Supplementary Information for:
The effect of sea level on glacial Indo-Pacific
climate
Pedro DiNezio1∗ & Jessica Tierney2
1
International Pacific Research Center, University of Hawaii,
2
∗
Woods Hole Oceanographic Institution,
To whom correspondence should be addressed; E-mail: [email protected].
1.
1.1.
Proxy data
Proxy database and selection criteria
We conducted a literature search, a perusal of publicly available data in paleoclimate archives
(NOAA’s National Climatic Data Centre’s Paleoclimatology database, http://www.ncdc.noaa.gov/
paleo/paleo.html, and Pangaea, http://www.pangaea.de) and contacted some authors directly to
collect proxy data for our synthesis. As stated in the Methods Summary, we applied the following criteria towards our proxy data selection: 1) the proxy used be interpreted to reflect
hydroclimate, 2) the data must contain information concerning hydroclimate during both the
Last Glacial Maximum (26.5–19 ka, corresponding to the timing of the sea-level lowstand 1 )
and the Late Holocene (0–4 ka) and 3) the proxy data site be located within our target region
of 25◦ S–20◦ N, 25–170◦ E. The latitudinal bounds of the target region were chosen to include
as much of the Indo-Pacific region as possible while excluding the northern tropics, where the
presence of the glacial ice sheets likely affects hydroclimate. The longitudinal bounds were
informed by the geographical extent of the Indian Ocean/Indo-Pacific Warm Pool (IPWP), and
also reflect the lack of proxy data from the central tropical Pacific east of 170◦ E. We did not
impose any criteria regarding the chronological control of the proxy archives in order to facilitate the inclusion of as many proxy records as possible, and under the assumption that in
most cases, sedimentation rates and chronology are sufficient to constrain the data to within
our broad target windows. Nevertheless, certain archives may have dating uncertainties that
exceed our target windows. To test the sensitivity of our results to chronological uncertainties,
we restricted our collection of proxy data to records that contain one or more radiocarbon,
U/Th, or well-constrained OSL dates near to or during the LGM and repeated the analyses
(Fig. S1). Imposing this restriction reduces the number of our terrestrial proxy sites from
53 to 37 (32 sites with robust data), and the marine sites from 54 to 26 (25 sites with robust
data). For the terrestrial proxies, we get the same result with the reduced matrix: HadCM3
1
outperforms the other models. For the marine proxies, the pattern of kappa results (Fig. S2)
is similar to the pattern when including all proxies (Fig. 3) but the kappa values are no longer
statistically significant because the number of proxy sites (N) is now reduced to 25. The severe
depopulation of the SSS proxy matrix reflects the fact that many of the marine records rely on
wiggle-matching benthic δ 18 O data at their site to the global benthic δ 18 O stack (e.g., LR04
2
) as a means of age control rather than relying on radiocarbon. It could be argued that this
technique is sufficient to constrain LGM data (given sufficient sampling rates) as the LGM
lowstand (26–19ka) is demarcated clearly by enriched benthic δ 18 O values, highlighting the
invariably subjective nature of identifying records as “better-dated” and “poorly-dated” by any
a priori metric.
In sum, this test suggests that our results are largely insensitive to chronological differences
between the records, but, in the case of the salinity proxies, it is clear that a critical number
of sites is needed to get meaningful results. This latter observation, along with the inherent
subjectivity in imposing a threshold for “well-dated” and “poorly-dated,” justifies our choice
to use a generous proxy screening criteria with regards to chronological control. In addition,
we emphasize that the core finding of the paper, which is that terrestrial and marine proxies –
each subject to independent types of dating uncertainties and proxy noise – both point towards
the same pattern of hydroclimatic change. Therfore the response of Indo-Pacific hydroclimate
to the exposure of the Sunda Shelf appears to be robust.
For many types of proxies, conversion of the proxy to a quantitative climate metric (precipitation, salinity) is difficult or else may carry large uncertainties and can complicate multi-proxy
synthesis 3 . Thus, for the present synthesis we chose to place proxy data into three basic
categories: evidence for drier or saltier conditions during the LGM (as compared to the Late
Holocene), no evidence for change during the LGM, or evidence for wetter or fresher conditions during the LGM. This categorical classification makes the conservative assumption that
the proxies can at the least give a robust indication of the direction of climatic change. We used
2
the interpretations of the authors of the individual proxy record studies to determine which category the data fall into, which in some cases is based on quantitative comparisons of the mean
and variance of LGM and Late Holocene data, and in other cases is necessarily qualitative (e.g.,
“increased dune activity implies drier conditions relative to present at the LGM”).
Tables S1 and S2 list the proxy data used in the synthesis, including their locations, source
references, and the corresponding categorical classification of hydroclimatic or salinity change
during the LGM relative to late Holocene conditions.
1.2.
Heinrich Events
Our target LGM window includes Heinrich Event 2 (ca. 23 ka), a millennial-scale climate
event triggered by a sudden advance of sea-ice in the North Atlantic 4 . We also recognize that
poor dating on some archives may conflate late-glacial climate with changes associated with
another Heinrich Event, Heinrich Event 1 (ca. 17 ka). Although Heinrich Events are North
Atlantic phenomena, it is well-known that tropical climate responded to these perturbations
(e.g., 5 ). Thus, Heinrichs 2 and 1 have the potential to overprint the proxy signature of LGM
conditions. In many continuous core sequences, the transition into Heinrich 1 is apparent and
clearly distinguishable from LGM conditions. Heinrich 2 poses more of a challenge, but it
is unlikely that in low-resolution, highly-integrated archives H2 would dominate the signal
of mean late-glacial conditions (although there may be site-specific exceptions, c.f. the East
African discussion below). In addition, the expected climatic response of a Heinrich Event –
dry conditions in the northern tropics counteracted by wet conditions in the southern tropics
(c.f. 6 ) – is not apparent in the spatial patterns of our proxy synthesis (Figs. 2a and 3a).
1.3.
Proxy data in key regions
Here we provide brief overviews of proxy evidence for LGM conditions in areas that are particularly diagnostic of a Sunda Shelf effect on tropical hydroclimate.
3
1.3.1.
