Assessment of the ability of CMIP6 models to simulate the heat content of the Arctic Ocean
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摘要: 本文利用PHC、ECCO2、SODA、GECCO3和CMIP6资料，分析了北冰洋热含量的水平分布特征、季节变化和长期变化趋势等，评估了CMIP6模式对北冰洋海洋热含量的模拟能力。研究发现，北冰洋海洋热含量表现出明显的季节变化：热含量在4月份最低，9月份最高；在历史情形下(1850−2014年)，相较观测和再分析资料，CMIP6多模式集合平均(MME)的上层500 m热含量在格陵兰海偏暖，在挪威海、巴伦支海和欧亚海盆偏冷，MME的全水深热含量在北冰洋几乎所有区域均偏暖，在格陵兰海偏差最大；CMIP6模式对北冰洋温度剖面模拟偏差较大，MME平均温度在1 000 m以深均高于观测和再分析资料。在未来情形下(2015−2100年)，MME表现出明显的北冰洋增暖情形，但绝大多数中国模式没有表现出明显的增暖情形。中国模式中，BCC-CSM2-MR和BCC-ESM1对北冰洋年平均热含量的模拟较差，CIESM对热含量季节和年代际变化模拟较差，FIO-ESM-2-0对北冰洋上层500 m年平均热含量及热含量季节和年代际变化的模拟都比较好。Abstract: The PHC, ECCO2, SODA, GECCO3 and CMIP6 data were used to analyze the horizontal distribution characteristics, seasonal variation and long-term trend of the Arctic Ocean heat content, and analyze the simulation ability of the CMIP6 models in this paper. The results show that the heat content of the Arctic Ocean shows obvious seasonal change, with the lowest in April and the highest in September. Under historical circumstances (1850−2014), compared with the observation and reanalysis data, the heat content of the upper 500 m of the CMIP6 models ensemble average (MME) is warmer in the Greenland Sea, colder in the Norwegian sea, Barents Sea and Eurasian Basin, while the whole water column heat content of MME is warmer in almost all regions of the Arctic Ocean, with the largest deviation in the Greenland Sea. CMIP6 models have a large deviation in the simulation of Arctic Ocean temperature profile, and the average temperature of MME is higher than the observation and reanalysis data at the depth of more than 1 000 m. In the future case (2015−2100), the simulation of ocean heat content of MME shows obvious Arctic Ocean warming, but most of the Chinese models show no obvious warming situation. BCC-CSM2-MR and BCC-ESM1 are poor in simulating the annual mean heat content of the Arctic Ocean, CIESM is poor in simulating the seasonal and interdecadal variations of ocean heat content, while FIO-ESM-2-0 is good in simulating the annual heat content of the upper 500 m, the seasonal and interdecadal variations of heat content of the Arctic Ocean.
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Key words:
- Arctic Ocean heat content /
- spatial distribution /
- seasonal variation /
- CMIP6 models /
- model assessment
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图 1 1992–2015年基于观测和再分析资料的北冰洋长期年平均上层500 m热含量水平分布
Fig. 1 Distribution of annual average upper 500 m heat content in the Arctic Ocean from 1992 to 2015 based on the observation reanalysis data
图 2 1992–2015年基于观测和再分析资料的北冰洋长期年均全水深热含量水平分布
Fig. 2 Distribution of annual average whole water heat content in the Arctic Ocean from 1992 to 2015 based on the observation reanalysis data
图 3 CMIP6多模式集合平均北冰洋热含量水平分布
