; K) speleothem δ18O from Lapa Grande, 515
Brazil16; L) Cariaco Basin grey scale record34; M) dinosterol δ2H from El Junco 516
Lake, Galápagos (this study). 517
20
518
519
520
521
Figure 2. Climatological precipitation averaged over A) Aug–Oct and B) Feb–522
April for the period 1980–2010. Precipitation data are from GPCP Version 2.2 523
Combined Precipitation Data Set for the period 1979-2010 524
(http://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html). The proxy records 525
referred to in this study are indicated by the colored symbols, which consist of the 526
dinosterol δD record from El Junco Lake, Galápagos (1°S, 89°W; this study), 527
greyscale record from the Cariaco Basin34, speleothem δ18O and Mg/Ca from 528
Heshang Cave, China61 and speleothem δ18O from Venado Cave, Costa Rica33, 529
Dongge Cave, China20, Paixão Cave and Padre Cave, eastern Brazil15, Lapa 530
Grande Cave, Brazil16, Cueva del Tigre Perdido, Peru62, and Bukit Assam Cave 531
21
in Malaysian Borneo36. The changes in climatological precipitation and upwelling 532
associated with the 8200 BP event (compared to prior and after the event) are 533
noted by the color coding. 534
535 536
537
Figure 3. El Junco Lake biomarker records with the timing of Lake Agassiz 538
drainage highlighted. A) dinosterol δ2H (∆ ), indicative of changes in total 539
precipitation; B) botryococcene accumulation rate (plotted on a log scale; (∆ ), 540
indicative of changes in precipitation due to changes in the frequency and/or 541
amplitude of El Niño events adapted from Zhang, et al. 32; C) the sign of the 542
inferred change in climatological rainfall due to processes not associated with El 543
Niño (∆ ). Circles at the top of the figure represent the age control points of the 544
sediment record; unfilled circles indicate 14C dates, filled circles indicate 210Pb 545
dates. The highlighted color bar (and the diamond with error bars near the top of 546
the figure) represent the timing of the terminal Lake Agassiz outburst at 8.33 kyr 547
with error ranges from 8.04–8.49 kyr 8. 548
549
550
22
551 552 553 554
555 556
Figure 4. Tropical Pacific upper ocean and surface wind response to hosing in the 557
CESM. A) SST (colors) and near-surface wind velocity (vectors); B) wind stress 558
curl (colors) and change in zonal wind stress (unfilled contours; contour spacing is 559
0.005 N/m2 and the negative contours are dashed) with the region from 5ºS to 5ºN 560
highlighted (colors poleward of this region have been muted for clarity); C) mean 561
subsurface temperature (colors) averaged across the equatorial region (2ºS to 2ºN) 562
and mean subsurface temperature from the control run (unfilled contours; contour 563
spacing is 2ºC) where the bold line represents the 20ºC isotherm. Changes were 564
23
calculated by subtracting the average over last 80 years of the four hosed ensemble 565
members from the average of 320 years of the preindustrial control run. 566
567 568
569 570 Figure 5. Tropical precipitation response to hosing in the CESM (colored contours) 571
compared to the response inferred from hydroclimate proxy records during the 8200 BP 572
event (colored symbols; see Fig. 2 for references). Climatological precipitation in the 573
CESM control run is indicated by the unfilled contours (contour interval is 4 mm/day; the 574
4 mm/day contour is solid and the 8 mm/day contour is dashed). 575
24
References (including Methods) 576
577
1 Alley, R. B. et al. Holocene climatic instability: A prominent, widespread event 578 8200 yr ago. Geology 25, 483-486, doi:10.1130/0091-579 7613(1997)025<0483:hciapw>2.3.co;2 (1997). 580
2 Barber, D. C. et al. Forcing of the cold event of 8,200 years ago by catastrophic 581 drainage of Laurentide lakes. Nature 400, 344-348, doi:10.1038/22504 (1999). 582
3 LeGrande, A. N. et al. Consistent simulations of multiple proxy responses to an 583 abrupt climate change event. Proc. Nat. Acad. Sci. U.S.A. 103, 837-842, 584 doi:10.1073/pnas.0510095103 (2006). 585
4 Rohling, E. J. & Palike, H. Centennial-scale climate cooling with a sudden cold 586 event around 8,200 years ago. Nature 434, 975-979, doi:10.1038/nature03421 587 (2005). 588
