3.1. Global LGM climate simulated with CCSM3
The annual global mean surface temperature is 7.9 °C in the CCSM3 LGM simulation, which is 6.9 °C colder than in the CCSM3 simulation of the recent past climate. This response is stronger than in most of the PMIP1 (1.9–9.2 °C colder than in the pre-industrial/recent past climate) and PMIP2 (3.4–5.5 °C colder than the pre-industrial climate) simulations as presented by Kageyama et al. (2006) and Braconnot et al. (2007). Temperature differences compared to the recent past climate are larger at mid and high latitudes in both hemispheres and, due to the increased albedo and surface elevation, most pronounced over the Laurentide and Fennoscandian ice sheets (Fig. 3). Our simulation primarily deviates from the PMIP2 mean (Braconnot et al., 2007, their Fig. 3) over the North Atlantic and North Pacific, differences are smaller over the Laurentide and Fennoscandian ice sheets. The upper-tropospheric circulation difference from the recent past climate is dominated by an amplification of the topographic wave over the Rocky Mountains and the Laurentide ice sheet in winter and with a smaller amplitude also in summer (Fig. 3). These changes are also found in the earlier period of the CCSM3 LGM simulation analysed by Otto-Bliesner et al. (2006). They are associated with a southward shift of the Atlantic and Pacific storm tracks resulting in a decrease in the high-latitude precipitation while mid-latitude precipitation increases in both winter and summer (Fig. 3). This shift in precipitation in the North Atlantic and North Pacific is a common feature in four PMIP2 simulations analysed by Lainé et al. (2009).
Figure 3. Summer (June–August, upper panels) and winter (December–February, lower panels) difference between the CCSM3 LGM simulation and the recent past climate. T2m (shading) and fraction of the surface covered by sea ice (contour at 50% sea ice coverage for CCSM3 LGM in red and the recent past in white) is shown in the left panels. Geopotential height at 300 hPa (contours every 50 geopotential meters, gpm) and relative changes in precipitation (shading). Negative geopotential height contours are coloured white and positive are coloured black, the zero contour is omitted. Units are °C and % in the left panels and gpm and % in the right panels.
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Changes in the oceans include a 50% decrease in the strength of the Atlantic Meridional Overturning Circulation and a decrease in annual mean SST with a maximum of 12 °C in the Nordic Seas and northern North Atlantic (not shown). The response of the Atlantic Meridional Overturning Circulation (AMOC) to LGM conditions differs among different PMIP coupled models. The response found in our simulation is relatively strong as compared to the 17% decrease in AMOC strength found by Otto-Bliesner et al. (2007) for the earlier period of this CCSM3 simulation (Brandefelt and Otto-Bliesner, 2009). It is also stronger than what is found in other coupled PMIP simulations. The AMOC slows down considerably (by 20–40%) during the LGM as compared to the modern climate in four PMIP models, there is a slight reduction in one model and four models show a substantial increase in AMOC strength (by 10–40%) (Otto-Bliesner et al., 2007; Weber et al., 2007).
The large-scale spatial pattern of winter and summer SST differences between the recent past and the LGM has the strongest response around 45°N, along the eastern North Atlantic coast and partly also in the Mediterranean. In the Norwegian Sea, the anomaly amounts to 10–12 °C (Fig. 4). The sea ice cover extends further equatorward in the North Atlantic, North Pacific and the Southern Ocean. The large-scale spatial pattern of winter and summer SST is similar in the different PMIP2 simulations (MARGO Project Members, 2009; their figures S4 and S5). In the Norwegian Sea, however, the magnitude of the anomaly varies among the PMIP2 simulations with a winter and summer cooling of 0–8 °C.
Figure 4. Summer (JAS, top) and winter (JFM, bottom) mean SST anomalies (LGM minus recent past) for the CCSM3 LGM simulation (left panel) and the MARGO compilation (right panel). The CCSM3 SSTs have been interpolated onto the same grid as the MARGO data. Grid boxes for which the CCSM3 simulated LGM SST falls within ±2 standard deviations of the MARGO proxy SST are indicated with white + signs in the right panels.
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The spatial pattern of the temperature anomaly in the North Atlantic and Nordic Seas coincides with the pattern of sea ice growth (not shown), i.e. the largest anomalies occur in the regions of more sea ice compared to recent past climate and smaller anomalies occur in regions that experience extensive sea ice already in the recent past climate. The same pattern in SST anomalies can be seen in the proxy data, but they also indicate that the summer and winter SST was possibly as warm or even warmer during LGM than in the recent past in parts of the Nordic Seas (Fig. 4). As shown in Fig. 4, the CCSM3-simulated LGM SST falls within ±2 standard deviations of the MARGO proxy SST for most grid boxes in the North Atlantic region in both summer and winter. For the remaining grid boxes, CCSM3 more often shows anomalies that are colder than indicated by the MARGO proxy data. Therefore, we conclude that CCSM3-simulated LGM SSTs are possibly too low in this region.
