ARTICLE IN PRESS. Quaternary Science Reviews 25 (2006)

Quaternary Science Reviews 2 (26) 282 212 Last Glacial Maximum temperatures over the North Atlantic, Europe and western Siberia: a comparison between PMIP models, MARGO sea surface temperatures and pollen-based reconstructions M. Kageyama a,, A. Laıˆne a, A. Abe-Ouchi b, P. Braconnot a, E. Cortijo a, M. Crucifix c, A. de Vernal d, J. Guiot e, C.D. Hewitt c, A. Kitoh f, M. Kucera g, O. Marti a, R. Ohgaito h, B. Otto-Bliesner i, W.R. Peltier j, A. Rosell-Mele k, G. Vettoretti j, S.L. Weber l,y.yu m, MARGO Project Members a LSCE/IPSL, CE Saclay, L Orme des Merisiers, Bâtiment 71, 91191 Gif-sur-Yvette Cedex, France b Center for Climate System Research, The University of Tokyo Kashiwa, 277-868, Japan c Hadley Centre for Climate Prediction and Research, Met Office, FitzRoy Road, Exeter EX1 3PB, Devon, UK d GEOTOP, Université du Québec Montréal, C.P. 8888, succursale Centre Ville, Montréal, Qué., Canada H3C 3P8 e CEREGE, UMR CNRS/Université Paul Cézanne 663, BP 8, F134 Aix-en-Provence cedex 4, France f Climate Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 3-2, Japan g Institut für Geowissenschaften, Eberhard-Karls Universität Tübingen, Sigwartstrasse 1, DE-7276 Tübingen, Germany h Frontier Research Center for Global Change (FRCGC), JAMSTEC, Yokohama City 236-1, Japan i Climate Change Research, National Center for Atmospheric Research, 18 Table Mesa Drive/P.O. Box 3, Boulder, CO 837, USA j Department of Physics, University of Toronto, 6 St. George Street, Toronto, Ont., Canada MS 1A7 k ICREA and Institute of Environmental Science and Technology (ICTA), Autonomous University of Barcelona (UAB), 8193 Bellaterra, Catalonia, Spain l Climate Variability Research, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 21, 373 AE De Bilt, The Netherlands m LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, P.O. Box 984, Beijing 129, People s Republic of China Received 1 June 2; accepted 9 February 26 Abstract Evaluating the ability of models to simulate climates different from the modern one is important for climate prediction. Here we present a first comparison between results from simulations of the Last Glacial Maximum climate and continental and surface ocean reconstructions for the North Atlantic, Europe and western Siberia. The simulations include prescribed sea surface temperature (SST) and slab-ocean atmospheric general circulation model runs performed within the PMIP1 project, and atmosphere ocean fully coupled runs performed after PMIP1 and within the PMIP2 project. The surface ocean reconstructions are from the MARGO project. Continental reconstructions are based on pollen data. Over the North Atlantic, most models simulate the strengthening of the SST meridional gradient suggested by the reconstructions, but most do not reproduce the LGM control SST anomaly at the right location, nor with the right amplitude. Over western Siberia, the model results are much improved when a new ice-sheet reconstruction (ICE-G) is used to force the models. The main discrepancy remains for western Europe winter temperatures, for which LGM control anomalies are significantly underestimated by all models. All models indicate that this region during the LGM experienced significantly higher interannual variability in coldest-month temperatures compared to the control runs. This increased variability could have conspired to bias the apparently extremely cold pollen-based temperature reconstructions. Crown Copyright r 26 Published by Elsevier Ltd. All rights reserved. Corresponding author. Tel.: +33 1 69 8 87 6. E-mail address: Masa.Kageyama@cea.fr (M. Kageyama). 277-3791/$ - see front matter Crown Copyright r 26 Published by Elsevier Ltd. All rights reserved. doi:1.116/j.quascirev.26.2.1

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 283 1. Introduction: lessons from atmospheric general circulation model simulations of the Last Glacial Maximum climate (PMIP1) During the first phase of the Palaeoclimate Modelling Intercomparison Project (Joussaume and Taylor, 199, 2; Braconnot, 24), atmospheric general circulations models (AGCMs) were used to simulate the climate of the Last Glacial Maximum (LGM, 21, years ago). The results were then evaluated over the continents against paleoclimatic reconstructions. The performance of the models in terms of simulating the climate over Europe and western Siberia has been mostly compared to pollenbased climatic reconstructions (Kageyama et al., 21). This comparison pointed to severe discrepancies between model results and climatic reconstructions over two areas. In western Europe and around the Mediterranean northern coast, the models underestimated the winter cooling and the year-round drying. On the other hand, over northwestern Siberia, the models overestimated the cooling, especially in summer. The boundary conditions and experimental design chosen for the PMIP1 LGM simulations could partly explain these discrepancies: The sea surface temperatures (SST) were either prescribed to the CLIMAP (1981) reconstructions or computed via a slab-ocean model, under the hypothesis that the oceanic meridional fluxes were identical to their modern values. Both methods represent strong assumptions. The CLIMAP (1981) very extensive winter sea-ice over the North Atlantic is a significant constraint on the atmospheric circulation and therefore, potentially, on European temperatures. Recent reconstructions based on assemblages of planktonic foraminifera (as in CLIMAP, 1981) (e.g. Pflaumann et al., 23), on dinoflagellate cyst assemblages (e.g. De Vernal et al., 1997) and on geochemical proxies (Barker et al., 2; Rosell-Mele et al., 24) have since suggested a less extensive sea-ice over the North Atlantic. The impact of warmer SSTs and a less extensive sea-ice has been investigated in two studies with results varying from a significantly warmer European coast (Pinot et al., 1999) to a very moderate change in the same region (Toracinta et al., 24). The PMIP1 slab-ocean experiments generally displayed SSTs warmer than CLIMAP (1981) and a less extensive sea-ice. The simulated continental temperature over western Europe is on average warmer in those runs, compared to the prescribed SST ones, especially north of 4 N (Kageyama et al., 21). This is not the case over central and eastern Europe, nor over western Siberia, where there is no systematic relationship between the LGM-pre-industrial anomaly in North Atlantic SST and that of the continental temperature, neither