Climate model benchmarking with glacial and mid-Holocene climates
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- Harrison, S.P., Bartlein, P.J., Brewer, S. et al. Clim Dyn (2014) 43: 671. doi:10.1007/s00382-013-1922-6
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Past climates provide a test of models’ ability to predict climate change. We present a comprehensive evaluation of state-of-the-art models against Last Glacial Maximum and mid-Holocene climates, using reconstructions of land and ocean climates and simulations from the Palaeoclimate Modelling and Coupled Modelling Intercomparison Projects. Newer models do not perform better than earlier versions despite higher resolution and complexity. Differences in climate sensitivity only weakly account for differences in model performance. In the glacial, models consistently underestimate land cooling (especially in winter) and overestimate ocean surface cooling (especially in the tropics). In the mid-Holocene, models generally underestimate the precipitation increase in the northern monsoon regions, and overestimate summer warming in central Eurasia. Models generally capture large-scale gradients of climate change but have more limited ability to reproduce spatial patterns. Despite these common biases, some models perform better than others.
KeywordsClimate-model evaluationClimate sensitivityLast Glacial MaximumMid-Holocene monsoonsPalaeoclimate Modelling Intercomparison Project
Simulations of the Last Glacial Maximum (LGM, ca 21,000 years ago) and mid-Holocene (MH, ca 6,000 years ago) are included for the first time in the set of climate-model simulations performed by the Coupled Model Intercomparison Project (CMIP5: Taylor et al. 2012). This development recognizes the unique opportunity to use paleoclimates to evaluate, or benchmark, the models that are used for future climate projections (Taylor et al. 2011; Braconnot et al. 2011). The LGM and MH are geologically recent times with strong and contrasting climate forcing (Braconnot et al. 2012), the response to which is documented by abundant palaeoenvironmental records (Harrison and Bartlein 2012). Braconnot et al. (2012) showed that climate models can successfully reproduce the first-order patterns of past climate changes, including the land-sea contrast and high-latitude amplification of temperature change and the impacts of changes in monsoon circulation on precipitation patterns, but are unable to reproduce the magnitude of observed regional changes in climate. Quantitative evaluation of climate models using paleoclimates has generally focused on specific regions or phenomena (e.g. Joussaume et al. 1999; Otto-Bliesner et al. 2009; Zhang et al. 2010; Valdes 2011; Braconnot et al. 2012; Harrison and Bartlein 2012). More systematic testing is now possible using global syntheses of paleoclimate reconstructions (e.g. MARGO Project Members 2009; Leduc et al. 2010; Bartlein et al. 2011; Schmittner et al. 2011).
Here we present an evaluation of the MH and LGM simulations from the CMIP5 archive against ten data sets that include annual and seasonal climate variables over the land and oceans. We begin by comparing the global performance and the geographic expression of simulated climate changes in the CMIP5 simulations with an earlier generation of LGM and MH simulations made during the second phase of the Palaeoclimate Modelling Intercomparison Project (PMIP2), to determine whether recent improvements in modelling schemes or the use of higher-resolution models has resulted in differences in model performance. We then provide an evaluation of the CMIP5 and PMIP2 models against the observational benchmarks, using standard metrics to assess different aspects of the goodness-of-fit and biases of individual models. Finally, we present an overall assessment of model performance to address the question of whether some models consistently perform better than others.
2 Data and methods
2.1 The benchmark data sets
There has been a long history of making quantitative climate reconstructions using biological, isotopic and geochemical records from land and ocean sites. More recently, community efforts have focused on creating synthetic global data sets for specific times and types of record. We have created benchmark data sets through combining the unique data points from existing syntheses, specifically pollen- and plant macrofossil-based reconstructions of land climate at the LGM and MH from Bartlein et al. (2011), surface ocean reconstructions from the MARGO data set for the LGM (MARGO Project Members 2009) and from the GHOST data set for the MH (Leduc et al. 2010), additional LGM land (5) and ocean (25) records from Schmittner et al. (2011) and Antarctic ice-core estimates of LGM temperatures from Braconnot et al. (2012). (Details of the original data sets, including their treatment of reconstruction uncertainties, are given in the SI.)