CCSM3-simulated LGM SST falls within ±2 standard deviations of the MARGO proxy SST for most grid boxes also in other regions of the globe (not shown). The MARGO data indicates moderate anomalies in the tropical SST (less than ±2 °C), whereas the CCSM3 LGM-simulated SSTs are colder than in the recent past climate by 2–4 °C. An average of PMIP2 tropical SSTs are 1.0–2.4 °C colder than pre-industrial values (Otto-Bliesner et al., 2009). The largest anomaly in the Southern Hemisphere found in the simulated LGM climate occurs around 50°S, which is colder by 2–8 °C in summer and winter. This anomaly occurs, similar to the Northern Hemisphere, in regions where the sea ice extent is increased. There is an indication also in the MARGO proxy SST data of regions of maximum differences compared to present-day conditions in the Southern Hemisphere around 50 °S in summer (January–March), whereas in winter (July–September) there are too few data points in this region to compare.
Sarnthein et al. (2003) conclude that sea ice only covered the Arctic Ocean and the western Fram Strait during LGM summer based on proxy data of SST. In contrast, sea ice spread far south across the Iceland Faroe Ridge during LGM winter. As compared to the proxy sea ice estimates made by Sarnthein et al. (2003), the CCSM3 LGM gives too much sea ice in the Labrador Sea and off the New Foundland coast in summer and too much ice in the central North Atlantic in winter (not shown).
Regardless of possible biases in SSTs, the simulated anomalies in annual mean temperatures over Europe in CCSM3 are similar to those obtained in the high-resolution atmosphere—only CCM3—simulations by Kim et al. (2008). They used proxy-based reconstructions of the SST as a lower boundary condition to their simulation indicating that the possible SST bias we report on here does not have a major influence on the annual mean temperature conditions over Europe. Similarly, Kageyama et al. (2006) reported that the relationship between SST anomalies in the North Atlantic and surface temperature anomalies over Europe is not straightforward for the warmest month of the year. They only found a significant relationship between anomalies in the western European region and the North Atlantic for temperatures of the coldest month.
3.2. European LGM climate simulated by RCA3
The very cold simulated LGM climate, with annual mean temperatures below 0 °C in all of Europe north of about 50°N and also in high-altitude regions in southern Europe, is clearly seen in Fig. 5. In winter, the situation is even more striking with the 0 °C line encompassing basically all of continental Europe and monthly mean temperatures below −40 °C over the northern parts of the ice sheet. These very large differences are partly due to the perennial snow/ice cover but also to a consequence of the high elevation of the ice sheet. During summer, the area with the lowest temperatures is more confined to the ice sheet, the extent of which is readily visible in Fig. 5. In ice-free parts of Europe temperature anomalies are stronger in the west as a consequence of the low SSTs in the North Atlantic in combination with the influence of prevailing northwesterlies in this region (Fig. 6). In winter when most parts of Europe are snow covered, the north–south gradient is less pronounced.
Figure 5. Mean temperatures of the warmest month, coldest month and year (top). In the lower row differences compared with recent past conditions are shown. Also shown are estimates of temperature anomalies based on proxy data (coloured circles (Wu et al., 2007) and squares (Allen et al., 2008). Unit: °C.
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Figure 6. Mean sea-level pressure (MSLP) and precipitation of the warmest month, coldest month and annual mean (top). In the lower row differences compared with recent past conditions are shown. Shown as coloured circles are precipitation estimates based on proxy data (Wu et al., 2007). Units: hPa for MSLP and mm month−1 for precipitation.
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Compared to the three high-resolution LGM simulations by Jost et al. (2005), the RCA3 results compares best with the HadRM simulation with a deviation of at most 3 °C (the other two being at least 5 °C warmer) in terms of temperature of the coldest month, at least for southern and central Europe. In northern Europe, the signature of temperature anomalies of the coldest month are different. While in RCA3 the maximum negative anomaly is over the ice sheet, all simulations in Jost et al. (2005) have maximum negative anomaly over the ocean. For annual precipitation RCA3 compares best with the LMDZHR simulation, which gives a similar pattern of precipitation anomaly. CCSR1 does not give enough details and HadRM simulates much more precipitation around the coastlines in southern Europe.
The RCA3 results from regions outside the ice sheet are compared with pollen-based proxy data of temperature differences with respect to the present climate from WU07 and the adjusted glacier temperatures from AL08 (Figs 5 and 7). For annual mean temperature, the difference between model and proxies is smaller than 2 °C in Spain, Italy and Greece. In the Alpine region, good agreement is seen for one of the records whereas RCA3 is warmer than indicated by the other proxies. In the Pyrenees, RCA3 is colder than the proxies. In Turkey, one proxy data is much warmer and one much colder. Compared to AL08, RCA3 simulates annual temperatures within the error bars for most locations, but tends to underestimate temperatures at the northernmost locations. We note that most proxy points are in close vicinity to high-altitude complex terrain areas such as the Alps and the Pyrenees making comparisons with the model results difficult.
