Asian monsoon climate during the Last Glacial Maximum: palaeo-data-model comparisons
Descripción
JOBNAME: No Job Name PAGE: 1 SESS: 11 OUTPUT: Wed Jun 26 15:00:00 2013 SUM: F71847F0 /Xpp84/wiley_journal/BOR/bor_v0_i0/bor_12032 Toppan Best-set Premedia Limited Proofreader: Elsie Delivery date: 26 Jun 2013 bs_bs_query
Journal Code: BOR Article No: BOR12032 Page Extent: 23
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Asian monsoon climate during the Last Glacial Maximum: palaeo-data − model comparisons
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AKKANEEWUT CHABANGBORN, JENNY BRANDEFELT AND BARBARA WOHLFARTH
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Chabangborn, A., Brandefelt, J. & Wohlfarth, B. 2013: Asian monsoon climate during the Last Glacial Maximum: palaeo-data – model comparisons. Boreas. 10.1111/bor.12032. ISSN 0300-9483.
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The Last Glacial Maximum (LGM) (23−19 ka BP) in the Asian monsoon region is generally described as cool and dry, due to a strong winter monsoon. More recently, however, palaeo-data and climate model simulations have argued 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 climatic conditions based on precipitation and effective moisture, and of sea surface temperatures. The comparison of the palaeo-data set to LGM simulations by the Climate Community System Model version 3 (CCSM3) shows fairly good agreement: a dry LGM climate in the western and northern part due to a strengthened winter monsoon and/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/or to uncertainties in the reconstructions. Differences are also observed between the reconstructed and simulated northern boundaries of the Intertropical Convergence Zone (ITCZ). The reconstructions estimate a more southern position over southern India and the Bay of Bengal, whereas CCSM3 simulates a more northern position. In Indochina, the opposite is the case. The palaeo-data indicate that climatic conditions changed around 20−19 ka BP, with some regions receiving higher precipitation and some experiencing drier conditions, which would imply a 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 correspondence between reconstructions and CCSM3 suggests that CCSM3 simulates LGM climate conditions over subtropical and tropical areas fairly well. The few high-resolution qualitative and quantitative palaeorecords available for the large Asian monsoon region make reconstructions however still uncertain.
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Akkaneewut Chabangborn and Barbara Wohlfarth, Department of Geological Sciences, Stockholm University, SE-106 91, Stockholm, Sweden; Jenny Brandefelt, The Swedish Nuclear Fuel and Waste Management Company, SE-111 64, Stockholm, Sweden; received 4th January 2013, accepted 31st May 2013.
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The Asian monsoon is one of the largest climate systems on Earth and affects a region that extends from the Arabian Sea to the South China Sea and from northern Australia to northern China (Wang et al. 2005). It has an important influence on Earth’s other climatic systems through transport of heat energy and humidity to higher latitudes (Zahn 2003; Clift & Plumb 2008; Maher 2008; Caley et al. 2011; Cook & Jones 2012). The NE monsoon transports cool and dry air masses over the continents during the winter season, whereas the SW and SE monsoons provide warm and wet conditions during summer (Ramage 1971; Wang et al. 2003, 2005; Zahn 2003; Holton 2004). These seasonal shifts are generally explained by insolation changes and associated differences in land−sea heat capacity. As a result of its dependence on insolation and land−sea thermal contrast, the Asian monsoon is also closely linked to the seasonal shift of the Intertropical Convergence Zone (ITCZ) (Chao & Chen 2001; Fleitmann et al. 2007; Clift & Plumb 2008). In summer, the circulation of humid air masses from the Indian Ocean, together with the northward shift of the ITCZ, causes rainfall over the Asian continent. In contrast, cool and dry climatic conditions develop in winter when the ITCZ shifts southward, allowing the winter monsoon to migrate over the continent. DOI 10.1111/bor.12032
The Asian monsoon is generally divided into two subsystems according to differences in summer monsoon circulation patterns: the South Asian Monsoon or Indian Ocean Monsoon (IOM) and the East Asian Monsoon (EAM). This division follows longitude 105°E, which extends along the eastern edge of the Tibetan plateau, across the Indochina Peninsula and through the Indonesian archipelago (Wang et al. 2003, 2005). The IOM is characterized by a distinct gyre generated from clockwise circulation across the equator. The EAM is a convergence of SW winds from the Indian Ocean and trade winds from the Pacific Ocean and also receives contributions from the subtropical front near China. Climate model scenarios suggest that a rise in global temperatures can have a significant impact on the intensity of seasonal rainfall in monsoonal Asia (IPCC 2012). As a faithful prediction of precipitation patterns is of great societal and economic importance, the performance of climate models needs to be tested using well-known extreme climate states in the past. The Last Glacial Maximum (LGM: 23−19 ka BP) is a time interval representing an extreme climate state with distinctly different environments from today (Mix et al. 2001). Global ice volumes had obtained their maximum, global sea level was at ∼130 m below present (Clark et al. 2009), and forested areas had become reduced © 2013 The Authors Boreas © 2013 The Boreas Collegium
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Akkaneewut Chabangborn et al.