In addition to combining existing syntheses, we have compiled and evaluated information from available speleothem records. Analyses of the stable isotopes, trace elements, luminescence and fabric of calcium carbonate precipitates (speleothems) from limestone caves can provide precisely dated information about long-term climate variability (e.g. Fairchild et al. 2006; Lachniet 2009). However, the interpretation of these records, and the unambiguous derivation of quantitative climate reconstructions, is dependent on site-specific conditions. We have reviewed and evaluated the published speleothem records based on the reliability of the age model, the presence of samples dated to either the MH or LGM, the availability of information about modern-day conditions (thus allowing quantitative calibration of the records), and the robustness of the inferences about the palaeorecord (see SI for details of the screening procedure). Of the 65 records examined, lack of information about modern conditions means that climate anomalies can only be provided for 37 sites (Figure S1) and only 6 of these provide quantitative reconstructions (see SI). Nevertheless, these speleothem reconstructions are useful because they provide information from regions not covered by other kinds of data.
Site-based reconstructions from each of these sources were combined to produce new quantitative reconstructions (with uncertainty estimates) of 10 climate variables on a common 2° by 2° resolution land or ocean grid. The climate variables are mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), accumulated temperature sum during the growing season (GDD5), mean annual precipitation (MAP), and the ratio (α) of actual to equilibrium evapotranspiration (i.e. evapotranspiration from a large, homogeneous well-watered surface: Raupach 2001) over land (Figures S2 and S3); summer (SSTsum), winter (SSTwin) and annual (SSTann) sea-surface temperatures; and number of months with sea-ice cover (SInmon) over the ocean (Figures S4 and S5). The gridded estimates were produced using simple averaging and the grid-cell uncertainty was calculated as the pooled estimate of the standard error. Inspection of these values shows that the uncertainties are much smaller than the standard errors of spatial averages of the reconstructions. These uncertainties are explicitly taken into account in the calculation of fuzzy distance (see SI).
2.2 Climate model simulations and processing of model outputs
The LGM and MH simulations are equilibrium experiments. The insolation, ice sheet and GHG forcings used for each experiment are described in Braconnot et al. (2012). Although other forcings are the same between the PMIP2 and CMIP5 experiments, the LGM ice-sheet forcing is different: in PMIP2 the ice sheets were specified from Peltier (2004) while in CMIP5 a blended product made from three more recent ice-sheet reconstructions was used (Braconnot et al. 2012).
We use the LGM and MH simulations in the CMIP5 (http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html) and PMIP2 (http://pmip2.lsce.ipsl.fr/database/access/opendap.shtml) archives as of 15th August 2012 (Table S6). Most of these simulations are made with ocean–atmosphere (OA) models. Some of the PMIP2 models simulated vegetation dynamics explicitly (i.e. were fully-coupled ocean–atmosphere-vegetation models, OAVs). Processes associated with the terrestrial and marine carbon cycle were ignored in PMIP2 experiments, but are included as interactive components of some of the models (here designated as OACs) in CMIP5. MH simulations are available for 13 OAs and 6 OAVs from the PMIP2 archive and 10 OA simulations and 5 OAC simulations from the CMIP5 archive. LGM simulations are available for 7 OA simulations and 2 OAV simulation from the PMIP2 archive, and 3 OA and 3 OAC simulations from the CMIP5 archive. There was no MH SSTann data archived by August 15th 2012 for the ECHAM, EARTH, FGOALSG2, and FGOALS2 models, no LGM sea-ice data for the IPSL4 model, and no SST or sea-ice data for the COSMOS model. For these models, comparisons were restricted to the subset of variables available.
Long-term means were calculated from the archived time-series data on individual model grids for five climate variables: near-surface air temperature (tas), precipitation flux (pr), cloud-area fraction (clt), sea-surface temperature (tos), and sea-ice fraction (sic). The temperature, precipitation and cloud-cover means were bi-linearly interpolated to a common 0.5° grid, in order to calculate bioclimatic variables (GDD5, MTWA, MTCO, MAT, MAP and α) for comparison with the benchmark data sets. Bioclimatic variables were calculated using the anomalies on the 0.5° grid using the approach of Prentice et al. (1992). The original routines of Cramer and Prentice (1988) and Prentice et al. (1993) were modified to include snow-moisture accounting and to use a multi-layer soil-characteristic data set (IGBP-DIS). Finally, the bioclimatic variables and sea-surface temperature and sea-ice fraction data were then regridded to the 2° × 2° grid of the palaeo-reconstructions, using simple averaging, to facilitate comparisons and for the calculation of ensemble-averages of model output. A detailed description of the model output processing is given in the SI.