in winter, nor in summer. This dispersion could be linked to the differences in the SSTs computed by the slab-ocean coupled to each AGCM, but also to the model-dependent reaction of the stationary waves to the SST difference (clearly illustrated in Pinot et al., 1999). This shows that the relationship between the LGM North Atlantic cooling/sea-ice expansion and the corresponding continental temperature anomalies over Europe and western Siberia is far from being completely understood and is probably quite modeldependent. The ice-sheet reconstruction used in these experiments was the Peltier (1994) ICE-4G reconstruction, in which the Fennoscandian ice-sheet extended far east over northwestern Siberia. This clearly was not consistent with pollen data being available in this region (Tarasov et al., 1999). The resolution of the PMIP1 models was, to present standards, low (48 points in longitude 36 in latitude for the coarsest resolution model) to medium (T42, equivalent to 2:8 2:8, for the highest resolution model). Given the local character inherent to the continental paleo-records, some of the model data discrepancies could be due to the rather low resolution of the models, especially in regions of complex topography or coastlines such as western Europe and the Mediterranean region. Pollard and Barron (23) and Jost et al. (2) have investigated this possibility, but found that in all but one of the models, the discrepancy between models and data remained at higher resolution. The Hadley Centre Regional Model simulated temperatures of the coldest month as cold as suggested by the reconstructions (Jost et al., 2) for western Europe but this appeared to be related to very cold temperatures over the adjacent sea-ice rather than the better resolution of local features on the continent. The PMIP1 simulations did not include any change to the land surface properties of the ice-free areas. The effect from changing the vegetation from forests to tundra or steppe over Europe and Siberia has a cooling effect of a few degrees (Kubatzki and Claussen, 1998; Levis et al., 1999; Wyputta and McAvaney, 21). However, in some of the PMIP1 modern climate simulations, the land cover is not the potential vegetation but the actual one, i.e., mostly, agriculture. In this case, the effect of imposing an LGM vegetation could be less. Furthermore, the impact of permafrost, which covers large fractions of the northern hemisphere mid and high latitudes at LGM, was not taken into account in the PMIP1 experiments. Its amplitude could be less than suggested by Renssen et al. (2) for the Younger Dryas case (Poutou, 23). The PMIP1 simulations did not include the impact of dust, which could also result in a few C cooling (Mahowald et al., 1999; Werner et al., 22).

284 ARTICLE IN PRESS M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 Since the PMIP1 project completion, several fully coupled atmosphere ocean models have been used to simulate the climate of the LGM. The first simulations have used the same ice-sheet reconstruction as in PMIP1 (Hewitt et al., 21, 23; Kitoh et al., 21; Kitoh and Murakami, 22; Kim et al., 22, 23; Peltier and Solheim, 22, 24; Shin et al., 23a,b) but slightly different experimental setups between the different models. In 22, the second phase of PMIP was launched (Harrison et al., 22), with the objective of evaluating fully coupled ocean atmosphere general circulation models which employ a common experimental methodology. The present study aims at reassessing the model performance in simulating the temperatures over Europe and western Siberia, and as such is a follow-up of Kageyama et al. (21). Given the possible sensitivity of the simulated climate over Europe to SST/sea-ice conditions over the adjacent ocean, we also include a comparison of the model results over the North Atlantic and the Nordic Seas to the available reconstructions from the MARGO project (Kucera et al., 2a, http://www.pangaea.de/projects/ MARGO/). 2. Models and experimental design The present study includes the PMIP2 fully coupled atmosphere ocean general circulation model (AOGCM) experiments available in the PMIP2 database as of January 1, 26 (http://www-lsce.cea.fr/pmip2/). These are the very first results from the PMIP2 project, put together in order to comply with the Third IPCC evaluation exercise. We compare these results to the PMIP1 (http:// www-lsce.cea.fr/pmip/) AGCM results and to the results from several of the post-pmip1 AOGCM simulations forced by the Peltier (1994) ice-sheet reconstruction: CCSM1.4-UToronto (Peltier and Solheim, 22, 24), MRI-CGCM1 (Kitoh et al., 21; Kitoh and Murakami, 22) and HadCM3 (Hewitt et al., 21, 23). Table 1 gives an exhaustive list of the models included in our comparison. The boundary conditions for each model/project are summarised in Table 2. The main differences in the experimental design are: (1) the change from prescribed SSTs to slab-ocean (both in PMIP1) and finally fully coupled ocean, (2) the change in the radiative forcing related to the imposed concentrations in greenhouse gases, (3) the use of the Peltier (24) ICE-G ice-sheet reconstruction in the PMIP2 experiments for the LGM experiment. This reconstruction has been constrained by the results from the Quaternary Environment of the Eurasian North (QUEEN) (Svendsen et al., 24) project for ice-sheet margins in the Eurasian sector. As a result, the Fennoscandian ice-sheet does not extend as far eastwards (Fig. 1). The Laurentide ice-sheet is also significantly higher than in the ICE-4G reconstruction, which could Table 1 General circulation models compared in this study Model (Abbreviation) Intercomparison Project Ocean Length of run analysed (Years) Reference climate CCC2. PMIP1 CLIMAP (1981) 1 Modern Slab-ocean 1 Modern CCM1 PMIP1 Slab-ocean 1 Modern CCSR1 PMIP1 CLIMAP (1981) 1 Modern ECHAM3 PMIP1 CLIMAP (1981) 1 Modern GENESIS1 (gen1) PMIP1 Slab-ocean 1 Modern GENESIS2 (gen2) PMIP1 CLIMAP (1981) 1 Modern Slab-ocean 1 Modern GFDL PMIP1 Slab-ocean 1 Modern LMCELMD4ter PMIP1 CLIMAP (1981) 1 Modern (lmcelmd4) Slab-ocean 1 Modern LMCELMD.3 (lmcelmd) PMIP1 CLIMAP (1981) 1 Modern MRI2 PMIP1 Slab-ocean 1 Modern UGAMP PMIP1 CLIMAP (1981) 1 Modern Slab-ocean 1 Modern UKMO PMIP1 Slab-ocean 1 Modern MRI-CGCM1 Fully coupled Control: 4 Modern LGM: CCSM1.4-UToronto (CCSM1.4-UT) Fully coupled 1 Modern HadCM3 Fully coupled Pre-industrial CCSM3 PMIP2 Fully coupled 1 Pre-industrial MIROC3.2 PMIP2 Fully coupled 1 Pre-industrial HadCM3M2 PMIP2 Fully coupled 3 Pre-industrial ECBILTCLIO PMIP2 Fully coupled 1 Pre-industrial IPSL-CM4-V1-MR PMIP2 Fully coupled 1 Pre-industrial FGOALS-1.g PMIP2 Fully coupled 1 Pre-industrial