considerably (Elenga et al. 2000; Tarasov et al. 2000; Williams et al. 2000; Yu et al. 2000a). The quasiequilibrium climate and the well-known boundary conditions make the LGM an excellent test period for climate models. Persistent cool and dry climatic conditions throughout the LGM are generally described for the Asian monsoon region, attributable to a strengthened winter monsoon (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; 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. van der 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 simulations of LGM climate (Bush 2002; Braconnot et al. 2007; Jiang et al. 2011; Ueda et al. 2011) suggest that conditions were wetter than reconstructed by terrestrial and marine palaeo-data. In addition, it has been proposed that the LGM climate in the Asian monsoon region was more variable and that short wet intervals punctuated generally cool and dry climatic conditions (Sun et al. 1999, 2000; Rashid et al. 2007; Saher et al. 2007; Govil & Naidu 2011; Mahesh et al. 2011). This would mean that the summer monsoon was periodically strengthened during a time interval that was generally dominated by winter monsoon conditions. Such submillennial scale oscillations in the strength of the summer monsoon may have occurred because of e.g. the El Niño-Southern Oscillation (ENSO) and atmosphere−ocean interactions (Wang et al. 2005); a southward shift of the ITCZ (Zhang & Delworth 2005; Broccoli et al. 2006; Braconnot et al. 2007) and of the western Pacific Warm Pool (De Deckker et al. 2002; Partin et al. 2007); and thermohaline circulation changes in the North Atlantic, which affected the Indian Ocean (Overpeck et al. 1996; Tiwari et al. 2009; Pausata et al. 2011; Stager et al. 2011). Other ideas explaining higher LGM monsoon precipitation relate to higher relative humidity (Bush & Philander 1998) and changes in aerosol concentrations (Ruddiman 2001; Clift & Plumb 2008), and to lower moist adiabatic lapse rates (Barmawidjaja et al. 1993; Flenley 1998) than at present. Qualitative and quantitative temperature changes can be relatively well reconstructed from proxies, especially in areas with clear climatic gradients. However, precipitation reconstructions are hampered by the fact that rainfall and its amounts have much more localized expressions (Dayem et al. 2010; Cook & Jones 2012). The drawbacks of marine and terrestrial palaeo-proxies as recorders of past monsoon precipitation have therefore been discussed extensively (see e.g. Sun et al. 1999, 2000; Tiwari et al. 2006 for further references). In par-
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ticular, the use of cave speleothem δ18O as a proxy for East Asian monsoon intensity has recently been challenged (Clemens et al. 2010; Dayem et al. 2010; Pausata et al. 2011; Maher & Thompson 2012). Here, we compile published palaeoenvironmental proxies for the Asian monsoon region and evaluate these in terms of qualitative precipitation and effective moisture to assess LGM summer monsoon variability on spatial and temporal scales. We compare these data sets to quantitative precipitation and effective moisture simulated by the Community Climate System Model version 3 (CCSM3), which has shown good correspondence to reconstructed LGM climate at high latitudes (Kjellström et al. 2009), and test whether CCSM3 is also able to faithfully simulate LGM monsoon precipitation over the Asian subtropics and tropics.
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Palaeo-proxies
Selection criteria
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Marine and terrestrial records (Table 1) were selected here according to the following criteria: (i) for evaluation of the spatial variability of LGM climatic conditions, the records should contain at least one 14C, U/Th, and/or TL date between 23 and 19 ka BP; (ii) records with age estimate errors of >1000 years, and 14C dates
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The palaeo-proxy data sets selected for this study include published terrestrial and marine records (here referred to as palaeo-data compilation) and the MARGO (2009) sea surface temperature (SST) synthesis (here referred to as MARGO09) (Table 1). We constrained our study area to the Asian monsoon region between 15°S and 40°N, and 40°E and 160°E (Fig. 1). Following Wang et al. (2003, 2005), the area was separated into the IOM and EAM subregions along longitude 105°E. The IOM subregion covers the area from the Indian Ocean in the south to the Tibetan Plateau in the north, and the EAM subregion covers large parts of China and the western Pacific Ocean (Fig. 1). The LGM land−sea configuration in the Asian monsoon region was distinctly different from the present day, owing to the marked sea-level lowstand. The LGM palaeogeography did not influence the IOM subregion as much as the EAM subregion, where a smaller South China Sea and exposure of the East China Sea shelf increased the land−sea thermal contrast. Sumatra, Java, and Borneo were connected to the Indochina peninsula and formed the so-called Sundaland, and northern Australia was linked to New Guinea, forming Sahulland (Fig. 1). As the areal extent of the exposed land was almost double that of today (De Deckker et al. 2002) and resulted in distinct environmental changes (Bird et al. 2005), the continental shelves of Sundaland and Sahulland (SSS) were treated as a third subregion (Fig. 1).
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LGM Asian monsoon climate
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Table 1. Palaeo-records and palaeo-proxies used for the compilation. The age assignments are based on MARGO project members (2009) (=M) values and published 14C, TL and U/Th dates. The number of dates between 25−17 ka BP for each sequence is given in parentheses. Site Site name no.
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Lat (°) Long (°) Elevation (m a.s.l.)
