Present-day status and future projection of spring Eurasian surface air temperature in CMIP5 model simulations
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The present study evaluates the performance of 19 climate models that participated in the coupled model intercomparison project phase 5 (CMIP5) in reproducing climatology, the standard deviation, and the dominant mode of spring surface air temperature (SAT) variations over the mid-high latitudes of Eurasia based on historical runs. Future change of the Eurasian spring SAT under the anthropogenic global warming is also examined by comparing the historical and RCP4.5 run. All the 19 CMIP5 models capture well the observed spatial structure of climatological spring SAT, with the pattern correlation coefficients all larger than 0.94. However, most of the models tend to underestimate the SAT over north Europe and north Siberia and overestimate the SAT south of 50°N. There exists large inter-model spreads in the standard deviation of the spring SAT. Most of the models capture realistically the observed dominant mode of interannual variations of spring SAT. Analyses show that the ability of a CMIP5 model in capturing the dominant mode of Eurasian spring SAT variations is connected with the model’s performance in representing the observed atmospheric circulation anomalies related to the Arctic Oscillation and the dominant mode of the atmospheric variations over Eurasia. Six best models are selected based on the ability in simulating the dominant mode of the spring SAT variations in the historical runs. These six models project an increase in the SAT climatology but a decrease in the standard deviation over most of Eurasia. These six models project a decrease in the explained variance as well as in the amplitude of the spring SAT and atmospheric anomalies related to the dominant mode.
KeywordsSpring Eurasian surface air temperature The dominant mode CMIP5 Future projection
We thank two anonymous reviewers for their constructive suggestions and comments, which helped to improve the paper. We also thank Mr. Shijie Zhou for providing outputs of the CMIP5 RCP4.5 experiments. This study is supported by the National Natural Science Foundation of China Grants (41605050, 41530425, 41775080, and 41661144016), and the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2016QNRC001). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and leads development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
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