East Africa
An early synthesis of proxy lake level data in East Africa suggested that some lakes in the
region may have experienced highstands during the LGM 7 . Subsequently, some argued that
the older lake chronologies are poorly dated and/or that the inferred LGM highstands likely occurred before the LGM, therefore the entirety of the East African region was drier than present
8
. However, in the last decade, well-dated sedimentary archives including cores recovered from
Lake Challa (located just to the East of Kilimanjaro) 9 and near the Zambezi River delta 10, 11 as
well as new proxy data from existing archives 12 provide well-constrained evidence of wetter
conditions along the eastern and southeastern African coast, in agreement with some of the
older pollen and lake stratigraphies from central Kenya 7 . Thus, we consider the older East
African proxy data “as-is”, e.g., in keeping with its original proxy interpretation.
Two sites in southeastern Africa contain conflicting evidence or interpretations for hydroclimatic change. Lake Malawi, a large rift lake near 12◦ S, has traditionally been interpreted to be
lower during the LGM on the basis of diatom distributions and carbon isotopes 8, 13 . However,
organic matter composition, hydrogen isotopes and mineralogical evidence suggests minimal
drying/no change
14, 15, 16
. The proximity of Lake Masoko, which experienced a highstand
during the LGM, has also promoted debate concerning the fidelity of diatoms as lake level indicators under glacial conditions 12 . Given the controversy and disagreement between proxies,
we treat this site as “not robust”, and it is not used in the kappa calculations. The second site of
interest is Lake Chilwa, in which an ancient shoreline was dated by the OSL technique to ca.
26-21 ka 17 . As this spans the majority of the LGM, we originally considered this evidence for
wetter conditions at the time. However, the authors attribute this highstand to Heinrich Event
2 and do not consider it representative of LGM conditions 17 , therefore, we also treat this site
as not robust.
Readers are also referred to 18 for further discussion of evidence for wet conditions during the
4
LGM in East Africa.
1.3.2.
Western Indian Ocean/Arabian Sea
The majority of the salinity proxy data from the Arabian Sea come from the study of
19
and
are based on δ 18 Oseawater reconstructions using paired foraminiferal δ 18 O and Mg/Ca measurements. While translation of δ 18 Oseawater to absolute salinity values during the LGM is complicated by the changing δ 18 Oseawater –salinity relationship20 , the directionality of the proxy is not
affected.
19
find substantial evidence for fresher conditions throughout the Arabian Sea, and
the extent of this freshening is further corroborated by
21
, who found abnormally light δ 18 O
values in foraminifera in the western Indian Ocean.
1.3.3.
Northern Australia
In agreement with the individual studies used in our synthesis, regional analyses of proxy data
in the northern Australian region indicate widespread drying relative to present-day conditions
22, 23
. There is one proxy record of paleo-floods in plunge pools that suggests a more active
hydrological cycle
24
and this site is included in our proxy synthesis. There is no indication,
however, of a regionally-coherent wettening that would be typical of the “wet-get-drier” (thermodynamic) response.
1.3.4.
Western Pacific
Fresher conditions during the western Pacific have long been recognized as a general feature
of foraminiferal-based investigations of paleoclimate in the region (e.g.,
thesis of δ 18 Oseawater reconstructions
27
25, 26
). A recent syn-
highlights the fresh conditions that prevailed in the
South China Sea, and at least two sites indicate that the freshening extended far into the western/central Pacific 28, 29 .
5
1.4.
Proxy search radius
The proxy data are not distributed equally in space and some proxy data sites are located
geographically close to one another. To avoid over-representing well-sampled regions in the
model-proxy comparison, we applied a 150 km search radius to each proxy site and combined
proxy information that fell within that distance of each other. The 150 km search radius is
based on observational studies showing that the spatial correlation of precipitation intensity
falls to an insignificant level beyond a separation distance between 100 and 200 km
30, 31, 32
.
Spatial data points that incorporate more than one proxy are designated by triangles in Figs. 2a
and 3a of the main text. Triangles in which the proxies agree on the sign of change are plotted
in the corresponding color. Triangles in which the combined proxies disagree are plotted as
black, are treated as “not robust,” and are not used in the kappa comparisons with the model
simulations. Similarly, we mark sites where multiple proxies disagree and/or the interpretation
is vague as “not robust” (e.g. see the East Africa section above).
2.
Paleoclimate simulations
We analyze changes in the climate of the IPWP during the LGM – relative to pre-industrial (PI)
conditions – in an ensemble of climate simulations performed with twelve ocean-atmosphere
climate models. The simulations were performed by modeling centers from the USA, Japan,
UK, Germany, France, China and are coordinated by the Paleoclimate Modeling Intercomparison Project (PMIP) 33 . Five LGM experiments were performed and coordinated by PMIP in
its second phase (PMIP2), one simulation was performed by NOAA/GFDL using the CM2.1
model
34
following PMIP2’s experimental protocol, and the remaining six simulations were
performed and coordinated by PMIP in its third phase (PMIP3). Table S3 lists the models
used, their resolution, and associated references. Further detailed descriptions of the models
can be found on the PMIP2 website (http://pmip2.lsce.ipsl.fr/pmip2/) and the Program for Climate Model Diagnosis and Intercomparison (PCMDI) website
6
(http://www-pcmdi.llnl.gov/ipcc/about ipcc.php).
All of the LGM (“21k oa” for PMIP2, “lgm” for PMIP3) and PI (“0k oa” for PMIP2, “piControl” for PMIP3) simulations were run with coordinated forcing and boundary conditions as described in the Methods Summary. However, the LGM simulations coordinated by PMIP2 and
PMIP3 used slightly different reconstructions of the ice sheet topography and snow cover extent. PMIP2 used the ICE-5G V1 35 reconstruction 33 , while PMIP3 used a blend of the ICE-6G
36
, MOCA 37 , and ANU 38 reconstructions (https://pmip3.lsce.ipsl.fr/wiki/doku.php/pmip3:design
:pi:final:icesheet). These differences are important for the simulation of mid-latitude circulation, but we see no evidence that they have a first-order effect on IPWP climate.
2.1.