Archive Marine
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Planktonic foraminifera Marine Planktonic foraminifera Terrestrial Pollen Terrestrial Pollen Terrestrial Pollen
Age References assignment M
Ding et al. (2002)
M
Spooner et al. (2005)
14
Hope (2009) Stuijts et al. (1988) van der Kaars & Dam (1997) van der Kaars et al. (2010) van der Kaars et al. (2000) Ding et al. (2002)
SHI9034
−9.10
111.01
−3330
2
SHI9016
−8.46
128.24
−1805
3 4 5
Kosipe valley −8.45 Situ Bayongbong swamp −7.18 Bandung basin −7.00
147.20 107.28 108.00
1965 1300 665
6 7 8
BAR94-42 SHI9014 SHI9006
−6.75 −5.77 −4.33
102.42 126.97 117.60
−2542 −3163 −1999
Marine Marine Marine
9 10 11 12
Sentarum lake di Atas lake Pea Sim-sim swamp Pee Bullok swamp
0.73 −1.07 2.29 2.28
112.10 100.77 98.89 98.98
35–50 1535 1450 1400
Terrestrial Terrestrial Terrestrial Terrestrial
13
K-12
2.69
127.74
−3510
Marine
14 15 16 17
2.69 3.06 3.53 4.00
127.74 102.64 141.87 114.00
−3510 20–30 −2282 ∼1000
18 19 20 21 22
K-12 Tasek Bera basin KH92-1-5cBX Cave in Gunung Buda National Park SO18302 SO18300 GIK17964-2 GIK17961-2 MD97-2142
Planktonic M foraminifera 14 Marine Pollen C (1) Terrestrial Hardwood remain 14C (1) Marine Alkenone M Terrestrial δ18O U/Th (9)
4.15 4.35 6.16 8.51 12.69
108.57 108.65 112.21 112.33 119.47
83 91 −1556 −1795 −1557
Marine Marine Marine Marine Marine
23 24
GIK17954-2 31-KL
14.80 18.75
111.53 115.87
−1520 −3360
Marine Marine
25
GIK17938-2
19.79
117.54
−2840
Marine
26
MD97-2148
19.80
117.54
−2830
Marine
27 28 29 30 31 32
GIK17940-2 Core 17940 Tianyang basin Huguang lake Toushe Basin DGKS9603
20.12 20.12 20.78 21.15 23.82 28.15
117.38 117.38 110.03 110.28 120.88 127.27
−1727 −1727 120 23 650 −1100
Marine Marine Terrestrial Terrestrial Terrestrial Marine
33 34 35 36 37 38 39
DGKS9603 Jintanwan Cave Hulu Cave Songjia Cave Weinan section Beizhuangcun section Pyonggeodong archaeological site Biwa lake A paddy field in Iwaya, Fukui prefecture Mikata Lake CH84-04 KH-79-3_L3 KT94-15_PC-9 MD85-674 SK-157-14
28.15 29.48 32.30 32.41 34.40 34.33 35.17
127.27 109.53 119.17 107.41 109.50 109.48 128.06
−1100 460 100 ∼680 600–1100 600–1100 100–300
Marine Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial
35.25 35.52
136.05 135.88
85 20
35.56 36.46 37.06 39.57 3.19 5.18
135.89 142.14 134.72 139.41 50.44 75.91
0 −2630 −935 −807 −4875 −3306
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Proxy
1
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C (1) C (1) 14 C (1) 14
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Pollen Pollen Planktonic foraminifera Pollen Pollen Pollen Pollen
14
C (2) C (1) M
14
14
C C C 14 C 14 14
(1) (2) (5) (4)
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Pollen Pollen Alkenone Alkenone Planktonic foraminifera Alkenone Planktonic foraminifera Planktonic foraminifera Planktonic foraminifera Alkenone Pollen Pollen Pollen Pollen Planktonic foraminifera Pollen δ18O δ18O δ18O Pollen Pollen Pollen
14
Anshari et al. (2001) Newsome & Flenley (1988) Maloney (1980) Maloney & McCormac (1996) Barmawidjaja et al. (1993) Barmawidjaja et al. (1993) Wüst & Bustin (2004) Ohkouchi et al. (1994) Partin et al. (2007)
C (1) C (1) M M M
Wang et al. (2009) Wang et al. (2009) Pelejero et al. (1999) Pelejero et al. (1999) Chen et al. (2003)
M M
Pelejero et al. (1999) Chen & Huang (1998)
M
Chen et al. (1999)
M
Chen et al. (2002)
14
M 14 C 14 C 14 C 14 C M
(2) (2) (2) (3)
14
Pelejero et al. (1999) Sun et al. (2000) Zheng & Lei (1999) Wang et al. (2010) Liew et al. (2006) Li et al. (2001)
C (2) U/Th (3) U/Th (3) U/Th (2) 14 C (1) 14 C (2) 14 C (3)
Xu et al. (2010) Cosford et al. (2010) Wang et al. (2001) Zhou et al. (2008) Sun et al. (1997) Wang & Sun (1994) Chung et al. (2006)
Terrestrial Pollen Terrestrial Pollen
14
Terrestrial Marine Marine Marine Marine Marine
14 C (2) M M M M 14 C (1)
Hayashi et al. (2010) Takahara & Takeoka (1992) Yasuda (1982) Bard et al. (unpublished) Ishiwatari et al. (2001) Ishiwatari et al. (2001) Bard et al. (1997) Ahmad et al. (2008)
Pollen Alkenone Alkenone Alkenone Alkenone δ18O
14
C (2) C (1)
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Table 1. Continued
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Site Site name no.
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
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Horton plains
6.81
80.83
49 50 51 52 53 54 55 56 57 58 59 60 61 62
MD77-191 MD77-169 MD77-194 TY93905/P Nilgiri hills MD77-195 RC12-344 Moomi cave, TY93929/P MD77-176 MD76-135 GeoB3005-1 MD76-131 MD76-131(C)
7.30 10.13 10.28 10.70 11.25 11.30 12.46 12.50 13.70 14.31 14.44 14.97 15.32 15.53
76.43 95.03 75.14 51.93 76.67 74.32 96.04 54.00 53.25 93.08 50.52 54.37 72.34 72.57
63 64 65
GeoB3007-1 MD77-181 117–723_Site
16.17 17.24 18.05
59.76 90.29 57.61
−1920 Marine −2271 Marine −816 Marine
66 67 68 69 70 71
MD77-180 MD77-202 SO93-126KL MD77-203 SO90-137KA SO90-93KL
18.28 19.13 19.97 20.42 23.12 23.59
89.51 60.41 90.03 59.34 66.48 64.22
−1986 −2427 −1250 −2442 −573 −1802
72
Bharatpur Bird Sanctuary wetland Phulara palaeolake Kathmandu basin Shudu lake, Ren Co Tham Rod archaeological site
27.12
77.52
29.33 27.67 27.90 30.73 19.57
80.13 85.22 99.95 96.68 98.89
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Lat (°) Long (°) Elevation (m a.s.l.)
