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Asian monsoon climate during the Last Glacial Maximum:palaeo-data–model comparisons

AKKANEEWUT CHABANGBORN, JENNY BRANDEFELT AND BARBARA WOHLFARTH

Chabangborn, A., Brandefelt, J. & Wohlfarth, B. 2013: Asian monsoon climate during the Last GlacialMaximum: palaeo-data–model comparisons. Boreas. 10.1111/bor.12032. ISSN 0300-9483.

The Last Glacial Maximum (LGM) (23–19 ka BP) in the Asian monsoon region is generally described as cool anddry, due to a strong winter monsoon. More recently, however, palaeo-data and climate model simulations haveargued for a more variable LGM Asian monsoon climate with distinct regional differences. We compiled,evaluated, and partly re-assessed proxy records for the Asian monsoon region in terms of wet/dry climaticconditions based on precipitation and effective moisture, and of sea surface temperatures. The comparison of thepalaeo-data set to LGM simulations by the Climate Community System Model version 3 (CCSM3) shows fairlygood agreement: a dry LGM climate in the western and northern part due to a strengthened winter monsoonand/or strengthened westerly winds and wetter conditions in equatorial areas, due to a stronger summer monsoon.Data–model discrepancies are seen in some areas and are ascribed to the fairly coarse resolution of CCSM3 and/orto uncertainties in the reconstructions. Differences are also observed between the reconstructed and simulatednorthern boundaries of the Intertropical Convergence Zone (ITCZ). The reconstructions estimate a more south-ern position over southern India and the Bay of Bengal, whereas CCSM3 simulates a more northern position. InIndochina, the opposite is the case. The palaeo-data indicate that climatic conditions changed around 20–19 kaBP, with some regions receiving higher precipitation and some experiencing drier conditions, which would implya distinct shift in summer monsoon intensity. This shift was probably triggered by the late LGM sea-level rise,which led to changes in atmosphere–ocean interactions in the Indian Ocean. The overall good correspondencebetween reconstructions and CCSM3 suggests that CCSM3 simulates LGM climate conditions over subtropicaland tropical areas fairly well. The few high-resolution qualitative and quantitative palaeo-records available for thelarge Asian monsoon region make reconstructions however still uncertain.

Akkaneewut Chabangborn ([email protected]) and Barbara Wohlfarth, Department of Geological Sciences,Stockholm University, SE-106 91, Stockholm, Sweden; Jenny Brandefelt, The Swedish Nuclear Fuel and WasteManagement Company, SE-111 64, Stockholm, Sweden; received 4th January 2013, accepted 31st May 2013.

The Asian monsoon is one of the largest climatesystems on Earth and affects a region that extends fromthe Arabian Sea to the South China Sea and fromnorthern Australia to northern China (Wang et al.2005). It has an important influence on Earth’s otherclimatic systems through transport of heat energy andhumidity to higher latitudes (Zahn 2003; Clift & Plumb2008; Maher 2008; Caley et al. 2011; Cook & Jones2012).

The NE monsoon transports cool and dry air massesover the continents during the winter season, whereasthe SW and SE monsoons provide warm and wet con-ditions during summer (Ramage 1971; Wang et al.2003, 2005; Zahn 2003; Holton 2004). These seasonalshifts are generally explained by insolation changes andassociated differences in land–sea heat capacity. As aresult of its dependence on insolation and land–seathermal contrast, the Asian monsoon is also closelylinked to the seasonal shift of the Intertropical Conver-gence Zone (ITCZ) (Chao & Chen 2001; Fleitmannet al. 2007; Clift & Plumb 2008). In summer, the circu-lation of humid air masses from the Indian Ocean,together with the northward shift of the ITCZ, causesrainfall over the Asian continent. In contrast, cool anddry climatic conditions develop in winter when theITCZ shifts southward, allowing the winter monsoonto migrate over the continent.

The Asian monsoon is generally divided into twosubsystems according to differences in summermonsoon circulation patterns: the South AsianMonsoon or Indian Ocean Monsoon (IOM) and theEast Asian Monsoon (EAM). This division follows lon-gitude 105°E, which extends along the eastern edge ofthe Tibetan plateau, across the Indochina Peninsulaand through the Indonesian archipelago (Wang et al.2003, 2005). The IOM is characterized by a distinctgyre generated from clockwise circulation across theequator. The EAM is a convergence of SW winds fromthe Indian Ocean and trade winds from the PacificOcean and also receives contributions from the sub-tropical front near China.

Climate model scenarios suggest that a rise in globaltemperatures may have a significant impact on theintensity of seasonal rainfall in monsoonal Asia (IPCC2012). As a faithful prediction of precipitation patternsis of great societal and economic importance, the per-formance of climate models needs to be tested usingwell-known extreme climate states in the past. The LastGlacial Maximum (LGM: 23–19 ka BP) is a time inter-val representing an extreme climate state with distinctlydifferent environments from today (Mix et al. 2001).Global ice volumes had obtained their maximum,global sea level was at ∼130 m below present (Clarket al. 2009), and forested areas had become reduced

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DOI 10.1111/bor.12032 © 2013 The AuthorsBoreas © 2013 The Boreas Collegium

considerably (Elenga et al. 2000; Tarasov et al. 2000;Williams et al. 2000; Yu et al. 2000a). The quasi-equilibrium climate and the well-known boundary con-ditions make the LGM an excellent test period forclimate models.

Persistent cool and dry climatic conditions through-out the LGM are generally described for the Asianmonsoon region, attributable to a strengthened wintermonsoon (e.g. van Campo et al. 1982; Huang et al.1997; Hodell et al. 1999; von Rad et al. 1999; Hope2001; Naidu 2004; Prabhu et al. 2004; White et al. 2004;Tiwari et al. 2006; Ansari & Vink 2007; Cosford et al.2010; Fleitmann et al. 2011). Other studies, however,argue for substantial precipitation during the LGM,such as palaeo-reconstructions from Sumatra (e.g. vander Kaars et al. 2010), the South China Sea (Sun et al.2000; Colin et al. 2010), and western China (Yu et al.2000a, b; 2003). Moreover, climate model simulationsof LGM climate (Bush 2002; Braconnot et al. 2007;Jiang et al. 2011; Ueda et al. 2011) suggest that condi-tions were wetter than reconstructed by terrestrial andmarine palaeo-data. Moreover, some paleo-recordsindicate that the LGM cool and dry climatic conditionswere punctuated by the short wet intervals (Sun et al.1999, 2000; Rashid et al. 2007; Saher et al. 2007; Govil& Naidu 2011; Mahesh et al. 2011). This wouldmean that the summer monsoon was periodicallystrengthened during a time interval that was generallydominated by winter monsoon conditions. Suchsubmillennial scale oscillations in the strength of thesummer monsoon may have occurred because ofe.g. the El Niño-Southern Oscillation (ENSO) andatmosphere–ocean interactions (Wang et al. 2005); asouthward shift of the ITCZ (Zhang & Delworth 2005;Broccoli et al. 2006; Braconnot et al. 2007) and of thewestern Pacific Warm Pool (De Deckker et al. 2002;Partin et al. 2007); and thermohaline circulationchanges in the North Atlantic, which affected theIndian Ocean (Overpeck et al. 1996; Tiwari et al. 2009;Pausata et al. 2011; Stager et al. 2011). Other ideasexplaining higher LGM monsoon precipitation relateto higher relative humidity (Bush & Philander 1998)and changes in aerosol concentrations (Ruddiman2001; Clift & Plumb 2008), and to lower moist adiaba-tic lapse rates (Barmawidjaja et al. 1993; Flenley 1998)than at present.

Qualitative and quantitative temperature changescan be relatively well reconstructed from proxies, espe-cially in areas with clear climatic gradients. However,precipitation reconstructions are hampered by the factthat rainfall and its amounts have much more localizedexpressions (Dayem et al. 2010; Cook & Jones 2012).The drawbacks of marine and terrestrial palaeo-proxiesas recorders of past monsoon precipitation have there-fore been discussed extensively (see e.g. Sun et al. 1999,2000; Tiwari et al. 2006 for further references). In par-ticular, the use of cave speleothem δ18O as a proxy for

East Asian monsoon intensity has recently been chal-lenged (Clemens et al. 2010; Dayem et al. 2010; Pausataet al. 2011; Maher & Thompson 2012).

Here, we compile published palaeoenvironmentalproxies for the Asian monsoon region and evaluatethese in terms of qualitative precipitation and effectivemoisture to assess LGM summer monsoon variabilityon spatial and temporal scales. We compare these datasets to quantitative precipitation and effective moisturesimulated by the Community Climate System Modelversion 3 (CCSM3), which has shown good corre-spondence to reconstructed LGM climate at high lati-tudes (Kjellström et al. 2009), and test whetherCCSM3 is also able to faithfully simulate LGMmonsoon precipitation over the Asian subtropics andtropics.

Palaeo-proxies

The palaeo-proxy data sets selected for this studyinclude published terrestrial and marine records (herereferred to as palaeo-data compilation) and theMARGO (2009) sea surface temperature (SST) synthe-sis (here referred to as MARGO09) (Table 1). We con-strained our study area to the Asian monsoon regionbetween 15°S and 40°N, and 40°E and 160°E (Fig. 1).Following Wang et al. (2003, 2005), the area was sepa-rated into the IOM and EAM subregions along longi-tude 105°E. The IOM subregion covers the area fromthe Indian Ocean in the south to the Tibetan Plateau inthe north, and the EAM subregion covers large parts ofChina and the western Pacific Ocean (Fig. 1).