5 Teller, J. T., Leverington, D. W. & Mann, J. D. Freshwater outbursts to the oceans 589 from glacial Lake Agassiz and their role in climate change during the last 590 deglaciation. Quat. Sci. Rev. 21, 879-887, doi:10.1016/s0277-3791(01)00145-7 591 (2002). 592
6 Ellison, C. R. W., Chapman, M. R. & Hall, I. R. Surface and deep ocean 593 interactions during the cold climate event 8200 years ago. Science 312, 1929-594 1932, doi:10.1126/science.1127213 (2006). 595
7 Kleiven, H. F. et al. Reduced North Atlantic Deep Water coeval with the glacial 596 Lake Agassiz freshwater outburst. Science 319, 60-64, 597 doi:10.1126/science.1148924 (2008). 598
8 Lewis, C. F. M., Miller, A. A. L., Levac, E., Piper, D. J. W. & Sonnichsen, G. V. 599 Lake Agassiz outburst age and routing by Labrador Current and the 8.2 cal ka 600 cold event. Quat. Int. 260, 83-97, doi:10.1016/j.quaint.2011.08.023 (2012). 601
9 Kobashi, T., Severinghaus, J. P., Brook, E. J., Barnola, J. M. & Grachev, A. M. 602 Precise timing and characterization of abrupt climate change 8200 years ago from 603 air trapped in polar ice. Quat. Sci. Rev. 26, 1212-1222, 604 doi:10.1016/j.quascirev.2007.01.009 (2007). 605
10 Hoffman, J. S. et al. Linking the 8.2 ka event and its freshwater forcing in the 606 Labrador Sea. Geophys. Res. Lett. 39, doi:10.1029/2012gl053047 (2012). 607
11 Morrill, C. & Jacobsen, R. M. How widespread were climate anomalies 8200 608 years ago? Geophys. Res. Lett. 32, doi:10.1029/2005gl023536 (2005). 609
12 Daley, T. J. et al. The 8200 yr BP cold event in stable isotope records from the 610 North Atlantic region. Global Planet. Change 79, 288-302, 611 doi:10.1016/j.gloplacha.2011.03.006 (2011). 612
13 Lea, D. W., Pak, D. K., Peterson, L. C. & Hughen, K. A. Synchroneity of tropical 613 and high-latitude Atlantic temperatures over the last glacial termination. Science 614 301, 1361-1364, doi:10.1126/science.1088470 (2003). 615
14 Chappellaz, J. et al. Synchronous changes in atmospheric CH4 and Greenland 616 climate between 40 kyr and 8 kyr BP. Nature 366, 443-445, 617 doi:10.1038/366443a0 (1993). 618
25
15 Cheng, H. et al. Timing and structure of the 8.2 kyr BP event inferred from delta 619 O-18 records of stalagmites from China, Oman, and Brazil. Geology 37, 1007-620 1010, doi:10.1130/g30126a.1 (2009). 621
16 Strikis, N. M. et al. Abrupt variations in South American monsoon rainfall during 622 the Holocene based on a speleothem record from central-eastern Brazil. Geology 623 39, 1075-1078, doi:10.1130/g32098.1 (2011). 624
17 Fleitmann, D. et al. Holocene ITCZ and Indian monsoon dynamics recorded in 625 stalagmites from Oman and Yemen (Socotra). Quat. Sci. Rev. 26, 170-188, 626 doi:10.1016/j.quascirev.2006.04.012 (2007). 627
18 Hong, Y. T. et al. Correlation between Indian Ocean summer monsoon and North 628 Atlantic climate during the Holocene. Earth Planet. Sci. Lett. 211, 371-380, 629 doi:10.1016/s0012-821x(03)00207-3 (2003). 630
19 Pausata, F. S. R., Battisti, D. S., Nisancioglu, K. H. & Bitz, C. M. Chinese 631 stalagmite delta O-18 controlled by changes in the Indian monsoon during a 632 simulated Heinrich event. Nat. Geosci. 4, 474-480, doi:10.1038/ngeo1169 (2011). 633
20 Wang, Y. J. et al. The Holocene Asian monsoon: Links to solar changes and 634 North Atlantic climate. Science 308, 854-857, doi:10.1126/science.1106296 635 (2005). 636
21 Gasse, F. Hydrological changes in the African tropics since the Last Glacial 637 Maximum. Quat. Sci. Rev. 19, 189-211, doi:10.1016/s0277-3791(99)00061-x 638 (2000). 639
22 Shanahan, T. M. et al. Paleoclimatic variations in West Africa from a record of 640 late Pleistocene and Holocene lake level stands of Lake Bosumtwi, Ghana. 641 Palaeogeog. Palaeoclimatol. Palaeoecol. 242, 287-302, 642 doi:10.1016/j.palaeo.2006.06.007 (2006). 643
23 Thompson, L. G. et al. Kilimanjaro ice core records: Evidence of Holocene 644 climate change in tropical Africa. Science 298, 589-593, 645 doi:10.1126/science.1073198 (2002). 646
24 Zebiak, S. E. & Cane, M. A. A model El Nino-Southern Oscillation. Monthly 647 Weather Review 115, 2262-2278, doi:10.1175/1520-648 0493(1987)115<2262:ameno>2.0.co;2 (1987). 649