Wet-get-drier response
In warmer (colder) climates atmospheric moisture tends to increase (decrease), governed by
the Clausius-Clapeyron (C-C) equation. Regions where there is climatological moisture convergence, such as the IPWP, will tend to have increased (decreased) moisture convergence in
response to warming (cooling), resulting in an enhanced (weakened) pattern of evaporation
minus precipitation (E – P)
39, 40
. The changes in moisture convergence can lead to changes
in either precipitation (P) or evaporation (E). This simple balance is relevant over the oceans,
where relative humidity is nearly constant, as well as over humid land regions. Over arid or
semiarid land surfaces, relative humidity can exhibit large changes and furthermore, the lack of
an unlimited water vapor supply constrains moisture convergence to remain small. These are
regions where the effect of the thermodynamic drying and associated “wet-get-drier” response
are negligible.
We use the annual-mean E – P estimated by the ERA40 reanalysis 41 for the 1979-2001 period
to compute the changes in E – P expected for the LGM from the effect of thermodynamic drying on moisture convergence. First, we assume that the change in moisture will drive changes in
P because E – P is dominated by P over the IPWP. We compute the change in P by multiplying
7
the annual-mean E – P with the C-C scaling (7%/K) and the models’ ensemble-mean tropical
mean cooling (-2.5 K). We normalize the P response by the spatial standard deviation over the
tropics (25◦ S-25◦ N) in order to define regions with negligible “wet-get-drier” response, such
as land regions with limited evapotranspiration. The normalization of E – P leads to wet and
dry regions with percentage changes in P that are larger than the 17.5% predicted by C-C for
a 2.5K cooling. This allows us to then vary the threshold for defining wet-dry conditions and
explore the sensitivity of the model-proxy agreement. We follow a similar procedure to estimate the signature of this mechanism on SSS, using annual-mean SSS values from 1950-2009
climatology 42 . We first remove the tropical-mean SSS, as changes in E – P drive deviations in
SSS from the tropical mean (SSS∗ ). We then multiply SSS* by the C-C scaling (7%/K) and the
models’ ensemble-mean tropical mean cooling (-2.5K). We acknowledge that SSS is directly
driven by E – P but also reflects changes in ocean advection, which our simple calculation
neglects.
3.
Sunda Shelf mechanism
The Sunda shelf – the areas of the Gulf of Thailand, the South China Sea, and the Java Sea
that were exposed due to lowered sea level during the LGM – cools severely in the HadCM3
and GFDL-CM2.1 LGM simulations (Fig. S3). This anomalous surface cooling suppresses
convection either by exceeding the threshold for convection or by generating anomalous temperature gradients with the surrounding ocean, favoring convection over the ocean. As a result,
HadCM3 (Fig. 4a) and GFDL-CM2.1 (Fig. S4a) simulate large anomalous subsidence over
the IPWP. However, we note that while HadCM3 and GFDL-CM2.1 simulate reductions in
ascending motion over the Sunda Shelf of a similar magnitude, the corresponding changes in
precipitation are much larger in HadCM3 than in GFDL-CM2.1 (Fig. 2b). Similarly, CCSM4.0
and GISS-E2-R also simulate strong cooling over the Sunda Shelf (Fig. S3), yet these models
do not simulate large reductions in convection. These differences may point towards a role
for deep convection parametrizations or land surface feedbacks; indeed, carbon isotopic data
8
suggest that savanna may have replaced forest cover during the LGM in at least the northern
half of the Shelf 43 . However, a full diagnosis of these effects requires further simulations and
thus is outside the scope of this synthesis paper.
The remaining models simulate weaker cooling and convective response over the Sunda Shelf
44
(Fig. S3 and Fig. 4). MIROC3.2 used a present-day land mask in the LGM experiment,
thus effectively not representing the effect of lower sea level on the geography of the Maritime
Continent. This model does not simulate enhanced cooling over the Sunda Shelf or a reduction
in ascending motion over exposed land areas (Fig. S4b), supporting the idea that the weakening
of convection over the Sunda Shelf simulated by HadCM3 and GFDL-CM2.1 is a response to
the changes in land-sea distribution. Moreover, MIROC3.2 simulates a stronger Pacific Walker
circulation (Fig. S4b) consistent with the mechanism weakening the Walker circulation in response to global warming 44 . This strongly suggests that in the absence of land-sea distribution
changes over the Maritime continent, the Indo-Pacific Walker circulation responds via the same
mechanism to both cooling and warming.
In sum, both the exposure of land and its enhanced cooling appear to be required for convection
to weaken. This could explain why a previous modeling study that did not specify LGM
cooling nor enhanced cooling over land 45 obtained opposite results; i.e. enhanced convection
over the Sunda shelf. In the absence of greenhouse gas- or ice sheet-driven cooling, the exposed
land warms (due to its reduced thermodynamic damping) favoring convection there. A more
comprehensive and systematic explanation of the Sunda shelf mechanism requires a set of
controlled simulations exploring the role of different convection schemes, ocean-atmosphere
coupling, and land surface parameterizations.
3.1.
Effect of the Sahul Shelf
The exposure of the land bridge connecting northern Australia with New Guinea, the Sahul
shelf, could also have local and remote impacts on hydroclimate. Not all the models simulate
9
drying/reduced convection over the the Sahul shelf (Fig. 2b). However, each model simulates
the same type of response over both Sunda and Sahul, reflecting the different sensitivities of
tropical convection over exposed land areas simulated by each model. HadCM3 is one of the
few models simulating enhanced drying over the Sahul shelf (Fig. 2b), however the changes are
not as large as those over the Sunda Shelf. The exposure of the Sahul Shelf could feasibly drive
regional responses, for instance the large-scale drying across northern Australia 46 . However,
the spatial patterns of mid-tropospheric vertical velocity change (Fig. S5) show a dominant role
for the Sunda shelf both reducing convection and associated drying of the IPWP and driving
remote responses, e.g. over East Africa.
4.
