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Article(id=1233907183444881559, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1233907180928299124, articleNumber=null, orderNo=null, doi=10.12284/hyxb2021147, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1609344000000, receivedDateStr=2020-12-31, revisedDate=1622563200000, revisedDateStr=2021-06-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1772115997420, onlineDateStr=2026-02-26, pubDate=1627142400000, pubDateStr=2021-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772115997420, onlineIssueDateStr=2026-02-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772115997420, creator=13701087609, updateTime=1772115997420, updator=13701087609, issue=Issue{id=1233907180928299124, tenantId=1146029695717560320, journalId=1149651085930835976, year='2021', volume='43', issue='7', pageStart='1', pageEnd='206', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772115996820, creator=13701087609, updateTime=1772115996820, updator=13701087609, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=35, endPage=51, ext={EN=ArticleExt(id=1233907183746871470, articleId=1233907183444881559, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Assessment of the ability of CMIP6 models to simulate the heat content of the Arctic Ocean, columnId=1233907181909766263, journalTitle=Haiyang Xuebao, columnName=Polar sea ice and climate change, runingTitle=null, highlight=null, articleAbstract=

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.

, correspAuthors=Xuezhi Bai, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2021 Pratacultural Science. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Long Xie, Xuezhi Bai, Shangmin Long), CN=ArticleExt(id=1233907188717121971, articleId=1233907183444881559, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=CMIP6模式对北冰洋海洋热含量的模拟能力评估, columnId=1233907182194978940, journalTitle=海洋学报, columnName=极地海冰与气候研究, runingTitle=null, highlight=null, articleAbstract=

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。

, correspAuthors=白学志, authorNote=null, correspAuthorsNote=
白学志（1969-），男，江苏省连云港市人，主要从事海洋环流、海气相互作用和极地海洋等方面研究。E-mail：
, copyrightStatement=版权所有©《海洋学报》编辑部 2021
谢龙，白学志，龙上敏. CMIP6模式对北冰洋海洋热含量的模拟能力评估[J]. 海洋学报，2021，43(7)：35–51Xie Long，Bai Xuezhi，Long Shangmin. Assessment of the ability of CMIP6 models to simulate the heat content of the Arctic Ocean[J]. Haiyang Xuebao，2021, 43(7)：35–51
, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=hudLQ9PZWrlMyszh8t7ArQ==, magXml=Hpte5FWTsKCoSGJSEKus7w==, pdfUrl=null, pdf=loNwZ4QKhr11l8yUZuwh+w==, pdfFileSize=4164116, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=iwiSGs9EjVy64Y85q7Ad+g==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=0LaX5swCqt5leHdn6koTQg==, mapNumber=null, authorCompany=null, fund=null, authors=

谢龙（1996—），男，安徽省淮北市人，主要从事北极热含量方面研究。E-mail：

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谢龙（1996—），男，安徽省淮北市人，主要从事北极热含量方面研究。E-mail：

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articleId=1233907183444881559, language=CN, label=图6, caption=中国模式与PHC的北冰洋上层500 m热含量水平分布偏差, figureFileSmall=bCl8BgtqhKm77lwcqltquQ==, figureFileBig=gVvkRqDQBW8Q0njfOi/IWQ==, tableContent=null), ArticleFig(id=1234047122543276054, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 7, caption=Deviation of the whole water column Arctic Ocean heat content of Chinese models with respect to PHC, figureFileSmall=mThwVAN9fnzBwJiB3z4zqA==, figureFileBig=mCtirtY1Cs52Je9Wxg653Q==, tableContent=null), ArticleFig(id=1234047122660716569, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图7, caption=中国模式与PHC的北冰洋全水深热含量水平分布偏差, figureFileSmall=mThwVAN9fnzBwJiB3z4zqA==, figureFileBig=mCtirtY1Cs52Je9Wxg653Q==, tableContent=null), ArticleFig(id=1234047122748796957, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 8, caption=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

, figureFileSmall=nRxCvNQjYswXMq9DVszfow==, figureFileBig=NDAC0ifeiLwtx3QsD53PRg==, tableContent=null), ArticleFig(id=1234047122853654561, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图8, caption=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