25 Battisti, D. S. & Hirst, A. C. Interannual variability in a tropical atmosphere ocean 650 model- influence of the basic state, ocean geometry and nonlinearity. J. Atmos. 651 Sci. 46, 1687-1712, doi:10.1175/1520-0469(1989)046<1687:iviata>2.0.co;2 652 (1989). 653
26 Clement, A. C., Seager, R., Cane, M. A. & Zebiak, S. E. An ocean dynamical 654 thermostat. J. Clim. 9, 2190-2196, doi:10.1175/1520-655 0442(1996)009<2190:aodt>2.0.co;2 (1996). 656
27 Fedorov, A. V. & Philander, S. G. A stability analysis of tropical ocean-657 atmosphere interactions: Bridging measurements and theory for El Nino. J. Clim. 658 14, 3086-3101, doi:10.1175/1520-0442(2001)014<3086:asaoto>2.0.co;2 (2001). 659
28 Roberts, W. H. G., Battisti, D. S. & Tudhope, A. W. ENSO in the Mid-Holocene 660 according to CSM and HadCM3. J. Clim. 27, 1223-1242, doi:10.1175/jcli-d-13-661 00251.1 (2014). 662
26
29 Atwood, A. R. & Sachs, J. P. Separating ITCZ- and ENSO-related rainfall 663 changes in the Galápagos over the last 3 kyr using D/H ratios of multiple lipid 664 biomarkers. Earth Planet. Sci. Lett. 404, 408-419 (2014). 665
30 Atwood, A. R. Mechanisms of Tropical Pacific Climate Change During the 666 Holocene.” University of Washington, (2015). 667
31 Atwood, A. R. & Sachs, J. P. Purification of dinosterol from complex mixtures of 668 sedimentary lipids for hydrogen isotope analysis. Org. Geochem. 48, 37-46, 669 doi:10.1016/j.orggeochem.2012.04.006 (2012). 670
32 Zhang, Z. H., Leduc, G. & Sachs, J. P. El Nino evolution during the Holocene 671 revealed by a biomarker rain gauge in the Galapagos Islands. Earth Planet. Sci. 672 Lett. 404, 420-434, doi:10.1016/j.epsl.2014.07.013 (2014). 673
33 Lachniet, M. S. et al. Tropical response to the 8200 yr BP cold event? Speleothem 674 isotopes indicate a weakened early Holocene monsoon in Costa Rica. Geology 32, 675 957-960, doi:10.1130/g20797.1 (2004). 676
34 Hughen, K. A., Overpeck, J. T., Peterson, L. C. & Trumbore, S. Rapid climate 677 changes in the tropical Atlantic region during the last deglaciation. Nature 380, 678 51-54, doi:10.1038/380051a0 (1996). 679
35 Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M. & Steinitz-Kannan, 680 M. Holocene changes in eastern tropical Pacific climate inferred from a 681 Galapagos lake sediment record. Quat. Sci. Rev. 27, 1166-1180, 682 doi:10.1016/j.quascirev.2008.02.015 (2008). 683
36 Chen, S., Hoffmann, S. S., Lund, D. C., Cobb, K. M. & Emile-Geay, J. A high-684 resolution speleothem record of western equatorial Pacific rainfall: Implications 685 for Holocene ENSO evolution. Earth Planet. Sci. Lett. 442, 61-71 (2016). 686
37 Moerman, J. W. et al. Diurnal to interannual rainfall δ18O variations in northern 687 Borneo driven by regional hydrology. Earth Planet. Sci. Lett. 369-370 (2013). 688
38 Frierson, D. M. W. & Hwang, Y. T. Extratropical Influence on ITCZ Shifts in 689 Slab Ocean Simulations of Global Warming. J. Clim. 25, 720-733, 690 doi:10.1175/jcli-d-11-00116.1 (2012). 691
39 Kang, S. M., Held, I. M., Frierson, D. M. W. & Zhao, M. The response of the 692 ITCZ to extratropical thermal forcing: Idealized slab-ocean experiments with a 693 GCM. J. Clim. 21, 3521-3532, doi:10.1175/2007jcli2146.1 (2008). 694
40 Zhang, R. & Delworth, T. L. Simulated tropical response to a substantial 695 weakening of the Atlantic thermohaline circulation. J. Clim. 18, 1853-1860, 696 doi:10.1175/jcli3460.1 (2005). 697
41 Cheng, W., Bitz, C. M. & Chiang, J. C. H. Adjustment of the global climate 698 system to abrupt changes in the Atlantic meridional overturning circulation. 699 Ocean Circulation: Mechanisms and Impacts, AGU Geophysical Monograph 700 Series 173, 295-313, doi:10.1029/173GM19 (2007). 701
42 Otto-Bliesner, B. L. & Brady, E. C. The sensitivity of the climate response to the 702 magnitude and location of freshwater forcing: last glacial maximum experiments. 703 Quat. Sci. Rev. 29, 56-73, doi:10.1016/j.quascirev.2009.07.004 (2010). 704
43 Timmermann, A. et al. The influence of a weakening of the Atlantic meridional 705 overturning circulation on ENSO. J. Clim. 20, 4899-4919, doi:10.1175/jcli4283.1 706 (2007). 707
27
44 Thompson, C. J. & Battisti, D. S. A linear stochastic dynamical model of ENSO. 708 Part I: Model development. J. Clim. 13, 2818-2832, doi:10.1175/1520-709 0442(2000)013<2818:alsdmo>2.0.co;2 (2000). 710