The LGM land–sea configuration in the Asianmonsoon region was distinctly different from thepresent day, owing to the marked sea-level lowstand.The LGM palaeogeography did not influence the IOMsubregion as much as the EAM subregion, where asmaller South China Sea and exposure of the EastChina Sea shelf increased the land–sea thermalcontrast. Sumatra, Java, and Borneo were connected tothe Indochina peninsula and formed the so-calledSundaland, and northern Australia was linked to NewGuinea, forming Sahulland (Fig. 1). As the areal extentof the exposed land was almost double that of today(De Deckker et al. 2002) and resulted in distinct envi-ronmental changes (Bird et al. 2005), the continentalshelves of Sundaland and Sahulland (SSS) were treatedas a third subregion (Fig. 1).

Selection criteria

Marine and terrestrial records (Table 1) were selectedhere according to the following criteria: (i) for evalua-tion of the spatial variability of LGM climatic condi-tions, the records should contain at least one 14C, U/Th,and/or TL date between 23 and 19 ka BP; (ii) recordswith age estimate errors of >1000 years, and 14C dates

2 Akkaneewut Chabangborn et al. BOREAS

Table 1. Palaeo-records and palaeo-proxies used for the compilation. The age assignments are based on MARGO project members (2009)(=M) data set and published 14C, TL and U/Th dates. The number of dates between 25–17 ka BP for each sequence is given in parentheses.

Siteno.

Site name Lat (°) Long (°) Elevation(m a.s.l.)

Archive Proxy Ageassignment

References

1 SHI9034 −9.10 111.01 −3330 Marine Planktonicforaminifera

M Ding et al. (2002)

2 SHI9016 −8.46 128.24 −1805 Marine Planktonicforaminifera

M Spooner et al. (2005)

3 Kosipe valley −8.45 147.20 1965 Terrestrial Pollen 14C (1) Hope (2009)4 Situ Bayongbong swamp −7.18 107.28 1300 Terrestrial Pollen 14C (1) Stuijts et al. (1988)5 Bandung basin −7.00 108.00 665 Terrestrial Pollen 14C (1) van der Kaars & Dam

(1997)6 BAR94-42 −6.75 102.42 −2542 Marine Pollen 14C (2) van der Kaars et al. (2010)7 SHI9014 −5.77 126.97 −3163 Marine Pollen 14C (1) van der Kaars et al. (2000)8 SHI9006 −4.33 117.60 −1999 Marine Planktonic

foraminiferaM Ding et al. (2002)

9 Sentarum lake 0.73 112.10 35–50 Terrestrial Pollen 14C (1) Anshari et al. (2001)10 di Atas lake −1.07 100.77 1535 Terrestrial Pollen 14C (2) Newsome & Flenley (1988)11 Pea Sim-sim swamp 2.29 98.89 1450 Terrestrial Pollen 14C (5) Maloney (1980)12 Pee Bullok swamp 2.28 98.98 1400 Terrestrial Pollen 14C (4) Maloney & McCormac

(1996)13 K-12 2.69 127.74 −3510 Marine Planktonic

foraminiferaM Barmawidjaja et al. (1993)

14 K-12 2.69 127.74 −3510 Marine Pollen 14C (1) Barmawidjaja et al. (1993)15 Tasek Bera basin 3.06 102.64 20–30 Terrestrial Hardwood remain 14C (1) Wüst & Bustin (2004)16 KH92-1-5cBX 3.53 141.87 −2282 Marine Alkenone M Ohkouchi et al. (1994)17 Cave in Gunung Buda

National Park4.00 114.00 ∼1000 Terrestrial δ18O U/Th (9) Partin et al. (2007)

18 SO18302 4.15 108.57 83 Marine Pollen 14C (1) Wang et al. (2009)19 SO18300 4.35 108.65 91 Marine Pollen 14C (1) Wang et al. (2009)20 GIK17964-2 6.16 112.21 −1556 Marine Alkenone M Pelejero et al. (1999)21 GIK17961-2 8.51 112.33 −1795 Marine Alkenone M Pelejero et al. (1999)22 MD97-2142 12.69 119.47 −1557 Marine Planktonic

foraminiferaM Chen et al. (2003)

23 GIK17954-2 14.80 111.53 −1520 Marine Alkenone M Pelejero et al. (1999)24 31-KL 18.75 115.87 −3360 Marine Planktonic

foraminiferaM Chen & Huang (1998)

25 GIK17938-2 19.79 117.54 −2840 Marine Planktonicforaminifera

M Chen et al. (1999)

26 MD97-2148 19.80 117.54 −2830 Marine Planktonicforaminifera

M Chen et al. (2002)

27 GIK17940-2 20.12 117.38 −1727 Marine Alkenone M Pelejero et al. (1999)28 Core 17940 20.12 117.38 −1727 Marine Pollen 14C (2) Sun et al. (2000)29 Tianyang basin 20.78 110.03 120 Terrestrial Pollen 14C (2) Zheng & Lei (1999)30 Huguang lake 21.15 110.28 23 Terrestrial Pollen 14C (2) Wang et al. (2010)31 Toushe Basin 23.82 120.88 650 Terrestrial Pollen 14C (3) Liew et al. (2006)32 DGKS9603 28.15 127.27 −1100 Marine Planktonic

foraminiferaM Li et al. (2001)

33 DGKS9603 28.15 127.27 −1100 Marine Pollen 14C (2) Xu et al. (2010)34 Jintanwan Cave 29.48 109.53 460 Terrestrial δ18O U/Th (3) Cosford et al. (2010)35 Hulu Cave 32.30 119.17 100 Terrestrial δ18O U/Th (3) Wang et al. (2001)36 Songjia Cave 32.41 107.41 ∼680 Terrestrial δ18O U/Th (2) Zhou et al. (2008)37 Weinan section 34.40 109.50 600–1100 Terrestrial Pollen 14C (1) Sun et al. (1997)38 Beizhuangcun section 34.33 109.48 600–1100 Terrestrial Pollen 14C (2) Wang & Sun (1994)39 Pyonggeodong

archaeological site35.17 128.06 100–300 Terrestrial Pollen 14C (3) Chung et al. (2006)

40 Biwa lake 35.25 136.05 85 Terrestrial Pollen 14C (2) Hayashi et al. (2010)41 A paddy field in Iwaya,

Fukui prefecture35.52 135.88 20 Terrestrial Pollen 14C (1) Takahara & Takeoka

(1992)42 Mikata Lake 35.56 135.89 0 Terrestrial Pollen 14C (2) Yasuda (1982)43 CH84-04 36.46 142.14 −2630 Marine Alkenone M MARGO (2009)44 KH-79-3_L3 37.06 134.72 −935 Marine Alkenone M Ishiwatari et al. (2001)45 KT94-15_PC-9 39.57 139.41 −807 Marine Alkenone M Ishiwatari et al. (2001)46 MD85-674 3.19 50.44 −4875 Marine Alkenone M Bard et al. (1997)47 SK-157-14 5.18 75.91 −3306 Marine δ18O 14C (1) Ahmad et al. (2008)

LGM Asian monsoon climate 3BOREAS

on carbonate bulk sediment were excluded; (iii)MARGO09 was temporally limited to the LGMchronozone level 2 (24–18 ka BP) (Mix et al. 2001)because the data set contains only few age estimatesfor the time interval 23–19 ka BP; (iv) all publish-ed 14C ages were recalibrated with the Calib6.0 online program (http://calib.qub.ac.uk/calib/calib.html) (Reimer et al. 2009). Age-depth curves were con-structed for palaeo-records with more than one agecontrol point. These were then used to assess the tem-poral variability of the Asian monsoon between 25 and17 ka BP. Palaeo-records containing one age estimatewere only used as supporting information for thespatial analysis.

Where possible raw data was obtained fromNOAA’s National Climatic Data Center (http://www.ncdc.noaa.gov/paleo/paleo.html), PANGAEA(http://www.pangaea.de), and the respective authors.Where raw data were not available, information wasdigitized from published sources.

Palaeo-proxy assessment

The MARGO09 data set could directly be compared tothe CCSM3 output, whereas other palaeo-proxies hadto be assessed in terms of qualitative precipitation andeffective moisture (precipitation minus evaporation,P-E), i.e. wet or dry climatic conditions. Qualitativeprecipitation and P-E were categorized for each type ofterrestrial proxy and were then compared to quantita-tive model output from CCSM3 (Fig. 2). Variations ofLGM summer monsoon intensity were identified bychanges in qualitative precipitation and P-E, and wereinterpolated to millennium-scale resolution for theindividual study sites.

Terrestrial proxies. – For pollen assemblages from ter-restrial archives (24 sites) and marine sequences (fivesites) (Fig. 1), we assigned each pollen taxon with >5%abundance to plant functional types (PFTs) that hadbeen established for China (Yu et al. 2000a), Japan

Table 1. Continued

Siteno.

Site name Lat (°) Long (°) Elevation(m a.s.l.)