, figureFileSmall=nRxCvNQjYswXMq9DVszfow==, figureFileBig=NDAC0ifeiLwtx3QsD53PRg==, tableContent=null), ArticleFig(id=1234047122962706471, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 9, caption=Seasonal variation of Arctic Ocean heat content based on the PHC observation data, figureFileSmall=2HFt6D19bd3L3KqYC/WZ5A==, figureFileBig=04nurBvKCDKw+ioo7BhCvQ==, tableContent=null), ArticleFig(id=1234047123063369770, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图9, caption=基于PHC观测资料的北冰洋热含量季节变化, figureFileSmall=2HFt6D19bd3L3KqYC/WZ5A==, figureFileBig=04nurBvKCDKw+ioo7BhCvQ==, tableContent=null), ArticleFig(id=1234047123168227373, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 10, caption=Decadal and seasonal variations of heat content in the Arctic Ocean of GECCO3、ECCO2 and SODA reanalysis data, figureFileSmall=/LloIw+tlDK5vr2gu4dY+g==, figureFileBig=JsZGr94w1Qb+3Tq7YhH/Ig==, tableContent=null), ArticleFig(id=1234047123277279280, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图10, caption=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, figureFileSmall=/LloIw+tlDK5vr2gu4dY+g==, figureFileBig=JsZGr94w1Qb+3Tq7YhH/Ig==, tableContent=null), ArticleFig(id=1234047123373748276, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 11, caption=Seasonal variation of the Arctic Ocean historical heat content of CMIP6 models (1850−2014), figureFileSmall=okAsJn4mOWjQ/zaBlVu8sQ==, figureFileBig=bMcudpFAm2EOKiKgPXkEWA==, tableContent=null), ArticleFig(id=1234047123591852087, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图11, caption=CMIP6模式模拟的北冰洋历史热含量季节变化（1850−2014年）, figureFileSmall=okAsJn4mOWjQ/zaBlVu8sQ==, figureFileBig=bMcudpFAm2EOKiKgPXkEWA==, tableContent=null), ArticleFig(id=1234047123696709687, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 12, caption=Taylor diagram of annual average heat content in history between CMIP6 models and PHC observation data (1850−2014), figureFileSmall=Oco55TyMIVb4lu9O8a46dA==, figureFileBig=5VNhFgirj4V//4cUZfh6RA==, tableContent=null), ArticleFig(id=1234047125126967353, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图12, caption=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）, figureFileSmall=Oco55TyMIVb4lu9O8a46dA==, figureFileBig=5VNhFgirj4V//4cUZfh6RA==, tableContent=null), ArticleFig(id=1234047125227630654, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 13, caption=Time series of the Arctic Ocean annual mean heat content of CMIP6 models, figureFileSmall=Mj5Yrxo6pVYLfE7SNyESEQ==, figureFileBig=QksQJ/muB2r6BEMhPr8P7Q==, tableContent=null), ArticleFig(id=1234047125340876869, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图13, caption=CMIP6模式模拟的北冰洋年平均热含量时间序列, figureFileSmall=Mj5Yrxo6pVYLfE7SNyESEQ==, figureFileBig=QksQJ/muB2r6BEMhPr8P7Q==, tableContent=null), ArticleFig(id=1234047125454123083, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 14, caption=Standard deviation of the Arctic Ocean heat content between models

a, b. Historical; c, d. future

, figureFileSmall=Y5gSx7gV9u6ziSnKZRTGsQ==, figureFileBig=/ZYyuoE6xRhFQcOdlN3KsQ==, tableContent=null), ArticleFig(id=1234047125546397772, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图14, caption=北冰洋CMIP6模式间热含量偏差的标准差

a, b. 历史；c, d. 未来

, figureFileSmall=Y5gSx7gV9u6ziSnKZRTGsQ==, figureFileBig=/ZYyuoE6xRhFQcOdlN3KsQ==, tableContent=null), ArticleFig(id=1234047125634478162, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 15, caption=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), figureFileSmall=qJXLwqUDREwYk7Nu2MLPrw==, figureFileBig=mDt6haxiTGKSY7kXGaxN1g==, tableContent=null), ArticleFig(id=1234047125735141464, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图15, caption=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）, figureFileSmall=qJXLwqUDREwYk7Nu2MLPrw==, figureFileBig=mDt6haxiTGKSY7kXGaxN1g==, tableContent=null), ArticleFig(id=1234047125831610458, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 16, caption=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), figureFileSmall=D9qNHL85g/abShtqTI7TxA==, figureFileBig=N37xvrOtvNs8FlcNMn4bVw==, tableContent=null), ArticleFig(id=1234047125915496542, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图16, caption=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）, figureFileSmall=D9qNHL85g/abShtqTI7TxA==, figureFileBig=N37xvrOtvNs8FlcNMn4bVw==, tableContent=null), ArticleFig(id=1234047125990994017, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 17, caption=The future heat content of the Arctic Ocean of CMIP6 multi-model ensemble mean, figureFileSmall=ZAZW4P+TmEQfb9NXhsZuZQ==, figureFileBig=EHhff2IDXX5sgdhMaF0K9w==, tableContent=null), ArticleFig(id=1234047126079074407, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图17, caption=CMIP6多模式集合平均未来北冰洋热含量水平分布, figureFileSmall=ZAZW4P+TmEQfb9NXhsZuZQ==, figureFileBig=EHhff2IDXX5sgdhMaF0K9w==, tableContent=null), ArticleFig(id=1234047126162960489, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Fig. 18, caption=The annual mean heat content in the Arctic Ocean of CMIP6 Chinese models in 2020–2050 and 2060–2090, figureFileSmall=BNqKJol54tujdGhkuiwCfA==, figureFileBig=TJBHRTK7uJ1hQ/HZOonIcw==, tableContent=null), ArticleFig(id=1234047126251040876, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=图18, caption=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, figureFileSmall=BNqKJol54tujdGhkuiwCfA==, figureFileBig=TJBHRTK7uJ1hQ/HZOonIcw==, tableContent=null), ArticleFig(id=1234047126334926960, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Table 1, caption=