45 Roberts, W. H. G. & Battisti, D. S. A new tool for evaluating the physics of 711 coupled atmosphere-ocean variability in nature and in general circulation models. 712 Clim. Dyn. 36, 907-923, doi:10.1007/s00382-010-0762-x (2011). 713
46 Atwood, A. R., Battisti, D. S., Wittenberg, A. T., Roberts, W. H. G. & Vimont, D. 714 J. Characterizing unforced multi-decadal variability of ENSO: A case study with 715 the GFDL CM2.1 coupled GCM. J. Clim. 29, 1161-1178, doi:10.1007/s00382-716 016-3477-9 (2016). 717
47 Chiang, J. C. H. & Bitz, C. M. Influence of high latitude ice cover on the marine 718 Intertropical Convergence Zone. Clim. Dyn. 25, 477-496, doi:10.1007/s00382-719 005-0040-5 (2005). 720
48 Clarke, A. J., Van Gorder, S. & Colantuono, G. Wind stress curl and ENSO 721 discharge/recharge in the equatorial Pacific. J. Phys. Oceanogr. 37, 1077-1091, 722 doi:10.1175/jpo3035.1 (2007). 723
49 Dong, B. & Sutton, R. T. Enhancement of ENSO variability by a weakened 724 Atlantic thermohaline circulation in a coupled GCM. J. Clim. 20, 4920-4939, 725 doi:10.1175/jcli4284.1 (2007). 726
50 Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO 727 representation in climate models: from CMIP3 to CMIP5. Clim. Dyn. 42, 1999-728 2018, doi:10.1007/s00382-013-1783-z (2014). 729
51 Deser, C. et al. ENSO and Pacific Decadal Variability in the Community Climate 730 System Model Version 4. J. Clim. 25, 2622-2651, doi:10.1175/jcli-d-11-00301.1 731 (2012). 732
52 Anchukaitis, K. J. & Tierney, J. E. Identifying coherent spatiotemporal modes in 733 time-uncertain proxy paleoclimate records. Clim. Dyn. 41, 1291-1306 (2013). 734
53 Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. Roy. 735 Meteorol. Soc. 106, 447-462, doi:10.1256/smsqj.44904 (1980). 736
54 Thompson, C. J. & Battisti, D. S. A linear stochastic dynamical model of ENSO. 737 Part II: Analysis. J. Clim. 14, 445-466, doi:10.1175/1520-738 0442(2001)014<0445:alsdmo>2.0.co;2 (2001). 739
55 Roberts, W. An Investigation into the Causes for the Reduction in the Variability 740 of the El Niño-Southern Oscillation in the Early Holocene in a Global Climate 741 Model Ph.D. thesis, University of Washington, (2007). 742
56 Russon, T., Tudhope, A. W., Hegerl, G. C., Schurer, A. & Collins, M. Assessing 743 the Significance of Changes in ENSO Amplitude Using Variance Metrics. J. 744 Clim. 27, 4911-4922, doi:10.1175/jcli-d-13-00077.1 (2014). 745
57 von Storch, H. & Zwiers, F. W. Statistical Analysis in Climate Research. 746 (Cambridge Univ. Press, 2003). 747
58 Stuiver, M. & Grootes, P. M. GISP2 oxygen isotope ratios. Quat. Res. 53, 277-748 283, doi:10.1006/qres.2000.2127 (2000). 749
59 Blunier, T., Chappellaz, J., Schwander, J., Stauffer, B. & Raynaud, D. Variations 750 in atmospheric methane concentration during the Holocene epoch. Nature 374, 751 46-49, doi:10.1038/374046a0 (1995). 752
28
60 Fleitmann, D. et al. Holocene forcing of the Indian monsoon recorded in a 753 stalagmite from Southern Oman. Science 300, 1737-1739, 754 doi:10.1126/science.1083130 (2003). 755
61 Liu, Y.-H. et al. Links between the East Asian monsoon and North Atlantic 756 climate during the 8,200 year event. Nat. Geosci. 6, 117-120, 757 doi:10.1038/NGEO1708 (2013). 758
62 van Breukelen, M. R., Vonhof, H. B., Hellstrom, J. C., Wester, W. C. G. & 759 Kroon, D. Fossil dripwater in stalagmites reveals Holocene temperature and 760 rainfall variation in Amazonia. Earth Planet. Sci. Lett. 275, 54-60, 761 doi:10.1016/j.epsl.2008.07.060 (2008). 762
763
Response of the tropical Pacific to abrupt climate change 8,200 years ago
Alyssa R. Atwood, David S. Battisti, C. M. Bitz, Julian P. Sachs
Extended Data (supplementary figures)
Figure S1. A) Speleothem δ18O (sub-annually resolved) from Bukit Assam Cave in
Malaysian Borneo 1; B) the variance of the 2-7 yr bandpass filtered time series in 60-yr
sliding windows (centered on the mid-point of each window; as in Fig. 1 of the
manuscript). The yellow square represents the variance in the 60-yr period following the
large δ18O enrichment at 8230 yr BP and the yellow circle represents the variance across
the entire 180-yr high resolution window (as in Fig. 1).