2.3 Comparison of PMIP2 and CMIP5 simulations
To compare the two generations of simulations, we calculated ensemble averages of the climate variables. The differences between the ensemble-average anomalies for individual variables illustrate the change in simulated patterns between the two generations (PMIP2 and CMIP5) of simulations. Given the number of available variables and grid cells (16,200), such comparisons will inevitably reveal many large or “significant” differences between the ensemble averages for individual variables. However, the issue is whether the different generations of simulations differ overall.
To more formally assess the differences in the ensemble averages between generations of simulations, we calculated Hotelling’s T2 statistic (Wilks 2011) on the climate anomalies, for each of the 2° × 2° grid cells for particular combinations of variables. Hotelling’s T2 is a multivariate generalization of the ordinary t-statistic that is appropriately used to examine differences in climate-model simulations (Chervin and Schneider 1976). Like the t-statistic, Hotelling’s T2 scales the difference between the means by a measure of the variability of the groups of observations being compared such that small values of the statistic could result from small differences in the means, or large variability among (in this case) models. The climate anomalies are approximately normally distributed and have similar variance between the two sets of simulations. Hotelling’s T2 is known to be sensitive to the trade-off between the number of observations and the number of variables (Rencher 2002), and so we limited the number of variables considered. In its application here, the multiple variables include either bioclimatic variables that are available globally (i.e. MAT, MTCO, MTWA and MAP), or selections of monthly temperature and precipitation (tas and pre for January, April, July and October), the observations are the individual model simulations grouped by simulation generation, and the null hypothesis is that the ensemble means are equal between groups. Separate comparisons were made for the MH and LGM simulations. Comparison of subsets of simulations within generations (e.g. CMIP5 OA vs. CMIP5 OAC) is not warranted owing to sample-size considerations.
A test statistic and associated significance level (p value) is obtained for each grid point for a specific comparison, and it is likely that some number of these local tests will appear to be significant (i.e. p < 0.05) simply by chance, and so a simple count of those tests to determine a global “field significance” may be misleading (the “false discovery rate” issue, see Wilks 2006). In other words, in the 16,200 individual tests, we should expect that five percent (810) would appear as significant even if the null hypothesis of no difference between simulation generations were true. We therefore applied the approach of Ventura et al. (2004) to evaluate the number of “significant” hypothesis tests in each comparison. In this approach, the sorted individual “local” p values (one at each grid point) are compared with a progression of false discovery rate (FDR) criteria (see Ventura et al. 2004, eqn. 2), and the proportion of the local p values that do not exceed those criteria provides support (or lack thereof) for rejecting a global null hypothesis of no difference between simulation generations. In practice, the FDR approach requires a larger number (than five percent) of local tests to have p values below the usual threshold (i.e. p < 0.05) before declaring the overall hypothesis of no difference in anomalies to be false. The anomaly patterns being compared are generally large in spatial scale, leading to correlations among the local tests, but Wilks (2006) shows that the FDR procedure is still robust in such a situation.
2.4 Metrics for comparison of reconstructed and simulated climate variables
Many metrics, each with different properties, have been used in the geosciences literature to compare observed and modelled quantities. Rather than focus on a single metric of model skill, we use a range of different measures to examine different aspects of model performance. We use medians and the interquartile range (IQR), calculated using only those grid cells where there are observations, to provide a basic measure of global agreement between model and observation. The IQR provides a measure of spatial variability in climate anomalies; comparison of simulated and reconstructed IQR therefore assesses the agreement in the amplitude of the anomalies. We assess the similarity of simulated and observed geographic patterning in climate anomalies using Kendall’s rank correlation coefficient tau (τ), which measures the similarity or difference of spatial patterns regardless of magnitudes (Kendall 1938). We present values as 1 − τ, which takes the values of 0 when patterns are identical and 1 when there is no correlation between them. Distance measures provide an overall measure of similarity. Fuzzy distance is a measure of the distance or dissimilarity between two quantities which takes account of measurement uncertainties: the effect of increasing uncertainty is to increase the fuzzy distance (Guiot et al. 1999; Tran and Duckstein 2002). The mathematical description of each of these metrics is given in the SI.