Figure 7. Comparison of simulated temperature (top) and precipitation (bottom) (horizontal axes) with proxy data (vertical axis) for the warmest month of the year (left), the coldest month of the year (middle) and annual mean (right). The vertical bars illustrated for the proxy data define the 95% confidence levels. The dashed lines represent the average of these errors. The numbers and letters corresponds to the specific sites in Fig. 2. Unit: °C for temperature, mm month−1 for precipitation.
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For the coldest month, large differences of more than 10 °C between model and proxies are seen in the four records in north eastern Europe (Fig. 7, middle). RCA3 tends to simulate a smaller anomaly in temperature than proxies indicate, but shows a fair agreement (mostly within a few degrees) in the southern half of Europe. There are some notable local exceptions to this where the model shows anomalies 15 °C smaller than the proxies (e.g. in Greece and Cyprus). However, the disagreement is not more than 5 °C for the sites in the surrounding regions implying that these specific sites may not be representative of the larger regional scale. We also note that as the uncertainty ranges in the proxy data are very large, the model-simulated temperatures are within the uncertainty estimates at all but three sites in northeastern Europe.
For the warmest month, the uncertainty ranges in the proxy records are much smaller than in winter. This means that the comparison between model and proxies has the potential of being more useful than in winter for which uncertainties are very large. RCA3 shows a larger anomaly in temperature than the proxies do at a majority of the locations, most notably in the Mediterranean region (Fig. 7, left), this may be a consequence of the low SSTs in the North Atlantic as simulated by CCSM3. At the same time, RCA3 is warmer than indicated by the proxies in eastern Europe.
Another way to evaluate simulated temperatures is to look at the extent of permafrost. Based on model simulations compared with temperature reconstructions, Renssen and Vandenberghe (2003, hereafter RV03) defines the southern limit of continuous permafrost as the line where the maximum annual mean temperature is below −8 °C and the mean winter temperature below −20 °C. Further, discontinuous permafrost develops when mean annual temperatures are −4 °C or below. The 0, −1, −4 and −8 °C isotherms for annual mean temperatures are similar in RCA3 to those in their reconstruction (not shown). The −20 °C isotherm for winter temperatures, on the other hand, is located further to the north in RCA3 in the North Sea/British Isles region implying that continuous permafrost would be less extensive and that RCA3 is possibly too warm in winter in this area. At the same time, however, the SSTs in RCA3 are roughly in agreement with the prescribed SSTs used by RV03 and colder compared to MARGO SSTs (cf. Fig. 4), indicating that any possible warm bias in parts of western Europe is not caused by biases in SSTs in the North Atlantic. Roche et al. (2006) estimates permafrost extent based on simulated temperature and gets about the same extent as RCA3.
Annual mean precipitation in RCA3 shows a broad maximum over the North Atlantic and the European continent (Figs 6 and 7). In winter, the differences w.r.t. the recent past climate resemble those in the annual mean but they are more pronounced. This means mostly dry anomalies, except in the Iberian Peninsula, the southern Alps and Italy (Fig. 6) where more precipitation is simulated in connection to the southward shift in the Atlantic storm tracks discussed earlier. The steep coastlines of western Fennoscandia and Scotland which today are facing the ocean and therefore get a lot of precipitation were, at the LGM, parts of the ice sheet that extends westward. Without the coastline orographic effect, precipitation is much smaller in these regions (Fig. 6). An area with more precipitation than in the recent past climate is the area of what is today the Baltic Sea. During the LGM, this area was an elevated part of the ice sheet in which RCA3 produces relatively large amounts of precipitation during summer.
All proxy data for precipitation are confined to southern Europe. A majority of the sites show reduced precipitation at the LGM for both summer and winter (Fig. 6), although the uncertainties are large, as described earlier. In terms of annual mean precipitation, model and proxy-based data agree fairly well on a decrease of around 300 mm yr−1. For the coldest month of the year, proxy-based data indicate small differences in precipitation compared with the recent past climate. The model, on the other hand, shows a spread with increased precipitation at some sites and decreases at others. Summarizing, for most of the sites, the model is within the uncertainty ranges defined for the proxies. However, there are exceptions to this relatively good agreement, as exemplified by site number 7 in southern Turkey. For this location, there is a large difference in altitude (about 1000 m, Table 2) between the actual site and the model grid elevation, as this is an area of strong gradients in topography. As RCMs, at the horizontal resolution we use here, cannot simulate local details of precipitation in mountainous areas, the large differences may be more of a representativity problem than an actual bias. It can also be noted from Fig. 6 that the pattern in precipitation change is noisy in its nature and that relatively small horizontal offsets from the sites may alter the correspondence between model and proxies.