Figure S2. Mean meridional streamfunction in the Atlantic in the CESM control and hosed
simulations: A) averaged over 320 years of the control simulation; B) averaged over the
last 80 years of the four hosed ensemble members.
Figure S3. Time evolution of the change in: A-C) sea ice concentration; D-F) SST (colors)
and wind stress (vectors); G-I) surface pressure (colors) and surface wind velocity (vectors)
in the CESM hosed runs with respect to 320 years of the control run. Time snapshots are
averages over: Year 0-1 (top), Year 10-11 (middle) and Year 50-51 (bottom). Each panel
shows the average of the four ensemble members.
Figure S4. Surface pressure (contours) from the control CESM integration and the
ensemble mean changes in the surface pressure (colors) and near-surface wind velocity
(vectors) in the CESM due to hosing. Changes were calculated by subtracting the average
over last 80 years of the four hosed ensemble members from the average of 320 years of
the preindustrial control run.
Figure S5. Tropical Pacific precipitation (colors) and zonal wind speed (unfilled
contours; m/s) in observations (top row), the CESM preindustrial control run (middle
row) and the hosed runs (bottom row) averaged over: A) the annual cycle; B) boreal fall
(Aug-Oct); and C) boreal spring (Feb-Apr). The observed climatological precipitation is
from GPCP Version 2.2 Combined Precipitation Data Set (averaged over 1979-2010) and
the observed surface winds are from ERA40 Reanalysis data. The control climatology is
the average of the 320-yr control PI CESM integration. The hosing climatology is the
ensemble average of the four 80-year hosing simulations.
Figure S6. Time series of 3-month running mean Niño 3 SSTAs in: A) 320 years of the
CESM preindustrial control run; B) the last 80 years of the four CESM hosed ensemble
members; C) 320 years of the LOAM with mean states from the preindustrial control run;
D) 320 years of the LOAM with mean states from one of the CESM hosed run.
Figure S7. EOF1 of tropical Pacific SSTAs in: A) 80 years of the preindustrial control
run of CESM; B) the last 80 years of one of the CESM hosed ensemble members; C) 80
years of the LOAM with mean states from the CESM preindustrial control run; D) 80
years of the LOAM with mean states from one of the CESM hosed ensemble members.
The fraction of total variance captured by each pattern is indicated in bold. The unfilled
contours in panels B and D are EOF1 from the respective control run. The LOAM SST
output was interpolated to the CESM ocean grid and smoothed with a 1–2–1 filter to
reduce the noise, as in Zebiak and Cane 2, Thompson 3 and Thompson 4.
Figure S8. Tropical Pacific SST and surface wind biases in the CESM preindustrial
control run. A) Mean annual SST and surface winds in observations; B) SST and surface
wind difference in 100 years of the CESM preindustrial control run relative to
observations. Observed SST data is from NOAA ERSST v3b (1971-2010) and observed
surface winds are from ERA40 Reanalysis data.
Figure S9. Ocean efficiency factors δT (left) and δW (right) in LOAM.
References
1 Chen, S., Hoffmann, S. S., Lund, D. C., Cobb, K. M. & Emile-Geay, J. A high-resolution speleothem record of western equatorial Pacific rainfall: Implications for Holocene ENSO evolution. Earth Planet. Sci. Lett. 442, 61-71 (2016).