These four metrics are calculated for each of the individual model simulations from PMIP2 and CMIP5. In addition, we calculate the metrics for various subsets of the models (all models, all the PMIP2 simulations, all the PMIP2 OA simulations, all the PMIP2 OAV simulations, all the CMIP5 simulations, all the CMIP5 OA simulations and all the CMIP5 OAC simulations). For each subset, we create an ensemble average by calculating average values for each grid cell across the suite of models; the global metrics are then calculated from these ensemble averages. For example, the median bias of the subset of CMIP5 models is calculated from an ensemble model created by taking the median value of the grid-cell values of all the CMIP5 models, grid cell by grid cell. The global metric is the “multi-model ensemble median bias” (see Gleckler et al. 2008).
2.5 Metric for overall evaluation of model performance
Rather than devising a single “skill score” for overall performance, which necessarily involves making arbitrary choices about the relative importance of individual variables and types of bias, we evaluate model performance for each climate variable and metric. This also obviates over-inflation of the skill score because of partial correlations among the variables. Following Gleckler et al. (2008) the metrics are normalized by the median model error to yield an evaluation of how well a given model compares to the typical model error. The median model error is calculated as the median of the global error for each individual model from CMIP5 and PMIP2. Thus, the normalization procedure uses the “median errors within the distribution of individual model errors” not the “multi-model ensemble median error”. This allows the metrics of the ensemble models created from the various subsets of models described above to be compared with the median model error. The normalization procedure yields negative values for models that perform better than the median model and positive values for models that perform worse. Values <−0.5 indicate that the models are 50 % better than the median model error, whereas values >0.5 indicate models that are 50 % worse than the median model error. In order to visualize these results, the models are ordered from best to worst, either based on an average of the normalized values across all of the variables and metrics, or alternatively for a single metric across all the variables.
3.1 Comparison of CMIP5 and PMIP2 simulations
3.2 Evaluation of LGM simulations
3.2.1 The glacial ocean
According to the reconstructions, ocean cooling occurs equally in both seasons. The models show larger cooling in summer (Fig. 5, Table S9). The PMIP2 OAV simulations produce colder oceans in both seasons than the PMIP2 OA or CMIP5 models, and again are more inconsistent with the seasonal reconstructions (Table S7). The mismatch between simulated and reconstructed annual (and seasonal) SSTs arises because the models overestimate the cooling in the tropics (30°N–30°S) and northern high-latitudes (>75°N). Conversely, they underestimate the cooling in the mid-latitudes (Figure S6). Between 45°–60°N, the ensemble median bias is larger than 1 °C. Some models have a bias >2° in this region (i.e. the simulated cooling is only about half of the reconstructed cooling).
The inter-quartile range (IQR) provides a measure of the spatial heterogeneity of an observed and/or simulated climate variable. This is not a measure of uncertainty of the median value, but rather shows the degree to which there is geographic variability in a given quantity. The IQR of the reconstructed SST anomalies is large (Fig. 5), both globally and in any zonal belt. Models consistently underestimate this variability (i.e. they do not capture the heterogeneity seen in the reconstructions even when gridded to the same scale as the model outputs) except north of 60°N. Globally, the IQR of the models is between 18 and 72 % of the reconstructed IQR of 2.7 °C, which suggests that the spatial correlation scale in the models may be longer than seen in the reconstructions.
There is considerable geographic patterning in reconstructed climate changes over land and ocean at the LGM. Cooling is most pronounced close to and downwind of the northern hemisphere ice sheets, less marked upwind of the Laurentide ice sheet, and small in the tropics. The spatial patterns in reconstructed LGM SSTann anomalies, as measured by 1 − τ, are not well predicted by the models (Table S9), with values ranging from 0.72 (ECBILT) to 0.96 (CNRM5). The seasonal SST anomalies show no correlation with the reconstructions, with all of the models obtaining values close to 1 for both SSTsum (0.94–1.03) and SSTwin (0.95–1.02).