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 28 Table 2 Boundary conditions and experimental design for the LGM simulations analysed in the present paper Experiment Ice-sheets Ocean Greenhouse gases PMIP1fix Peltier (1994) CLIMAP (1981) ðco 2 Þ¼2 ppm ð34 ppmþ ðch 4 Þ¼3 ppb PMIP1cal Peltier (1994) Slab-ocean ðn 2 OÞ¼19 ppb (Raynaud et al., 1993; Leuenberger and Siegenthaler, 1992) Peltier and Solheim (24) Peltier (1994) Fully coupled ocean ðco 2 Þ¼2 ppm ð3 ppmþ ðch 4 Þ¼4 ppb ð1714 ppbþ ðn 2 OÞ¼27 ppb ð311 ppbþ (Petit et al., 1999) Kitoh et al. (21) Peltier (1994) Fully coupled ocean ðco 2 Þ¼2 ppm ð34 ppmþ Hewitt et al. (23) Peltier (1994) Fully coupled ocean ðco 2 Þ¼2 ppm ð28 ppmþ PMIP2 Peltier (24) Fully coupled ocean ðco 2 Þ¼18 ppm ð28 ppmþ ðch 4 Þ¼3 ppb ð76 ppbþ ðn 2 OÞ¼2 ppb ð27 ppbþ (Monnin et al., 21; Dallenbach et al., 2; Fluckiger et al., 1999) In addition to the boundary conditions and experimental design features described in the table, the following characteristics are valid in all cases: the orbital parameters have been set to their 21 ka BP values and land surface characteristics for ice-free land are equal to their modern values. The greenhouse gases concentrations for the control run have been indicated in brackets. Note that the control corresponds either to the present climate or to the preindustrial climate. 4 4-12 6 12-12 6 12 7 7-6 6-6 6 1 1 2 2 3 3 Fig. 1. Last Glacial Maximum topography (in m above sea-level) and land-ice (thick black lines) limits from the Peltier (1994) ICE4G (left hand side) and Peltier (24) ICEG (right hand side) reconstructions. have a remote influence on the European climate via modified stationary waves and storm-tracks (Kageyama and Valdes, 2). There are also differences in the control simulations. Most post-pmip1 models use modern greenhouse gases (GHG) concentrations for their control run, while all PMIP2 experiments and the post-pmip1 HadCM3 run use pre-industrial values. Contrary to the PMIP1 models, which often differed from the IPCC models of that time (their version was older and/or resolution lower), the PMIP2 models are generally those that have been used for the most recent IPCC simulations. 3. Sea surface temperatures 3.1. Sea surface temperature reconstructions for the North Atlantic and the Nordic Seas MARGO (multiproxy approach for the reconstruction of the Glacial Ocean surface, http://www.pangaea.de/ Projects/MARGO/) is an international program launched in September 22 with the objectives of improving the pioneering results of the CLIMAP group (climate longrange investigation, mapping, and prediction, CLIMAP, 1976, 1981) in reconstructing ocean surface conditions

286 ARTICLE IN PRESS M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 during the LGM. Whereas CLIMAP mainly used planktonic foraminifer data as paleoindicators of the LGM sea surface conditions, the MARGO project follows a multiproxy approach as well as the use of different transfer function techniques and better quality-assessed datasets. In addition, the CLIMAP LGM time slice was not precisely defined and in some cases probably included Heinrich events, whereas the MARGO project uses a better constrained chronostratigraphy and defines the LGM as the interval between 19 and 23 ka, which excludes Heinrich events (Mix et al., 21). In this paper, we use the reconstructions of the North Atlantic and the Nordic Seas SSTs from various transfer function approaches based on census counts of assemblages of planktonic foraminifera (Kucera et al., 2b), Mg/Ca ratios of planktonic foraminifera (Barker et al., 2; Meland et al., 2), dinoflagellate cyst assemblages (De Vernal et al., 2) and alkenone unsaturation ratios (Rosell-Mele et al., 24). The reconstruction based on census counts of assemblages of planktonic foraminifera uses different transfer function techniques (artificial neural networks, revised analog method and SIMMAX with and without distance weighting). The SSTs used in this study and the error bars associated with them in Figs. 2 and 3 consist of the average and the standard deviation calculated from the results of the four techniques. This foraminifera-based reconstruction and the one based on census counts of dinoflagellate cysts provide summer (July August September; jas), winter (January February March; jfm) and annual glacial SSTs. The error bars for the dinocyst-based reconstructions represents the calibration method uncertainty. Although the whole annual cycle must be important in determining the relative distribution of the different species, reconstructions of SSTs might be more robust for the summer season, during which planktonic populations experience maximum development. In the present work, we have assigned the freezing value ð 1:8 CÞ to all samples in regions where both dinocysts and foraminifera indicate perennial sea-ice. This essentially concerns the region off the east coast of Greenland (see Kucera et al., 2b, figure 2; De Vernal et al., accepted for publication). The Mg/Ca temperatures are calcification temperatures of planktonic foraminifera shells whose production nowadays spikes in spring and summer at mid to high latitudes (Barker et al., 2). The estimates for the northern North Atlantic and the Nordic Seas (Meland et al., 2) have all been obtained from N. pachyderma sin. and calibrated using jas temperatures, since this species mainly calcifies in summer. They will therefore be represented on the model data Control 2 ccc2. 2 1 1 2 3 3 4 4 6 6 7 7 8 LGM 8 ccm1 gen1 gen2 gfdl lmcelmd4 mri2 ugamp ukmo HadCM3 MRI-CGCM1 CCSM1.4-UT CLIMAP Climatology 2 1 1 2 3 3 4 4 6 6 7 7 8 8 foraminifera MgCa alkenone dinocyst Control 2 2 1 1 2 3 3 4 4 6 6 7 7 8 8 LGM 2 2 1 1 2 3 3 4 4 6 6 7 7 8 8 CCSM3 HadCM3M2 MIROC3.2 ECBILTCLIO IPSL-CM4-V1-MR FGOALS-1.g foraminifera MgCa alkenone dinocyst 1 - -1 LGM - Control -1-2 2 3 3 4 4 6 6 7 7 8 8 (a) latitude 1 - -1 LGM - Control -1-2 2 3 3 4 4 6 6 7 7 8 8 (b) latitude Fig. 2. (a) Sectorial averages (3 W 2 E) of the summer (July August September) sea surface temperatures over the North Atlantic and the Nordic Seas, compared with sea surface temperature reconstructions, for the PMIP1 and post-pmip1 simulations. The Mg/Ca estimates are from Meland et al. (2). (b) Same as (a) for the PMIP2 models, in coloured solid lines. The PMIP1 and post-pmip1 results are indicated in grey.