2 Zebiak, S. E. & Cane, M. A. A model El Nino-Southern Oscillation. Monthly Weather Review 115, 2262-2278, doi:10.1175/1520-0493(1987)115<2262:ameno>2.0.co;2 (1987).
3 Thompson, C. J. Initial conditions for optimal growth in a coupled ocean-atmosphere model of ENSO. J. Atmos. Sci. 55, 537-557, doi:10.1175/1520-0469(1998)055<0537:icfogi>2.0.co;2 (1998).
4 Thompson, C. J. A linear, stochastic, dynamical model of El Niño /Southern Oscillation PhD thesis, University of Washington, (1998).
Response of the tropical Pacific to abrupt climate change 8,200 years ago 1
Alyssa R. Atwood1 David S. Battisti2, C. M. Bitz2, Julian P. Sachs1 2
3
Supplementary Information 4
5
1. Inferring Climatological Rainfall Changes and El Niño Rainfall Changes from El Junco 6
Lake Biomarker Records 7
From the available evidence, we infer that sedimentary dinosterol δ2H (Fig. 2A) reflects the 8
long-term mean (climatological) lake δ2H and thus changes in the climatology of mean annual 9
rainfall owing to the “amount effect”. From the C34 botryococcene data, we infer that 10
the sedimentary accumulation rate of C34 botryococcene (Fig. 2B) scales with the 11
climatological rainfall associated with El Niño events (i.e. the total rain that fell during El Niño 12
events divided by the total amount of time in the sedimentary interval). This inference is 13
equivalent to assuming that, during a given interval, the total accumulation of botryococcenes in 14
the sediment scales with the total rain that fell during El Niño events. Under this assumption, 15
changes in the botryococcene accumulation rate reflect changes in climatological rainfall due to 16
changes in El Niño rainfall. Changes in the climatological rainfall associated with El Niño events 17
(and therefore botryococcene accumulation rate) should be sensitive to changes in the frequency, 18
amplitude, and/or duration of El Niño rainfall events. The botryococcene accumulation rate thus 19
provides a broad (statistical) measure of the variability of El Niño rainfall events. Due to the 20
temporal resolution of the biomarker data, all rainfall reconstructions represent averages over 21
decadal and longer timescales. 22
Based on these inferences, we propose that rainfall at El Junco Lake can be reconstructed in 23
the following way: 24
∆ = − ∙ ∆( ) (2.1) 25
∆ = ∙ ∆( ( )) (2.2) 26
where ∆ is the total change in climatological rainfall, ∆ is the change in climatological 27
rainfall attributable to El Niño events (i.e. the change in El Niño mean annual rainfall), and 28
is the sedimentary botryococcene accumulation rate (in μg cm-2yr-1). The botryococcene 29
accumulation rate is related to the botryococcene concentration in the following way: 30
= ( ∗ ∗ )/ (2.3) 31
where [botryo] is the botryococcene concentration (in μg/g), DBD is the dry bulk density of the 32
sediment (in g/cm3), d is the thickness of the sediment sample (in cm), and t is the duration of 33
time represented by the sediment sample (in yr). In this study, an average DBD has been 34
estimated from the top 15 cm of sediment. Future work will include incorporating improved 35
estimates of DBD based on down-core gamma ray density measurements. 36
The El Junco Lake biomarker records are shown in Fig. 2. Positive (negative) values of ∆ 37
indicate an increase (decrease) in climatological rainfall and positive (negative) values of ∆ 38
indicate an increase (decrease) in climatological rainfall due to an increase (decrease) in El Niño 39
rainfall. It is important to note the distinction between El Niño rainfall reconstructions (as 40
presented here) and El Niño SSTA reconstructions, since the frequency and amplitude of rainfall 41
anomalies in the eastern Pacific during El Niño events can change without a corresponding 42
change in the amplitude of SSTAs. For instance, 1 demonstrated, using future climate simulations 43
from the Coupled Model Intercomparison Project phase 3 and phase 5, that extreme rainfall in 44
the Niño 3 region increases in response to a weakened mean zonal SST gradient in the tropical 45
Pacific due to enhanced probability of anomalous deep convection in the eastern Pacific under a 46
given SSTA. Possible mechanisms of increased (decreased) El Niño rainfall as recorded by the 47
botryococcene records therefore include: (1) decreased (increased) mean zonal SST gradient in 48
the tropical Pacific, (2) increased (decreased) variance of ENSO SSTAs, and/or (3) a shift in the 49
distribution of ENSO SSTAs to more eastern Pacific (central Pacific) El Niño events. ∆ should 50
be more sensitive to changes in Eastern Pacific (EP) El Niño events as compared to Central 51
Pacific (CP) events due to the location of El Junco Lake in the eastern equatorial Pacific 2,3. 52
This interpretation of the El Junco Lake biomarker records matches that presented in 53