Models introduction

, figureFileSmall=null, figureFileBig=null, tableContent=
模式所属机构（国别）网格
　　注：黑色加粗字体表示的为中国模式。
ACCESS-CM2CSRIO-BOM(澳大利亚)360×300×50
BCC-CSM2-MRBCC(中国)360×232×40
BCC-ESM1BCC(中国)360×232×40
CAMS-CSM1-0CAMS(中国)360×200×50
CAS-ESM2-0CAS(中国)360×196×30
CanESM5CCCMA(加拿大)360×291×45
CESM2NCAR(美国)360×180×33
CESM2-WACCMNCAR(美国)360×180×33
CIESMTHU(中国)320×384×60
CNRM-CM6-1CNRM-CERFACS(法国)362×294×75
CNRM-ESM2-1CNRM-CERFACS(法国)362×294×75
EC-Earth3EC-Earth(欧洲)362×292×75
EC-Earth3-VegEC-Earth(欧洲)362×292×75
FGOALS-f3-LCAS(中国)360×218×30
FGOALS-g3CAS(中国)360×218×30
FIO-ESM-2-0FIO(中国)320×384×60
GFDL-ESM4NOAA-GFDL(美国)360×180×35
HadGEM3-GC31-LLMOHC(英国)360×330×75
INM-CM4-8INM(俄国)360×180×33
INM-CM5-0INM(俄国)360×180×33
IPSL-CM6A-LRIPSL(法国)362×332×75
MIROC-ES2LMIROC(日本)360×256×63
MIROC6MIROC(日本)360×256×63
MPI-ESM1-2-HRMPI-M(德国)802×404×40
MPI-ESM1-2-LRMPI-M(德国)256×220×40
MRI-ESM2-0MRI(日本)360×180×61
NESM3NUIST(中国)362×292×46
TaiESM1AS-RCEC(中国)320×384×60
UKESM1-0-LLMOHC(英国)360×330×75
), ArticleFig(id=1234047126410424435, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=表1, caption=

模式介绍

, figureFileSmall=null, figureFileBig=null, tableContent=
模式所属机构（国别）网格
　　注：黑色加粗字体表示的为中国模式。
ACCESS-CM2CSRIO-BOM(澳大利亚)360×300×50
BCC-CSM2-MRBCC(中国)360×232×40
BCC-ESM1BCC(中国)360×232×40
CAMS-CSM1-0CAMS(中国)360×200×50
CAS-ESM2-0CAS(中国)360×196×30
CanESM5CCCMA(加拿大)360×291×45
CESM2NCAR(美国)360×180×33
CESM2-WACCMNCAR(美国)360×180×33
CIESMTHU(中国)320×384×60
CNRM-CM6-1CNRM-CERFACS(法国)362×294×75
CNRM-ESM2-1CNRM-CERFACS(法国)362×294×75
EC-Earth3EC-Earth(欧洲)362×292×75
EC-Earth3-VegEC-Earth(欧洲)362×292×75
FGOALS-f3-LCAS(中国)360×218×30
FGOALS-g3CAS(中国)360×218×30
FIO-ESM-2-0FIO(中国)320×384×60
GFDL-ESM4NOAA-GFDL(美国)360×180×35
HadGEM3-GC31-LLMOHC(英国)360×330×75
INM-CM4-8INM(俄国)360×180×33
INM-CM5-0INM(俄国)360×180×33
IPSL-CM6A-LRIPSL(法国)362×332×75
MIROC-ES2LMIROC(日本)360×256×63
MIROC6MIROC(日本)360×256×63
MPI-ESM1-2-HRMPI-M(德国)802×404×40
MPI-ESM1-2-LRMPI-M(德国)256×220×40
MRI-ESM2-0MRI(日本)360×180×61
NESM3NUIST(中国)362×292×46
TaiESM1AS-RCEC(中国)320×384×60
UKESM1-0-LLMOHC(英国)360×330×75
), ArticleFig(id=1234047126481727607, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=EN, label=Table 2, caption=

Standard deviation, center root mean square difference and correlation of annual average heat content in history between CMIP6 models and PHC observation data

