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谢文强, 王双双, 延晓冬. 2022. CMIP6全球气候模式对中国年平均日最高气温和最低气温模拟的评估[J]. 气候与环境研究, 27(1): 63−78. doi: 10.3878/j.issn.1006-9585.2021.21027
引用本文: 谢文强, 王双双, 延晓冬. 2022. CMIP6全球气候模式对中国年平均日最高气温和最低气温模拟的评估[J]. 气候与环境研究, 27(1): 63−78. doi: 10.3878/j.issn.1006-9585.2021.21027
XIE Wenqiang, WANG Shuangshuang, YAN Xiaodong. 2022. Evaluation on CMIP6 Global Climate Model Simulation of the Annual Mean Daily Maximum and Minimum Air Temperature in China [J]. Climatic and Environmental Research (in Chinese), 27 (1): 63−78. doi: 10.3878/j.issn.1006-9585.2021.21027
Citation: XIE Wenqiang, WANG Shuangshuang, YAN Xiaodong. 2022. Evaluation on CMIP6 Global Climate Model Simulation of the Annual Mean Daily Maximum and Minimum Air Temperature in China [J]. Climatic and Environmental Research (in Chinese), 27 (1): 63−78. doi: 10.3878/j.issn.1006-9585.2021.21027

CMIP6全球气候模式对中国年平均日最高气温和最低气温模拟的评估

Evaluation on CMIP6 Global Climate Model Simulation of the Annual Mean Daily Maximum and Minimum Air Temperature in China

  • 摘要: 选取CMIP6历史模拟试验26个模式数据,以CN05.1数据作为观测资料,对1961~2014年中国年平均最高气温和最低气温变化模拟能力进行评估。结果表明:1961~2014年,中国年均最高气温和最低气温均存在上升的趋势。最高气温增长速率为2.15°C/100 a;最低气温增长速率为3.92°C/100 a,约为最高气温增长速率的两倍。CMIP6模式都能模拟出这种长时间尺度的变化趋势,但不同模式模拟能力存在一定差异,模式间离散度达到0.38°C/100 a(最高气温)和0.41°C/100 a(最低气温)。模式中BCC-ESM1和EC-Earth3模式对这两种趋势的模拟效果最好。CMIP6模式可以较好地模拟出中国范围内的最高气温和最低气温空间分布特征。中国范围内,大部分模式模拟结果与观测呈正相关的格点所占比例分别为82%(最高气温)和97%(最低气温),模拟结果具有明显的地域性。对于气候平均态,CMIP6模式可以较好地模拟出最高最低气温空间分布特征,对于整个中国东部地区,最高最低气温模拟结果的模式间标准差均在3°C以内,一致性较高,在西部地区差异较大,青藏高原地区达到6°C以上。GISS-E2-1-G和MRI-ESM2-0可以很好地模拟出1961~2014年中国最高气温和最低气温经验正交分解(Empirical Orthogonal Function, EOF)主要模态及其时间演变。总体来说,CMIP6模式对中国年均最高气温和最低气温的气候态空间分布以及变化趋势等方面,具备较好的模拟能力。

     

    Abstract: Simulations for China’s annual average maximum and minimum surface air temperature by CMIP6 models were evaluated, referring to observations from CN05.1 data. Results show that the annual average maximum and minimum surface air temperature in China from 1961 to 2014 had increasing trends. The maximum surface air temperature increased at a rate of 2.15°C/100 a. The growth rate of the minimum air temperature was 3.92°C/100 a, which was about twice the growth rate of the maximum air temperature. CMIP6 models can simulate trends over long time scales, but there were large differences in the simulation ability of different models. The dispersion between models reached 0.38°C/100 a (maximum air temperature) and 0.41°C/100 a (minimum air temperature). BCC-ESM1 and EC-Earth3 had the best performance in simulating the trends of the maximum and minimum air temperature, respectively. CMIP6 models can well simulate the spatial distribution of the climatological maximum and minimum air temperature in China. Proportions of grid points where the most of the model simulations correlated positively with observations were 82% (maximum air temperature) and 97% (minimum air temperature) in China. Simulation results of the maximum and minimum air temperature in the whole of eastern China had obvious geographical characteristics with a standard deviation within 3°C, showing a high consistency. The variation was significant in the western region and reached more than 6°C in the Tibetan Plateau. GISS-E2-1-G and MRI-ESM2-0 can well simulate the main EOF (empirical orthogonal function) modes and principal components of the maximum and minimum air temperature in China during 1961–2014. In summary, CMIP6 models can well simulate the spatial distribution of the climatological maximum and minimum air temperature and interannual trends of the maximum and minimum air temperature in China.

     

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