Atwood 4 and is similar, though not identical, to that presented in Atwood and Sachs (2014) and 54
Zhang et al. (2014), based on the addition of new data and recent advances in our understanding 55
of the proxy records and their climatic implications. An overview of the important updates to the 56
biomarker data and their interpretations is provided in Section 3, below. 57
2. Inferring changes in residual rainfall from El Junco Lake biomarker records 58
Due to the sensitivity of rainfall at El Junco Lake to the climatological position of the eastern 59
Pacific ITCZ, we propose that further climatic information can be gained by comparing the 60
changes in inferred total climatological rainfall to the changes in climatological rainfall 61
associated with El Niño events. We first assume that changes in climatological rainfall (∆ ) at 62
El Junco Lake is comprised of two contributions: (1) changes in climatological rainfall 63
associated with changes in El Niño rainfall (∆ ) and (2) changes in (residual) climatological 64
rainfall due to processes unassociated with El Niño (∆ ; i.e. changes in the climatological 65
position of the ITCZ, changes in the Hadley circulation, or changes in localized convective 66
processes). This is represented in the following equation: 67
∆ = ∆ +∆ . (2.4) 68
We therefore propose that changes in residual rainfall (i.e. changes in climatological rainfall 69
unassociated with changes in El Niño rainfall) can be inferred in the following way. From Eq. 70
(2.4), we have: 71
∆ = ∆ − ∆ . 72
Substituting Eq. (2.2) and (2.3) into (2.4), we therefore obtain 73
∆ = − ∙ ∆( ) − ∙ ∆( ( )) . (2.5) 74
Since the proportionality constants β and γ are unknown (although both are greater than 75
zero), ∆ cannot be fully reconstructed. However, the sign of ∆ is constrained for periods in 76
which −∆(dinosterol δD) and ∆( ( )) are of opposite sign – that is, for periods in which 77
the inferred change in total climatological rainfall opposes the change in climatological rainfall 78
associated with El Niño events. We thus limit our interpretation of ∆ to its sign during such 79
periods. Positive (negative) values of ∆ indicate an increase (decrease) in climatological 80
rainfall due to processes unassociated with El Niño. 81
The primary utility of the ∆ reconstructions is to help interpret the source of the most 82
prominent mean rainfall changes at El Junco Lake. E.g. a positive value of ∆ indicates that the 83
increase in ∆ was due an increase in ∆ that was greater than the decrease in ∆ (i.e. that 84
the source of the increased climatological rainfall was from non-El Niño contributions). For 85
instance, a positive ∆ would result if the increase in climatological rainfall associated with a 86
southward shifted ITCZ in the eastern Pacific was greater than a coincident decrease in El Niño 87
contributions to climatological rainfall. In contrast, a negative value of ∆ indicates that the 88
decrease in ∆ was due a decrease in ∆ that was greater than the increase in ∆ (i.e. that the 89
source of the decreased climatological rainfall was from non-El Niño contributions, such as a 90
northward shift of the ITCZ). The El Junco Lake rainfall reconstructions are shown in Fig. 2 for 91
the last 9,100 years. 92
By design, the periods of inferred ∆ highlight periods in which ∆ was opposed by ∆ 93
(or, equivalently, ∆ opposed ∆ ). Such opposing changes in ∆ and ∆ appear to account 94
for many of the largest hydrologic changes in El Junco Lake over the last 9,100 years (Fig. 2). 95
3. Revisions to Previous Biomarker-Based El Junco Lake Rainfall Reconstructions 96
Interpretation of the biomarker data presented here is consistent with Atwood 4 and follows that 97
presented in Atwood and Sachs 5 and Zhang, et al. 6 with the following exceptions: 98
a. Zhang, et al. 6 propose that changes in botryococcene concentration scale with changes in 99
the frequency of El Niño events. In this study, we adopt what we believe to be a more 100
conservative interpretation of the data. Here, as in Atwood (2015), we infer that the 101
sedimentary botryococcene accumulation rate is proportional to climatological rainfall 102
associated with El Niño events. In adopting this interpretation, we allow for the 103
possibility that the sedimentary botryococcene accumulation rate is sensitive to changes 104
in the frequency, amplitude, and duration of El Niño rainfall events (as determined 105
through their combined influence on climatological rainfall associated with El Niño 106
events). 107
b. Atwood 4 uses the botryococcene δ2H to infer the mean isotopic composition of El Junco 108
Lake water during El Niño events and assumes that, to first order, changes in lake water 109
δ2H during El Niño events are driven by changes in the mean monthly rainfall during 110