, figureFileSmall=null, figureFileBig=null, tableContent=
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
PHC1.000 01.000 00.000 00.000 01.000 01.000 0
ACCESS-CM21.163 61.572 80.672 60.873 90.817 10.861 5
BCC-CSM2-MR0.775 62.012 71.317 12.361 80.085 90.131 0
BCC-ESM10.833 92.258 51.357 92.591 10.089 10.135 8
CAMS-CSM1-00.684 11.526 30.598 41.421 00.811 80.437 6
CAS-ESM2-01.217 01.324 50.791 70.993 40.761 80.667 2
CESM21.147 81.924 40.410 01.802 50.936 30.337 9
CESM2-WACCM1.105 01.714 50.385 51.542 50.937 70.454 7
CIESM1.321 81.956 70.703 91.735 40.851 70.464 3
CNRM-CM6-10.854 40.968 90.543 00.510 00.841 60.866 4
CNRM-ESM2-10.915 11.083 50.524 90.537 20.853 40.870 0
CanESM51.089 71.362 50.661 20.795 90.803 10.815 8
EC-Earth30.929 01.265 50.467 80.912 50.884 90.696 9
EC-Earth3-Veg0.973 91.267 90.474 60.856 50.884 70.739 0
FGOALS-f3-L1.025 61.298 90.573 20.793 20.840 10.792 2
FGOALS-g31.054 41.425 90.684 00.832 00.779 50.820 9
FIO-ESM-2-01.101 81.233 00.389 00.938 50.936 00.664 9
GFDL-ESM41.137 01.480 50.541 10.986 90.879 50.749 0
HadGEM3-GC31-LL1.099 51.223 50.558 90.581 10.862 40.882 4
INM-CM4-80.881 01.232 40.566 30.931 40.826 00.669 9
INM-CM5-00.883 11.344 60.484 10.815 80.875 00.665 4
IPSL-CM6A-LR0.943 71.144 40.507 10.991 60.865 50.579 5
MIROC61.212 81.654 60.546 01.285 30.895 80.630 3
MIROC-ES2L1.156 21.425 00.620 40.773 90.844 10.853 3
MPI-ESM1-2-HR1.143 01.432 20.499 80.986 80.899 70.725 2
MPI-ESM1-2-LR1.171 81.626 80.547 41.346 40.884 70.563 5
MRI-ESM2-01.115 32.316 70.409 02.233 60.931 00.297 4
NESM30.744 11.097 61.286 51.555 30.068 20.097 5
TaiESM11.234 71.778 00.529 71.602 90.908 70.447 7
UKESM1-0-LL1.076 71.251 30.602 10.628 50.834 40.867 4
), ArticleFig(id=1234047126574002298, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233907183444881559, language=CN, label=表2, caption=

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

, figureFileSmall=null, figureFileBig=null, tableContent=
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
PHC1.000 01.000 00.000 00.000 01.000 01.000 0
ACCESS-CM21.163 61.572 80.672 60.873 90.817 10.861 5
BCC-CSM2-MR0.775 62.012 71.317 12.361 80.085 90.131 0
BCC-ESM10.833 92.258 51.357 92.591 10.089 10.135 8
CAMS-CSM1-00.684 11.526 30.598 41.421 00.811 80.437 6
CAS-ESM2-01.217 01.324 50.791 70.993 40.761 80.667 2
CESM21.147 81.924 40.410 01.802 50.936 30.337 9
CESM2-WACCM1.105 01.714 50.385 51.542 50.937 70.454 7
CIESM1.321 81.956 70.703 91.735 40.851 70.464 3
CNRM-CM6-10.854 40.968 90.543 00.510 00.841 60.866 4
CNRM-ESM2-10.915 11.083 50.524 90.537 20.853 40.870 0
CanESM51.089 71.362 50.661 20.795 90.803 10.815 8
EC-Earth30.929 01.265 50.467 80.912 50.884 90.696 9
EC-Earth3-Veg0.973 91.267 90.474 60.856 50.884 70.739 0
FGOALS-f3-L1.025 61.298 90.573 20.793 20.840 10.792 2
FGOALS-g31.054 41.425 90.684 00.832 00.779 50.820 9
FIO-ESM-2-01.101 81.233 00.389 00.938 50.936 00.664 9
GFDL-ESM41.137 01.480 50.541 10.986 90.879 50.749 0
HadGEM3-GC31-LL1.099 51.223 50.558 90.581 10.862 40.882 4
INM-CM4-80.881 01.232 40.566 30.931 40.826 00.669 9
INM-CM5-00.883 11.344 60.484 10.815 80.875 00.665 4
IPSL-CM6A-LR0.943 71.144 40.507 10.991 60.865 50.579 5
MIROC61.212 81.654 60.546 01.285 30.895 80.630 3
MIROC-ES2L1.156 21.425 00.620 40.773 90.844 10.853 3
MPI-ESM1-2-HR1.143 01.432 20.499 80.986 80.899 70.725 2
MPI-ESM1-2-LR1.171 81.626 80.547 41.346 40.884 70.563 5
MRI-ESM2-01.115 32.316 70.409 02.233 60.931 00.297 4
NESM30.744 11.097 61.286 51.555 30.068 20.097 5
TaiESM11.234 71.778 00.529 71.602 90.908 70.447 7
UKESM1-0-LL1.076 71.251 30.602 10.628 50.834 40.867 4
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CMIP6模式对北冰洋海洋热含量的模拟能力评估
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谢龙 1 , 白学志 1, * , 龙上敏 1
海洋学报 | 极地海冰与气候研究 2021,43(7): 35-51
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海洋学报 | 极地海冰与气候研究 2021, 43(7): 35-51
CMIP6模式对北冰洋海洋热含量的模拟能力评估
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谢龙1 , 白学志1, * , 龙上敏1
作者信息
  • 1河海大学 海洋学院，江苏 南京 210098

    • 谢龙（1996—），男，安徽省淮北市人，主要从事北极热含量方面研究。E-mail：

通讯作者:

白学志（1969-），男，江苏省连云港市人，主要从事海洋环流、海气相互作用和极地海洋等方面研究。E-mail：
Assessment of the ability of CMIP6 models to simulate the heat content of the Arctic Ocean
Long Xie1 , Xuezhi Bai1, * , Shangmin Long1
Affiliations
  • 1College of Oceanography, Hohai University, Nanjing 210098, China
出版时间: 2021-07-25 doi: 10.12284/hyxb2021147
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摘要
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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。

北冰洋热含量  /  空间分布  /  季节变化  /  CMIP6模式  /  模式评估

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.

