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PrecipitationSurface Temperature Relationship in the IPCC CMIP5 Models


doi: 10.1007/s00376-012-2130-8

  • 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 simulating 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 Northern 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 variability 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 discrepancies between models and observations remain to be revealed with detailed and comprehensive evaluations in various aspects.
  • [1] Shang-Min LONG, Kai-Ming HU, Gen LI, Gang HUANG, Xia QU, 2021: Surface Temperature Changes Projected by FGOALS Models under Low Warming Scenarios in CMIP5 and CMIP6, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 203-220.  doi: 10.1007/s00376-020-0177-5
    [2] TIAN Di, GUO Yan*, DONG Wenjie, 2015: Future Changes and Uncertainties in Temperature and Precipitation over China Based on CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 487-496.  doi: 10.1007/s00376-014-4102-7
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    [6] XIN Xiaoge, CHENG Yanjie, WANG Fang, WU Tongwen, and ZHANG Jie, 2013: Asymmetry of Surface Climate Change under RCP2.6 Projections from the CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 796-805.  doi: 10.1007/s00376-012-2151-3
    [7] Xiaolei CHEN, Yimin LIU, Guoxiong WU, 2017: Understanding the Surface Temperature Cold Bias in CMIP5 AGCMs over the Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1447-1460.  doi: 10.1007s00376-017-6326-9
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Manuscript History

Manuscript received: 17 June 2012
Manuscript revised: 17 August 2012
通讯作者: 陈斌, bchen63@163.com
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PrecipitationSurface Temperature Relationship in the IPCC CMIP5 Models

    Corresponding author: WU Renguang; 
  • 1. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong
  • 2. Center for Monsoon and Environment Research/Department of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275

Abstract: 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 simulating 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 Northern 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 variability 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 discrepancies between models and observations remain to be revealed with detailed and comprehensive evaluations in various aspects.

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