Archive
Proxy
2100–2300 Terrestrial Pollen
Age References assignment 14
C (2)
M M M M 14 C (1) M 14 C (3) U/Th (8) M M M M M M
Premathilake (2006) Premathilake & Risberg (2003) Sonzogni et al. (1998) Sonzogni et al. (1998) Sonzogni et al. (1998) Sonzogni et al. (1998) Rajagopalan et al. (1997) Sonzogni et al. (1998) Rashid et al. (2007) Shakun et al. (2007) Sonzogni et al. (1998) Sonzogni et al. (1998) Sonzogni et al. (1998) Budziak et al. (2000) Sonzogni et al. (1998) Cayre et al. (1999)
M M M
Budziak et al. (2000) Sonzogni et al. (1998) Godad et al. (2011)
M M M M 14 C (4) M
Sonzogni et al. (1998) Sonzogni et al. (1998) Sonzogni et al. (1998) Sonzogni et al. (1998) von Rad et al. (1999) Schulz & Emeis (unpublished) Sharma & Chatterjee (2007) Kotlia et al. (2010) Fujii & Sakai (2002) Cook et al. (2011) Tang et al. (2000) Wattanapituksakul (2006)
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−1254 −2360 −1222 ∼−1500 2200 ∼−1200 −2140 ∼1000 −2490 −1375 −1895 −2316 −1230 −1230
Marine Marine Marine Marine Terrestrial Marine Marine Terrestrial Marine Marine Marine Marine Marine Marine
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Marine Marine Marine Marine Marine Marine
Alkenone Alkenone Alkenone Alkenone δ13C Alkenone δ18O δ18O Alkenone Alkenone Alkenone Alkenone Alkenone Planktonic foraminifera Alkenone Alkenone Planktonic foraminifera Alkenone Alkenone Alkenone Alkenone δ18O Alkenone
51
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73 74 75 76 77
174 Terrestrial Pollen 1500–1700 1303 3630 4450 600–1170
Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial
Pollen Pollen Pollen Pollen Fauna remains
14
C (1)
14
C (2) C (3) C (4) 14 C (3) TL (1) 14 14
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on carbonate bulk sediment were excluded; (iii) MARGO09 was temporally limited to the LGM chronozone level 2 (24−18 ka BP) (Mix et al. 2001) because the data set contains only few age estimates for the time interval 23−19 ka BP; (iv) all published 14C ages were recalibrated with the Calib 6.0 online program (http://calib.qub.ac.uk/calib/calib. html) (Reimer et al. 2009). Age-depth curves were constructed for palaeo-records with more than one age control point. These were then used to assess the temporal variability of the Asian monsoon between 25 and 17 ka BP. Palaeo-records containing one age estimate were only used as supporting information for the spatial analysis. Where possible raw data was obtained from NOAA’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 was digitized from published sources.
Palaeo-proxy assessment
62
The MARGO09 data set could directly be compared to the CCSM3 output, whereas other palaeo-proxies had to be assessed in terms of precipitation and qualitative effective moisture (precipitation minus evaporation, P-E), i.e. wet or dry climatic conditions. Qualitative precipitation and P-E were categorized for each type of terrestrial proxy and were then compared to quantitative model output from CCSM3 (Fig. 2). Variations of LGM summer monsoon intensity were identified by changes in qualitative precipitation and P-E were interpolated to millennium-scale resolution for the individual study sites.
63 64 65 66 67 68 69 70 71 72 73 74
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Terrestrial proxies. – For pollen assemblages from terrestrial archives (24 sites) and marine sequences (five sites) (Fig. 1), we assigned each pollen taxon with >5% abundance to plant functional types (PFTs) that had been established for China (Yu et al. 2000a), Japan
76 77 78 79 80
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LGM Asian monsoon climate
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Colour online, B&W in print
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Fig. 1. Location of the compiled palaeo-data sets and MARGO (2009) sea surface temperature sites used in this study. The Asian monsoon region 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 TerrainBase 5-min global bathymetry/topography data set (National Geophysical Data Center 1995). See Table 1 for details on the sites. This figure is available in colour at http://www.boreas.dk.
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Fig. 2. Flow chart illustrating the different steps of the compilation, evaluation of proxies, and comparisons to the Climate Community System Model v. 3. E = evaporation; LGM = Last Glacial Maximum; P = precipitation; PFTs = plant functional types; SST = sea surface temperature.
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Akkaneewut Chabangborn et al.
