Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models
Zhijie Ta, Yang Yu, Lingxiao Sun, Xi Chen, Guijin Mu, Ruide Yu
Abstract
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Zhijie Ta, Yang Yu, Lingxiao Sun, Xi Chen, Guijin Mu, Ruide Yu
Abstract
Open-access reader
The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.
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The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.
Key concepts: Coupled model intercomparison project, Cru, Precipitation, HadCM3, Environmental science, Climatology, Climate model, Climate simulation