Precipitation and surface temperature are two important quantities whose variations are closely related through various physical processes. In the present study, we evaluated the precipitation-surface temperature (P-...Precipitation and surface temperature are two important quantities whose variations are closely related through various physical processes. In the present study, we evaluated the precipitation-surface temperature (P-T) relationship in 17 climate models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the IPCC Assessment Report version 5. Most models performed reasonably well at simulat- ing the large-scale features of the P-T correlation distribution. Based on the pattern correlation of the P-T correlation distribution, the models performed better in November-December-January-February-March (NDJFM) than in May-June-July-August-September (MJJAS) except for the mid-latitudes of the North- ern Hemisphere, and the performance was generally better over the land than over the ocean. Seasonal dependence was more obvious over the land than over the ocean and was more obvious over the mid- and high-latitudes than over the tropics. All of the models appear to have had difficulty capturing the P-T correlation distribution over the mid-latitudes of the Southern Hemisphere in MJJAS. The spatial variabil- ity of the P-T correlation in the models was overestimated compared to observations. This overestimation tended to be larger over the land than over the ocean and larger over the mid- and high-latitudes than over the tropics. Based on analyses of selected model ensemble simulations, the spread of the P-T correlation among the ensemble members appears to have been small. While the performance in the P-T correlation provides a general direction for future improvement of climate models, the specific reasons for the discrep- ancies between models and observations remain to be revealed with detailed and comprehensive evaluations in various aspects.展开更多
基金supported by the National Key Basic Research Program of China(Grant No.2009CB421404)the National Natural Science Foundation of China(Grant No.41175076)+2 种基金the Fundamental Research Funds for the Central Universities(Grant No.11lgjc10)the support of a Direct Grant of the Chinese University of Hong Kong(Grant No.2021105)a Hong Kong Research Grants Council Project(CUHK No.403612)
文摘Precipitation and surface temperature are two important quantities whose variations are closely related through various physical processes. In the present study, we evaluated the precipitation-surface temperature (P-T) relationship in 17 climate models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the IPCC Assessment Report version 5. Most models performed reasonably well at simulat- ing the large-scale features of the P-T correlation distribution. Based on the pattern correlation of the P-T correlation distribution, the models performed better in November-December-January-February-March (NDJFM) than in May-June-July-August-September (MJJAS) except for the mid-latitudes of the North- ern Hemisphere, and the performance was generally better over the land than over the ocean. Seasonal dependence was more obvious over the land than over the ocean and was more obvious over the mid- and high-latitudes than over the tropics. All of the models appear to have had difficulty capturing the P-T correlation distribution over the mid-latitudes of the Southern Hemisphere in MJJAS. The spatial variabil- ity of the P-T correlation in the models was overestimated compared to observations. This overestimation tended to be larger over the land than over the ocean and larger over the mid- and high-latitudes than over the tropics. Based on analyses of selected model ensemble simulations, the spread of the P-T correlation among the ensemble members appears to have been small. While the performance in the P-T correlation provides a general direction for future improvement of climate models, the specific reasons for the discrep- ancies between models and observations remain to be revealed with detailed and comprehensive evaluations in various aspects.