(Takahara et al. 2000), and South-East Asia (Pickett et al. 2004) by the Global Paleovegetation Mapping (BIOME6000) project (Prentice & Webb 1998) (Table 2, Fig. 2). For the IOM domain, we used the PFTs established by Kramer et al. (2010) because the BIOME6000 project for the Indian continent is still in progress. The PFTs established by BIOME6000 and by Kramer et al. (2010) are based on a relationship between pollen taxa and modern climatic variables, i.e. mean temperature of the coldest month, growing degree days, and moisture index of each pollen taxa, as well as elevation, rainfall, and fractional sunshine hour (Prentice et al. 1996). The PFTs were then assigned to biomes (Table 2, Fig. 2) based on the assumption that all existing plant taxa share the same climatic conditions and that outlying taxa may have been longtransported from a different climatic region (Prentice et al. 1996). However, in a few cases, when pollen assemblages could not be assigned to only one biome, they were categorized as mixed biome (Table 2). The biomes were approximated to qualitative precipitation and P-E, based on the relationship between biomes and mean annual precipitation and temperature, as suggested by Mader (2010). Inferred P-E was grouped into three categories, representing low (1), medium (2), and high (3) (Table 3). The climatic conditions assessed from land-based archives were then compared to the CCSM3 model output (Fig. 2). Pollen assemblages from marine sequences represent runoff from the hinterland and, as such, a mix of pollen sources. These were therefore not used for comparisons with the CCSM3 simulation. As the reconstructed biomes may not show large temporal 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 variations in grassland expansion or runoff, respectively, and/or precipitation/P-E and, as such, shifts in summer monsoon strength over time. For speleothem δ18O values we assumed that speleothems grow in a closed system (ΔT ∼0°C) and that variations in δ18O mirror the amount of precipitation over the site (Wang et al. 2001; Yuan et al. 2004). δ18O values for each speleothem archive (five sites) were averaged between 23 and 19 ka BP to represent mean LGM values for each site and to allow for a generalized comparison between individual sites (Table 4). This generalized approach minimizes local factors that can influence the δ18O composition of the speleothems and high-frequency oscillations, and facilitates intersite comparisons and relative precipitation estimates. Qualitative precipitation derived from speleothem δ18O was categorized based on significant differences in LGM mean δ18O values (Table 4). Low mean δ18O values in respect to the LGM mean signify high pre-
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cipitation (3) and high mean δ18O values low precipitation (1) (Table 3). For investigation of summer monsoon variability over time, we averaged δ18O values for each speleothem using a five-point running mean between 25 and 17 ka BP. Average values for each site were then compared to the LGM mean δ18O value for individual speleothem sites to assess shifts in relatively wet or dry climatic conditions. Climatic conditions derived from other terrestrial proxies, i.e. δ13C of bulk sediment, hardwood and faunal remains, and archaeological evidence, were used as supporting information and follow the interpretations of the respective authors.
58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
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Marine proxies. – For planktonic foraminifera (Globigerinoides ruber) δ18O values we assumed that sealevel was stable (Clark & Mix 2002; Clark et al. 2009) and that major salinity changes did not occur during the LGM (Schulz et al. 1998; Clark & Mix 2002) (Fig. 2). The SST difference between the LGM and the present-day was calculated using the MARGO09 alkenone and the World Ocean Atlas (WOA) 1998 (Conkright et al. 1998) data sets (Table 5). This difference was then used to calculate the LGM P-E for selected marine δ18O records (four sites) (Table 6) (Tiwari et al. 2006) at locations, which were not significantly affected by the LGM sea-level lowstand. Calculation of P-E was based on the assumption of a 0.25‰ increase in δ18O with a SST decrease of 1°C (Erez & Luz 1983). Moreover, we assume a constantly averaged global ocean effect of ∼1.1‰ higher than present during the LGM sea-level lowstand (Adkins et al. 2002; Ravelo & Hillaire-Marcel 2007). P-E calculated for four sites and based on the LGM δ18O mean values of planktonic foraminifera (G. ruber; Table 6) is used as an approximation of mean LGM precipitation and allows for an easier qualitative comparison between marine sites. We assigned the calculated P-Es to two qualitative categories: high P-E and high precipitation (3); low P-E and low precipitation (1) (Table 3) using the same procedure as for the mean δ18O speleothem values.
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Climate model output data
102 103 104
The Community Climate System Model version 3 The climate model output used here is from the CCSM3, which is a fully coupled global atmosphere− ocean−sea-ice−land surface climate model (Collins et al. 2006). The model has been successfully employed to simulate extreme climatic states in the past (e.g. Kjellström et al. 2009; Brandefelt et al. 2011). The LGM simulation that was analysed here has been described in detail by Otto-Bliesner et al. (2006a) and Brandefelt & Otto-Bliesner (2009).
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105 106 107 108 109 110 111 112 113
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LGM Asian monsoon climate
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Table 2. Assignment of LGM pollen assemblages to biomes using plant functional types (PFTs). See text for further explanations.