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 287 Control Control 2 1 1 2 3 3 4 4 6 6 7 7 8 8 LGM ccc2. ccm1 gen1 gen2 gfdl lmcelmd4 mri2 ugamp ukmo HadCM3 MRI-CGCM1 CCSM1.4-UT CLIMAP Climatology 2 1 1 2 3 3 4 4 6 6 7 7 8 8 LGM CCSM3 HadCM3M2 MIROC3.2 ECBILTCLIO IPSL-CM4-V1-MR FGOALS-1.g foraminifera MgCa alkenone dinocyst 2 2 1 1 foraminifera MgCa alkenone dinocyst 1 1 2 3 3 4 4 6 6 7 7 8 8 2 3 3 4 4 6 6 7 7 8 8 LGM - Control LGM - Control 1 1 1 1 2 3 3 4 4 6 6 7 7 8 8 (a) latitude 2 3 3 4 4 6 6 7 7 8 8 (b) latitude Fig. 3. (a) Same as Fig. 2a but for the annual averages. The Mg/Ca estimates are from Barker et al. (2). (b) Same as Fig. 2b but for the annual averages. comparisons for summer in the present paper. The error bars associated with these reconstructions are based on duplicate measurements which gave a 1 sigma error of :8 mmol=mol (Meland, personal communication). These values have been used to determine the error bars for each temperature estimate via the formula from Meland et al. (2). On the other hand, the five estimates for the southern North Atlantic (Barker et al., 2) were obtained from different species of foraminifera (G. ruber and G. bulloides). The reconstructed temperatures will be considered to represent annual mean SSTs in our comparison, and not summer temperatures, because at these latitudes and for those species, the bloom does not strictly occur in summer. Barker et al. (2) also report that these reconstructions have been mostly interpreted as annual mean SSTs. Finally, the comparison of the reconstructions obtained from core tops with modern data (top panels, Fig. 3) shows a good agreement for annual mean SSTs. The error bars on these reconstructions are based on the same considerations as those from Meland et al. (2). The alkenone proxy is based on the measurement of the relative abundance of long-chain alkenones (Brassell et al., 1986). These molecules are produced by a few, but ubiquitous, species of marine algae. To convert data into temperature values, two alternative empirical calibration approaches have been used. The first one was based on measuring the U K 37 ratio in laboratory cultures (Prahl et al., 1988). This calibration was later confirmed from a second calibration based on marine surface sediments (e.g. Mu ller et al., 1998), which is valid for the globe except in some coastal sites and in Arctic waters. This study showed that global sedimentary values of U K 37 have a higher correlation factor with annual ocean temperatures (from World Ocean Atlas, 1998) at m depth than for any other depths and seasons. Therefore, SST estimates from U K 37 derived from this calibration can be interpreted as reflecting annual mean values at the ocean surface. At high latitudes, however, this is unlikely to be the case given the strong seasonal contrasts in phytoplankton production, and alkenone-ssts may better reflect summer temperatures (Haug et al., 2). Given this uncertainty on the season recorded by the alkenone proxy, we have chosen to represent the core-top and LGM estimates (for which the same calibration was used) on the annual and summer model data comparison figures (Figs. 2 and 3). These are discussed in the following sections. The LGM control anomalies, also represented on these figures, are computed from the LGM and core-top estimates. The error bars on the LGM reconstructed temperatures represent the combined uncertainties related to the analytical procedure and the calibration method.

288 ARTICLE IN PRESS M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 The dinoflagellate and alkenone proxies are related to primary productivity and reflect conditions in the photic zone, i.e., in the upper part of the mixed layer ( m), whereas planktonic foraminifers may inhabit different depths in the water column and some species, such as the polar taxon N. pachyderma sin. often occupy mesopelagic layers, below the pycnocline (e.g. Be and Tolderlund, 1971; Kohfeld et al., 1996; Simstich et al., 23; Hillaire-Marcel et al., 24). Since growth and calcification of N. pachyderma sin. in the glacial North Atlantic may have occurred at depths greater than 1 m, the transfer function and Mg/Ca temperatures based on foraminifera could represent subsurface conditions. Despite uncertainty in the precise depth of the planktonic populations, all calibrations in the MARGO project, for the different proxies, used the seasonal means of surface temperature at 1 m from World Ocean Atlas (1998), and the reconstructed temperatures thus represent the upper layer of the ocean. Further studies will investigate the simulated LGMmodern ocean temperature differences in the upper layers of the ocean, but for the present study, we have chosen to only compare the MARGO SST reconstructions with the SST simulated by the models. Those SST reconstructions from different proxies and methods are used for comparison with the results of the different models (Table 2) for the summer season (jas) and the annual mean. This is a first-step comparison which gives a broad view of the range of the model results and the paleoreconstructions (this latter dispersion being more specifically discussed in De Vernal et al., accepted), and the differences between them. We did not perform the comparison for the winter season, given the potentially less accurate reconstructions and the important impact of the air temperature over sea ice rather than of SSTs for Europe and western Siberia winter temperature (see Section 4). 