Seasonality
Precipitation and salinity proxies do not necessarily record annual-mean conditions. However,
the diversity of proxies used in this study – many of which will have a different seasonal bias
due to local oceanography and ecology – minimizes the possibility of a systematic seasonality
bias in our categorical synthesis maps. Furthermore, to the extent that the mechanisms have a
dominant annual-mean response, even a proxy with a seasonal bias will capture the signature of
the annual-mean response. We investigate this issue by assessing the proxy-model agreement
for changes in precipitation and SSS over different seasons. We find that the changes in precipitation during DJF and JJA simulated by HadCM3 exhibit the same large-scale patterns of a
dry IPWP and wet eastern Africa as seen in the annual mean (Fig. S6). IPSL-CM4, in contrast,
simulates drying of the IPWP that migrates towards the northern (southern) hemisphere in DJF
(JJA) (Fig. S7). The JJA pattern shows statistically significant agreement with the terrestrial
proxies (κ = 0.35). However, the JJA drying of the IPWP simulated by IPSL-CM4 does not
translate into a pattern of SSS that shows good agreement with the marine proxies because
this model does not simulate a salty Bay of Bengal nor fresh western Pacific, neither seasonally nor in the annual mean. All other models do not show statistically significant changes
for DJF or JJA. In the present day climate, rainfall and SSS exhibit a marked seasonal con10
trast between the northern and southern hemispheres associated with the monsoons (Fig. S8),
yet their seasonality is not as strong in the deep tropics, especially over the equatorial Indian
Ocean, Sumatra, and equatorial East Africa. These are the same locations where the Sunda
shelf mechanism, as simulated by HadCM3, has the strongest signals. Conversely, an enhancement of the present-day seasonal contrast (Fig. S8) does not appear to explain the patterns of
rainfall and SSS change inferred from the proxies, especially over eastern Africa, where proxies show an east-west gradient at the LGM, but present-day seasonality exhibits a north-south
contrast. Thus, while it is likely that some of the off-equatorial drying over southeast Asia
and northern Australia suggested by the proxies could be due to changes in the monsoons, the
Sunda shelf mechanism is the most parsimonious explanation for the changes over the equatorial Indian Ocean and Maritime Continent. In sum, seasonality does not affect our primary
result that HadCM3 provides the best match for the proxy data.
5.
5.1.
Proxy-Model comparison
Representation of model data
To best compare the simulations with the information provided by the terrestrial precipitation proxies, we plot each model’s precipitation change as a percentage of the annual mean
precipitation in the preindustrial climate (Fig. 1b). For salinity, we use absolute units because unlike precipitation, the small range of global ocean salinities constrains the percentage
changes to values of ca. 5% or less. As the salinity proxies are corrected for the global change
in mean ocean salinity during the LGM due to the presence of the ice sheets (for SSS transfer functions, 1 psu, and for δ 18 Oseawater , 1h,
47
), we also remove the global mean salinity
change (∼1 psu) from the salinity fields of the model data for comparison. Note, however, that
while all PMIP3 LGM simulations included this 1 psu change as an initial boundary condition,
among the PMIP2 LGM simulations only CCSM3.0 and GFDL CM2.1 had a 1 psu salinity
increase applied. Therefore, we only removed the mean ocean salinity change of 1 psu from
11
those models where it had been applied. Last, to compare the models with the proxies, we
placed the model simulation data into the same three-category system, varying the threshold
over which the model data is considered wetter/drier or fresher/saltier from 1–40% and 0.1-1
psu, respectively (see Figs. 2c and 3c).
5.2.
Weighted Cohen’s κ
As described in the Methods, the Cohen’s κ statistic 48 is a measure of categorical data agreement between two “raters” (in our case the proxies vs. each model) who classify N items (the
locations with proxy data) into C mutually exclusive categories (the hydroclimate response
defined either as drier, unchanged, or wetter; or saltier, unchanged, or fresher). For instance,
the categorical data for HadCM3 when the precipitation cut-off is 13% (Figure 1b) is:
Model
Proxy
Drier
Unchanged
Wetter
Drier
22
10
1
Unchanged
3
4
0
Wetter
1
1
5
and for FGOALS1.0g with a 5% cut-off:
Model
Proxy
Drier
Unchanged
Wetter
Drier
20
5
8
Unchanged
1
1
5
Wetter
4
1
2
Cohen’s κ is then defined as the observed fractional agreement (po ) relative to the probability
of random agreement (pe ):
κ=
po − pe
1 − pe
12
where po is the fractional agreement among the raters (the sum of the diagonal elements divided by the number of items, N ) and pe is the probability that the raters agree due to random
chance (computed from the observed data as the frequency of occurrence of each category, i.e.
the product of the sum of the respective rows normalized by N ). As noted in the Methods
Summary, we use a modified version of this Cohen’s κ, the weighted Cohen’s κ
49
, in which
multiplying the data matrix by a weight matrix penalizes models for a total miss (e.g., drier
when it should be wetter, weight = 0) more than a near miss (e.g., drier when it should be no
change, weight = 0.5).
13
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Table S1. List of the terrestrial paleoclimate proxy data used in the LGM synthesis, with
locations, hydroclimatic change category (W=wetter than present, NC=no change, D=drier
than present, NR=not robust) and associated references.