Arctic Ocean heat content  /  spatial distribution  /  seasonal variation  /  CMIP6 models  /  model assessment
谢龙, 白学志, 龙上敏. CMIP6模式对北冰洋海洋热含量的模拟能力评估. 海洋学报, 2021 , 43 (7) : 35 -51 . DOI: 10.12284/hyxb2021147
Long Xie, Xuezhi Bai, Shangmin Long. Assessment of the ability of CMIP6 models to simulate the heat content of the Arctic Ocean[J]. Haiyang Xuebao, 2021 , 43 (7) : 35 -51 . DOI: 10.12284/hyxb2021147
正文
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1 引言
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自20世纪70年代以来，全球气温持续升高，对北极产生了深刻的影响。21世纪以来，北极的气温变化是全球平均水平的两倍，这被称为“北极放大”现象[1]。与此同时，北极海冰覆盖范围呈不断减小的趋势[2-5]，海冰厚度和海冰密集度也持续降低[6-7]，在2007年夏季出现了有卫星观测以来海冰覆盖面积的最低值[5, 8]。一般认为北极海冰范围会继续减小，在2030年可能会出现在夏季北极无冰的情况[4]。
海冰是北极气候系统的一个重要组成部分，控制着海洋–大气界面上的热量、水、动量和气体交换。夏季海冰范围的减小会增加海洋对太阳辐射的吸收，一方面加速海冰的融化，另一方面越来越多的开阔海面对大气产生异常加热，形成正反馈效应[9-10]。研究表明，北极增暖的反馈主要是冰雪反照率反馈，海冰的减少被认为是北极放大现象的关键因素[1]。过去30年的海冰减退极大地改变了北冰洋上混合层的热收支[11]。在全球变暖和北极海冰不断减少的背景下，北冰洋的热含量必将发生变化，不同区域热含量变化情况也不尽相同。因此，研究北冰洋热含量的变化及其影响因素对于研究北冰洋海冰变化及气候变化有重要意义。
北冰洋热含量表现出明显的季节变化。Serreze等[12]利用PHC（Polar Science Center Hydrographic Climatology）资料计算得到北冰洋热含量的季节变化。夏季（7−9月）北冰洋热含量增量最大，9月热含量达到最大值，11−12月热损失最大，随后热含量持续下降，4月达到最小值。
在海盆内部，Timmermans等[13]研究发现，在1987–2017年的30年里，加拿大海盆热含量增加了几乎1倍，主要原因是海冰减少导致上层海洋吸收更多的太阳辐射。M.S.和Thompson[14]利用同化资料研究了1958–2005年间北冰洋上层1 000 m热含量的长期变化特征。研究发现，北冰洋热含量的季节循环主要受上层海洋净热通量的正负控制，而年际变化主要与北大西洋的经向热平流有关。Lique和Steele[15]利用高分辨率全球海洋海冰模式ORCA025对1968–2007年间北冰洋热含量变化进行REOF分析，发现北冰洋28%的热含量变化是由季节性无冰区大气热通量的季节性和年际变化驱动的，31%的热含量变化受到大西洋水通过弗拉姆海峡的热含量变化影响。
历史观测数据显示，北冰洋在过去几十年中主要的气候参数，如：北极表层气温、北冰洋淡水含量、中层（150～1 000 m）大西洋水温度以及陆缘冰厚度等都发生了明显变化[16-17]。在北欧海中，大西洋水是一个表层水团，温度最大值出现在海洋表面。在弗拉姆海峡中，它汇入西斯匹次卑尔根海流的地方，温度在次表层出现最大值。大西洋水通过弗拉姆海峡和巴伦支海进入欧亚海盆后，继续向下游输运[18-19]，大部分大西洋水绕北冰洋循环流动，大多数作为一股“边缘”流沿着大陆架坡流动[20]，流动过程中大西洋水的温度和盐度在逐渐降低。在弗拉姆海峡，大西洋水温度大约为3℃，在北冰洋内部温度下降到0.4℃。大西洋水的核心也随着大西洋水在北冰洋内部的流动而从表层向下流动，从弗拉姆海峡的200 m下降到加拿大海盆中的500 m[21]。
暖而咸的大西洋水分布在北冰洋各个区域，对于北冰洋的热量平衡起着特殊的作用，但是大西洋水向上释放的热量能否影响北极海冰仍然存在很多争论[22-23]。随着21世纪以来北冰洋内大西洋水异常增暖[24]以及欧亚海盆内“大西洋化”现象向东拓展，越来越多学者认为大西洋水的热量对于北冰洋海冰的变化具有重要的作用[25-26]。Polyakov等[27]利用三维冰–海模型实验估算出在40年后大西洋水热通量每增加0.5 W/m2将会导致28～35 cm的海冰融化。