3.2. Comparison to the model results 3.2.1. Comparison for the modern situation The SST pattern in the North Atlantic is at first order zonal. Figs. 2 and 3 show the sectorial averages of the simulated SSTs over the eastern part of the North Atlantic (between 3 W and 2 E) as a function of latitude, along with the SST reconstructions, for the pre-industrial runs (control, upper panels), the LGM runs (middle panels) and the anomalies between the two (LGM control, lower panels) for the summer and annual mean, respectively. We chose to perform the zonal average over the eastern part of the North Atlantic ocean because it is the sector which is the most densely covered by proxy data, especially foraminifera (Fig. 4). The upper panels of Figs. 2 and 3 indicate that most models stand within 2 3 1C around the climatological HadISST curve (Rayner et al., 23), especially between 2 N and N. The dispersion of models increases north of N. The models bias w.r.t. the climatology and the inter-model differences are very similar for the summer and annual averages. The results of the slab models (dashed lines in Figs. 2a and 3a) have low spreading between each other compared with fully coupled models and generally show good consistency with the climatology (although slightly colder on average), except for GENESIS1 which simulates significantly colder temperatures south of 4 N and between N and 67 N and CCM1 SSTs which are very cold north of N. This general tendency to low spreading and close match to climatology can be explained by the modern oceanic meridional fluxes used in the slab models. The range of the results of the fully coupled models is larger, especially for the summer season (Figs. 2a (post- PMIP1 runs) and b (PMIP2 runs), upper panels), although the warm temperatures simulated by MRI-CGCM1 and CCSM1.4-UT can be attributed to the use of modern concentrations of GHG rather than pre-industrial ones. The upper panels of Figs. 2 and 3 also show the modern SSTs at the sites included in the foraminifera and dinoflagellate datasets. These are very close to the zonally averaged climatological curve, which indicates that the location of those cores does not exhibit strong regional signals but rather are good indicators of the mean zonal temperature at the given latitudes. The alkenone-based SST and the annual reconstructions derived from Mg/Ca concentrations for the control are not the climatological value obtained at the core location (as it is the case for the foraminifera and dinoflagellate), but are a reconstruction of the ocean temperature from coretop sediments with the same calibration used for the LGM reconstruction. The comparison with the climatological curve therefore does not indicate the degree of zonal dispersion at the core locations but a degree of confidence in the SST reconstruction. For instance, this SST reconstruction yields particularly cold temperatures between 3 N and 4 N in summer (Fig. 2). The same alkenone-based reconstruction plotted in comparison with annual means obtained from other proxies and models (Fig. 3, upper panel) shows that it may indeed better represent annual mean SSTs rather than summer SSTs in this range of latitudes. Nevertheless, the alkenone-based reconstruction further north is closer to the summer climatology than to the annual-mean climatology. The season represented by this proxy seems to depend on the conditions found at different latitudes and might be difficult to predict in a very different context like the LGM case before further investigation is carried out to better understand this behaviour. 3.2.2. LGM SSTs and LGM control SST anomalies The dispersion between the LGM runs is larger than in the CTRL ones (Figs. 2 and 3, middle panels). For the slabocean models the ranking of the results in terms of temperature is usually conserved. For example, GEN- ESIS1, which was among the coldest slab models for the control, is even colder than the other slab models for the LGM, with sea ice forming as far south as 2 N (SSTs at

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 289 the freezing point of 1:8 C). MRI2, which was also among the coldest models between 2 N and Ninthe control run, simulates much colder SSTs than most slab models at those latitudes under LGM conditions. In the same way, the relative ranking of coupled models in temperature is generally similar under preindustrial and LGM conditions. For instance, IPSL-CM4-V1-MR, which is one of the coldest models in the low to mid-latitudes in the control run keeps this characteristics in the glacial run. The cold temperatures simulated by CCSM3 between 4 N and N is also present in both runs. At high latitudes, the relatively warm and cold behaviours of HadCM3M2 and FGOALS, respectively, are also preserved under LGM conditions. Despite this large dispersion, the models suggest a stronger meridional SST gradient north of 4 N in the LGM runs, associated with a moderate cooling in the tropics compared to present conditions, but with cold temperatures extending far south in the extra-tropics (the sea-ice edge reaches N for the coldest models). This feature of a stronger SST gradient is also suggested by the foraminifera-based reconstructions between 4 and N, with roughly uniform temperatures farther north, and by the Mg/Ca reconstructions, also giving a similar magnitude CCSM1.4-UT 8 7 6 4 3 MRI-CGCM1 HadCM3 ccc2. 8 7 6 4 3 gfdl ccm1 7 6 4 3 lmcelmd4 gen1 8 7 6 4 3 ukmo mri2 8 7 6 4 3 gen2 ugamp -8-6 -4-2 8 7 6 4 3-8 -6-4 -2 CLIMAP (a) -1-13 -11-9 -7 - -3-1 1 3 Fig. 4. LGM-control anomalies in the computed summer sea surface temperatures in the PMIP1 slab-ocean and fully coupled post-pmip1 ocean atmosphere models. The CLIMAP (1981) reconstructions are also given for comparison. Coloured squares represent the reconstructions from the assemblages of planktonic foraminifera (Kucera et al., 2b), on the same colour scale as the model results. The reference sea surface temperatures for the reconstructions are the 1 m temperatures from World Ocean Atlas (1998). (b) Same as Fig. 4a but for the PMIP2 simulations.