The fuzzy distance provides an overall measure of model performance. Models with different median biases, for example, can nevertheless have a similar distance score if the model with the larger bias captures the spatial variability or patterning better. For example, GISS.E2 has a slightly better overall score than MPI (ESM) for SSTann (Table S9) because although GISS.E2 displays a larger bias it has a more realistic range of variability. The distance measures confirm that models generally reproduce SSTann (1.18–1.50) better than SSTwin (1.28–1.71), which in turn is better than SSTsum (1.44–1.93), consistent with the unrealistically larger cooling in summer than winter. The distance measures for SInmon show that the mismatch between simulated and observed sea-ice cover is typically one to 2 months, although FGOALS1 has a bias of 4 months.
3.2.2 The glacial continents
The reconstructions show year-round cooling over the continents at the LGM (Fig. 5). Based on regions with reconstructions, MAT was reduced by 6.4 °C. Winter cooling was greater than summer cooling (−9.6 °C compared to −4.3 °C). All but two models underestimate the reconstructed annual cooling, with the largest median bias nearly 3.5 °C and eight models having a bias larger than 1 °C. MIROC (ESM) and HADCM3 (OAV) overestimate the reconstructed cooling (Fig. 5). The OA version of HadCM3 also produces a greater year-round cooling than most other models (close to the reconstructed change), but the OA version of MIROC is comparatively warm compared to other models. The underestimation of MAT is driven by underestimation of winter cooling. All the models underestimate the MTCO reduction; the smallest median bias is +2.4 °C and the largest +7.3 °C. Some models underestimate and some overestimate the reconstructed summer cooling. All models underestimate the LGM reduction in MAP over land, consistent with the underestimation of the change in temperature (Li et al. in press). As a result, most models also underestimate the increase in aridity (reduction in α). The bias in median MAP ranges from +10 to +334 mm; nine models produce changes in median MAP that are less than half of the reconstructed change. The discrepancies between simulated and reconstructed α are smaller, because the smaller-than-reconstructed reduction in precipitation is offset by the smaller-than-reconstructed reduction in temperature.
The IQR of modeled LGM land climates is smaller than reconstructed for most, but not all, variables (Fig. 5). The simulated spatial variability in MTWA is consistently smaller than shown by the reconstructions: simulated MTWA IQR is 29–86 % of the reconstructions. With the exception of the COSMOS model (112 %), the simulated IQR of GDD5 is between 40 and 70 % of the reconstructions. However, the simulated IQR of MAT ranges from much smaller to somewhat larger (27–145 %), that for MTCO is from 26 to 103 %, and that for MAP is 53–116 %. The variability in α is always larger than shown by the reconstructions (155–419 %).
The geographic patterns in the sign of the changes over land at the LGM during winter (MTCO) are in general moderately well predicted, with values of 1 − τ ranging from 0.58 to 0.82 (Table S9). The prediction of GDD5 is also moderately good (0.58–0.82). Three models score 1 or >1 for MTWA, i.e. there is no correlation between the simulated and observed patterns, but the range for the other models (0.63–0.97) is comparable to the other seasonal variables. The simulation of the geographic patterns in MAT is also moderately good (0.57–0.89), with the exception of a single model (CNRM3.3), which scores 1. The simulation of geographic patterning in α is poor, with a range of values between 0.72 and 0.88 (Table S9). However, the simulation of the spatial pattern in precipitation is poorer, with nine models having values of 1 − τ greater than 1. In general, the prediction of LGM temperature anomaly patterns appears to be better over land than over the ocean. This is apparent in comparing e.g. MAT with SSTann where 15 out of 17 models have 1 − τ values of <0.80 for MAT compared to only 8 out of 16 models with values <0.80 for SSTann. However, the simulation of seasonal climate over land is very much better than seasonal climates over the ocean, where all of the models have values of close to 1 (i.e. no correlation) for geographic patterning over the ocean.