Archive Proxy Ageassignment

References

48 Horton plains 6.81 80.83 2100–2300 Terrestrial Pollen 14C (2) Premathilake (2006)Premathilake & Risberg(2003)

49 MD77-191 7.30 76.43 −1254 Marine Alkenone M Sonzogni et al. (1998)50 MD77-169 10.13 95.03 −2360 Marine Alkenone M Sonzogni et al. (1998)51 MD77-194 10.28 75.14 −1222 Marine Alkenone M Sonzogni et al. (1998)52 TY93905/P 10.70 51.93 ∼−1500 Marine Alkenone M Sonzogni et al. (1998)53 Nilgiri hills 11.25 76.67 2200 Terrestrial δ13C 14C (1) Rajagopalan et al. (1997)54 MD77-195 11.30 74.32 ∼−1200 Marine Alkenone M Sonzogni et al. (1998)55 RC12-344 12.46 96.04 −2140 Marine δ18O 14C (3) Rashid et al. (2007)56 Moomi cave, 12.50 54.00 ∼1000 Terrestrial δ18O U/Th (8) Shakun et al. (2007)57 TY93929/P 13.70 53.25 −2490 Marine Alkenone M Sonzogni et al. (1998)58 MD77-176 14.31 93.08 −1375 Marine Alkenone M Sonzogni et al. (1998)59 MD76-135 14.44 50.52 −1895 Marine Alkenone M Sonzogni et al. (1998)60 GeoB3005-1 14.97 54.37 −2316 Marine Alkenone M Budziak et al. (2000)61 MD76-131 15.32 72.34 −1230 Marine Alkenone M Sonzogni et al. (1998)62 MD76-131(C) 15.53 72.57 −1230 Marine Planktonic

foraminiferaM Cayre et al. (1999)

63 GeoB3007-1 16.17 59.76 −1920 Marine Alkenone M Budziak et al. (2000)64 MD77-181 17.24 90.29 −2271 Marine Alkenone M Sonzogni et al. (1998)65 117–723_Site 18.05 57.61 −816 Marine Planktonic

foraminiferaM Godad et al. (2011)

66 MD77-180 18.28 89.51 −1986 Marine Alkenone M Sonzogni et al. (1998)67 MD77-202 19.13 60.41 −2427 Marine Alkenone M Sonzogni et al. (1998)68 SO93-126KL 19.97 90.03 −1250 Marine Alkenone M Sonzogni et al. (1998)69 MD77-203 20.42 59.34 −2442 Marine Alkenone M Sonzogni et al. (1998)70 SO90-137KA 23.12 66.48 −573 Marine δ18O 14C (4) von Rad et al. (1999)71 SO90-93KL 23.59 64.22 −1802 Marine Alkenone M MARGO (2009)72 Bharatpur Bird

Sanctuary wetland27.12 77.52 174 Terrestrial Pollen 14C (1) Sharma & Chatterjee

(2007)73 Phulara palaeolake 29.33 80.13 1500–1700 Terrestrial Pollen 14C (2) Kotlia et al. (2010)74 Kathmandu basin 27.67 85.22 1303 Terrestrial Pollen 14C (3) Fujii & Sakai (2002)75 Shudu lake, 27.90 99.95 3630 Terrestrial Pollen 14C (4) Cook et al. (2011)76 Ren Co 30.73 96.68 4450 Terrestrial Pollen 14C (3) Tang et al. (1999, 2000)77 Tham Rod

archaeological site19.57 98.89 600–1170 Terrestrial Fauna remains TL (1) Wattanapituksakul (2006)


http://calib.qub.ac.uk/calib/calib.html

http://calib.qub.ac.uk/calib/calib.html

http://www.ncdc.noaa.gov/paleo/paleo.html

http://www.ncdc.noaa.gov/paleo/paleo.html

http://www.pangaea.de

Fig. 1. Location of the compiled palaeo-data sets and MARGO (2009) sea surface temperature sites used in this study. The Asian monsoonregion is separated into three subregions: the Indian Ocean Monsoon, the East Asian Monsoon, and the Sundaland and Sahulland Shelves.The LGM palaeo-shoreline (thick black contour line) and land topography (green, yellow, and red contour lines) are based on the TerrainBase5-min global bathymetry/topography data set (National Geophysical Data Center 1995). See Table 1 for details on the sites. This figure isavailable in colour at http://www.boreas.dk.

Fig. 2. Flow chart illustrating the differentsteps of the compilation, evaluation ofproxies, and comparisons to the ClimateCommunity System Model v. 3. E =evaporation; LGM = Last GlacialMaximum; P = precipitation; PFTs =plant functional types; SST = sea surfacetemperature.


http://www.boreas.dk

(Takahara et al. 2000), and South-East Asia (Pickettet al. 2004) by the Global Paleovegetation Mapp-ing (BIOME6000) project (Prentice & Webb 1998)(Table 2, Fig. 2). For the IOM domain, we used thePFTs established by Kramer et al. (2010) because theBIOME6000 project for the Indian continent is stillin progress. The PFTs established by BIOME6000 andby Kramer et al. (2010) are based on a relationshipbetween pollen taxa and modern climatic variables,i.e. mean temperature of the coldest month, growingdegree days, and moisture index of each pollen taxa, aswell as elevation, rainfall, and fractional sunshine hour(Prentice et al. 1996). The PFTs were then assigned tobiomes (Table 2, Fig. 2) based on the assumption thatall existing plant taxa share the same climatic condi-tions and that outlying taxa may have been long-transported from a different climatic region (Prenticeet al. 1996). However, in a few cases, when pollenassemblages could not be assigned to only one biome,they were categorized as mixed biome (Table 2). Thebiomes were approximated to qualitative precipitationand P-E, based on the relationship between biomes andmean annual precipitation and temperature, as sug-gested by Mader (2010). Inferred P-E was grouped intothree categories, representing low (1), medium (2), andhigh (3) (Table 3). The climatic conditions assessedfrom land-based archives were then compared to theCCSM3 model output (Fig. 2). Pollen assemblagesfrom marine sequences represent runoff from the hin-terland and, as such, a mix of pollen sources. Thesewere therefore not used for comparisons with theCCSM3 simulation.

As the reconstructed biomes may not show largetemporal differences, we chose to employ the increase/decrease of non-arboreal pollen taxa (e.g. Artemisia,Compositae, Rosaceae, Chenopodiaceae, Poaceae)or other major ecological group variations (e.g.Pteridophyta spores) as tentative indicators for varia-tions in grassland expansion or runoff, respectively,and/or precipitation/P-E and, as such, shifts in summermonsoon strength over time.

For speleothem δ18O values we assumed thatspeleothems grow in a closed system (ΔT ∼0°C) andthat variations in δ18O mirror the amount of precipita-tion over the site (Wang et al. 2001; Yuan et al. 2004).δ18O values for each speleothem archive (five sites) wereaveraged between 23 and 19 ka BP to represent meanLGM values for each site and to allow for a generalizedcomparison between individual sites (Table 4). Thisgeneralized approach minimizes local factors that caninfluence the δ18O composition of the speleothems andhigh-frequency oscillations, and facilitates intersitecomparisons and relative precipitation estimates.Qualitative precipitation derived from speleothem δ18Owas categorized based on significant differences inLGM mean δ18O values (Table 4). Low mean δ18Ovalues in respect to the LGM mean signify high pre-

cipitation (3) and high mean δ18O values low precipita-tion (1) (Table 3). For investigation of summermonsoon variability over time, we averaged δ18O valuesfor each speleothem using a five-point running meanbetween 25 and 17 ka BP. Average values for each sitewere then compared to the LGM mean δ18O value forindividual speleothem sites to assess shifts in relativelywet or dry climatic conditions.

Climatic conditions derived from other terrestr-ial proxies, i.e. δ13C of bulk sediment, hardwood andfaunal remains, and archaeological evidence, were usedas supporting information and follow the interpreta-tions of the respective authors.

Marine proxies. – For planktonic foraminifera (Glo-bigerinoides ruber) δ18O values we assumed that sea-level was stable (Clark & Mix 2002; Clark et al. 2009)and that major salinity changes did not occur duringthe LGM (Schulz et al. 1998; Clark & Mix 2002)(Fig. 2).

The SST difference between the LGM and thepresent-day was calculated using the MARGO09alkenone and the World Ocean Atlas (WOA) 1998(Conkright et al. 1998) data sets (Table 5). This differ-ence was then used to calculate the LGM P-E forselected marine δ18O records (four sites) (Table 6)(Tiwari et al. 2006) at locations, which were not signifi-cantly affected by the LGM sea-level lowstand. Calcu-lation of P-E was based on the assumption of a 0.25‰increase in δ18O with a SST decrease of 1°C (Erez & Luz1983). Moreover, we assume a constantly averagedglobal ocean effect of ∼1.1‰ higher than present duringthe LGM sea-level lowstand (Adkins et al. 2002;Ravelo & Hillaire-Marcel 2007). P-E calculated forfour sites and based on the LGM δ18O mean values ofplanktonic foraminifera (G. ruber; Table 6) is used asan approximation of mean LGM precipitation andallows for an easier qualitative comparison betweenmarine sites. We assigned the calculated P-Es to twoqualitative categories: high P-E and high precipitation(3); low P-E and low precipitation (1) (Table 3) usingthe same procedure as for the mean δ18O speleothemvalues.

Climate model output data

The Community Climate System Model version 3

The climate model output used here is from theCCSM3, which is a global, coupled ocean-atmosphere-sea ice-land surface climate model (Collins et al. 2006;Brandefelt & Otto-Bliesner 2009). The model has beensuccessfully employed to simulate extreme climaticstates in the past (e.g. Kjellström et al. 2009; Brandefeltet al. 2011). The LGM simulation that was analysedhere has been described in detail by Otto-Bliesner et al.(2006a) and Brandefelt & Otto-Bliesner (2009).


Table 2. Assignment of LGM pollen assemblages to biomes using plant functional types (PFTs). See text for further explanations.

Siteno.