Longitude
132.30
113.50
127.45
Latitude
-22.80
-22.05
-19.80
Country/Region
Central Australia
Western Australia
Northwestern Australia
46.92
37.87
125.00
145.70
146.63
146.30
118.38
35.50
121.59
131.00
-19.78
-18.24
-17.45
-17.37
-16.83
-16.63
-16.35
-15.50
-13.17
-13.00
Madagascar
Zambezi River Delta
Northwestern Australia
Northeastern Australia
Northeastern Australia
Northeastern Australia
Eastern Indian Ocean
Malawi
North Australian Basin
Northwestern Australia
34.00
-12.00
Malawi
Core/Site ID
Lake Lewis
Fr10/95-GC17
Lake Gregory, Fitzroy
Basin
Lake Tritrivakely
64PE203-80
Carpenter’s Gap
Lynch’s Crater
Lake Euramoo
ODP Site 820
SO-14-08-05
Lake Chilwa
MD98-2167
Lily pond, Wangi
falls, Waterfall creek
Lake Malawi
140.00
118.07
33.76
-12.00
-10.78
-9.33
Northern Australia
Lombok Ridge
Malawi/Rungwe Mtns
Gulf of Carpentaria
G6-4
Lake Masoko
29.75
36.00
-9.08
-8.00
Zambia
Tanzania
107.80
107.50
102.42
30.00
-7.20
-7.10
-6.08
-6.00
West Java
Java
Sumatra
East Africa
Lake Cheshi
Eastern Arc Mountains
Bandung Basin
Situ Bayongbong
BAR94-42
Lake Tanganyika
126.97
143.00
133.45
35.82
29.50
37.70
-5.77
-5.50
-5.00
-3.92
-3.50
-3.32
Banda Sea
Papua New Guinea
Aru Sea
Tanzania
Eastern Central Africa
Kenya/Tanzania
SHI-9014
Tari basin
MD98-2175
Lake Manyara
Multiple sites
Lake Challa
121.30
-2.50
Sulawesi, Indonesia
Wanda Site
140.50
100.77
33.00
117.50
36.33
36.17
-2.33
-1.07
-1.00
-0.75
-0.68
-0.42
Irian Jaya
Sumatra
Tanzania/Uganda/Kenya
Kalimantan
Kenya
Kenya
37.00
37.53
112.10
124.89
0.00
0.05
0.85
1.23
Kenya
Kenya
Kalimantan
Sulawesi, Indonesia
Lake Hordorli
Danau di Atas
Lake Victoria
Mahakam Delta
Lake Naivasha
Lake
NakuruElementaita
Aberdare Mountains
Sacred Lake
Lake Sentarum
Lake Tondano
103.80
31.00
1.40
1.67
Singapore
Albert
Nee Soon
Lake Albert
24
Proxy
high dune activity
pollen
lake desiccation and
dune activity
diatoms
CBT index, δDwax
phytoliths
pollen, charcoal
pollen
pollen, charcoal
dust flux
shorelines/lake level
pollen
evidence for paleofloods
δ 13 Cwax , vivianite,
δDwax , diatoms, OM
characterization
lake level
pollen, charcoal
runoff (magnetic susceptibility), pollen
lake level
pollen
Category
D
D
D
Reference
50, 51
52
53, 54
D
W
D
D
D
D
D
NR
D
W
55
10, 56
57
58
59
60
61
17
62, 63
24
NR
8, 14, 15, 13, 16
D
D
W
64
65
12, 66
NC
W
67
68
pollen
pollen
pollen
diatoms, trace metals,
δDwax
pollen, charcoal
pollen, charcoal
pollen
lake level
pollen
BIT index, δDwax ,
lake level
pollen, site nearly desiccated
pollen, charcoal
pollen
lake desiccation
pollen
lake level
lake level
D
NC
NC
D
69
70
71
72, 73, 74
D
NC
D
W
D
W
75
76
62, 63
7, 77, 78, 79
80
18, 9
D
81
NC
NC
D
D
W
D
82
70, 83
84, 85
86
7, 77, 79
7, 77, 79
W
D
D
D
87
88, 89
90
91
D
D
92
93
pollen
δ 13 COC
site nearly desiccated
pollen, diatoms, lake
desiccation
pollen
lake level
Longitude
99.03
107.88
101.70
113.77
114.00
108.57
108.65
36.00
112.21
125.83
117.55
119.45
75.23
41.60
76.75
44.30
54.00
72.50
102.90
Latitude
2.25
2.78
3.22
3.82
4.00
4.15
4.35
5.00
6.16
6.48
8.52
10.47
10.50
11.10
11.25
11.96
12.50
15.50
17.10
Country/Region
Sumatra
South China Sea
Peninsular Malaysia
Northern Borneo
Borneo (Malaysia)
South China Sea
South China Sea
Kenya
South China Sea
Davao Gulf, Phillipines
Palawan, Phillipines
Palawan, Phillipines
Pakistan
Ethiopia/Djibouti
Southern India
Gulf of Aden
Socotra
Pakistan
Northeast Thailand
Core/Site ID
Pea Bullok
SO18323
Batu Cave
Niah Cave
Gunang Buda
SO18302
SO18300
Lake Turkana
SO17964
MD06-3075
Gangub Cave
Makangit Cave
MD77-194
Lake Abhe
Nilgiri Hills
P178-15P
Moomi Cave
MD76-131
Nong Pa Kho
25
Proxy
pollen
pollen
bat guano δ 13 C
bat guano δ 13 C
speleothem δ 18 O
pollen
pollen
lake level
pollen
pollen
bat guano δ 13 C
bat guano δ 13 C
pollen
lake level
peat δ 13 C
δDwax
speleothem δ 18 O
pollen
charcoal
Category
NC
D
D
NC
D
NC
D
D
NC
D
D
D
D
W
D
D
D
D
D
Reference
94
95
43
43
96
95
95
7, 77, 79
97
98
43
43
99
100
101, 102
Tierney et al, in prep
103
99
104
Table S2. List of the marine paleoclimate proxy data used in the LGM synthesis, with locations, salinity change category (F=fresher than present, NC=no change, S=saltier than present,
NR=not robust) and associated references.