The fuzzy distance scores (Table S9) suggest that model performance is better for MAT (2.21–3.57) than for seasonal temperatures (MTCO: 3.61–6.24; MTWA 3.05–6.30). This is probably because, despite generally underestimating the annual cooling, the models capture the spatial variability of temperature changes moderately well. The range of the distance scores for MAP (135.91–387.37) and α (0.09–0.17) reflect the differences in the median biases: there is a large range for MAP but only small differences in the scores for α between the models.
3.3 Evaluation of mid-Holocene simulations
3.3.1 The mid-Holocene ocean
The observed IQR of SSTann is 1.2 °C, considerably smaller than the estimate obtained for the LGM. However, as at the LGM, the models consistently underestimate the heterogeneity in SSTs, with IQR values between 17 and 56 % of the observed. The CMIP5 models generally show more heterogeneity at high northern and southern latitudes (Figure S10) than the PMIP models, but even so they underestimate the IQR values at these latitudes.
MH SST reconstructions are sparse (Figure S5), but show only moderate warming in the tropics and more pronounced warming in the northern mid- to high-latitudes. In the simulations (Fig. 3), the tropics are characterized by lower SSTs and there is a strong gradient in warming from the mid- to high-latitudes of the northern hemisphere. The geographic patterns in simulated MH SSTann anomalies, as measured by 1 − τ, are poorly predicted by the models (Table S11), with values ranging from 0.78 (MRI2) to 1.12 (HadCM3). Six models have values >1 (i.e. show some degree of anti-correlation with observations). The fuzzy distance measures (0.45–0.75) reflect this poor performance (Table S9).
3.3.2 The mid-Holocene continents
The MH, in regions with reconstructions, is characterized by slightly warmer summers, longer growing seasons, and increased precipitation relative to present (Fig. 6). This pattern reflects the distribution of the reconstructions, which are biased towards the northern hemisphere where insolation was increased in summer and reduced in winter relative to today (leading to little overall change in MAT) and increased seasonality amplified monsoonal rainfall. The models consistently underestimate the reconstructed change in MAP and α. As a result of underestimating the increase in precipitation (and α), and therefore presumably underestimating latent heat flux, the models overestimate summer warming by 0.56–2.27 °C. The simulated changes in MH winter temperature are not consistent between models. Some simulate warmer-than-present (and too warm) winters, others produce cooler-than-present (and too cool) winters. Biases in simulated MAT changes reflect the biases in the seasonal temperatures. Models that produce lower than reconstructed MAT tend to have more winter cooling and less summer warming than other models. Models that produce MAT warmer than reconstructed, tend to be warmer in both seasons.
Simulated MH land climates show consistently less spatial variability than the reconstructed climates. The IQR values of MAT (14–46 %), MTCO (14–56 %), MTWA (22–55 %), GDD (15–57 %) and MAP (17–39 %) from the simulations are consistently smaller than those of the reconstructions. The IQR of α ranges from 48 to 130 % of the reconstructions.
There is considerable geographic patterning in reconstructed climate changes over land at the MH (Figure S3), with a clear temperature gradient between the tropics and the northern extratropics. Enhanced northern-hemisphere monsoon circulation gives rise to increased precipitation in the monsoon core region but increased aridity in regions of descending air. Models’ ability to simulate geographic patterning in the sign of climates in the MH is poorer than at the LGM. For example, 1 − τ values for MAT range from 0.87 (IPSL and MRI2fa (OAV)) to 1.07 (FGOALS1). The best scores are in the range from 0.77 (for MAP) to 0.93 (for MTWA), and for all of the variables (other than MAT) between a third to a half of the models have scores of >1 indicating no correlation with the observations.
Despite the fact that the simulated geographic patterning is poorer in the MH than at the LGM, comparison of the fuzzy distance measures for each variable show that the overall performance of the models is better for the MH than the LGM (i.e. the biases are less extreme). Thus, the range of the fuzzy distances for MH MTCO is 1.34–1.91 compared to 3.61–6.24 for the LGM (Table S9, Table S10). The difference in the ranges is similar for MTWA (1.33–2.51 for the MH, 3.05–6.30 for the LGM). As in the LGM, the range for MAT (1.05–1.42) is better than for the seasonal temperatures. Similarly, the range of the fuzzy distances for MH MAP is 76–110, whereas the range for the LGM is 135–388.