those events. Changes in botryococcene δ2H thus provide a measure of changes in the 111
average amplitude of El Niño rainfall events. This interpretation is an update from that 112
presented in Atwood and Sachs 5, in which botryococcene δ2H was interpreted in terms 113
of a change in “El Niño activity”, defined as a change in the average frequency and/or 114
intensity of El Niño events. Atwood and Sachs 5 further used the botryococcene 115
concentration data to fill in missing values of botryococcene δ2H during periods in which 116
the concentration was too low for isotopic analysis (justified by the correlation between 117
the down-core botryococcene δ2H and concentration values). 118
c. Another distinction between the results presented in Atwood and Sachs 5 and this work is 119
the interpretation of changes in residual climatological rainfall (i.e. climatological rainfall 120
that is unassociated with El Niño). Here, as in Atwood (2015), we reconstruct changes in 121
residual climatological rainfall (∆ ) based on differences in the dinosterol δ2H and 122
botryococcene accumulation rate data (versus the dinosterol δ2H and botryococcene δ2H 123
data as in Atwood and Sachs 5. However, due to the correlation between the down-core 124
botryococcene δ2H and concentration, our conclusions concerning changes in residual 125
climatological rainfall have not been substantially modified. In addition, in an effort to 126
adopt a more conservative approach to the reconstruction of ∆ , in this study we define 127
only the sign of ∆ (as relative changes in its magnitude are dependent on the 128
proportionality constants β and γ in Eq. 2.5, which are not yet empirically constrained). 129
4. Sensitivity tests in LOAM 130
Sensitivity tests were performed in LOAM to determine which components of the tropical 131
Pacific background state were responsible for the decreased growth rate of the ENSO mode 132
under hosing. In these experiments, individual components of the mean fields from the hosing 133
run were prescribed in LOAM with all other mean fields provided from the control run. Table 1 134
shows the configuration and results of these experiments. 135
5. Mean state biases in CESM 136
CESM with CAM4 is recognized as a top-performing GCM with regard to its simulation of 137
tropical Pacific climate variability (Deser et al., 2012; Bellenger et al., 2014). However, tropical 138
Pacific mean state biases are still present in CESM, including a cold SST bias in the NE tropical 139
Pacific that extends along the equator, a warm SST bias along the coast of South America and 140
the northern subtropical Pacific, and equatorial easterlies that are too strong across the central 141
Pacific and in the far eastern equatorial Pacific (Fig. S8). 142
143
Supplementary Tables 144
Table S.1. Summary of the constants and tuning parameters used in LOAM (the governing 145
equations in LOAM are provided in the Supplementary material of Atwood et al., 2016). 146
Symbol Value Parameter
A Rayleigh damping constant 1.57607 ∙ 10
β Meridional gradient of Coriolis force 2.2915 ∙ 10 Coupling coefficient 1.4
, b Reference temperatures 303K, 5400 K
Thermal damping coefficient 125 days
Mean upper layer depth 150 m
Surface layer depth 50 m
Subsurface layer depth 100 m ( ) Ocean efficiency factor See Fig. S1 ( ) Ocean efficiency factor See Fig. S1 ′ Reduced gravity 5.6 cm
Ocean mechanical damping 8.7 months
Surface layer damping 2 days
, Density of the atmosphere, ocean 1.275 , 1.026 ∙ 10 ,
Gravity wave speed in atmosphere, ocean 60 , 2.9
Atmospheric drag coefficient 1.5 × 10
WBR Western boundary reflection 0.7
References 148 149 1 Cai, W. J. et al. Increasing frequency of extreme El Nino events due to greenhouse 150
warming. Nature Climate Change 4, 111-116, doi:10.1038/nclimate2100 (2014). 151 2 Ashok, K., Behera, S. K., Rao, S. A., Weng, H. & Yamagata, T. El Niño Modoki and its 152
possible teleconnection. Journal of Geophysical Research 112, C11007 (2007). 153 3 Murphy, B. F., Ye, H. & Delage, F. Impacts of variations in the strength and structure of 154
El Nino events on Pacific rainfall in CMIP5 models. Clim. Dyn. 44, 3171-3186, 155 doi:10.1007/s00382-014-2389-9 (2015). 156
4 Atwood, A. R. Mechanisms of Tropical Pacific Climate Change During the Holocene.” 157 University of Washington, (2015). 158
5 Atwood, A. R. & Sachs, J. P. Separating ITCZ- and ENSO-related rainfall changes in the 159 Galápagos over the last 3 kyr using D/H ratios of multiple lipid biomarkers. Earth Planet. 160 Sci. Lett. 404, 408-419 (2014). 161
6 Zhang, Z. H., Leduc, G. & Sachs, J. P. El Nino evolution during the Holocene revealed 162 by a biomarker rain gauge in the Galapagos Islands. Earth Planet. Sci. Lett. 404, 420-163 434, doi:10.1016/j.epsl.2014.07.013 (2014). 164
165
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