29 ARTICLE IN PRESS M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 CCSM3 HadCM3M2 MIROC3.2 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 ECBILTCLIO IPSL-CM4-V1-MR FGOALS-1.g (b) 1 13 11 9 7 3 1 Fig. 4. (Continued) 1 3 in the cooling for summer (Kucera et al., 2b; Meland et al., 2). North of N, LGM reconstructions based on dinoflagellate cyst assemblages exhibit about 1 1C warmer SSTs than the foraminifera ones. At these latitudes, the latter reconstructions are mainly based on N. pachyderma sin., which dominates the assemblages and which is also used for Mg/Ca measurements. As seen in Section 3.1, the different proxies could indicate temperatures at different depths. This could explain some of the differences between the reconstructions. However, dinoflagellate assemblages also show quite uniform temperatures north of N, which translates in a positive northward gradient in the temperature anomalies. At these latitudes, despite a spread of the order of 1C in the model anomalies and specific modeldependent behaviours, the overall behaviour of the models also consists in an SST anomaly weakening with latitude (Figs. 2 and 3, lower panels). Figs. 4a and b show the spatial distribution of the temperature anomalies for the different models for the summer season, along with the planktonic foraminifera SST reconstructions. For the slab-ocean models (Figs. 4a, lower panels), the structure of the SST anomaly is very zonal, with the largest anomaly between and 7 N. Since these models do not include any change in heat transport associated with differences in ocean currents and/ or deep ocean circulation, this mainly represents a thermal response to the atmospheric cooling. When the dynamics of the ocean is taken into account, as it is the case for the fully coupled models (Fig. 4a, upper panels and Fig. 4b), the temperature anomalies are usually more regional, the strongest cooling being generally located off New Foundland and extending into the mid-atlantic. Changes in the Gulf Stream and the Labrador currents, along with the surface wind forcing, should be further analysed in each model to better understand this feature. Also, changes in the strength of the thermohaline circulation and in the location of the convection sites, resulting in a change in the northward ocean heat transport, have to be considered in order to understand the spatial distribution of the SST anomalies. For example, PMIP2 ocean models (Fig. 4b) continue to transport heat northward in the eastern part of the North Atlantic, explaining the small temperature differences in this region. 4. Continental temperatures over Europe and western Siberia 4.1. Temperature reconstructions based on pollen data As in Kageyama et al. (21), we compare the model results to pollen-based temperature reconstructions. The LGM vegetation in Europe and western Siberia is very different from the present vegetation (dominated by agriculture) or the potential vegetation for the present climate (Ramankutty and Foley, 1999). It mainly consists of steppe with significant elements of tundra (steppetundra), except in Siberia where taiga is also present. This vegetation difference is consistent with a significant decrease in winter temperature and annual precipitation, especially in western Europe and on the northern Mediterranean coast. The quantification of these climatic anomalies has been performed for Europe and western Siberia by Peyron et al. (1998) and Tarasov et al. (1999), who used a neural network technique coupled with a plant functional type approach. The reconstructions for Europe and the northern Mediterranean coast by Peyron et al. (1998) have since been updated through the use of a more extensive calibration dataset (Peyron et al., 2). The new reconstructions of the temperature of the coldest month are included in the confidence intervals of the previous reconstructions, but show a slightly less intense cooling over western Europe (compare figure 7 with figure 2 from

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 291 Kageyama et al. (21) for the temperature of the coldest month). The temperature of the warmest month, which had only been reconstructed for central and eastern Europe and Siberia (Tarasov et al., 1999), has also been assessed for western Europe and the Mediterranean coast. Both the temperatures of the coldest month and of the warmest month have been used in the high resolution model data comparison by Jost et al. (2). The reconstructions (coloured squares and diamonds in Figs. and 6 for the exact values and associated confidence intervals) show that the LGM temperature of the warmest month is around 1 1C cooler than the modern value for the European sector. The cooling (between and 1C) is smaller for western Siberia. For the temperature of the coldest month (Fig. 7), there is a strong gradient in the LGM control climate anomalies, from the western sector, where they reach 2 C, to western Siberia, where they remain at around 1 C. These low values are consistent, at least for western Europe, with the climatic reconstruction based on beetles (Guiot et al., 1993) and those based on permafrost limits (Renssen and Vandenberghe, 23). Indeed these authors showed that permafrost reached the latitude N in its continuous form and 4 N in its discontinuous form. Using the temperature limits they give for these types of permafrosts, the absolute annual temperature should have been between 4 and 8 C and the coldest month temperature between 2 and 2 C. In southern Italy, Allen et al. (1999) evaluated the winter cooling to 2 C (during the LGM) up to 12 C during the warmer episodes of the glacial times. Furthermore, Guiot et al. (2) have used a process-based vegetation model (BIOME3) in an inverse mode to reconstruct from pollen data the most probable climate under precipitation seasonality change and under lowered CO 2 atmospheric concentration. Applied to LGM pollen data from Greece and Italy, they showed that winter was ca 1 2 1C colder than the present, in agreement with the above climate reconstruction, and that CO 2 effects do not bias winter climate reconstructions in southern Europe. For summer, Guiot et al. (1999) showed that, in Europe as well as in Siberia, CO 2 does not appear to be responsible of any bias in the above reconstructions. 