Fig. 3 The Arctic Ocean heat content of multi-model ensemble mean
图 4 北冰洋热含量水平分布偏差
Fig. 4 Deviation of the Arctic Ocean heat content with respect to PHC and SODA, respectively
图 5 北冰洋各海盆和格陵兰海长期年平均垂直温度剖面
Fig. 5 Long-term annual mean vertical temperature profiles of the Arctic Ocean basins and Greenland Sea
图 6 中国模式与PHC的北冰洋上层500 m热含量水平分布偏差
Fig. 6 Deviation of the upper 500 m Arctic Ocean heat content of Chinese models with respect to PHC
图 7 中国模式与PHC的北冰洋全水深热含量水平分布偏差
Fig. 7 Deviation of the whole water column Arctic Ocean heat content of Chinese models with respect to PHC
图 8 CMIP6模式平均温度剖面与PHC温度剖面的温度偏差
水平条形图代表温度偏差，正值代表模式偏暖，负值代表模式偏冷
Fig. 8 The temperature deviation between the average temperature profile of the CMIP6 models and the PHC temperature profile
Horizontal bar represents temperature deviation, positive value means that the model simulation is warmer than the PHC, negative value means model simulation is colder than PHC
图 9 基于PHC观测资料的北冰洋热含量季节变化
Fig. 9 Seasonal variation of Arctic Ocean heat content based on the PHC observation data
图 10 基于GECCO3、ECCO2、SODA再分析资料的北冰洋热含量年代际变化和季节变化
Fig. 10 Decadal and seasonal variations of heat content in the Arctic Ocean of GECCO3、ECCO2 and SODA reanalysis data
图 11 CMIP6模式模拟的北冰洋历史热含量季节变化（1850−2014年）
Fig. 11 Seasonal variation of the Arctic Ocean historical heat content of CMIP6 models (1850−2014)
图 12 CMIP6模式与PHC观测资料历史年平均热含量之间的空间泰勒图（1850−2014年）
Fig. 12 Taylor diagram of annual average heat content in history between CMIP6 models and PHC observation data (1850−2014)
图 13 CMIP6模式模拟的北冰洋年平均热含量时间序列
Fig. 13 Time series of the Arctic Ocean annual mean heat content of CMIP6 models
图 14 北冰洋CMIP6模式间热含量偏差的标准差
a, b. 历史；c, d. 未来
Fig. 14 Standard deviation of the Arctic Ocean heat content between models
a, b. Historical; c, d. future
图 15 北冰洋上层500 m历史热含量的CMIP6模式间EOF第一模态分布（a）和模式间序列(b）（1850–2014年）
Fig. 15 Distribution of the first EOF mode between CMIP6 models of the historical upper 500 m heat content of the Arctic Ocean (a) and sequence between models (b) (1850–2014)
图 16 北冰洋历史全水深热含量的CMIP6模式间EOF第一模态（a）和模式间序列（b）（1850–2014年）
Fig. 16 Distribution of the first EOF mode between CMIP6 models of the historical whole water column heat content of the Arctic Ocean (a) and sequence between models (b) (1850–2014)
图 17 CMIP6多模式集合平均未来北冰洋热含量水平分布
Fig. 17 The future heat content of the Arctic Ocean of CMIP6 multi-model ensemble mean
图 18 CMIP6中国模式模拟2020–2050年、2060–2090年北冰洋年平均热含量水平分布
Fig. 18 The annual mean heat content in the Arctic Ocean of CMIP6 Chinese models in 2020–2050 and 2060–2090
表 1 模式介绍
Tab. 1 Models introduction
