Evaluation on CMIP6 Global Climate Model Simulation of the Annual Mean Daily Maximum and Minimum Air Temperature in China
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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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图 1 观测结果（红点）及CMIP6多模式模拟（绿点：多模式集合平均，白点：模式模拟结果的中位数点）的中国最高最低气温变化线性趋势分布（图形与中轴线的距离越大表示模式模拟结果为该值的数量多）
Figure 1. Linear trend distributions of China-wide average maximum and minimum air temperature from observations (red) and CMIP6 models (green: Multi-model ensemble, white: Median point of simulations) (larger distance between the graph and the central axis indicates a greater number of model simulations that result in that value)
图 2 观测（实线）、CMIP6集合平均（虚线）以及CMIP6多模式模拟（阴影）1961～2014中国平均（a）最高气温距平和（b）最低气温距平
Figure 2. Observed (solid line), CMIP6 ensemble mean (dashed line), and CMIP6 models simulated (shaded) annual (a) maximum air temperature anomalies and (b) minimum air temperature anomalies over China from 1961 to 2014
图 3 CMIP6模式模拟1961～2014中国年均最高气温评估结果：（a）NorESM2-MM模式与观测相关性；（c）KIOST-ESM模式与观测相关性；（e）多模式集合模拟结果（MME）（阴影部分表示该格点有95%以上的模式呈现出同样的相关性变化）；（b）、（d）、（f）分别为（a）、（c）、（e）三个模式相关关系的显著性检验结果（绿色部分表示该格点可以通过0.05的显著性检验，图f中阴影部分表示该格点有80%以上的模式在该格点的相关性是显著的）
Figure 3. Evaluation results of the annual average maximum air temperature in China from 1961 to 2014 simulated by CMIP6 models: (a) correlation between the NorESM2-MM simulation and observation; (c) correlation between the KIOST-ESM simulation and observation; (e) multi-model ensemble results (MME) (the shaded part indicates that 95% of the patterns in the grid show the same variation in correlation); (b), (d), and (f) are the significance test results of the correlation between models in (a), (c), and (e), respectively (the green part indicates that the grid point can pass the 0.05 significance test, and the shaded part in Fig. 3f indicates that more than 80% of the patterns in the grid point are significantly correlated)
图 4 CMIP6模式模拟1961～2014中国年均最低气温评估结果：（a）CanESM5模式与观测相关性；（c）INM-CM5-0模式与观测相关性；（e）多模式集合模拟结果（MME）（阴影部分表示该格点有95%的模式呈现出同样的相关性变化）;（b）、（d）、（f）分别为（a）、（c）、（e）三个模式相关关系的显著性检验结果（绿色部分表示该格点可以通过0.05水平的显著性检验，4f中阴影部分表示该格点有80%以上的模式在该格点的相关性是显著的）
Figure 4. Evaluation results of the annual average minimum air temperature in China simulated by the CMIP6 model from 1961 to 2014: (a) Correlation between the CanESM5 simulation and observation; (c) correlation between the INM-CM5-0 simulation and observation; (e) multi-model ensemble results (MME) (the shaded part indicates that 95% of the patterns in the grid show the same variation in correlation); (b), (d), and (f) are the significance test results of the correlation between models in (a), (c), and (e), respectively (the green part indicates that the grid point can pass the 0.05 significance test, and the shaded part in Fig. 4f indicates that more than 80% of the patterns in the grid point are significantly correlated)
图 5 （a、b）CMIP6模式模拟的、（c、d）CN05.1数据、（e、f）CMIP6集合平均（MME）模拟的1995～2014年气候态中国最高（左侧）和最低（右侧）气温空间分布
Figure 5. Spatial distributions of China’s annual mean maximum (left panel) and minimum (right panel) air temperature from 1995 to 2014 simulated by the (a, b) CMIP6 models, (c, d) CN05.1 data, and (e, f) CMIP6 ensemble mean (MME)
图 6 CMIP6模式模拟1995～2014年平均的中国（a）最高气温和（b）最低气温空间分布的模式间标准差
Figure 6. Standard deviation of the spatial distribution of the CMIP6 model-simulated (a) maximum and (b) minimum air temperature in China averaged from 1995 to 2014
图 7 CMIP6模式1995～2014年平均的中国（a）最高气温和（b）最低气温空间分布模拟结果的Taylor图（红点：多模式集合平均；REF：观测；到REF的距离：中心化均方根误差；半径：标准差之比；方位角：空间相关系数；绿色符号：模式中相关性最高的前三个模式；橙色符号：模式中标准差与观测最为接近的前三个模式；数字：与表1中序号对应的模式的评估结果）