Major pollen component during the LGM(>5% in the pollen diagram)

Biome References References for biomeidentification

3 Nothofagus, Poaceae Tropical deciduousbroadleaf forest andwoodland

Hope (2009) Pickett et al. (2004)

4 Dacrycarpus imbricatus, Altingia, Castanopsis comp.,Quercus

Warm-temperate rain forest Stuijts et al. (1988) Pickett et al. (2004)

5 Low-montane forest II: Dacrycarpus, Distylium,Dodonaea, Engelhardia, PodocarpusLow-montane forest I: Altingia,Castanopsis/Lithocarpus, Eugenia, QuercusSubmontane forest: Celtis,Helicia,Moraceae/Urticacae, Palmae, Trema

Tropical deciduousbroadleaf forest andwoodland

van der Kaars &Dam (1997)

Pickett et al. (2004)

6 Dipterocarpaceae, Eucalyptus type (Myrtaceae),Leguminosae (Fabaceae), Macaranga type(Euphorbiaceae), Rutaceae, Sapotaceae/Meliaceae,Lithocarpus (Fagaceae), Quercus (Fagaceae),Dacrycarpus, Distylium (Hamamelidaceae), Engelhardia(Juglandaceae), Podocarpus (Podocarpaceae),Cyperaceae, Poaceae

Warm-temperate rain forest van der Kaars et al.(2010)


7 Macaranga/Mollotus, Oleaceae, Lithocarpus,Podocarpus, Cyperaceae, Poaceae, Eucalyptus


van der Kaars et al.(2000)


9 Gluta renghas (Anacardiaceae), Calophyllum(Guttiferae), Gymnosperm sumatrana (Casuarinaceae),Palaquium Type I (4 colporate grain/Sapotaceae),Planchonella Type (Sapotaceae), Longetia(Euphorbiaceae), Sterculiaceae, Symplocos comp.(Symplocaceae), Quercus (Fagaceae),Macaranga/Mallotus (Euphorbiaceae)


Anshari et al. (2001) Pickett et al. (2004)

10 Dacrycarpus, Dacrydium, Lithocarpus/Castanopsis,Quercus comp., Symingtonia, Hamamelidaceae,Elaeocarpus comp., Iltex comp., Medinilla comp.,Myrsine comp., Myrtaceae, Vaccinium comp.,Cyperaceae, Poaceae, Eriocaulon, Tricolporate psilate,Fillices, Lycopodium

Warm-temperate rain forest Newsome & Flenley(1988)


11 Dacrycarpus, Dacrydium, Engelhardia comp., Eugeniacomp., Lithocarpus/Cartanopsis comp., Quercus comp.,Symingtonia comp., Ericaceae

Wet sclerophyll forest,change to tropical broadleafforest and woodland

Maloney (1980) Pickett et al. (2004)

12 Dacrydium, Cartanopsis comp., Quercus, Eugenia,Engelhardia, Symingtonia populnea, Vaccinium comp.,Myrsine, Cyatheaceae

Warm-temperate rain forest Maloney &McCormac (1996)


14 Podocarpus, Dacrycarpus, Lithocarpus/Castanopsis Warm-temperate rain forest Barmawidjaja et al.(1993)


18 Quercus, Euphorbiaceae, Palmae, Cyperaceae Tropical deciduousbroadleaf forest andwoodland

Wang et al. (2009) Pickett et al. (2004)

19 Quercus, Palmae, Oleaceae, Rubiaceae, Rutaceae,Cyperaceae, Poaceae

Tropical deciduousbroadleaf forest, woodland,and steppe

Wang et al. (2009) Pickett et al. (2004)

28 Pinus, Artemisia Mix of steppe and montanerain forest/montane conifers

Sun et al. (2000) Yu et al. (2000a)

29 Quercus (evergreen), Casta nopsis, Papilionaceae,Urticaceae, Taxodiaceae, Cyperaceae, Poaceae,Artemisia

Mix of steppe and warmmixed forest

Zheng & Lei (1999) Yu et al. (2000a)

30 Castanopsis-Lithocarpus, Pinus, Quercus (evergreen),Quercus (deciduous), Poaceae, Artemisia, Cyperaceae,

Mix of steppe and warmmixed forest

Wang et al. (2010) Yu et al. (2000a)

31 Pinus, Ilex, Alnus, Quercus, Cyclobalanopsis,Castanopsis, Symplocos, Ulmus, Ligustrum, Salix,Artemisia, Cyperaceae, Poaceae

Broadleaf evergreen/warmmixed forest

Liew et al. (2006) Yu et al. (2000a)

33 Tsuga, Pinus, Quercus (evergreen), Betulaceae, Quercus(deciduous), Poaceae, Cyperaceae, Chenopodiaceae,Compositae, Artemisia

Dominated by steppe andwarm mixed forest

Xu et al. (2010) Yu et al. (2000a)

37 Artemisia, Compositae Steppe Wang & Sun (1994) Yu et al. (2000a)


The atmospheric component model of CCSM3 is theNCAR (National Centre for Atmospheric Research)Community Atmospheric Model version 3 (CAM3)with a horizontal resolution of approximately 2.8×2.8°.

The land model has the same grid resolution as theatmospheric model and includes a river routing scheme.The ocean and sea ice models have a grid resolution ofapproximately 1×1°.

Table 2. Continued

Siteno.

Major pollen component during the LGM(>5% in the pollen diagram)

Biome References References for biomeidentification

38 Abies, Tsuga, Picea, Pinus subgeneous Haplaxylon,Pinus undifference, Cupressaceae, Salix, Carpinus,Betula, Quercus subgenus Lepidobalanus, Alnus,Poaceae, Artemisia

Cool mixed forest Hayashi et al. (2010) Takahara et al.(2000)

39 Cruciferae, Poaceae, Cyperaceae, Leguminoceae,Liliaceae

Steppe Sun et al. (1997) Yu et al. (2000a)

40 Piceae, Salix, Betula, Quercus (Lepidobalanus),Artemisia, Compositae, Poaceae

Mix of cool mixed forestand steppe

Chung et al. (2006) Yu et al. (2000a)

41 Abies, Tsuga, Cryptomeria, Betula, Ulmus, Alnus,Poaceae, Cyperaceae, Lysichiton, Copositae

Mix of cool mixed forestand steppe

Takahara & Takeoka(1992)

Takahara et al.(2000)

42 Picea, Abies, Tsuga, Pinus (Haploxylon), Ulmus,Quercus (Lepidobalanus), Salix, Betula, Corylus, Alnus,Cyperaceae, Poaceae, Lysichiton, Umbelliferae,Artemisia, Compositae

Cool mixed forest Yasuda (1982) Takahara et al.(2000)

48 Chenopodiaceae Steppe Premathilake (2006)

73 Pinus, Tsuga, Quercus (deciduous), Quercus(evergreen), Castanopsis, Alnus, Carpinus, Betula,Poaceae, Artemisia, Chenopodiaceae

Cool mixed forest Fujii & Sakai (2002) Kramer et al. (2010)

74 Abies, Picea, Cupressaceae, Quercus (deciduous),Quercus (evergreen), Betula, Poaceae, Cyperaceae,Asteraceae, Artemisia, Rosaceae

Mix of steppe and coolmixed forest

Cook et al. (2011) Kramer et al. (2010)

75 Pinus, Cedrus, Picea, Abies, Larix, Quercus, Ulmus,Loniceae, Urticaceae, Poaceae, Cyperaceae,Caryophyllaceae, Tubuliflorae, Linguiliflorae,Artemisia, Polygonum, Primulaceae

Mix of steppe and coolmixed forest

Kotlia et al. (2010) Kramer et al. (2010)

76 Chenopodiaceae, Artemisia Steppe Tang et al. (1999,2000)

Kramer et al. (2010)

Table 3. Assignment of qualitative precipitation minus evaporation (effective moisture; P-E) for biome and planktonic foraminifera, andprecipitation for speleothem mean δ18O values. Biomes were roughly approximated to P-E by comparison to a relationship between biome andmean annual temperature and precipitation suggested by Mader (2010). Mean δ18O values obtained from planktonic foraminifera (Globigerinaruber) were converted to P-E, whereas those of speleothem were directly used to represent LGM qualitative precipitation. These assignmentswere used to attribute qualitative P-E to the biomes in Fig. 4B, i.e. 1 = dry; 2 = medium; 3 = wet. The grey box represents medium precipita-tion, which was not considered in the qualitative precipitation assessments for the speleothem mean δ18O values and planktonic foraminiferaP-E assessments in order to avoid any overestimation.

Palaeo-proxyassessment

Effective moisture Qualitative precipitationspeleothem δ18O values

Biome P-E

3 Warm-temperate rain forest, tropical deciduous broadleaf forest and woodland,wet sclerophyll forest, and broadleaf evergreen/warm forest

<−1.4 <−7.70

2 Mix of steppe and warm mixed forest1 Cool mixed forest, mix of steppe and cool mixed forest and steppe >0.3 >−0.58

Table 4. LGM mean δ18O values for selected speleothems.

Site # Site name Time interval (ka BP) LGM mean δ18O (‰) value References

17 Gunung Buda National Park 24.3–17.8 −7.70±0.05 Partin et al. (2007)34 Jintanwan Cave 24.1–17.7 −6.20±0.22 Cosford et al. (2010)35 Hulu cave 24.2–18.8 −6.29±0.33 Wang et al. (2001)36 Songjia cave 19.8–17.5 −9.26±0.14 Zhou et al. (2008)56 Moomi cave 24.2–17.3 −0.58±0.17 Shakun et al. (2007)


Tab

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Ann

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ean

sea

surf

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tem

pera

ture

s(S

STs)

sim

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and

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nam

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TC

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3

(°C

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09

(°C

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TW

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98

(°C

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STM

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09-C

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3

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)ΔS

STC

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98

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era

26.1

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0.85

27.7

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2SH

I901

6P

lank

toni

cfo

ram

inif

era

26.9

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1827

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28.2

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74−1

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48

SHI9

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Pla

nkto

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24.8

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1795

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224

31-K

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lank

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2P

lank

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626

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97-2

148

Pla

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330.

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252

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723_

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enon

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enon

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0.34

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90-9

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1.45

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soon

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enon

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25.7

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554

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one

25.8

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61M

D76

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Alk

enon

e25

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0.23

25.2

0±1.

5028

.25

0.10

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5−3

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62M

D76

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(C)

Pla

nkto

nic

fora

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0.23

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1±0.