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4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
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Site no. Major pollen component during the LGM (>5% in the pollen diagram) 3
Nothofagus, Poaceae
4
Dacrycarpus imbricatus, Altingia, Castanopsis comp., Quercus Low-montane forest II: Dacrycarpus, Distylium, Dodonaea, Engelhardia, Podocarpus Low-montane forest I: Altingia, Castanopsis/Lithocarpus, Eugenia, Quercus Submontane forest: Celtis,Helicia, Moraceae/Urticacae, Palmae, Trema 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 Macaranga/Mollotus, Oleaceae, Lithocarpus, Podocarpus, Cyperaceae, Poaceae, Eucalyptus
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5
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6
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7
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9
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10
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11
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12
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14
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18
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19
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53
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28
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7
33
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) 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 Dacrycarpus, Dacrydium, Engelhardia comp., Eugenia comp., Lithocarpus/Cartanopsis comp., Quercus comp., Symingtonia comp., Ericaceae
Biome
References
References for biome identification
Tropical deciduous broadleaf forest and woodland Warm-temperate rain forest Tropical deciduous broadleaf forest and woodland
Hope (2009)
Pickett et al. (2004)
Stuijts et al. (1988)
Pickett et al. (2004)
van der Kaars & Dam (1997)
Pickett et al. (2004)
Warm-temperate rain forest
van der Kaars et al. (2010)
Pickett et al. (2004)
Tropical deciduous broadleaf forest and woodland Tropical deciduous broadleaf forest and woodland
van der Kaars et al. (2000)
Pickett et al. (2004)
Anshari et al. (2001)
Pickett et al. (2004)
Newsome & Flenley (1988)
Pickett et al. (2004)
Maloney (1980)
Pickett et al. (2004)
Maloney & McCormac (1996)
Pickett et al. (2004)
Barmawidjaja et al. (1993) Wang et al. (2009)
Pickett et al. (2004)
Wang et al. (2009)
Pickett et al. (2004)
Sun et al. (2000)
Yu et al. (2000)
Zheng & Lei (1999)
Yu et al. (2000)
Wang et al. (2010)
Yu et al. (2000)
Liew et al. (2006)
Yu et al. (2000)
Xu et al. (2010)
Yu et al. (2000)
Warm-temperate rain forest
Wet sclerophyll forest, change to tropical broadleaf forest and woodland Warm-temperate rain Dacrydium, Cartanopsis comp., Quercus, Eugenia, Engelhardia, Symingtonia populnea, Vaccinium comp., forest Myrsine, Cyatheaceae Podocarpus, Dacrycarpus, Lithocarpus/Castanopsis Warm-temperate rain forest Quercus, Euphorbiaceae, Palmae, Cyperaceae Tropical deciduous broadleaf forest and woodland Quercus, Palmae, Oleaceae, Rubiaceae, Rutaceae, Tropical deciduous Cyperaceae, Poaceae broadleaf forest, woodland, and steppe Pinus, Artemisia Mix of steppe and montane rain forest/montane conifers Mix of steppe and warm Quercus (evergreen), Castanopsis, Papilionaceae, mixed forest Urticaceae, Taxodiaceae, Cyperaceae, Poaceae, Artemisia Castanopsis-Lithocarpus, Pinus, Quercus (evergreen), Mix of steppe and warm Quercus (deciduous), Poaceae, Artemisia, Cyperaceae, mixed forest Pinus, Ilex, Alnus, Quercus, Cyclobalanopsis, Broadleaf evergreen/warm Castanopsis, Symplocos, Ulmus, Ligustrum, Salix, mixed forest Artemisia, Cyperaceae, Poaceae Dominated by steppe and Tsuga, Pinus, Quercus (evergreen), Betulaceae, warm mixed forest Quercus (deciduous), Poaceae, Cyperaceae, Chenopodiceae, Compositae, Artemisia
Pickett et al. (2004)
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Table 2. Continued
1
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Akkaneewut Chabangborn et al.
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Artemisia, Compositae Abies, Tsuga, Picea, Pinus subgeneous Haplaxylon, Pinus undifference, Cupressaceae, Salix, Carpinus, Betula, Quercus subgeneous Lepidobatanus, Alnus, Poaceae, Artemisia Cruciferae, Poaceae, Cyperaceae, Leguminoceae, Liliaceae Piceae, Salix, Betula, Quercus (Lepidobalanus), Artemisia, Compositae, Poaceae Abies, Tsuga, Cryptomeria, Betula, Ulmus, Alnus, Poaceae, Cyperaceae, Lysichiton, Copositae Picea, Abies, Tsuga, Pinus (Haploxylon), Ulmus, Quercus (Lepidobalanus), Salix, Betula, Corylus, Alnus, Cyperaceae, Poaceae, Lysichiton, Umbelliferae, Artemisia, Compositae Chenopodiaceae Pinus, Tsuga, Quercus (deciduous), Quercus (evergreen), Castanopsis, Alnus, Carpinus, Betula, Poaceae, Artemisia, Chenopodiceae Abies, Picea, Cupressaceae, Quercus (deciduous), Quercus (evergreen), Betula, Poaceae, Cyperaceae, Asteraceae, Artemisia, Rosaceae Pinus, Cedrus, Picea, Abies, Larix, Quercus, Ulmus, Loniceae, Urticaceae, Poaceae, Cyperaceae, Caryophyllaceae, Tubuliflorae, Linguiliflorae, Artemisia, Polygonum, Primulaceae Chenopodiaceae,) Artemisia
Biome
References
References for biome identification
Steppe Cool mixed forest
Wang & Sun (1994) Yu et al. (2000) Hayashi et al. (2010) Takahara et al. (2000)
Steppe
Sun et al. (1997)
Yu et al. (2000)
Mix of cool mixed forest and steppe Mix of cool mixed forest and steppe Cool mixed forest
Chung et al. (2006)
Yu et al. (2000)
Takahara & Takeoka (1992) Yasuda (1982)
Takahara et al. (2000) Takahara et al. (2000)
Steppe Cool mixed forest
Premathilake (2006) Fujii & Sakai (2002)
Kramer et al. (2010)
Mix of steppe and cool mixed forest
Cook et al. (2011)
Kramer et al. (2010)
Mix of steppe and cool mixed forest
Kotlia et al. (2010)
Kramer et al. (2010)
Steppe
Tang et al. (2000
Kramer et al. (2010)
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The atmospheric component model of CCSM3 is the NCAR (National Centre for Atmospheric Research) Community Atmospheric Model version 3 (CAM3) with a horizontal resolution of approximately 2.8×2.8°.
32 33 34 35
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The land model has the same grid resolution as the atmospheric model and includes a river routing scheme. The ocean and sea-ice models have a grid resolution of approximately 1×1°.