3.4 Assessment of overall model performance
No model performs better than the median model error across all climate variables at the LGM (Figs. 7a, 8a). Nevertheless, the PMIP2 OAV models perform very much worse than the median model error with respect to bias across most variables compared to their OA counterparts (Fig. 7a), as does FGOALS1 and models of the CNRM family (CNRM3.3, CNRM5). These models also perform much worse than the median model error with respect to the IQR metric. Newer versions of a particular model family do not necessarily perform better: CCSM for example performs much better than the median model error for ocean temperature and summer conditions over the land, whereas CCSM4 scores worse than the median model error for these variables. IPSL5 shows an improvement in performance compared to IPSL4 with respect to biases in MAT, MAP and α, but a degradation in the simulation of seasonal and annual SST.
In the MH (Fig. 7b, Table S9), there is more consistency among the models for the 1 − τ and the fuzzy distance metrics (i.e. most models perform only slightly better or worse than the median model error) than for median bias. With the exception of α, there is also considerable consistency between models for the IQR ratio. The simulation of MAP (e.g. as measured by bias: Fig. 8b) is more consistent among models than the simulation of any other climate variable. Although there are models that perform much better than the median model error for MAT, MTCO, MTWA and GDD, there are more models that perform much worse than the median model error for these variables. In contrast, there are more models that perform better than worse than the median model error for SSTann and α. Some models (e.g. ECHAM, ECBILT.LV) consistently perform better than the median model error across all variables as measured by bias, while some models perform better than the median model error for six out of the seven (GISS.E2, ECHAM (OAV), MPI (ESM)) variables. No model consistently performs worse than the median model error across all variables, but some models frequently are very much worse than the median model across several variables (e.g. MIROC (ESM), MRI2, CCSM4, IPSL4, FGOALS2). There is little consistency between the ranking of models with respect to the median model error in the LGM and MH simulations. Furthermore, the ranking of the models depends critically on the choice of metrics and/or variables included (compare Figs. 7 and 8).
3.5 Relationship between LGM biases and climate sensitivity
Braconnot et al. (2012) showed that the PMIP2 models reproduce the first-order signals of LGM and MH climate changes. The overall behavior of the CMIP5 models is not different from the PMIP2 models. The models capture major features of past climates such as the differential response of land and ocean to warming/cooling, and the tendency for temperature changes in the higher latitudes to be more extreme than changes in the tropics. Model realism in respect to these first-order signals is important because these signals are features of future projections (Meehl et al. 2007; Sutton et al. 2007; Allan and Soden 2008; Izumi et al. 2013; Li et al. in press). However, although the CMIP5 models have similar success in capturing large-scale climate changes, they display mismatches of similar magnitude between simulated and reconstructed regional climates as their predecessors.
Evaluation of the LGM simulations could provide a constraint on climate sensitivity, by assuming that the model that best reproduces reconstructed LGM climates is likely to have the most realistic climate sensitivity under modern conditions. Using this approach, we estimate a climate sensitivity of 2.7 °C. This is comparable to the estimate of 2.7 ± 0.22 °C made by Annan and Hargreaves (2012) based on the PMIP2 LGM multi-model ensemble and 2.8 °C (with a 90 % confidence range of 1.6–4.7 °C) obtained using an explicitly Bayesian approach with the PMIP2 ensemble by Schmidt et al. (2013). Schmittner et al. (2011), using results from a single model ensemble constrained by glacial MAT anomalies, estimated median climate sensitivity as 2.3 °C with a likely range of 1.4–4.3 °C. The range of climate sensitivities for the CMIP5 (Andrews et al. 2012) and PMIP2 (Crucifix 2006; M Crucifix unpublished data) models is small; the multi-model ensemble used here does not provide a tighter constraint on climate sensitivity than Schmittner et al. (2011). Within this group of models, global biases in simulated LGM temperature are only weakly related to climate sensitivity (Fig. 8). Furthermore, there are opposite biases in ocean and land temperatures: ocean temperatures are globally low and land temperatures globally high, compared with the reconstructions.