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668

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0.27

26.4

0±1.

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0.86

−2.2

8−1

.42


For the LGM boundary conditions, insolation wasset to be constant at 1365 W m−2 and the concentrationof greenhouse gases followed those reported from ice-cores (Otto-Bliesner et al. 2006a). The tropical incom-ing insolation during the LGM was lower (∼−4 W m−2)than during the pre-industrial period (AD 1800)between July and November (Otto-Bliesner et al.2006a). The CCSM3 output suggests that radia-tive forcing decreased by ∼2.76 W m−2, of which∼2.20 W m−2 can be attributed to a lower CO2 concen-tration. The LGM CCSM3 continental ice sheet,topography, coastline (∼−120 m), and bathymetry arebased on the LGM ICE-5G reconstruction of Peltier(2004).

For the LGM simulation, CCSM3 was started frompre-industrial boundary conditions. The model runreached a quasi-steady state after 800 model years, butwas continued for another ∼1000 years (Brandefelt &Otto-Bliesner 2009). Model output from the last 300model years is considered here to represent the annualaverage simulated climate for the LGM (c. 21 ka BP).CCSM3 output was interpolated to 5×5° using theClimate Data Operation program in order to comparewith MARGO09. Mean annual evaporation was calcu-lated based on the direct proportion between surfacelatent heat flux and evaporation amount. CCSM3simulated precipitation, P-E, and SSTs were used forcomparisons with the qualitative precipitation and P-Eestimates derived from the palaeo-data compilation,and the quantitative SSTs of MARGO09 (Fig. 2). Thedifferences in air temperature, SST, and precipitationrate between the LGM and the recent past (RP) climate(years 1960–2000; Otto-Bliesner et al. 2006b) are dis-cussed below.

CCSM3 LGM simulation

CCSM3 simulates warmer mean annual air tempera-tures (21–24°C) from the equator to latitude 20°Nduring the LGM, compared with the surroundingregions (Fig. 3A). Mean annual air temperatures areslightly lower over the exposed Sundaland and

Sahulland than over surrounding areas and there is adistinct decrease in air temperatures on the ArabianPeninsula, near the Himalayan Mountains and over theTibetan Plateau. In contrast, temperatures only gradu-ally decrease northward in the western Pacific Ocean.LGM mean annual air temperatures are distinctlycooler than in the RP in the entire Asian Monsoonregion (Fig. 3B). A maximum air temperature differ-ence between LGM and RP of ∼−5°C is seen over theexposed Sundaland and Sahulland, in the westernArabian Sea region, over the Himalayan Mountains,and along the east coast of China. The LGM and RPannual mean air temperature difference is, however,lower (∼−3°C) in the Bay of Bengal and in the EastChina Sea.

Simulated LGM SSTs generally decrease from theequator northwards (Fig. 3C), but are higher in the Bayof Bengal as compared to the western Arabian Sea.Cooling between LGM and RP SST values is highest inthe western Arabian Sea and in the NW Pacific, withonly minor differences in the Bay of Bengal, in the EastChina Sea, and in the Indonesian Gateways (Fig. 3D).

CCSM3 simulates high LGM mean annual precipi-tation of >2500 mm a−1 between 5° and 10°S over theIndian and Pacific Oceans (Fig. 3E). The westernArabian Sea region and NW India are very dry andmean annual precipitation amounts to <500 mm a−1. Incontinental regions, mean annual precipitationdecreases to 1000 mm a−1 between 10° and 20°N and to<500 mm a−1 north of 20°N. Exceptions are the Bay ofBengal, the Malaysian Peninsula, and the South ChinaSea, where mean annual precipitation of 1500–2000 mm a−1 are simulated (Fig. 3E). The differencebetween LGM and RP mean annual precipitation sug-gests a significantly lower LGM mean annual precipi-tation in the Indonesian archipelago and in the westernArabian Sea (Fig. 3F). In contrast, LGM precipitationin the Bay of Bengal, including the west coast of theIndochina Peninsula and the East China Sea, is higherthan during the RP.

The distribution of P-E calculated from CCSM3output data generally resembles that of mean annual

Table 6. LGM mean P-E derived from planktonic foraminifera (G. ruber) δ18O (Tiwari et al. 2006).

Site # Site name LGM mean δ18O (‰)values (PDB standard)

ΔSSTLGM-Present1 (°C) P-E2 References

6 BAR94-42 ∼−1.00 −2.17 1.56 van der Kaars et al. (2010)47 SK-157-14 −0.96±0.43 −2.42 1.46 Ahmad et al. (2008)55 RC12-344 −1.19±0.17 −2.44 1.67 Rashid et al. (2007)70 SO90-137KA 0.09±0.09 −2.98 0.27 von Rad et al. (1999)

1Difference between reconstructed LGM SSTs inferred from alkenones (MARGO 2009) and present-day SSTs from the World Ocean Atlasdata set (Conkright et al. 1998) from sites in the vicinity.2P-E is based on the assumption that δ18O increases by 0.25‰ with a SST decrease of 1°C (Erez & Luz 1983) and a constantly averagedglobal ocean effect of ∼1.1‰ higher than present during the sea-level lowstand of the LGM (Adkins et al. 2002; Ravelo & Hillaire-Marcel2007).


Fig. 3. CCSM3 simulated climate parameters for the Last Glacial Maximum (LGM) compared to the Recent Past (RP). The land-seatopography is based on the LGM shoreline in the TerrainBase 5-min global bathymetry/topography data set (National Geophysical DataCenter 1995). A. LGM mean annual air temperature (°C). B. Difference between LGM and RP mean annual temperatures (°C). C. LGM meanannual sea surface temperatures (SSTs; °C). D. Difference between LGM and RP SSTs (°C). E. LGM mean annual precipitation (mm a−1). F.Difference between mean LGM and RP precipitation (mm a−1). G. LGM mean annual effective moisture (P-E). This figure is available incolour at http://www.boreas.dk.



precipitation simulated by CCSM3. P-E shows dry con-ditions along the west coast of the Arabian Sea and wetconditions near the equator (Fig. 3G). There are dis-tinct differences in continental regions, where CCSM3simulates low precipitation, but medium to high P-E.This indicates very low evaporation over these areasduring the LGM.

To compare quantitative model output to qualitativeprecipitation reconstructed from palaeo-data, weassumed here that precipitation amounts of <1000,1000–2000, and >2000 mm a−1 correspond to low, med-ium, and high precipitation, respectively. Furthermore,simulated P-E of <0, 0 to 800, and >1200 mm a−1 wereassumed to correspond to low, medium, and high P-E,respectively, as estimated from the proxy data.

Palaeo-data–climate modeloutput comparison

Indian Ocean monsoon subregion

δ18O values measured on speleothems from the westernArabian Sea (#56, Shakun et al. 2007) (Table 4) havebeen interpreted as representing arid LGM conditions.In addition, the low P-E calculated for marine site #70(von Rad et al. 1999) from the continental margin off theIndus Delta suggests low precipitation (Table 3). AridLGM conditions agree well with CCSM3 simulations oflow precipitation (<500 mm a−1) (Fig. 4A) and low P-E(<−400 mm a−1) (Fig. 4B) over the western part of theIOM domain. High P-E reconstructed off southernIndia (#47, Ahmad et al. 2008) and in the Bay of Bengal(#55, Rashid et al. 2007) is of a similar value as P-E atsite #6 (van der Kaars et al. 2010) in Sundaland, wheresubstantial LGM precipitation has been inferred frommultiproxy analyses (Table 6). The CCSM3 simulationalso compares well to the palaeo-data assessment hereand suggests high P-E (1000–>1200 mm a−1) over thesouthern and eastern IOM domain (Fig. 4B).

Data−model discrepancies are visible for Sri Lanka,southern India, northwestern Thailand, and the north-ern part of the IOM subregion (Fig. 4A, B). Chen-opodiaceae are the only pollen taxa present in LGMsediments from Sri Lanka (#48, Premathilake &Risberg 2003; Premathilake 2006) and were originallyinterpreted as representing a dry (xerophytic) forest(Premathilake 2006). Here, we assigned these assem-blages to a steppe biome because the majority ofChenopodiaceae pollen are drought-tolerant species(Kotlia et al. 2010; Kramer et al. 2010) (Table 2), andinfer dry LGM conditions (Table 3). The dominance ofdry-tolerant C4 plants in sediments from the NilgiriHills (#53, Rajagopalan et al. 1997) provides addi-tional support for a dry LGM climate. In contrastto the palaeo-proxy assessment of dry conditions,CCSM3 simulates intermediate P-E (∼0–200 mm a−1)(Fig. 4B) and precipitation (∼1500 mm a−1).

Dry conditions can also be assessed from terrestrialsites in the northern part of the IOM subregion(Fig. 4B), i.e. a distinct barren pollen zone in LGMsediments from northern India (#72, Sharma &Chatterjee 2007), reconstruction of steppe and coolmixed forest biomes for LGM sequences in Nepal (#74,Fujii & Sakai 2002; Hayashi et al. 2009; #73, Kotliaet al. 2010) and western China (#75, Cook et al. 2011),and inferred steppe biomes on the Tibetan Plateau(#76, Tang et al. 1999, 2000) (Tables 2, 3). CCSM3simulates low precipitation for these areas (Fig. 4A),but medium P-E (0–600 mm a−1) over Sri Lanka andsouth of India, and for the northern IOM subregion(Fig. 4B). Medium simulated P-E over the northernIOM subregion may be explained by very low evapo-ration owing to low surface temperatures near theHimalayan Mountains.