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37 38 39 40 41 42 43
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60 61
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Table 3. Assignment of qualitative precipitation minus evaporation (effective moisture; P-E) for biome and planktonic foraminifera, and precipitation for speleothem mean δ18O values. Biomes were roughly approximated to P-E by comparison to a relationship between biome and mean annual temperature and precipitation suggested by Mader (2010). Mean δ18O values obtained from planktonic foraminifera (Globigerina ruber) were converted to P-E, whereas those of speleothem were directly used to represent LGM qualitative precipitation. These assignments were 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 precipitation, which was not considered in the qualitative precipitation assessments for the speleothem mean δ18O values and planktonic foraminifera P-E assessments in order to avoid any overestimation.
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Effective moisture
Qualitative precipitation speleothem δ18O values
Biome
P-E
Warm-temperate rain forest, tropical deciduous broadleaf forest and woodland, wet sclerophyll forest, and broadleaf evergreen/warm forest Mix of steppe and warm mixed forest Cool mixed forest, mix of steppe and cool mixed forest and steppe
−0.58
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Table 4. LGM mean δ18O values for selected speleothems.
53
Site #
Site name
Time interval (ka BP)
LGM mean δ18O (‰) value
References
54 55 56 57 58
17 34 35 36 56
Gunung Buda National Park Jintanwan Cave Hulu cave Songjia cave Moomi cave
24.3–17.8 24.1–17.7 24.2–18.8 19.8–17.5 24.2–17.3
−7.70±0.05 −6.20±0.22 −6.29±0.33 −9.26±0.14 −0.58±0.17
Partin et al. (2007) Cosford et al. (2010) Wang et al. (2001) Zhou et al. (2008) Shakun et al. (2007)
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59 60 61 62
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37 38 39 40 41
Bay of Bengal
50 58 64 66 68
49 51 54 61 62
Indian Ocean Monsoon
32 33 34 35 36 MD77-169 MD77-176 MD77-181 MD77-180 SO93-126KL
MD77-191 MD77-194 MD77-195 MD76-131 MD76-131(C)
MD85-674 TY93905/P TY93929/P MD76-135 GeoB3005-1 GeoB3007-1 117–723_Site MD77-202 MD77-203 SO90-93KL
Alkenone Alkenone Alkenone Alkenone Alkenone
Alkenone Alkenone Alkenone Alkenone Planktonic foraminifera
Alkenone Alkenone Alkenone Alkenone Alkenone Alkenone Planktonic foraminifera Alkenone Alkenone Alkenone
Planktonic foraminifera Alkenone Planktonic foraminifera Planktonic foraminifera Planktonic foraminifera Alkenone Planktonic foraminifera Alkenone Alkenone Alkenone
Planktonic foraminifera Planktonic foraminifera Planktonic foraminifera Planktonic foraminifera Alkenone Alkenone Alkenone
Proxies
25.88±0.23 25.50±0.27 25.67±0.28 25.71±0.26 25.54±0.27
26.06±0.20 25.99±0.18 25.81±0.19 25.10±0.23 25.00±0.23
24.48±0.19 22.82±0.24 22.58±0.26 21.47±0.28 22.12±0.25 22.77±0.26 21.94±0.30 22.62±0.29 22.16±0.30 22.15±0.33
26.24±0.16 24.42±0.19 24.74±0.19 24.75±0.18 24.75±0.18 24.56±0.19 22.10±0.24 16.19±0.45 4.53±0.44 NAN
26.12±0.27 26.98±0.18 27.03±0.15 26.90±0.19 26.79±0.16 25.42±0.18 25.27±0.18
SSTCCSM3 (°C)
25.90±1.50 26.90±1.50 26.10±1.50 26.30±1.50 26.40±1.50
25.80±1.50 25.70±1.50 25.80±1.50 25.20±1.50 28.51±0.85
25.60±1.50 26.00±1.50 24.40±1.50 24.60±1.50 24.70±1.50 25.10±1.50 25.87±0.85 23.00±1.50 22.50±1.50 23.60±1.50
26.81±1.26 22.40±1.50 24.71±1.26 25.18±1.26 25.31±1.26 22.40±1.50 25.38±1.26 14.70±1.50 17.50±1.50 17.80±1.50
25.60±0.85 27.72±0.85 27.98±1.26 26.56±0.85 27.60±1.50 24.80±1.50 25.00±1.50
SSTMARGO09 (°C)
28.34 28.27 27.97 28.06 27.82
28.22 28.35 28.45 28.25 28.25
27.12 26.03 26.40 26.61 26.37 26.73 25.95 26.39 25.75 26.58
28.49 27.32 26.68 26.54 26.53 26.38 23.94 18.50 16.86 14.15
27.78 28.26 28.74 28.45 29.18 28.36 28.30
SSTWOA98 (°C)
0.02 1.40 0.43 0.59 0.86
−0.26 −0.29 −0.01 0.10 3.51
1.12 3.18 1.82 3.13 2.58 2.33 3.93 0.38 0.34 1.45
0.57 −2.02 −0.03 0.43 0.56 −2.16 3.28 −1.49 12.97 NAN
−0.51 0.74 0.95 −0.34 0.81 −0.62 −0.27
ΔSSTMARGO09-CCSM3 (°C)
−2.46 −2.77 −2.30 −2.35 −2.28
−2.16 −2.36 −2.64 −3.15 −3.25
−2.64 −3.21 −3.82 −5.14 −4.25 −3.96 −4.01 −3.77 −3.59 −4.43
−2.25 −2.90 −1.94 −1.79 −1.78 −1.82 −1.84 −2.31 −12.33 NAN
−1.66 −1.28 −1.71 −1.55 −2.39 −2.94 −3.03
ΔSSTCCSM3-WOA98 (°C)
−2.44 −1.37 −1.87 −1.76 −1.42
−2.42 −2.65 −2.65 −3.05 0.26
−1.52 −0.03 −2.00 −2.01 −1.67 −1.63 −0.08 −3.39 −3.25 −2.98
−1.68 −4.92 −1.97 −1.36 −1.22 −3.98 1.44 −3.80 0.64 3.65
−2.17 −0.54 −0.76 −1.89 −1.58 −3.56 −3.30
ΔSSTMARGO09-WOA98 (°C)
BOREAS
East Arabian Sea
46 52 57 59 60 63 65 67 69 71
West Arabian Sea
Indian Ocean Monsoon
22 23 24 25 26 27 28 29 30 31
63
22 23 24 25 26 27 32 43 44 45
East Asian Monsoon
12 13 14 15 16 17 18 19 20 21
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SHI9034 SHI9016 SHI9006 K-12 KH92-1-5cBX GIK17964-2 GIK17961-2
1 2 8 13 16 20 21
Sunda and Sahul Shelves
5 6 7 8 9 10 11 MD97-2142 GIK17954-2 31-KL GIK17938-2 MD97-2148 GIK17940-2 DGKS9603 CH84-04 KH-79-3_L3 KT94-15_PC-9
Site name
Site #
Subregion
Table 5. Annual mean sea surface temperatures (SSTs) simulated by CCSM3 and reconstructed by MARGO (2009). These are compared to the World Ocean Atlas data set (WOA97) (Conkright et al. 1998). See Fig. 1 for the location of the sites, and Fig. 5 for data−model comparisons.