由于观测资料的稀缺，海–冰耦合模式是研究北极海–冰–气系统的重要工具[28-29]。第六次国际耦合模式比较计划（Coupled Model Intercomparison Project Phase 6，CMIP6）是CMIP实施20多年来参与的模式数量最多、设计的数值实验最丰富、所提供的模拟数据最为庞大的一次。这些数据将支撑未来5～10年的全球气候研究，基于这些数据的研究成果将构成未来气候评估和气候谈判的基础[30]。因此，CMIP6模式对北极地区气候模拟的准确与否具有重要意义。本文拟基于观测资料，评估CMIP6模式对北冰洋热含量的模拟能力。
本文采用PHC观测资料、ECCO2（The Estimating the Circulation and Climate of the Ocean Project Version 2）、SODA（Simple Ocean Data Assimilation）和GECCO3（German Contribution to the Estimating the Circulation and Climate of the Ocean Project Version 3）再分析资料以及29套CMIP6模式资料对北冰洋热含量的水平分布、季节变化和长期变化趋势等进行研究，同时评估了CMIP6模式对北冰洋海洋热含量的模拟能力。
2 数据和方法
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2.1 数据
本文采用PHC的温度数据，包括气候态年平均、季节平均和月平均数据。数据的空间范围为全球海洋89.5°S～89.5°N，环全球经度，水平空间分辨率为1°×1°。其中，季节平均数据仅包括冬季和夏季两个季节，月平均数据只有海洋上层1 500 m的数据。
本文采用了SODA、ECCO2和GECCO3再分析资料。SODA海洋数据集是由全球简单海洋资料同化分析系统产生，该系统是美国马里兰大学于20世纪90年代初开发的分析系统，其目的是为气候研究提供一套与大气再分析资料相匹配的海洋再分析资料[31]。SODA资料时间范围为1980年1月至2015年12月，时间分辨率为逐月，空间范围为74.75°S～89.75°N，环全球经度，水平空间分辨率为1°×1°。ECCO2资料来自美国喷气动力实验室（Jet Propulsion Laboratory, JPL），是基于美国麻省理工学院的海洋环流模式（Massachusetts Institude of Technology general circulation model, MITgcm）[32]。数据时间范围为1992年1月至2016年12月，时间分辨率为3 d，空间范围为全球海洋89.875°S～89.875°N，环全球经度，水平空间分辨率为0.25°×0.25°。本文还使用了同样基于MITgcm的GECCO3再分析资料。GECCO是德国对ECCO的进一步发展，GECCO3则是GECCO和GECCO2的延续。GECCO3数据时间范围为1948年1月至2018年1月，时间分辨率为逐月，空间范围为全球海洋89.5°S～89.5°N，环全球经度，水平空间分辨率为1°×1°。
世界气候研究计划“耦合模拟工作组”组织的CMIP6正在进行中，全球33家机构的约112个气候模式版本注册参加[30]。本文选取了CMIP6历史气候模拟实验下的29个模式（1850年1月至2014年12月）以及未来试验场景SSP126（在SSP1（低强迫情景）基础上对RCP2.6情景的升级（辐射强度在2100年达到2.6 W/m2））下的25个模式（2015年1月至2100年12月，4个中国模式BCC-ESM1、CAS-ESM2-0、CIESM和TaiESM1在SSP126实验场景下没有提供结果）。CMIP6模式数据在使用过程中均插值到1°×1°的规则网格上，模式所属机构和网格分辨率见表1。
2.2 热含量计算方法
本文采用Lique和Steele[15]给出的计算公式来计算海洋热含量
$HC\left( {t,x,y} \right) = \int_z {{\rho _0}{C_{\rm{p}}}} \left[ {T\left( {t,x,y,z} \right) - {T_{\rm{ref}}}} \right]{\rm{d}}z{{，}}$
式中，T是给定点的温度；Tref是参考温度，取Tref = –0.27℃，–0.27℃是利用PHC数据计算得到的气候态平均温度；通过计算，我们发现海水密度与海水比热容取定值和变化值对计算热含量影响很小，因此本文取海水密度ρ0=1 026 kg/m3，海水比热容Cp=4 000 J/(kg·℃)。计算区域为60°～90°N，环全球经度，计算深度分别为海洋上层500 m和全水深。