4.2. Model data comparison The new temperature reconstructions used in Jost et al. (2) are reconstructions of the temperatures of the coldest and the warmest months. We have therefore computed these variables from the model results by first computing the mean annual cycle and then taking the warmest/coldest month from this climatology. This is consistent with the calibration dataset used for the reconstructions from pollen assemblages. The warmest month for the modern climate simulations is July on the European continent, except near the Atlantic ocean and the Mediterranean Sea where it can be August (not shown). The size of this zone of oceanic influence is model dependent, it is very small in HadCM3M2, CCSM3, MIROC3.2, IPSL-CM4-MR and FGOALS-1.g but spreads out over most of western Europe and the Mediterranean area in CCSM1.4-UT. In the LGM simulations, most models simulate their temperature maxima over Europe in July as well. However, the warmest LGM month simulated by MIROC3.2 over present-day Northern France, Germany and Poland is August, and the zone in which CCSM1.4-UT simulates its warmest temperatures in August is reduced to France and Spain. Therefore, although most models simulate a temperature maximum over Europe in July, there can be deviations from this situation according to the location (in particular near the coasts) and the time period (modern climate vs LGM). As far as the thermal minimum is concerned, most models simulate it in January over the continent, but for large zones, which are dependent on the model and the time period, the minimum is simulated either in February (especially in the modern climate simulations) or in December (especially in the LGM simulations). Hence, to get the largest possible consistency with the calibration of the pollen reconstructions, it is necessary to compute the actual temperature of the warmest/coldest month rather than assuming they are the months of July/January, respectively. 4.2.1. Temperature of the warmest month The analysis of the PMIP1 simulations (Kageyama et al., 21) showed that the main factor driving the LGM control differences in the temperature of the warmest month over Europe and western Siberia was the presence of the Fennoscandian ice-sheet. The largest anomalies, in the prescribed SST as well as in the slab ocean experiments, were indeed located over the ice-sheet. The second factor would be the change in SSTs over the North Atlantic and the Mediterranean Sea, but the relationship between these SSTs and continental temperature is not straightforward. For instance, the slab-ocean simulation that yields the largest LGM-modern SST anomaly in the mid-latitudes is GENESIS1 (Figs. 4 and 2, lower panel). This model is far from simulating the coldest anomaly in the temperature of the warmest month in the adjacent western Europe sector (Fig. 6, upper panel), nor over the other two sectors, except over the ice-sheet east of 1 E. Furthermore, most slabocean AGCMs simulated a North Atlantic SST anomaly smaller than the CLIMAP (1981) one, but the entanglement of the simulated temperatures of the warmest month anomalies for the fixed SST and slab-ocean simulations shows how difficult it is to relate SSTs with continental temperatures, even for regions close to the North Atlantic ocean such as western Europe. Fig. (left column) shows the anomaly in the temperature of the warmest month simulated by the fully coupled models over the North Atlantic, Nordic Seas, Europe and western Siberia, along with the corresponding continental reconstructions. The coldest anomalies (a cooling larger than 18 1C) are reached over the Fennoscandian

292 ARTICLE IN PRESS M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 CCSM3 HADCM3M2 MIROC3.2 ECBILTCLIO IPSL_CM4_MR FGOALS-1.g CCSM1.4-UT HADCM3 MRI-CGCM1 6 3 3 6 9 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 8 7 6 4 3 6 3 3 6 9 3 24 18 12 6 3 3 Fig.. Surface air temperature over the North Atlantic, the Nordic Seas, Europe and western Siberia for the fully coupled ocean atmosphere models forced by the Peltier (24) ICE-G reconstruction for the top four lines, and the Peltier (1994) ICE-4G reconstruction for the bottom three lines. Lefthand side: LGM CTRL anomaly in the temperature of the warmest month. Right-hand side: same but for the coldest month. The minimum (left column) and maximum (right-hand side) sea-ice extents are indicated (orange line for the pre-industrial run, red line for the LGM run). Pollen-based reconstructions are shown with the same colour scale. Diamonds correspond to the Peyron et al. (1998) reconstructions, updated in Jost et al. (2), squares correspond to the Tarasov et al. (1999) reconstructions. The CTRL for the pollen-based reconstructions is the 196 199 average. ice-sheet and the North Atlantic/Nordic Seas sea-ice if present (see for instance the CCSM1.4-UToronto results). The cooling decreases south of the ice-sheet. Over Europe, for longitudes between 1 W and E, the simulated cooling amounts to 6 12 1C for most models, in broad agreement with the reconstructions. HadCM3, MRI- CGCM1 and ECBILTCLIO simulate a more modest cooling (3 6 1C), which is in agreement with the reconstructions

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 293 Twarm, LGM-CTRL longitudes [-1:1], latitudes [3:7] - -1-1 -2-2 -3 3 3 4 4 6 6 7 longitudes [1:], latitudes [3:7] - -1-1 -2-2 -3 3 3 4 4 6 6 7 longitudes [:9], latitudes [3:7] - -1-1 -2-2 -3 3 3 4 4 6 6 7 (a) latitude Twarm, LGM-CTRL longitudes [-1:1], latitudes [3:7] - -1-1 -2-2 -3 3 3 4 4 6 6 7 longitudes [1:], latitudes [3:7] - -1-1 -2-2 -3 3 3 4 4 6 6 7 longitudes [:9], latitudes [3:7] - -1-1 -2-2 -3 3 3 4 4 6 6 7 (b) latitude Jost et al 2 Tarasov et al 1999 ccsr1 gen2 echam3 mri2 ccc2. ugamp lmcelmd4 lmcelmd ccm1 gen2 gen1 mri2 ccc2. ugamp ukmo lmcelmd4 gfdl HadCM3 CCSM1.4-UT MRI-CGCM1 Jost et al 2 Tarasov et al 1999 HadCM3M2 MIROC3.2 FGOALS-1.g IPSL-CM4-V1-MR CCSM3 ECBILTCLIO Fig. 6. (a) Sectorial averages (land points only) of the simulated temperature of the warmest month for three sectors: (1 W 1 E) (top), (1 E E) (middle), ( E 9 E) (bottom), along for the temperature reconstructions for each sectors (dots with confidence interval). Dotted lines: PMIP1 prescribed SST simulations. Dashed lines: PMIP1 slab-ocean simulations. Dot-dashed lines: fully coupled models forced by the Peltier (1994) ICE-4G reconstruction. (b) Same as a for the PMIP2 models (solid coloured lines). PMIP1 and post-pmip1 model results are indicated in grey. for two data points in France. As noticed from the previous analysis of the PMIP1 fixed SST and slab-ocean simulations, it is difficult to establish a relationship between the temperature of the warmest month over the ocean and over the adjacent continent. For instance, MIROC3.2 simulates a 6 12 1C cooling over southwestern Europe but an oceanic cooling over the adjacent North Atlantic between 3 and 6 1C. On the other hand, MRI-CGCM1 simulates a moderate cooling over southwestern Europe (3 6 1C) but a larger one (between 6 and 12 1C) over the whole North Atlantic. This lack of correlation is further discussed in Section.1. The anomalies simulated by the models over western Siberia are very sensitive to the ice-sheet reconstruction used to force the model (Fig. 1): CCSM1.4-UT, HadCM3 and MRI-CGCM1 (lower three maps) use the Peltier