The palaeo-data and model comparison showsalso less correspondence over western Thailand(#77, Wattanapituksakul 2006). Here faunal remainsand δ18O values measured on molluscs in LGMsediments, were interpreted as indicating climaticconditions similar to today (Shoocongdej 2000,2006; Wattanapituksakul 2006; Marwick & Gagan2011), i.e. a precipitation of around 2000 mm a−1

(Trikanchanawattana 2005). However, CCSM3 simu-lates low LGM precipitation (<500 mm a−1; Fig. 4A).

Sundaland and Sahulland subregion

Pollen assemblages from western (#12, Maloney 1980;#10, Newsome & Flenley 1988; #11, Maloney &McCormac 1996), southern (#4, Stuijts et al. 1988; #5,van der Kaars & Dam 1997; #6, van der Kaars et al.2010), and central (#9, Anshari et al. 2001) Sundaland,from the northern Molucca Sea region (#14,Barmawidjaja et al. 1993), the Banda Sea (#7, van derKaars et al. 2000), and the eastern part of the SSSsubregion (#3, Hope 2009) (Fig. 4) all suggest the exist-ence of warm temperate rain forest and tropical decidu-ous broadleaf forest and woodland biomes on exposedland areas (Table 2). Similarly, macrofossil finds ofDipterocarpaceae (#15, Wüst & Bustin 2004) are indi-cators for warm-temperate rain forests (Pickett et al.2004). Pollen records collected near the NorthSundaland palaeo-river mouth (#18–19, Wang et al.2009) also mirror tropical broadleaf forest andwoodland biomes in the hinterland (Table 2). Takentogether, these biomes point to wet climatic conditionsin large parts of the SSS subregion. This is supportedby low LGM mean δ18O value of cave speleothem fromBorneo (#17, Partin et al. 2007) (Table 4).Consistent with these palaeo-reconstructions, wetconditions are also shown by CCSM3 simulationsof high precipitation (>2500 mm a−1; Fig. 4A) andhigh P-E (>800 mm a−1) over the SSS domain(Fig. 4B).


East Asian monsoon subregion

Warm mixed forest and steppe biomes are recon-structed for sites in southern China (#29, Zheng & Lei1999; #30, Wang et al. 2010) (Table 2). These biomescompare well to those inferred from pollen in marine

sediments from the South China Sea (#28, Sun et al.2000), which suggest mixed steppe, montane rainforest, or montane conifer forest biomes on the adja-cent continent (Table 2). The biome assessment indi-cates intermediate P-E, which compares well toCCSM3 simulations of medium P-E (0–200 mm a−1)

Fig. 4. Palaeo-data–CCSM3 comparison for the LGM. Quantitative precipitation and P-E (mm a−1) modelled by CCSM3 is compared toqualitative precipitation (A) and P-E (B) inferred from the palaeo-data compilation. The land-sea topography is based on the LGM shorelinein the TerrainBase 5-min global bathymetry/topography data set (National Geophysical Data Center 1995). For easier comparisons betweenpalaeo-data and model results, the simulated quantitative precipitation and P-E were categorized as low (<1000 mm a−1), medium (1000–2000 mm a−1), and high (>2000 mm a−1), and low (<0 mm a−1), medium (0–800 mm a−1), and high (>800 mm a−1), respectively. See Table 3 fordetails on the qualitative assessment of the palaeo-proxies. This figure is available in colour at http://www.boreas.dk.



over the inland of southern China (Fig. 4B). Moreover,the low simulated P-E (0 to −200 mm a−1) over Koreaand Japan corresponds well to a mix of steppe and coolmixed forest biomes (#42, Yasuda 1982; #41,Takahara & Takeoka 1992; #39, Chung et al. 2006;#40, Hayashi et al. 2010), which imply arid climaticconditions during the LGM (Fig. 4B).

Reconstructed P-E based on biomes and simulatedby CCSM3 show marked discrepancies along coastalareas and in central China. A palaeovegetation recordfrom Taiwan (#31, Liew et al. 2006), which is inter-preted as representing a broadleaf evergreen/warmmixed forest biome (Table 2), for example points tohigher LGM P-E as compared to sites from the SouthChina mainland (#29, Zheng & Lei 1999; #30, Wanget al. 2010). In contrast, CCSM3 simulates low P-Eover this area (Fig. 4B). Pollen stratigraphies obtainedfrom the Chinese loess plateau (#38, Wang & Sun 1994;#37, Sun et al. 1997) mainly consist of non-arborealpollen assemblages and are assigned to tundra andsteppe biomes (Table 2). These suggest predominantlydry climatic conditions (Fig. 4B, Table 3). However,the CCSM3 LGM simulation indicates intermediateP-E (∼400 mm a−1).

A difference also exists between reconstructed andmodelled precipitation and precipitation/humidityinferred from LGM speleothem δ18O records. Themean LGM δ18O values of speleothems from Jintawan(#34, Cosford et al. 2010) and Hulu (#35, Wang et al.2001) Caves would suggest comparably high precipita-tion (Fig. 4A), as LGM mean δ18O values are similar tothose from Gunung Buda National Park (#17, Partinet al. 2007), but distinctly different from Moomi Cave(#56, Shakun et al. 2007) (Table 4). CCSM3 simulatedvalues of <1000 mm a−1 for continental regions between20 and 35°N (Fig. 4A) are however at odds with aninterpretation of high precipitation amounts from caveδ18O values (Wang et al. 2001; Yuan et al. 2004;Cosford et al. 2010).

Pollen assemblages in marine sediments from theEast China Sea (#33, Xu et al. 2010) contain mixedsignals of desert, tundra, and warm mixed forestbiomes (Table 3). These mirror river transport and/ornear-shore vegetation and are thus difficult to interpret.

Comparison between CCSM3 output data andMARGO09 SST reconstructions

Reconstructed SSTs in the Asian monsoon region aremostly based on planktonic foraminifera transfer func-tions and on unsaturated alkenones measured in cocco-lithophores (MARGO 2009). The LGM mean value ofthe reconstructed SSTs is ∼25°C, which is ∼2°C lowerthan present-day mean SSTs (Conkright et al. 1998).

Alkenone-based SSTs gradually decrease from∼26°C near the equator to ∼23°C north of 10°N in the

Arabian Sea (Fig. 5). SSTs reconstructed for sites #62and #65 based on planktonic foraminifera are higherthan those estimated for close-by sites (Fig. 5). CCSM3simulated SSTs also decrease northward, but are gen-erally higher in the eastern Arabian Sea (25–26°C) ascompared to the western Arabian Sea (22–24°C)(Table 5, Fig. 5). The data and model output comparewell for the eastern Arabian Sea, but simulated SSTsare generally 2–3°C lower than reconstructed values.

Reconstructions for the Bay of Bengal suggest SSTsof ∼26°C, except for at site #58, where SSTs are slightlyhigher. The reconstructions compare generally well tosimulated SSTs. Reconstructions and model outputshow that SSTs in the Bay of Bengal were comparableto those in the Lombok Strait of the Indonesian Gate-ways (Fig. 5, Table 5).

SST reconstructions from the Indonesian Gatewayare based on planktonic foraminifera only (sites #1–2,8, 13) and show generally high mean values (∼27°C),which are only 1°C lower than those of the present.(Table 5). Alkenone-based SSTs at site #16, which islocated northeast of the Indonesian Gateway in thewestern tropical Pacific, are in a similar range (Fig. 5).Simulated and reconstructed SSTs show very goodagreement (Table 5, Fig. 5).

In the EAM subregion, reconstructed and modelledSSTs generally decrease from south to north, similar topresent-day mean SSTs (Table 5), and reach less than∼18°C close to the Sea of Japan (Table 5). In this partof the study area, the resolution of CCSM3 is howevertoo coarse to allow for comparisons. In the SouthChina Sea SST reconstructions are, with a few excep-tions, in good agreement with CCSM3 simulated SSTs(Fig. 5, Table 5). SSTs derived from alkenones andplanktonic foraminifera at sites #20–21 and sites #24and #26, respectively, display values comparable tosimulated SSTs (∼25°C). However, for site #22 plank-tonic foraminifera-based SSTs are ∼27°C and thushigher than simulated, and for sites #23 and #27,alkenone-based SSTs are ∼22°C, which is much lowerthan estimated from planktonic foraminifera (Fig. 5,Table 5).

Temporal variability of the Asian monsoonduring the LGM

Pollen and speleothem δ18O records with at least twodates spanning the time interval between 25 and 17 kaBP (Fig. 6) are used here to discuss the temporal vari-ability of the Asian summer monsoon during the LGM.

In the IOM subregion, records from Sri Lanka (#48,Premathilake & Risberg 2003; Premathilake 2006),Yemen (#56, Shakun et al. 2007), and northern India(#74, Fujii & Sakai 2002; Hayashi et al. 2009; #73,Kotlia et al. 2010) indicate mainly dry climatic condi-tions until between c. 19 and c. 20 ka BP and a shift to


wetter conditions thereafter (Fig. 6). Pollen records forLake Shudu (#75, Cook et al. 2011) in the NE part ofthe IOM region do not show any significant vegetationchanges. However, sediment mineral magnetic proper-ties change abruptly at around 20 ka BP, which mightsignify a change from lower to higher runoff and assuch a shift in precipitation patterns (Cook et al. 2011).

One pollen record from western Sumatra (#10,Newsome & Flenley 1988) shows a change from wetterto drier conditions c. 20 ka BP, which compares wellwith precipitation reconstructions from Borneo (#17,Partin et al. 2007) (Fig. 6). In contrast, pollen recordsfrom the Sumatra plateau (#11, Maloney 1980; #12,Maloney & McCormac 1996), which is located in the

Fig. 5. LGM sea surface temperature (SST; °C) in the Asian monsoon region reconstructed by planktonic foraminifera and alkenones(MARGO 2009) compared with CCSM3 simulated SSTs. The land-sea topography is based on the LGM shoreline in the TerrainBase 5-minglobal bathymetry/topography data set (National Geophysical Data Center 1995). The filled circles and squares represent MARGO (2009)SSTs and the underlying colours are CCSM3 simulated SSTs. This figure is available in colour at http://www.boreas.dk.