3 结果
收起
3.1 历史时期北冰洋热含量水平分布特征
3.1.1 观测和再分析资料
图1和图2分别为观测和再分析资料气候年平均上层500 m和全水深热含量水平分布，再分析资料在计算时选取公共时间段：1992年1月至2015年12月。
由图可以看出，不论是上层500 m还是全水深，观测和再分析资料的北冰洋热含量水平分布都表现出在北欧海（北欧海是对格陵兰海、冰岛海和挪威海的统称）较高，在挪威海最高的特征。这主要是受到沿着挪威陆坡北向流动的挪威海大西洋暖流和自弗拉姆海峡向南流动的东格陵兰寒流的影响[33-36]。
在上层500 m，观测和再分析资料的北冰洋热含量水平分布都表现出由弗拉姆海峡至北冰洋中心逐渐降低，在欧亚海盆为正，在加拿大海盆为负的特征。这是因为随着大西洋水不断向北冰洋内部流动温度也在逐渐降低，热含量也逐渐减小。对于全水深，北冰洋热含量在挪威海最高，在格陵兰海、欧亚海盆和加拿大海盆热含量为负。在北冰洋内部的海盆边缘出现热含量的极大值，这主要是因为温暖的北大西洋水主要是沿着大陆坡在北极海盆周围流动[20]。相较于上层500 m，北冰洋全水深热含量在欧亚海盆由正值变为负值。
3.1.2 CMIP6 模拟结果和偏差
图3给出了CMIP6多模式集合平均结果（MME）的热含量水平分布。如图所示，MME的北冰洋热含量水平分布也表现出挪威海最高的特征。MME上层500 m热含量水平分布与观测比较接近：在挪威海最高，由弗拉姆海峡至北冰洋中心逐渐降低。MME全水深热含量在北冰洋几乎所有海域全部为正，同观测和再分析资料相差较大。
用MME减去PHC和SODA，得到热含量偏差如图4所示（蓝色代表MME偏冷，橙色代表MME偏暖）。由图可知，在上层500 m，MME热含量在格陵兰海暖偏差最大，在挪威海、巴伦支海及欧亚海盆偏冷。对于全水深热含量，MME在几乎所有海域均偏暖，同样在格陵兰海暖偏差最大，仅在巴伦支海部分海域偏冷。
为了研究模式模拟偏差产生的原因，图5给出了PHC、再分析资料和CMIP6模式模拟的垂直温度剖面。在加拿大海盆，模拟的温度剖面分布与观测较为相似，都是表层温度较低，至500 m深度左右温度达到最高值，随后温度缓慢降低，2 000 m以深后温度基本保持不变。但是模拟的温度在整个水柱明显高于观测和再分析资料，约0.8℃，因此模式模拟的上层500 m和全水深热含量在加拿大海盆偏暖。
在欧亚海盆，模拟的上层500 m热含量偏冷，而全水深热含量偏暖。欧亚海盆上层500 m温度剖面如图5c所示。由图可知，模式模拟的上层500 m温度垂直剖面与PHC和再分析资料相比有较大差异：PHC和再分析资料温度最高值出现在300 m水深上下，约为0.9℃，随后温度逐渐降低，至2 000 m水深上下达到最低值，在–0.5～–1℃之间。而模式模拟的MME温度最高值出现在水深800 m上下，约为1℃，明显偏深。在上层500 m，MME温度略低于PHC和再分析资料。但随着水深逐渐增加，在500 m以深，MME温度逐渐高于PHC和再分析资料，至温度稳定后，MME温度高于PHC和再分析资料接近2℃。这导致了模式模拟的欧亚海盆上层500 m热含量偏冷，而全水深热含量偏暖。CMIP6模式对于入流大西洋水的模拟仍然是亟待解决的问题，几乎没有模式能较准确地模拟出北冰洋中大西洋水的垂直结构。
在格陵兰海，MME温度垂直剖面与PHC和再分析资料存在较大偏差：模式模拟的温度剖面随着深度增加先降低后增加，在约300 m水深达到最高值6℃左右，随后温度逐渐降低，至1 500 m水深后稳定在2℃左右，而PHC和再分析资料的温度垂直剖面则随着水深增加温度逐渐降低，由表层的6～10℃至1 000 m以深稳定在–2℃。在800 m以深，MME温度显著高于观测和再分析资料，温度差甚至达到4℃，因此格陵兰海出现最大热含量暖偏差。
3.1.3 中国模式模拟能力
为了了解中国模式对于北冰洋热含量的模拟情况，用中国模式模拟结果减去PHC得到中国模式与PHC上层500 m和全水深热含量偏差如图6和图7所示。在上层500 m，大部分中国模式都在格陵兰海偏暖，在挪威海和巴伦支海偏冷，其中CIESM的暖偏差最大。FGOALS-f3-L、FGOALS-g3、FIO-ESM-2-0和CAS-ESM2-0在欧亚海盆和加拿大海盆偏冷，其余中国模式偏暖。对于全水深热含量，大部分模式都在巴伦支海偏冷。FGOALS-f3-L在加拿大海盆偏冷，FGOALS-g3在加拿大海盆和欧亚海盆偏冷，其他中国模式在加拿大海盆和欧亚海盆均呈明显偏暖的特征。
为了更好地了解MME和中国模式与PHC资料的偏差，通过计算不同资料在0～100 m、100～300 m、300～750 m、750～1 500 m和1 500～3 000 m不同深度的平均温度，用MME和中国模式减去PHC，得到模式平均温度剖面与PHC的温度偏差如图8所示。