294 ARTICLE IN PRESS M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 Tcold, LGM-CTRL longitudes [-1:1], latitudes [3:7] -1-2 -3-4 3 3 4 4 6 6 7-1 -2-3 -4 3 3 4 4 6 6 7-1 -2-3 longitudes [1:], latitudes [3:7] longitudes [:9], latitudes [3:7] -4 3 3 4 4 6 6 7 (a) latitude -1-2 -3 Tcold, LGM-CTRL -4 3 3 4 4 6 6 7-1 -2-3 -4 3 3 4 4 6 6 7-1 -2-3 longitudes [-1:1], latitudes [3:7] longitudes [1:], latitudes [3:7] longitudes [:9], latitudes [3:7] -4 3 3 4 4 6 6 7 (b) latitude Jost et al 2 Tarasov et al 1999 ccsr1 gen2 echam3 mri2 ccc2. ugamp lmcelmd4 lmcelmd ccm1 gen2 gen1 mri2 ccc2. ugamp ukmo lmcelmd4 gfdl HadCM3 CCSM1.4-UT MRI-CGCM1 Jost et al 2 Tarasov et al 1999 HadCM3M2 MIROC3.2 FGOALS-1.g IPSL-CM4-V1-MR CCSM3 ECBILTCLIO Fig. 7. (a) Same as Fig. 6a but for the temperature of the coldest month. (b) Same as Fig. 6b but for the temperature of the coldest month. (1994) ICE-4G reconstruction, which extends far east over western Siberia. Of these three models, only HadCM3 simulates a weaker cooling over western Siberia than over western Europe, which is the broad gradient indicated by the reconstructions. To the extreme north of the sector, the ice-sheet imposes very cold temperatures while the reconstructions indicate a very mild change. In the six simulations forced by the new Peltier (24) ICE-G reconstruction, all models except FGOALS-1.g simulate weaker cooling over western Siberia than over western Europe. Two of the models even simulate a slight warming over northwestern Siberia. To account for the model dispersion and the confidence interval of the reconstructions, we have represented sectorial averages for the same sectors as in Kageyama et al. (21), i.e. western Europe (1 W 1 E), central and eastern Europe (1 E) and western Siberia ( 9 E). All PMIP1, 2 and additional fully coupled model results are represented in Fig. 6. The dispersion of the fully coupled models is slightly larger than that of the PMIP1

M. Kageyama et al. / Quaternary Science Reviews 2 (26) 282 212 29 simulations, over the western sector, as is the case for the SST anomalies (Fig. 2, lower panel). ECBILTCLIO stands on the warm side, its simulated anomaly standing between 2 and C outside the ice-covered area. CCSM1.4-UT stands on the cold side, especially between 4 and N, with temperature anomalies reaching 1 C. This dispersion of the model results is in fact comparable to the confidence interval of the reconstructions. Most models are in broad agreement with the reconstructions over the western European sector. For the central and eastern sector (Fig. 6, middle and lower panels), the dispersion of the fully coupled model results is slightly smaller (although comparable) to that of the PMIP1 models, indicating that the source of intermodel differences is more related to differences in the atmosphere or the land-surface treatment in the models than to the differences in SSTs. For the central and eastern Europe sector (Fig. 6, middle panel), apart from a few points between N and 6 N, model results are in good agreement with reconstructions. The discrepancy might not be as large as it appears on this figure because these three points are located near the eastern edge of the sector, where temperature anomalies would be smaller than the average over the sector, as shown in Fig. for the fully coupled models. This is especially the case for the models forced by the ICE-G reconstruction. The impact of the ICE-G reconstruction used in the PMIP2 simulation is very clear on the lower panel of Fig. 6, which shows the model data comparison for western Siberia. South of 6 N, apart from the CCM1 which simulates too cold anomalies, model results compare well with the reconstructions. North of 6 N, the only agreement is reached by five of the six PMIP2 models, which used the ICE-G reconstructions. All other models simulated temperature anomalies 2 1C too cold compared to the reconstructions. The hypothesis for model data discrepancy that was proposed in our PMIP1 study (Kageyama et al., 21, and Section 1), i.e. that the simulated temperatures of the warmest month over western Siberia were too cold because of the imposed ice sheet in this region, is therefore confirmed through the simulations forced by the ICE-G reconstructions. 4.2.2. Temperature of the coldest month The reconstructions of the anomalies of the temperatures of the coldest month show a large SW NE gradient, from western Europe where they stand around 2 C, to western Siberia where they equal ca 1 C. The results from the PMIP1 model data comparison showed that the models were not able to simulate such a gradient in the temperature anomalies, mainly because they were not able to simulate the large cooling depicted by the reconstructions for western Europe and the Mediterranean area. The updated reconstructions over this area result in ca 1C smaller anomalies, but they still suggest a significant SW NE gradient, as shown in Figs. and 7. The results from the PMIP1 runs analysed in Kageyama et al. (21) showed that the largest cooling of the temperature of the coldest month was associated with extensive sea-ice and by the ice-sheets. The influence of the sea-ice is clearly shown by the fixed SST experiments, which were forced by the extensive CLIMAP (1981) sea-ice for the LGM runs. In the slab-ocean experiments, the cooling was smaller, and the relative influence of the ice-sheet larger (Kageyama et al., 21, Figure 6). Fig. (right column) shows the simulated anomalies of the temperature of the coldest month for the fully coupled models. These anomalies can easily be compared to the simulated anomalies of the temperatures of the warmest month (left column). Values of the pollen-based reconstructions are represented with the same colour scale. For all but one of the models, the simulated cooling in the temperature of the coldest month is much larger than the cooling of the temperature of the warmest month, as suggested by reconstructions. Also, most models simulate a cooling over western Siberia weaker than over western Europe, as suggested by the reconstructions. This gradient in the simulated cooling is stronger in the PMIP2 simulations forced by the Peltier (24) ICE-G reconstruction. This could result from the direct influence of the different location of the ice-sheet, but also from the influence of the Fennoscandian topography on the atmospheric general circulation. The remote impact of a much higher Laurentide ice-sheet in ICE-G compared to ICE- 4G cannot be excluded either, the European climate being sensitive to the state of the Laurentide ice-sheet via the influence of stationary waves and storm-tracks (Kageyama and Valdes, 2). Over the western Europe sector (Fig. 7, upper panel), the anomalies in the temperature of the coldest month simulated by the fixed SST models, forced by the CLIMAP (1981) SST and sea-ice reconstruction, are colder than those simulated by the slab-ocean models, which simulated SST anomalies significantly warmer than CLIMAP (1981), except for the GENESIS1 model. This is not the case in the central Europe and western Siberian sectors, for which there is no systematic grouping of the models according to the type of PMIP1 simulation. This impact of the North Atlantic sea-ice/sst anomalies is also clear in the comparison of the coupled simulation results. CCSM1.4- UT simulates sea-ice as far south as 3 S, which is further south than the CLIMAP (1981) reconstruction. The associated anomalies of the temperatures of the coldest month are extremely cold, over western Europe as well as further East, all the way to 9 E. CCSM3 also simulates quite an extensive sea-ice maximum for the LGM, although not as large as CCSM1.4-UT. This appears to be associated with a smaller anomaly of the temperature of the coldest month over the northern Mediterranean coast. Furthermore, the anomalies on the western Siberia sector are also much smaller, which results in the pattern of this anomaly being much less zonal than in CCSM1.4-UT. The example of ECBILTCLIO or HadCM3 would further prove this relationship between the presence of extensive