Fig. 6. Qualitative precipitation change inferred from biomes and δ18O of speleothems in the Asian monsoon region between 25 and 17 ka BP.Thick lines represent the calibrated age points and thin lines are interpolations between these. The black and grey colours represent relativelywet and dry climatic conditions, respectively. See Table 1 for details on the sites and Fig. 1 for locations of the sites.



rain shadow of the mountains (Fig. 1), indicate insteadthat relatively dry conditions before 20 ka BP were fol-lowed by wetter conditions.

For the EAM subregion, pollen records from SouthChina (#30, Wang et al. 2010) and Taiwan (#31, Liewet al. 2006) suggest that P-E gradually decreased atc. 20 and c. 19 ka BP, respectively. Reconstructed pre-cipitation from sites around 30°N (#35, Wang et al.2001; #36, Zhou et al. 2008; #34, Cosford et al. 2010)also imply a change between c. 20 and c. 19 ka BP, butin the opposite direction, i.e. from relatively dry torelatively wet conditions. Pollen records from Japanshow either low P-E throughout the LGM (#40,Hayashi et al. 2010), or relatively wet climatic condi-tions c. 22.5 ka BP, which were followed by a drierclimate until 19 ka BP and again wetter conditionsthereafter (#42, Yasuda 1982).

Reconstructed and modelled position ofthe ITCZ

Proxy-based reconstructions of LGM climatic condi-tions can be compared to precipitation calculated fromCCSM3 output data according to the method outlinedby Braconnot et al. (2007) to provide insight into thegeographical extent of the summer monsoon, i.e. thenorthern boundary of the ITCZ during the LGM.

In the CCSM3 LGM simulation, the northernboundary of the ITCZ (defined by high precipitation

south of 30°N) is located at ∼5°N in the westernArabian Sea (Fig. 7). Over southern India and South-East Asia the mean position of the ITCZ in CCSM3fluctuates over an area between ∼10° and ∼15°N and issituated at approximately ∼20°N in the western PacificOcean. Although proxy-based climatic patterns for thetime interval 23–20 ka BP (Fig. 5) compare well withthe simulated position of the ITCZ in the westernArabian Sea, reconstructions and simulations divergein the eastern Arabian Sea, over southern India andIndochina. In the reconstruction, the northern bound-ary of the ITCZ shifts northwards in the Bay of Bengaland reaches ∼20–25°N over southern China (Fig. 7).

The shift in precipitation and P-E patterns recon-structed from proxies in Yemen (#56, Shakun et al.2007), Sri Lanka (#48, Premathilake & Risberg 2003;Premathilake 2006), and northern India (#74, Fujii &Sakai 2002; Hayashi et al. 2009; #73, Kotlia et al. 2010)between c. 20 and 19 ka BP (Fig. 6) seems to indicatea strengthening of the summer monsoon and anorthward shift of the ITCZ (Fig. 7). In addition,speleothems from central China (#35, Wang et al. 2001;#34, Cosford et al. 2010) show changes in δ18O compo-sition. This could be interpreted either as reflectinghigher precipitation derived from the EAM (Wanget al. 2001; Yuan et al. 2004; Dykoski et al. 2005) or asa shift in the source region of the precipitation (Clemenset al. 2010; Pausata et al. 2011; Maher & Thompson2012). As the major source of precipitation in this areais derived from the South China Sea, changes in the δ18O

Fig. 7. Simulated and reconstructed northern boundary of the Intertropical Convergence Zone (ITCZ) during the LGM in the Asian monsoonregion compared to its present-day January and July position (Stager et al. 2011). The land-sea topography is based on the LGM shoreline inthe TerrainBase 5-min global bathymetry/topography data set (National Geophysical Data Center 1995). Present-day northern boundary ofthe ITCZ in January and July (yellow line); CCSM3 simulated ITCZ (orange line); reconstructed position of the ITCZ at 23–20 ka BP (bluedashed line); northward shift of the ITCZ at around 19 ka BP (green dashed line). This figure is available in colour at http://www.boreas.dk.



composition may reflect a northward movement of themean position of the ITCZ (Fig. 7)

Discussion

CCSM3 simulations and qualitative P-E and precipita-tion estimates based on the palaeo-data compilationare in good agreement for the western and southernpart of the Asian monsoon region. Both show a weaksummer monsoon and dry climatic conditions in thewestern IOM subregion, and a strengthened summermonsoon and wetter climatic conditions off southernIndia, in the Bay of Bengal and over the SSS domain(Fig. 4A, B). Discrepancies between terrestrial data andmodel output are visible in India, near the HimalayanMountains, and in central China, where palaeo-proxiessuggest drier conditions than simulated, and in NWThailand and Taiwan, where proxies indicate wetterconditions than simulated (Fig. 4A, B).

The observed differences between palaeo-proxiesand CCSM3 simulations may be attributable to anumber of factors: (i) the coarse resolution of CCSM3may not allow the detection of ocean–atmosphereinteractions (Marchant et al. 2007); this may explainwhy the model does not capture the climatic conditionssuggested by the palaeo-data; (ii) the selected LGMboundary conditions (e.g. SSTs, PFTs, dust) can influ-ence atmospheric dynamics (Otto-Bliesner et al. 2009;Hargreaves et al. 2011; Jiang et al. 2011); LGM dust,for example, is not parameterized well in CCSM3(Otto-Bliesner et al. 2006a) and it is believed that dustwould have had a major impact on precipitation pat-terns given the many available dust sources (Kohfeld &Harrison 2001); (iii) qualitative P-E and precipitationwere not here proper classification cause of uncertaintyin reconstructed conditions assessments; (iv) the influ-ences of wind speed and/or surface temperature onevaporation make interpretations of P-E more compli-cated than of precipitation; (v) the individual sites andproxies may represent local conditions that were notrepresentative for a larger region.

The obvious differences between speleothem andbiome proxies and between speleothem proxies and themodel simulation in central China are more difficult toexplain (Fig. 4A, B). The low LGM δ18O values ofspeleothems from Jintanwan and Hulu caves wereoriginally interpreted as a signal of East Asianmonsoon intensity (Wang et al. 2001; Cosford et al.2010). However, recent work has challenged this viewand suggests instead that the signal seen in speleothemδ18O values is due to precipitation originating fromdifferent sources, i.e. the South China Sea and thePacific Ocean, and thus reflects changes in synopticcirculation patterns (Clemens et al. 2010; Pausata et al.2011; Maher & Thompson 2012). This hypothesisneeds to be tested further, but could explain the dis-

crepancies between biome- and speleothem-inferredprecipitation reconstructions, and between simulatedand speleothem-inferred precipitation.

The disagreement between planktonic foraminifera-and alkenone-based SSTs at neighbouring sites could bedue to multiple factors, such as e.g. differences in depthhabitat, nutrients, and seasonal productivity, or to acombination of these (e.g. Ishiwatari et al. 2001; Kuceraet al. 2005; Otto-Bliesner et al. 2009). SST estimatesderived from MARGO09 and simulated SSTs generallyshow comparable trends throughout the whole region,although absolute values differ by 1–2°C (Table 5).Modelled and reconstructed SSTs are highest aroundthe equator and in the Indonesian Gateway and gradu-ally decrease from the equator northwards in the Indianand Pacific Oceans (Fig. 5). High SSTs in the SSS sub-region, especially in the Indonesian Gateways, indicatethat a connection existed between the Pacific and IndianOceans during the LGM and that warm surface waterswere able to flow from the West Pacific Warm Pool intothe eastern Indian Ocean. The Indonesian throughflowdecreased by 6% in the CCSM3 LGM simulation(Otto-Bliesner et al. 2006a). However, a higher resolu-tion simulation on a general circulation model resultedin a much larger decrease of 30% (Žuvela-Aloise 2005).Moreover, the coarse resolution of the CCSM3 simula-tion likely leads to less agreement north of 10°N in thewestern part of the Arabian Sea (#57, #59, #60, #63,#65, and #74), where some studies have suggestedstrong upwelling during the LGM (Anand et al. 2008;Naidu 2004). For the EAM domain, CCSM3 simulatesan abrupt decrease north of 25°N. This pattern can beexplained by northward circulation of the upper ocean,as suggested by Otto-Bliesner et al. (2006a, 2009) andHargreaves et al. (2011).

The simulated and reconstructed LGM climate pat-terns generally correspond well with other modellingefforts, such as those performed within PMIP2(Braconnot et al. 2007) and individual atmosphere-ocean general circulation model (Bush 2002; Uedaet al. 2011). All of these simulations show dry climaticconditions over the Arabian Sea and India, and wetconditions over Sundaland and Sahulland. Simulationsperformed within PMIP2 suggest that LGM precipita-tion was higher near the South China Sea (Braconnotet al. 2007) and regional LGM data–model compari-sons for China using PMIP2 output (Jiang et al. 2011)indicate that climatic conditions were wetter in parts ofsouthern and eastern China and drier in the west andnorth of China. Our palaeo-data compilation compareswell to both these studies. CCSM3, however, simulatesan opposite climatic pattern, with wetter climate in thewest than in the east of China. This simulation wouldbe in agreement with a compilation of lake-level databy Yu et al. (2000b), which was interpreted as reflectinga wetter LGM climate in western China. The lake-level data set can also be interpreted as reflecting


lower evaporation rather than higher precipitation(Herzschuh 2006).