3 4
1 2
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LGM Asian monsoon climate 9
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10 1
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Akkaneewut Chabangborn et al.
BOREAS
Table 6. LGM mean P-E derived from planktonic foraminifera (G. ruber) δ18O (Tiwari et al. 2006).
2 3
Site #
Site name
LGM mean δ18O (‰) values (PDB standard)
ΔSSTLGM-Present1 (°C)
P-E ratio2
References
4 5 6 7
6 47 55 70
BAR94-42 SK-157-14 RC12-344 SO90-137KA
∼−1.00 −0.96±0.43 −1.19±0.17 0.09±0.09
−2.17 −2.42 −2.44 −2.98
1.56 1.46 1.67 0.27
Van der Kaars et al. (2010) Ahmad et al. (2008) Rashid et al. (2007) von Rad et al. (1999)
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8 9 10 11 12
1
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65
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Difference between reconstructed LGM SSTs inferred from alkenones (MARGO 2009) and present-day SSTs from the World Ocean Atlas data set (Conkright et al. 1998) from sites in the vicinity. 2 The P-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 averaged global ocean effect of ∼1.1‰ higher than present during the sea-level lowstand of the LGM (Adkins et al. 2002; Ravelo & Hillaire-Marcel 2007).
13
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14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
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For the LGM boundary conditions, insolation was set to be constant at 1365 W m−2 and the concentration of greenhouse gases followed those reported from icecores (Otto-Bliesner et al. 2006a). The tropical incoming 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 radiative forcing decreased by ∼2.76 W m−2, of which ∼2.20 W m−2 can be attributed to a lower CO2 concentration. The LGM CCSM3 continental ice sheet, topography, coastline (∼−120 m), and bathymetry are based on the LGM ICE-5G reconstruction of Peltier (2004). For the LGM simulation, CCSM3 was started from pre-industrial boundary conditions. The model run reached a quasi-steady state after 800 model years, but was continued for another ∼1000 years (Brandefelt & Otto-Bliesner 2009). Model output from the last 300 model years is considered here to represent the annual average simulated climate for the LGM (c. 21 ka BP). CCSM3 output was interpolated to 5×5° using the Climate Data Operation program in order to compare with MARGO09. Mean annual evaporation was calculated based on the direct proportion between surface latent heat flux and evaporation amount. CCSM3 simulated precipitation, P-E, and SSTs were used for comparisons with the qualitative precipitation and P-E estimates derived from the palaeo-data compilation, and the quantitative SSTs of MARGO09 (Fig. 2). The differences in air temperature, SST, and precipitation rate between the LGM and the recent past (RP) climate (years 1960–2000; Otto-Bliesner et al. 2006b) are discussed below.
48
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CCSM3 LGM simulation
50 51 52 53 54
CCSM3 simulates warmer mean annual air temperatures (21–24°C) from the equator to latitude 20°N during the LGM, compared with the surrounding regions (Fig. 3A). Mean annual air temperatures are slightly lower over the exposed Sundaland and
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Sahulland than over surrounding areas and there is a distinct decrease in air temperatures on the Arabian Peninsula, near the Himalayan Mountains and over the Tibetan Plateau. In contrast, temperatures only gradually decrease northward in the western Pacific Ocean. LGM mean annual air temperatures are distinctly cooler than in the RP in the entire Asian Monsoon region (Fig. 3B). A maximum air temperature difference between LGM and RP of ∼−5°C is seen over the exposed Sundaland and Sahulland, in the western Arabian Sea region, over the Himalayan Mountains, and along the east coast of China. The LGM and RP annual mean air temperature difference is, however, lower (∼−3°C) in the Bay of Bengal and in the East China Sea. Simulated LGM SSTs generally decrease from the equator northwards (Fig. 3C), but are higher in the Bay of Bengal as compared to the western Arabian Sea. Cooling between LGM and RP SST values is highest in the western Arabian Sea and in the NW Pacific, with only minor differences in the Bay of Bengal, in the East China Sea, and in the Indonesian Gateways (Fig. 3D). CCSM3 simulates high LGM mean annual precipitation of >2500 mm a−1 between 5° and 10°S over the Indian and Pacific Oceans (Fig. 3E). The western Arabian Sea region and NW India are very dry and mean annual precipitation amounts to
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