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This paper is dedicated to the analysis of winter cold spells over Western Europe in the simulations of the 5th phase of the Coupled Model Intercomparison Project (CMIP5). Both model biases and responses in a warming climate are discussed using historical simulations and the 8.5 W/m2 Representative Concentration Pathway (RCP8.5) scenario, respectively on the 1979–2008 and 2070–2099 periods. A percentile-based index (10th percentile of daily minimum temperature, Q10) with duration and spatial extent criteria is used to define cold spells. Related diagnostics (intensity, duration, extent, and severity as a combination of the former three statistics) of 13 models are compared to observations and suggest that models biases on severity are mainly due to the intensity parameter rather than to duration and extent. Some hypotheses are proposed to explain these biases, that involve large-scale dynamics and/or radiative fluxes related to clouds. Evolution of cold spells characteristics by the end of the century is then discussed by comparing RCP8.5 and historical simulations. In line with the projected rise of mean temperature, “present-climate” cold spells (computed with the 1979–2008 10th percentile, Q10P) are projected to be much less frequent and, except in one model, less severe. When cold spells are defined from the future 10th percentile threshold (“future-climate” cold spells, Q10F), all models simulate a decrease of their intensity linearly related to the seasonal mean warming. Some insights are given to explain the inter-model diversity in the magnitude of the cold spells response. In particular, the snow-albedo feedback is suggested to play an important role, while for some models changes in large-scale dynamics are also not negligible. 相似文献
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Influence of the Eurasian snow cover on the Indian summer monsoon variability in observed climatologies and CMIP3 simulations 总被引：3，自引：1，他引：2
The present study is aimed at revisiting the possible influence of the winter/spring Eurasian snow cover on the subsequent Indian summer precipitation using several statistical tools including a maximum covariance analysis. The snow–monsoon relationship is explored using both satellite observations of snow cover and in situ measurements of snow depth, but also a subset of global coupled ocean–atmosphere simulations from the phase 3 of the Coupled Model Intercomparison Project (CMIP3) database. In keeping with former studies, the observations suggest a link between an east–west snow dipole over Eurasia and the Indian summer monsoon precipitation. However, our results indicate that this relationship is neither statistically significant nor stationary over the last 40 years. Moreover, the strongest signal appears over eastern Eurasia and is not consistent with the Blanford hypothesis whereby more snow should lead to a weaker monsoon. The twentieth century CMIP3 simulations provide longer timeseries to look for robust snow–monsoon relationships. The maximum covariance analysis indicates that some models do show an apparent influence of the Eurasian snow cover on the Indian summer monsoon precipitation, but the patterns are not the same as in the observations. Moreover, the apparent snow–monsoon relationship generally denotes a too strong El Niño-Southern Oscillation teleconnection with both winter snow cover and summer monsoon rainfall rather than a direct influence of the Eurasian snow cover on the Indian monsoon. 相似文献
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Climate Dynamics - During the 2012–2013 winter, the negative phase of the North Atlantic Oscillation (NAO) predominated, resulting in a cold winter over Europe and northern Asia punctuated by... 相似文献
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European temperatures and their projected changes under the 8.5 W/m2 Representative Concentration Pathway scenario are evaluated in an ensemble of 33 global climate models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Respective contributions of large-scale dynamics and local processes to both biases and changes in temperatures, and to the inter-model spread, are then investigated from a recently proposed methodology based on weather regimes. On average, CMIP5 models exhibit a cold bias in winter, especially in Northern Europe. They overestimate summer temperatures in Central Europe, in association with a greater diurnal range than observed. The projected temperature increase is stronger in summer than in winter, with the highest summer warming occurring over Mediterranean regions. Links between biases and sensitivities are evidenced in winter, suggesting a potential influence of snow cover biases on the projected surface warming. A brief analysis of daily temperature extremes suggests that the intra-seasonal variability is projected to decrease (slightly increase) in winter (summer). Then, in order to understand model discrepancies in both present-day and future climates, we disentangle effects of large-scale atmospheric dynamics and regional physical processes. In particular, in winter, CMIP5 models simulate a stronger North-Atlantic jet stream than observed and, in contrast with CMIP3 results, the majority of them suggests an increased frequency of the negative phase of the North-Atlantic Oscillation under future warming. While large-scale circulation only has a minor contribution to ensemble-mean biases or changes, which are primarily dominated by non-dynamical processes, it substantially affects the inter-model spread. Finally, other sources of uncertainties, including the North-Atlantic warming and local radiative feedbacks related to snow cover and clouds, are briefly discussed. 相似文献
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A set of global atmospheric simulations has been performed with the ARPEGE-Climat model in order to quantify the contribution of realistic snow conditions to seasonal atmospheric predictability in addition to that of a perfect sea surface temperature (SST) forcing. The focus is on the springtime boreal hemisphere where the combination of a significant snow cover variability and an increasing solar radiation favour the potential snow influence on the surface energy budget. The study covers the whole 1950?C2000 period through the use of an original snow mass reanalysis based on an off-line land surface model and possibly constrained by satellite snow cover observations. Two ensembles of 10-member AMIP-type experiments have been first performed with relaxed versus free snow boundary conditions. The nudging towards the monthly snow mass reanalysis significantly improves both potential and actual predictability of springtime surface air temperature over Central Europe and North America. Yet, the impact is confined to the lower troposphere and there is no clear improvement in the predictability of the large-scale atmospheric circulation. Further constraining the prescribed snow boundary conditions with satellite observations does not change much the results. Finally, using the snow reanalysis only for initializing the model on March 1st also leads to a positive impact on predicted low-level temperatures but with a weaker amplitude and persistence. A conditional skill approach as well as some selected case studies provide some guidelines for interpreting these results and suggest that an underestimated snow cover variability and a misrepresentation of ENSO teleconnections may hamper the benefit of an improved snow initialization in the ARPEGE-Climat model. 相似文献
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