The strengthened LGM winter monsoon over theIOM subregion could be explained by a decrease inland–sea thermal contrast, because of relatively lowSSTs (Table 5) and similar ice cover on the TibetanPlateau as compared to today (Owen et al. 2002; Owen2009). This would have caused lower air temperatures,which in turn could explain CCSM3 simulations ofvery low evaporation (i.e. low precipitation andmedium P-E; Fig. 3E, G). Moreover, decreased inflowfrom the Pacific Ocean through the Indonesian Gate-ways reduced Indian Ocean equatorial currents andshifted the Indian Ocean warm pool eastwards(Schneider 1998), which hampered the transport ofhumid air masses to the western IOM domain.

The dry climatic conditions in the northern part ofthe EAM subregion can be explained by a strengthenedwinter monsoon caused by a stronger Siberian high-pressure system (Ding et al. 1995; Zheng et al. 1998;Yang et al. 2004; Cosford et al. 2010) and by a decreasein land–sea thermal contrast during summertime (Jiang& Lang 2010). Strengthening westerly winds (An et al.2012) may also have contributed to the dry climaticconditions over the northern part of the EAM subre-gion. Wetter climatic conditions, with a strengthenedsummer monsoon in the SSS and southern EAMdomains resulted from increased land–sea thermal con-trasts because of the large emerged land areas on theSunda and Sahul continental shelves (Fig. 1) andbecause of a decrease in SSTs (Table 5).

The palaeo-data compilation shows that majorchanges in precipitation patterns from dry to wet andwet to dry occurred around 20–19 ka BP. The time-lag of about 1000 years between individual sites for theobserved shift is probably related to dating uncer-tainties. The change in precipitation patterns seemsto reflect a strengthening and northward shift ofthe summer monsoon, which was triggered bypalaeogeographical and palaeoceanographical changesthat took place between 20 and 19 ka BP. The shiftfrom dry to wetter climate conditions in the IOM sub-region coincides with the gradual rise in sea level atc. 20 ka BP, which was followed by a rapid increase atc. 19 ka BP (Yokoyama et al. 2000; Hanebuth et al.2009, 2011). The rise in sea level would have allowedincreased throughflow of warm waters from the Pacificto the Indian Ocean and transport of high-humidity airmasses that had been concentrated in the SSS domainand the Bay of Bengal, to the west. The west-ward movement of high-humidity air masses afterc. 20–19 ka BP led to increased precipitation over Indiaand the Arabian Sea and to decreased precipitationover the SSS and southern EAM subregions. Althoughchanges in precipitation patterns can be observed overthe entire Asian monsoon region, these were locallydifferent because of topographical effects. Precipitation

increased for example on the leeward side of the Hima-layan Mountains (#74, Fujii & Sakai 2002; Hayashiet al. 2009; #73, Kotlia et al. 2010) and along theeastern side of the Sumatra highland (#11, Maloney1980; #12, Maloney & McCormac 1996), which islocated in the rain shadow of the mountain range. Theonset or strengthening of the summer monsoon around20–19 ka BP in the northern EAM domain could beexplained by a gradual weakening of the Siberian high-pressure system and/or strengthened westerly winds(An et al. 2012), which dominated during the LGM.These climatic changes seem to have occurred slightlyearlier than those described for China, where thechange towards wetter conditions took place between18.5 and 17.5 ka BP (Herzschuh 2006).

Proxy-based precipitation reconstructions of thenorthern boundary of the ITCZ suggest a distinctnorthward shift at around 20–19 ka BP. Prior to 20 kaBP, the northern boundary of the ITCZ was located ataround 5°N in the Indian Ocean, shifted northwards inthe Bay of Bengal, and crossed southern China aroundlatitude 20–25°N (Fig. 7). After 20–19 ka BP, the ITCZshifted northward, reaching above 10°N in the IndianOcean, 20°N in the Bay of Bengal, and 30°N overChina. The reconstructed and modelled northernboundaries of the ITCZ do not correspond well witheach other. For example, CCSM3 simulates the bound-ary over southern India at around 15°N, whereas thereconstruction suggests a more southerly location. Forthe Bay of Bengal and over the Indochina Peninsula thesimulated northern boundary is at around 10–15°N,which is more southern than inferred from the recon-structions (Fig. 7). Although the CCSM3 simulatedITCZ compares well to the ensemble of PMIP2 runsover southern India, the picture is more complicatedover the Indochina Peninsula and southern China(Braconnot et al. 2007). Some PMIP2 models indicatethat the northern boundary of the ITCZ reached20–30°N, whereas others locate it at around 10–15°N.The HadCM2 model, which includes vegetation inter-actions, supports a more northerly position of theITCZ (Braconnot et al. 2007), which is in line with ourreconstructions. The obvious differences betweenproxy data and model simulations may be because ofthe coarse resolution of the models and the differentparameterization schemes used (i.e. different modelsshow divergent results). However, the data–model con-trast also illustrates inherent uncertainties of the proxyreconstructions. The few available palaeo-records onlyprovide qualitative information regarding precipitationchanges. This makes it difficult to assess the amount ofrainfall, which would help to delineate a more preciseposition of the northern boundary of the ITCZ.

Our data–model comparison shows that the LGMclimate in the Asian monsoon region was more variablethan generally assumed (e.g. van Campo et al. 1982;Huang et al. 1997; Hodell et al. 1999; von Rad et al.


1999; Hope 2001; Naidu 2004; Prabhu et al. 2004; Whiteet al. 2004; Tiwari et al. 2006; Ansari & Vink 2007;Cosford et al. 2010; Fleitmann et al. 2011). Althoughcool and dry climates persisted in the western and north-ern parts of the region, due to a strengthened wintermonsoon, higher precipitation prevailed in equatorialareas (e.g. over Sundaland and Sahulland), in the SouthChina Sea, and in southern China. The overall goodcorrespondence between reconstructed and simulatedclimate parameters, such as precipitation, effectivemoisture, and SSTs, suggests that CCSM3 can beregarded as simulating precipitation and SSTs well overtropical and subtropical areas (Figs 4, 5). However, theoverall low density of high-resolution qualitative andquantitative palaeo-records in the Asian monsoonregion makes reconstructions still uncertain, especiallysince precipitation is much more localized than tempera-ture. High-quality proxy studies, which would betterallow quantification of LGM precipitation, are there-fore of great importance to allow further tests of theperformance of all climate models and thus increase thecreditability of the simulations.

Conclusions

Proxy records from marine and terrestrial palaeo-datasets for the Asian monsoon region have been compiled,evaluated, and partly re-assessed in terms of LastGlacial Maximum (LGM) (23-19 ka BP) precipitation,effective moisture (P-E) and sea surface temperatures(SSTs). Qualitative palaeoclimate estimates were thencompared to CCSM3 model output data. The palaeo-proxy compilation and the model–data comparisonallowed us to discuss past changes in Asian monsoonstrength in three different subregions, the Indian Oceanmonsoon (IOM), the Sunda-Sahulland (SSS) and theEast Asian monsoon (EAM), on temporal and spatialscales. They also provide estimates for the location ofthe northern boundary of the ITCZ during the LGM.

• CCSM3 simulations and reconstructed palaeo-dataindicate a weak LGM summer monsoon over mostof the IOM subregion and over the northern part ofthe EAM subregion, and a strengthened summermonsoon over the SSS domain.

• Data–model discrepancies are seen over southernIndia, where proxies suggest dry conditions,whereas the model simulates wet conditions; overNW Thailand and southern China, where proxiesestimate wet conditions and the CCSM3 outputpoints to dryness; and central China, wherespeleothem proxies indicate wetter conditions thanthose modelled and reconstructed based on biomes.Data–model discrepancies may be caused by thelow resolution of the model and/or the choice ofboundary conditions, but could also be due to

inherent uncertainties in the proxy data, or thatproxies reflect local, rather than regional climaticconditions.

• CCSM3 simulated SSTs agree well with theMARGO (2009) data set in the SSS domains, in theBay of Bengal, and in the Eastern Arabian Sea.

• The compiled palaeo-records show a distinct changein Asian monsoon intensity at around 20–19 ka BP.Climate conditions changed from dry to wet inthe IOM and in the northern EAM subregions,and from wet to dry in the SSS and in the southernEAM subregions. These shifts were triggered bypalaeogeographical and palaeoceanographicalchanges that affected the area.

• The reconstructed and simulated northern bounda-ries of the ITCZ diverge over southern India, theBay of Bengal, and Indochina. Reconstructionsestimate a more southern position over southernIndia and the Bay of Bengal, and a more northernposition over Indochina. In contrast, CCSM3 simu-lates a more northern and southern position,respectively.

• The overall good correspondence between recon-structions and CCSM3 simulations suggests thatCCSM3 can be regarded as simulating precipitationwell over subtropical and tropical areas. The fewhigh-resolution qualitative and quantitative palaeo-records available for the large Asian monsoonregion make reconstructions however still uncertainand many more good records are needed to faith-fully reconstruct precipitation in this climaticallysensitive area.

Acknowledgements. – This research is financed through SwedishResearch Council (VR) grants 621-2008-2855, 348-2008-6071, and621-2011-4684. We thank Hildred Crill for language advice, RobertGraham, Francesco Muschitiello, and Sakonwan Chawchai for criti-cally reading the manuscript, Jason Cosford for providing theJintanwan speleothem data set, and Rodrigo Caballero, LudvigLöwemark, Agatha de Boer, Sakonwan Chawchai, and DanieleReghellin for helpful discussions. The speleothem and planktonicforaminifera δ18O data sets were retrieved from NOAA andPANGAEA. We also thank Min-Te Chen, Pai-Sen Yu, Sander vander Kaars, Ping Fu, and Zhen Li for many helpful suggestions withrespect to the palaeo-proxy records. The careful review and com-ments made by D. Jiang and an anonymous reviewer greatlyimproved the manuscript.

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