Longitude
111.83
108.51
108.37
166.15
113.50
110.51
115.00
166.28
119.50
120.70
121.03
120.00
121.79
122.42
121.70
120.99
120.67
51.77
129.24
128.64
128.17
103.25
117.90
100.13
90.00
159.36
129.79
146.14
44.78
67.34
90.00
141.77
127.74
73.88
90.08
60.25
90.52
125.83
111.82
113.42
126.50
139.64
121.30
90.03
57.74
51.93
115.31
96.04
90.02
82.00
57.35
Latitude
-24.74
-24.46
-23.95
-23.00
-22.13
-19.41
-17.64
-15.79
-15.31
-14.98
-14.01
-13.85
-13.08
-11.52
-11.30
-10.95
-10.83
-10.15
-9.09
-8.79
-8.50
-5.94
-4.69
-1.49
-1.00
0.32
1.13
1.25
1.25
1.90
2.00
2.03
2.68
5.07
5.18
5.39
5.85
6.30
6.37
6.47
6.51
8.02
8.80
9.13
10.50
10.77
10.90
12.46
12.70
14.04
14.32
Country/Region
Western Australia
Western Australia
Western Australia
Coral Sea
Western Australia
Western Australia
Western Australia
Coral Sea
Timor Sea
Timor Sea
Timor Sea
Timor Sea
Timor Sea
Timor Sea
Timor Sea
Timor Sea
Timor Sea
Western Indian Ocean
Timor Sea
Timor Sea
Timor Sea
West Sumatra
Makassar Strait
West Sumatra
Bay of Bengal
Ontong Java Plateau
West Pacific
West Pacific Warm Pool
Arabian Sea
Arabian Sea
Bay of Bengal
West Pacific Warm Pool
Molucca Sea
Arabian Sea
Bay of Bengal
Arabian Sea
Bay of Bengal
Mindanao
South China Sea
South China Sea
West Pacific
Caroline Basin
Sulu Sea
Bay of Bengal
Arabian Sea
Arabian Sea
South China Sea
Andaman Sea
Bay of Bengal
Bay of Bengal
Arabian Sea
Core/Site ID
Fr10/95-GC20
Fr10/96-GC10
Fr10/95-GC11
MD06-301
Fr10/95-GC17
Fr10/95-GC14
Fr10/95-11
ODP 828A
SO18506
SO18500
Fr10/95-5
SO18507
MD01-2378
SO18473
SO18475
SO18476
SO18477
WIND 28K
SO18462
SO18460
SO18459
GeoB 10038-4
MD98-2162
GeoB 10029-4
RC14-35
ODP806B
MD01-2386
MD97-2138
IOE-143KK
SO28-18KL
RC14-37
MD97-2140
K12
MD90-0963
SK157-14
SO28-11KL
RC14-39
MD98-2181
SO18267
MD01-2390
MD06-3067
3cBX
MD97-2141
RC12-339
SO42-87KL
NIOP 905
SO17957
RC12-344
RC12-340
SK218/1
SO42-74KL
26
Proxy
radiolaria transfer function
radiolaria transfer function
radiolaria transfer function
δ 18 Osw
radiolaria transfer function
radiolaria transfer function
δ 18 O + foram transfer SST
δ 18 O + foram transfer SST
δ 18 Osw
δ 18 Osw
δ 18 O + foram transfer SST
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Oruber
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
foram transfer function
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
foram transfer function
δ 18 Osw
δ 18 O + foram transfer SST
δ 18 Oruber +alkenone SST
δ 18 Oruber
δ 18 Osw
foram transfer function
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
foram transfer function
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
foram transfer function
δ 18 Osw
δ 18 Osw
Category
NC
F
F
F
F
F
NC
S
NC
S
S
F
F
S
S
S
S
F
NC
NC
S
S
NC
NC
NC
F
NC
F
S
F
NC
F
NC
S
S
F
NC
S
F
F
NC
F
F
F
F
NC
S
NC
NC
S
F
Reference
105
105
105
28
105
105
106
106
27
27
106
27
27
27
27
27
27
21
27
27
27
107
108, 27
107
109
25
27
110
19
19
109
111
106
112
113
19
109
114
27
115
27
29
26
109
19
116
27
117
109
118
19
Longitude
83.58
111.53
90.00
72.97
72.85
68.76
112.90
69.05
119.45
58.80
90.00
126.24
113.48
118.34
36.33
Latitude
14.71
14.76
15.00
15.25
15.48
15.52
16.09
17.08
17.25
17.38
17.50
-8.46
-22.08
-9.65
-20.40
Country/Region
Bay of Bengal
South China Sea
Bay of Bengal
Arabian Sea
Arabian Sea
Arabian Sea
South China Sea
Arabian Sea
South China Sea
Arabian Sea
Bay of Bengal
Banda Sea
Western Australia
Indonesia
Mozambique Channel
Core/Site ID
VM29-19
SO17954
RC12-343
ORV SK17
SK117-GC8
SO42-26KL
SO17950
SO42-36KL
SO17927
TN47-6GGC
MD77-181
SHI-9016
MD00-2361
MD98-2165
MD79-257
27
Proxy
δ 18 Osw
δ 18 Osw
foram transfer function
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
δ 18 Osw
foram transfer function
δ 18 O + foram transfer SST
δ 18 O + foram transfer SST
δ 18 Osw
δ 18 O + foram transfer SST
Category
NC
F
S
S
S
F
F
F
F
NC
S
S
F
NC
NC
Reference
119
27
109
116
120
19
27
19
27
19
109
121
122
123
123
Table S3. List of models used in the LGM and PI simulations, their atmospheric and oceanic
resolution, and associated references. References for GISS-E2-R, IPSL-CM5A-LR, MPI-ESM
are not yet available.
Model
Institution, Country
Model Resolution
Atmosphere (lat.
× Ocean (lat. × long.)
long.)
Reference
National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, USA
LASG/Institute of Atmospheric Physics,
China
Institut Pierre Simon Laplace, France
Center for Climate System Research (University of Tokyo), National Institute for
Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC), Japan
National Center for Atmospheric Research,
USA
UK Met Office Hadley Centre, UKa
2◦ ×2.5◦ L24
1/3◦ –1◦ ×1◦ L50
34
T42 L26 (2.8◦ ×2.8◦ )