Albedo feedback associated with changes in vegetation cover should amplify land cooling (de Noblet et al. 1996; Jahn et al. 2005), particularly in mid- to high-latitudes, and indeed the single pair of PMIP2 simulations which allow us to evaluate the impact of vegetation feedbacks show a substantial additional LGM land cooling (1.2 °C colder than the OA version of the model). However, this simulation also produces (unrealistically) colder oceans than the OA simulation.
The increased atmospheric dust loading at the LGM (Kohfeld and Harrison 2001; Maher et al. 2010), which should contribute to increased cooling, is not included in the LGM experimental design. Model-based estimates (Claquin et al. 2003; Mahowald et al. 2006) show the change in dust forcing is larger over land than over ocean, and the magnitude and even the sign of the forcing varies latitudinally. This could potentially contribute to the latitudinal differences found in the simulated temperature biases (see e.g. Schmittner et al. 2011). Mahowald et al. (2006) showed a small positive forcing over the equatorial oceans, which could help to explain why the present generation of models tends to overestimate SST cooling in the tropics. These two experiments (Claquin et al. 2003; Mahowald et al. 2006) are both constrained by observed LGM dust fluxes but give different results for the magnitude of the global dust forcing (−0.9 and −2.0 Wm−2) and also show different spatial patterns. Furthermore, these dust-forcing estimates do not take account of interactions between dust and clouds. These limitations make it impossible to infer the extent to which inclusion of dust forcing would substantially reduce the biases in simulated LGM temperatures.
The MH simulations also show systematic biases that are different over land and ocean, and between seasons. Land temperature anomalies, particularly in summer, are generally too high and SST anomalies too low. The models underestimate precipitation changes in the regions with the largest summer warming. This bias probably reflects problems in the simulation of land–atmosphere heat fluxes. Wohlfahrt et al. (2004) showed that the IPSL OA model overestimated MH aridity and summer warming in central Eurasia, and this problem was exacerbated by vegetation feedbacks. The MH PMIP2 OAV simulations (except FOAM) show more warming than the OA version of each model, i.e. the inclusion of interactive vegetation amplifies a problem already present in the OA model.
Previous model evaluations have emphasized regions where models capture the direction of observed climate changes but underestimate those changes (Braconnot et al. 2012). But models can either over- or under-estimate seasonal climate changes. Over-estimation of one variable can be related to underestimation of another, making it possible to infer potential causes of model biases. The discrepancies between simulated and reconstructed climates are generally common to all models. All models overestimate the reconstructed summer cooling of the tropics at the LGM, just as all models underestimate the MH increase of Afro-Asian monsoon precipitation. Explanations of these discrepancies must lie in features common to all models. Nevertheless, some models perform better than others. This is particularly noticeable at the LGM, where the climate-change signal is large, and more consistent across seasons and regions.
Paleoclimate benchmarking provides an independent evaluation of climate models, focusing attention on how well models can simulate climate change. Our results suggest that although models and data are in agreement on the direction and spatial pattern of the large-scale features of climate change (Braconnot et al. 2012; Schmidt et al. 2013; Izumi et al. 2013; Li et al. in press), there are still shortcomings in the amplitude of simulated changes. Recent work by Hargreaves et al. (2013) has shown that this is not a function of the resolution at which the data-model comparisons are made. It is likely that the incorporation of the dust forcing (for the LGM) and improvements to the simulation of vegetation feedbacks (for both LGM and MH) will improve the ability of state-of-the-art models to reproduce past climate changes. However, incorporation of such feedbacks does not obviate the need for continued efforts by modelling groups to achieve accurate simulations of fundamental climate processes.
We thank our PMIP colleagues who contributed to the production of the benchmark syntheses, and the modelling groups who contributed to the CMIP5 and PMIP2 archives. The analyses and figures are based on data archived at CMIP5 or PMIP2 by March 15th 2012. We thank Hanna Sundquist for her contribution to the compilation of speleothem data, Ann Henderson-Sellers and Gavin Schmidt for their thought-provoking comments, Pascale Braconnot for her persistence in cross-checking the metrics, Sabina Serneels for editorial assistance, and Gavin and Pascale for comments on earlier versions of the text.
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