Figure 7. Taylor diagrams of simulation results for the average spatial distribution of (a) maximum and (b) minimum air temperature in China from 1995 to 2014 (red dot: Multi-model ensemble; REF: Observation; distance from REF point: Centralized root mean square error; radial distance: Ratio of standard deviation; azimuthal position: Spatial correlation coefficient; Green symbols: The top three patterns with the highest correlation among the patterns; Orange symbols: The first three models in which the standard deviation is closest to the observation; Numbers: Evaluation results for the pattern corresponding to the ordinal number in Table 1)
图 8 26个CMIP6模式与观测1961～2014年中国最低气温EOF前两个模态的空间和时间相关系数（ACC1代表第一模态空间相关系数；ACC2代表第二模态空间相关系数；TCC1代表第一模态时间序列的相关系数；TCC2代表第二模态时间序列的相关系数）
Figure 8. Spatial and temporal correlation coefficients for the first two EOF principal components of the minimum air temperature during 1961–2014 over China between the CMIP6 models and CN05.1 (ACC1 and ACC2 indicate the spatial correlation coefficients for the first and second EOF principal components, respectively. TCC1 and TCC2 correspond to the temporal correlation coefficients for the first and second principal components, respectively)
图 9 观测的最高气温EOF分解的（a）第一、（b）第二空间模态及（c、d）其时间序列
Figure 9. (a) First and (b) second spatial modes of the observed maximum air temperature EOF decomposition and (c, d) their principal components (PCs)
图 11 观测的最低气温EOF分解的（a）第一、（b）第二空间模态及（c、d）其时间序列
Figure 11. (a) First and (b) second spatial modes of the observed minimum air temperature EOF decomposition and (c, d) their principal components (PCs)
表 1 CMIP6模式简介
Table 1. Information of CMIP6 models
序号 模式名称 国家 所属机构 分辨率（纬度×经度） 1 ACCESS-CM2 澳大利亚 英联邦科学与工业研究组织（CSIRO）与气象局（BOM） 1.875°×1.25° 2 ACCESS-ESM1-5 澳大利亚 英联邦科学和工业研究组织（CSIRO） 1.875°×1.24° 3 AWI-CM-1-1-MR 德国 阿尔弗雷德·韦格纳研究所（AWI），赫尔姆霍兹极地和海洋研究中心（HCPMR） 0.9375°×0.9375° 4 AWI-ESM-1-1-LR 德国 阿尔弗雷德·韦格纳研究所（AWI） 1.875°×1.875° 5 BCC-CSM2-MR 中国 北京气候中心（BCC） 1.125°×1.125° 6 BCC-ESM1 中国 北京气候中心（BCC） 2.8125×2.8125° 7 CanESM5 加拿大 加拿大气候建模和分析中心（CCCma） 2.8125°×2.8125° 8 EC-Earth3 瑞典 欧共体地球联合会（EC） 0.703°×0.703° 9 EC-Earth3-Veg 瑞典 欧共体地球联合会（EC） 0.703°×0.703° 10 EC-Earth3-Veg-LR 瑞典 欧共体地球联合会（EC） 1.125°×1.125° 11 FGOALS-f3-L 中国 中国科学院大气物理研究所（IAP，CAS）大气科学和地球流体力学数值模拟国家重点实验室 1.25°×1.25° 12 FGOALS-g3 中国 中国科学院大气物理研究所（CAS） 2.0°×2.0° 13 GFDL-CM4 美国 美国国家海洋和大气管理局地球物理流体动力学实验室（GFDL） 1.25°×1.25° 14 GFDL-ESM4 美国 美国国家海洋和大气管理局地球物理流体动力学实验室（GFDL） 1.25°×1.0° 15 GISS-E2-1-G 美国 美国宇航局戈达德空间研究所（GISS） 2.5°×2.0° 16 INM-CM4-8 俄罗斯 俄罗斯科学院数值数学研究所（INMRAS） 2.0°×1.5° 17 INM-CM5-0 俄罗斯 俄罗斯科学院数值数学研究所（INMRAS） 2.0°×1.6° 18 IPSL-CM6A-LR 法国 皮埃尔·西蒙·拉普拉斯学院（IPSL） 2.5°×1.25° 19 KIOST-ESM 韩国 韩国海洋科学技术研究所（KIOST） 1.875°×1.875° 20 MIROC6 日本 日本海洋地球科学技术厅（JAMSTEC） 1.40625°×1.40625° 21 MPI-ESM-1-2-HAM 德国 马克斯普朗克气象研究所（MPI-M） 1.975°×1.975° 22 MPI-ESM1-2-HR 德国 马克斯普朗克气象研究所（MPI-M） 0.9375°×0.9376° 23 MPI-ESM1-2-LR 德国 马克斯普朗克气象研究所（MPI-M） 1.875°×1.875° 24 MRI-ESM2-0 日本 日本气象厅气象研究所（JMA） 1.125°×1.126° 25 NorESM2-MM 挪威 挪威气候中心（NorCC） 1.25°×0.9375° 26 NESM3 中国 南京信息工程大学（NUIST） 1.875°×1.875° 
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表 2 观测和CMIP6模式模拟的中国最高气温和最低气温变化线性趋势
Table 2. Linear trends of China’s average maximum and minimum air temperature from observations and CMIP6 models simulations
数据来源 最高气温变化的线性
趋势/°C（100 a）−1最低气温的线性趋
势/°C（100 a）−1观测（CN05.1） 2.15 3.92 ACCESS-CM2 1.76 1.87 ACCESS-ESM1-5 2.60 2.75 AWI-CM-1-1-MR 2.36 2.60 AWI-ESM-1-1-LR 2.36 2.59 BCC-CSM2-MR 1.81 2.03 BCC-ESM1 2.15 2.33 CanESM5 2.89 3.11 EC-Earth3 3.45 3.91 EC-Earth3-Veg 2.74 3.14 EC-Earth3-Veg-LR 2.26 2.55 FGOALS-f3-L 2.38 2.58 FGOALS-g3 2.03 2.25 GFDL-CM4 2.44 2.63 GFDL-ESM4 1.52 1.67 GISS-E2-1-G 2.04 2.24 INM-CM4-8 1.53 1.71 INM-CM5-0 1.59 1.80 IPSL-CM6A-LR 1.78 2.06 KIOST-ESM 2.49 2.84 MIROC6 1.49 1.60 MPI-ESM-1-2-HAM 1.86 2.05 MPI-ESM1-2-HR 1.48 1.68 MPI-ESM1-2-LR 2.01 2.19 MRI-ESM2-0 2.02 2.28 NESM3 2.36 2.38 NorESM2-MM 1.83 1.91 多模式集合（MME） 2.12 2.34
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