1◦ ×1◦ L33
124
2.5◦ ×3.75◦ L19
T42 L20 (2.8◦ ×2.8◦ )
1–2◦ ×2◦ L31
0.5–1.4◦ ×1.4◦ L43
125
126
T42 L26 (2.8◦ ×2.8◦ )
1/3–1◦ ×1◦ L40
127, 128
2.5◦ ×3.75◦ L19
1.25◦ ×1.25◦ L20
129
2◦ ×25◦ L40
1◦ ×1.25◦ L32
2.5◦ ×3.75◦ L19
T63 L47 (1.4◦ ×0.9◦ )
1.25◦ ×0.9◦ L26
1–2◦ ×2◦ L31
1.4◦ ×1.4◦ L44
1/3–1◦ ×1◦ L60
130, 131
T159
L48
(1.125◦
×1.125◦ )
T127 L31 (1.4◦ ×1.4◦ )
L26
0.5◦ ×1◦ L50
132
1/3–1◦ ×1◦ L42
133
PMIP2
GFDL-CM2.1
FGOALS-g1.0
IPSL-CM4
MIROC3.2
CCSM3.0
HadCM3
PMIP3
GISS-E2-R
IPSL-CM5A-LR
MPI-ESM
CCSM4.0
MRI-CGCM3
CNRM-CM5
NASA Goddard Institute for Space Studies,
USA
Institut Pierre Simon Laplace, France
Max Planck Institute, Germany
National Center for Atmospheric Research,
USA
Meteorological Research Institute, Japan
Centre
National
de
Mètèorologiques, France
Recherches
28
20
(b)
30˚N
0.0
Simulated rainfall change
0.1
0.2
0.3
Cohen’s κ
0.4
CNRM−CM5
CCSM4.0
MRI−CGCM3
MPI−ESM−P
IPSL−CM5A−LR
HadCM3
GISS−E2−R
120˚E
CCSM3.0
60˚E
MIROC3.2
30˚S
IPSL−CM4
10
GFDL−CM2.1
0˚
30
FGOALS−1.0g
30˚N
Proxy−model agreement
Wet−get−drier
Drier
No Change
Wetter
Not robust
(c)
∆P (%)
Proxy network
drier−wetter threshold
(a)
0.5
Wet−get−drier
GFDL−CM2.1
FGOALS−1.0g
κmax=0.11 (∆P=1 %)
κmax=0.19 (∆P=1 %)
κmax=0.13 (∆P=5 %)
0˚
30˚S
30˚N
IPSL−CM4
MIROC3.2
CCSM3.0
κmax=0.05 (∆P=13 %)
κmax=0.20 (∆P=14 %)
κmax=0.22 (∆P=1 %)
0˚
30˚S
30˚N
HadCM3
GISS−E2−R
IPSL−CM5A−LR
κmax=0.45* (∆P=13 %)
κmax=0.10 (∆P=21 %)
κmax=0.01 (∆P=47 %)
0˚
30˚S
30˚N
MPI−ESM−P
CCSM4.0
MRI−CGCM3
κmax=0.20 (∆P=1 %)
κmax=0.02 (∆P=31 %)
κmax=0.07 (∆P=22 %)
60˚E
60˚E
0˚
30˚S
CNRM−CM5
30˚N
120˚E
120˚E
κmax=0.22 (∆P=21 %)
−50 −40 −30 −20 −10 0
0˚
10 20 30 40 50
∆P (%)
Model−proxy agree
Model−proxy do not agree
30˚S
60˚E
120˚E
Figure S1. Reconstructed and simulated changes in rainfall during the Last Glacial Maximum
(LGM) and Cohen’s κ results, as in Figure 2 of the main text but including only proxy data
with one or more radiocarbon, U/Th, or well-constrained OSL dates near to or during the LGM.
HadCM3 is still the only model with a significant match.
29
(c)
Proxy−model agreement
1.00
∆SSS (psu)
0.50
(b) Simulated sea−surface salinity change
30˚N
0.0
0.1
0.3
Cohen’s κ
0.4
CNRM−CM5
CCSM4.0
MRI−CGCM3
MPI−ESM−P
IPSL−CM5A−LR
HadCM3
0.2
GISS−E2−R
CCSM3.0
120˚E
60˚E
MIROC3.2
30˚S
IPSL−CM4
0.25
Wet−get−drier
0˚
0.75
GFDL−CM2.1
30˚N
saltier−fresher threshold
Saltier
No Change
Fresher
Not robust
FGOALS−1.0g
Proxy network
(a)
0.5
Wet−get−drier
GFDL−CM2.1
FGOALS−1.0g
κmax=0.12 (∆SSS=0.10 psu)
κmax=0.26 (∆SSS=0.10 psu)
κmax=0.10 (∆SSS=1.40 psu)
0˚
30˚S
30˚N
IPSL−CM4
MIROC3.2
CCSM3.0
κmax=0.08 (∆SSS=0.90 psu)
κmax=0.26 (∆SSS=0.50 psu)
κmax=0.09 (∆SSS=0.40 psu)
0˚
30˚S
30˚N
HadCM3
GISS−E2−R
IPSL−CM5A−LR
κmax=0.28 (∆SSS=0.90 psu)
κmax=0.20 (∆SSS=0.60 psu)
κmax=0.21 (∆SSS=0.20 psu)
0˚
30˚S
30˚N
MPI−ESM−P
CCSM4.0
MRI−CGCM3
κmax=0.08 (∆SSS=0.80 psu)
κmax=0.18 (∆SSS=0.10 psu)
κmax=0.08 (∆SSS=0.70 psu)
0˚
30˚S
CNRM−CM5
30˚N
60˚E
120˚E
60˚E
120˚E
κmax=0.11 (∆SSS=0.10 psu)
−1.0
0˚
−0.5
0.0
0.5
1.0
∆SSS (psu)
Model−proxy agree
Model−proxy do not agree
30˚S
60˚E
120˚E
Figure S2. Reconstructed and simulated changes in salinity during the Last Glacial Maximum
(LGM) and Cohen’s κ results, as in Figure 3 of the main text but including only proxy data with
one or more radiocarbon, U/Th, or well-constrained OSL dates near to or during the LGM. The
kappa data are similar to the results using the full proxy matrix (Fig. 3 of the main text), but the
values are no longer significant at the 95% level due to the depopulation of the proxy matrix to
only 25 proxies.
30
30˚N
2416
IPSL−CM4
(−2.67 oC)
oC)
16
FGOALS−1.0g
(−2.29
2
4
24
24
24
124
GFDL−CM2.1
(−3.51 oC)
6
24
24
24
24
24
24
0˚
24
24
24
24
24
24
24
24
24
30˚S
16
1
o
MIROC3.2
(−2.07
24 C)
2
24
4
24
oC)
24
HadCM3
(−2.55
24
24
24
24
0˚
6
CCSM3.0
(−2.04 oC)
24
24
24
24
24
16
30˚N
24
24
24
24
24
24
24
30˚S
30˚N
24
1264
GISS−E2−R
(−2.54 oC)
4
24
24
126
IPSL−CM5A−LR
(−4.24 oC)
2
4
24
24
24
24
24
24
24
24
24
24
24
24
24
o
24
CNRM−CM5
(−1.55
16
24 C)
oC)
216
MRI−CGCM3
(−2.75
4
24
24
CCSM4
16 (−2.50 oC)
24
24
24
24
24
24
30˚S
24
24
24
24
30˚N
2416
MPI−ESM−P
(−2.35 oC)
24
24
24
0˚
24
24
24
24
24
24
24
24
60˚E
24
24
24
24
120˚E
60˚E
−6
−5
24
24
24
−7
24
24
24
24
24
24
24
30˚S
24
24
0˚
24
24
24
24
120˚E
−4
∆Ts
−3
−2
−1
0
(oC)
Figure S3. Change in surface temperature (Ts ) simulated by PMIP models in response to LGM
forcing. Values over the ocean correspond to sea surface temperature (SST). Contours show
the annual-mean Ts simulated in the PI simulation. The contour interval is 2◦ C. The coastlines
correspond to the 120 m isobath of the present day ocean bathymetry.