模式 所属机构（国别） 网格 ACCESS-CM2 CSRIO-BOM(澳大利亚) 360×300×50 BCC-CSM2-MR BCC(中国) 360×232×40 BCC-ESM1 BCC(中国) 360×232×40 CAMS-CSM1-0 CAMS(中国) 360×200×50 CAS-ESM2-0 CAS(中国) 360×196×30 CanESM5 CCCMA(加拿大) 360×291×45 CESM2 NCAR(美国) 360×180×33 CESM2-WACCM NCAR(美国) 360×180×33 CIESM THU(中国) 320×384×60 CNRM-CM6-1 CNRM-CERFACS(法国) 362×294×75 CNRM-ESM2-1 CNRM-CERFACS(法国) 362×294×75 EC-Earth3 EC-Earth(欧洲) 362×292×75 EC-Earth3-Veg EC-Earth(欧洲) 362×292×75 FGOALS-f3-L CAS(中国) 360×218×30 FGOALS-g3 CAS(中国) 360×218×30 FIO-ESM-2-0 FIO(中国) 320×384×60 GFDL-ESM4 NOAA-GFDL(美国) 360×180×35 HadGEM3-GC31-LL MOHC(英国) 360×330×75 INM-CM4-8 INM(俄国) 360×180×33 INM-CM5-0 INM(俄国) 360×180×33 IPSL-CM6A-LR IPSL(法国) 362×332×75 MIROC-ES2L MIROC(日本) 360×256×63 MIROC6 MIROC(日本) 360×256×63 MPI-ESM1-2-HR MPI-M(德国) 802×404×40 MPI-ESM1-2-LR MPI-M(德国) 256×220×40 MRI-ESM2-0 MRI(日本) 360×180×61 NESM3 NUIST(中国) 362×292×46 TaiESM1 AS-RCEC(中国) 320×384×60 UKESM1-0-LL MOHC(英国) 360×330×75 注：黑色加粗字体表示的为中国模式。 
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表 2 CMIP6模式与PHC观测资料历史年平均热含量之间的标准偏差、中心均方根差和相关系数
Tab. 2 Standard deviation, center root mean square difference and correlation of annual average heat content in history between CMIP6 models and PHC observation data
模式 标准偏差 中心均方根差 相关系数 上层500 m 全水深 上层500 m 全水深 上层500 m 全水深 PHC 1.000 0 1.000 0 0.000 0 0.000 0 1.000 0 1.000 0 ACCESS-CM2 1.163 6 1.572 8 0.672 6 0.873 9 0.817 1 0.861 5 BCC-CSM2-MR 0.775 6 2.012 7 1.317 1 2.361 8 0.085 9 0.131 0 BCC-ESM1 0.833 9 2.258 5 1.357 9 2.591 1 0.089 1 0.135 8 CAMS-CSM1-0 0.684 1 1.526 3 0.598 4 1.421 0 0.811 8 0.437 6 CAS-ESM2-0 1.217 0 1.324 5 0.791 7 0.993 4 0.761 8 0.667 2 CESM2 1.147 8 1.924 4 0.410 0 1.802 5 0.936 3 0.337 9 CESM2-WACCM 1.105 0 1.714 5 0.385 5 1.542 5 0.937 7 0.454 7 CIESM 1.321 8 1.956 7 0.703 9 1.735 4 0.851 7 0.464 3 CNRM-CM6-1 0.854 4 0.968 9 0.543 0 0.510 0 0.841 6 0.866 4 CNRM-ESM2-1 0.915 1 1.083 5 0.524 9 0.537 2 0.853 4 0.870 0 CanESM5 1.089 7 1.362 5 0.661 2 0.795 9 0.803 1 0.815 8 EC-Earth3 0.929 0 1.265 5 0.467 8 0.912 5 0.884 9 0.696 9 EC-Earth3-Veg 0.973 9 1.267 9 0.474 6 0.856 5 0.884 7 0.739 0 FGOALS-f3-L 1.025 6 1.298 9 0.573 2 0.793 2 0.840 1 0.792 2 FGOALS-g3 1.054 4 1.425 9 0.684 0 0.832 0 0.779 5 0.820 9 FIO-ESM-2-0 1.101 8 1.233 0 0.389 0 0.938 5 0.936 0 0.664 9 GFDL-ESM4 1.137 0 1.480 5 0.541 1 0.986 9 0.879 5 0.749 0 HadGEM3-GC31-LL 1.099 5 1.223 5 0.558 9 0.581 1 0.862 4 0.882 4 INM-CM4-8 0.881 0 1.232 4 0.566 3 0.931 4 0.826 0 0.669 9 INM-CM5-0 0.883 1 1.344 6 0.484 1 0.815 8 0.875 0 0.665 4 IPSL-CM6A-LR 0.943 7 1.144 4 0.507 1 0.991 6 0.865 5 0.579 5 MIROC6 1.212 8 1.654 6 0.546 0 1.285 3 0.895 8 0.630 3 MIROC-ES2L 1.156 2 1.425 0 0.620 4 0.773 9 0.844 1 0.853 3 MPI-ESM1-2-HR 1.143 0 1.432 2 0.499 8 0.986 8 0.899 7 0.725 2 MPI-ESM1-2-LR 1.171 8 1.626 8 0.547 4 1.346 4 0.884 7 0.563 5 MRI-ESM2-0 1.115 3 2.316 7 0.409 0 2.233 6 0.931 0 0.297 4 NESM3 0.744 1 1.097 6 1.286 5 1.555 3 0.068 2 0.097 5 TaiESM1 1.234 7 1.778 0 0.529 7 1.602 9 0.908 7 0.447 7 UKESM1-0-LL 1.076 7 1.251 3 0.602 1 0.628 5 0.834 4 0.867 4
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