Kuhlemann et al. (2008) study the change in difference between ELA temperature and SST between LGM and present-day conditions in the Mediterranean region. They suggest that the atmospheric lapse rate in parts of that area was noticeably steeper (8.5–10 °C km−1) at LGM than today leading to more unstable conditions. This reduction in stability, in combination with intrusions of cold air from the north, would favour convective precipitation in the southern Alps, Apennines and Dinarides; especially on the upwind flank of the mountain regions. Our simulation shows 25–50% more precipitation than in the simulated recent past on an annual mean basis in these regions, possibly supporting their suggestion. The lapse rate is steeper in the LGM simulation than in a corresponding simulation of present-day conditions in large areas around the Mediterranean for most of the year; although statistically significant, the difference is small (0–0.4 °C km−1). This indicates that the increased precipitation in these areas are not only a result of stronger convection, at least not in the model. In northern Europe the simulated LGM lapse rate is weaker (1–2 °C km−1) indicating more stable conditions, especially in winter (Fig. 8).
Figure 8. Difference in annual average atmospheric lapse rate (difference between 700 and 900 hPa levels) between the LGM simulation and the simulation of the recent past, ice sheets and areas with elevation higher than 500 m are masked out. Units: °C km−1.
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3.3. European LGM vegetation simulated by LPJ-GUESS
The vegetation model simulates vegetation reminiscent of tundra and/or montane woodland over ice-free parts of central and southern Europe (Fig. 9). Short, cool summers and low CO2 concentrations limit primary production and tree growth. Boreal needle-leaved trees dominate the tree canopy in the more continental climate of eastern Europe, whereas low growing season heat sums limit establishment to broadleaved deciduous trees of the mountain birch type in western Europe.
Figure 9. Proportion of vegetation cover comprising broadleaved trees, needle-leaved trees and herbaceous vegetation resulting from the LPJ-GUESS simulation forced by the initial climate from RCA3. The numbers indicate the fraction of a certain type of vegetation in the vegetated part of each grid box.
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A pollen-based reconstruction of vegetation in ice-free parts of Europe at LGM indicate that forested areas were more or less absent (Harrison and Prentice, 2003). This is supported by a reconstruction based on a wider range of different data including both pollen data and plant macrofossils (Ray and Adams, 2001). Both these studies suggest that central and southern Europe instead was dominated by steppe or dry shrubland vegetation. Much lower rainfall than today and reduced plant water-use efficiency associated with lower CO2 concentrations in the atmosphere have been invoked as causes of low plant-available moisture that would limit the occurrence of trees in favour of grasses (Harrison and Prentice, 2003). The semi-open landscapes simulated by LPJ-GUESS over parts of Southern Europe suggest a greater representation of trees than the data-based reconstructions. A study using an equilibrium biosphere model based on a range of GCM-reconstructed climates likewise suggests the presence of boreal forest vegetation over southern Europe at LGM (Kaplan et al., 2003). Biases are possible in both the models and the data-based reconstructions; however, one likely explanation for this discrepancy is that the cold distributional limits for trees in the models are calibrated to the observed modern distributions of dominant taxa in relation to coldest month mean temperatures. The physiological control of these limits is, however, more closely tied to absolute minimum temperatures, representing extreme cold events that result in meristematic freezing and plant death (Woodward and Williams, 1987). Although absolute minimum and coldest month mean temperatures are correlated with one another, the difference between them is likely to have been larger during LGM compared with today's climate. This is supported by the simulated LGM climate (not shown explicitly, but see, e.g. the increased winter time variability in Fig. 13). This will tend to result in a coldward bias in the simulated distributions of tree taxa at LGM. It should also be noted that LPJ-GUESS was not set up to simulate shrubs in this study; LPJ-GUESS was applied with the minimum number of PFTs necessary to represent biogeographic patterns and shifts of relevance for accounting for land–atmosphere feedbacks in the context of our study. Although shrubs can be represented in LPJ-GUESS, tree PFTs with corresponding phenology and bioclimatic constraints provide a sufficient proxy for the purposes of our study (see also Smith et al. 2011).
Figure 13. Top panel: simulated inter-annual standard deviation at LGM; bottom panel: difference in interannual standard deviation between the LGM simulation and the simulation of the recent past, for temperature (left panel) and precipitation (right panel). All panels show winter conditions. Units: °C for temperature, mm month−1 for precipitation.
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Consequently, the simulated woody vegetation may also be considered to represent shrubland and woody tundra which were elements of the European vegetation during the LGM (Harrison and Prentice, 2003).