31
20
-40
30oE
60oE
90oE
150oE
120oE
180oE
150oW
120oW
Maritime Continent
90oW
S. America
Sunda Shelf New Guinea
-20
-40
-20
-20
-60
400
-40
-4
0
-40
-20
-20
pressure (hPa)
200
800
1000
20
-20
0
600
20
-60
-40
Africa
(b)
20
-40
1000
0
0
-6
800
-4
-40
-60
600
-20
400
0
-2
-20
200
20
0
20
pressure (hPa)
(a)
-20
30oE
60oE
-20
90oE
Africa
120oE
180oE
150oE
150oW
120oW
S. America
Maritime Continent
Sunda Shelf New Guinea
-25
-15
-10
-5
-2
0
90oW
2
5
10
15
25
Δω (hPa day-1)
Figure S4. Changes in vertical velocity (ω) over the equatorial Indo-Pacific simulated by (a)
GFDL-CM2.1 and (b) MIROC3.2 in response to LGM forcing (colors). The ω changes are
averaged over the 5◦ S–5◦ N latitude band. Contours are mean-annual ω simulated in the preindustrial control experiment. The contour intervals represent 10 hPa day−1 . Note that the
colorscale is not linear.
32
20
−2
40
−20
−20
0
−20
−40
40
20
−40
0
−4
∆ω500 LGM
20
20
0
20
−20
0
−2
20
0
20
30˚S
20
−2
−20
0˚
40
−2
20
20
−2
−4−020 0
20
30˚N
20
40
−40
20
−2−40
0
−20
20
20
20
−20
−40
−40
−2
0
−40
−20
40
−20
−20
20
20
40
20
20
60˚E
90˚E
120˚E
150˚E
180˚
150˚W
120˚W
90˚W
∆ω500 (hPa day−1)
−20 −15 −10 −8
−4
−2
0
2
4
8
10
15
20
Figure S5. Changes in vertical velocity at the 500 hPa level (ω 500 ) simulated by HadCM3 in
response to LGM forcing. Contours are mean-annual ω simulated in the pre-industrial control
experiment. The contour intervals represent 10 hPa day−1 . Note that the colorscale is not
linear.
33
8
4
8
4
8
8
8
8
4
8
8
8
4
8
8
8
8
4
8
4
8
8
4
8
8
4
8
8
30˚S
4
4
8
8
4
4
8
κmax=0.37* (∆P=9 %)
(b)
4
4
0˚
4
κmax=0.41* (∆P=2 %)
(a)
30˚N
4 4
8
8
8
4
4
8
4
4
4
8.0
4.0
2.0
1.0
0.5
0.0
−0.5
−1.0
−2.0
−4.0
−8.0
∆P (mm day−1)
HadCM3 simulated rainfall and salinity change DJF and JJA
Model−proxy agree
Model−proxy do not agree
(c) κ
*
max=0.29
κmax=0.25 (∆SSS=0.10 psu)
(d)
(∆SSS=0.40 psu)
34
35
34
35
36
35
34 33
34
32
32
33
35
30˚S
36
34
0˚
32
35
33
34
35
35
60˚E
2
34 33
3
34
35
120˚E
60˚E
34
0.5
32
34 33
32
34
34
1.0
33
33
33
34
120˚E
34
0.0
34
−0.5
∆SSS (psu)
30˚N
−1.0
Model−proxy agree
Model−proxy do not agree
Figure S6. Change in precipitation and sea surface salinity during (a,c) DJF and (b,d) JJA simulated by HadCM3 in response to LGM forcing. Contours show the annual-mean precipitation
and SSS simulated in the PI simulation. The contour intervals are 2 mm/day and 1 psu respectively. Circles indicate the proxy estimates of precipitation or SSS change (fill color) and
proxy-model agreement (border color). Black and red (yellow) borders indicates agreement
and disagreement respectively. The coastlines correspond to the 120 m isobath of the present
day ocean bathymetry. The maximum Cohens κ and the corresponding optimal threshold for
defining drier/wetter or saltier/fresher conditions is shown for each panel. Asterisks indicate
statistically significant (p < 0.05) Cohens κ values.
34
30˚N
κmax=0.06 (∆P=39 %)
4
(a)
4
κmax=0.35* (∆P=10
%)
8
(b)
4
4
8
4
8
8
8
4
4
4
8
8
8
8
0˚
4
4
4
4
4
8
4
8
8
8
8
8
4
8
4
8 8
8
4
4
4
30˚S
8.0
4.0
2.0
1.0
0.5
0.0
−0.5
−1.0
−2.0
−4.0
−8.0
∆P (mm day−1)
IPSL−CM4 simulated rainfall and salinity change DJF and JJA
Model−proxy agree
Model−proxy do not agree
(c)
κmax=0.07 (∆SSS=0.60 psu)
(d)
35
κmax=0.10 (∆SSS=0.60 psu)
35
35
0˚
36
36
3435
35
34
35
0.5
0.0
−0.5
35 35
36
36
1.0
∆SSS (psu)
30˚N
−1.0
30˚S
36
36
60˚E
120˚E
60˚E
120˚E
Figure S7. As in Figure S6, but for IPSL-CM4.
35
Model−proxy agree
Model−proxy do not agree
(a)
JJA minus DJF rainfall
2
30˚N
4
6
4
6
4
4
2
4
6
6
4
2
4
6
6
4
2
2
8
8
8
8
2
2
2
30˚S
4
2
6
6
4
86
8
4
2
0˚
6
60˚E
120˚E
−8.0 −4.0 −2.0 −1.0 −0.5 0.0 0.5 1.0 2.0 4.0 8.0
∆P (mm/day)
(b) SON minus MAM sea−surface salinity
30˚N
35
34.5
0˚
35
34.5
34
35
35.5
34.5
35
35.
5
30˚S
35.5
120˚E
60˚E
−1.0
−0.5
0.0
0.5
1.0
∆SSS (psu)
Figure S8. Observed seasonal contrast in climatological (a) rainfall and (b) sea-surface salinity (SSS). The rainfall and SSS contrasts are the difference between JJA minus DJF and SON
minus MAM climatologies respectively. The contour intervals are 2 mm/day and 0.5 psu respectively.
36