由图可知，对于加拿大海盆，在上层100 m，MME和所有中国模式均偏冷。MME偏冷约0.5℃，除了NESM3，其他中国模式偏差较大，超过1℃。在100～300 m水深，MME偏暖约0.5℃，CIESM偏暖较小，NESM3偏暖接近2℃，其余中国模式仍旧偏冷，但偏差较上层500 m减小。在300 m以深，除了FGOALS-g3和FGOALS-f3-L一直偏冷，NESM3在1 500 m以深偏冷，其余中国模式均偏暖。BCC-CSM2-MR和BCC-ESM1偏差最大，基本都偏暖2℃，CIESM和TaiESM1次之。对于欧亚海盆，在上层100 m，MME和所有中国模式均偏冷超过1℃。在100～300 m水深，部分中国模式由偏冷变为偏暖，偏差较上层100 m均有减小。在300 m以深，温度偏差情况类似于加拿大海盆。在格陵兰海，除了BCC-CSM2-MR和BCC-ESM1在上层100 m偏冷，NESM3在上层300 m偏冷，其余中国模式在所有深度处均偏暖。MME在100～300 m偏暖超过6℃。偏差最大的模式是100～300 m水深的CIESM，最大偏差接近10℃。从750 m以深，MME和中国模式的偏差逐渐减小。
从以上分析可以看出，加拿大海盆和欧亚海盆温度偏差的特征比较相似，在格陵兰海情况则不同。同时，从温度偏差的绝对值考虑，格陵兰海温度偏差在–4～10℃，远超加拿大海盆和欧亚海盆的–3～3℃。这也解释了为什么MME与PHC的暖偏差在格陵兰海最大。
3.2 北冰洋海洋热含量的季节和年代际变化
3.2.1 季节变化
利用PHC月均数据计算得到的PHC热含量的季节变化如图9所示。由图可知，北冰洋热含量表现出明显的季节变化：夏季（7–9月）北冰洋热含量增量最大，9月份达到最大值。11–12月热损失最大，随后热含量持续下降，4月份达到最小值。
GECCO3、ECCO2和SODA 3种再分析资料的热含量季节和年代际变化如图10所示。其中，GECCO3时间范围是1948–2018年，ECCO2时间范围是1992–2016年，SODA时间范围是1980–2015年，在计算年代际变化时使用十三点滑动平均的方法除去季节信号。
由图10c和图10d可知，在上层500 m，再分析资料表现出同PHC相同的季节变化特征：热含量在9月份最高，4月份最低。ECCO2的上层海洋热含量最高，SODA和GECCO3次之，PHC最低。对于全水深，ECCO2和SODA热含量季节变化特征同上层500 m情况相同。GECCO3热含量在10月份最低，2月份最高，同SODA和ECCO2相差较大。
由图10a和图10b可知，在上层500 m，GECCO3在1948–1988年间热含量呈下降趋势。在1992–2016年，再分析资料热含量表现出一致的上升趋势。对于全水深，GECCO3与ECCO2和SODA表现出较大的差异。GECCO3的热含量从1948–1963年迅速上升，再经历至1978年的缓慢上升之后，基本保持不变，与ECCO2和SODA的热含量明显上升趋势相差较大。ECCO2和SODA则表现出明显的热含量上升趋势。结合图10c和图10d，我们发现，GECCO3对于上层500 m热含量的模拟与SODA和ECCO2比较接近，但在全水深时偏差较大。
CMIP6模式的北冰洋历史热含量季节变化如图11所示。由图可知，CMIP6模式模拟的上层500 m热含量的季节变化特征与观测和再分析资料相似：热含量在9月份最高，4月份最低。MME的模拟结果同观测和再分析资料比较接近。CMIP6模式全水深热含量也表现出与观测和再分析资料相同的季节变化。中国模式CIESM的热含量远高于其他模式，模拟较差。不同模式间热含量差异非常大。
3.2.2 模式评估
图12给出了CMIP6模式与PHC观测资料的北冰洋年平均热含量之间的泰勒图。黑色实线代表标准偏差，蓝色虚线代表相关系数，粉红虚线代表中心均方根差。由图可知，在上层500 m，大部分CMIP6模式模拟能力比较接近，相关系数在0.7～0.95之间，标准偏差在0.75～1.5之间。在上层500 m，中国模式BCC-CSM2-MR、BCC-ESM1和NESM3年平均热含量与PHC的相关系数非常小，模拟相对较差，FIO-ESM-2-0、CESM2-WACCM和CESM2的模拟相对较好。相较上层500 m，CMIP6模式全水深热含量的空间泰勒图分布比较分散，偏差相对较大。MRI-ESM2-0、BCC-CSM2-MR和BCC-ESM1的模拟相对较差，CNRM-CM6-1、CNRM-ESM2-1和Had-GEM3-GC31-LL模拟相对较好。
为方便查阅，表2给出了图12空间泰勒图的标准偏差、中心均方根差和相关系数的具体数值。
CMIP6模式模拟的1850–2100年北冰洋上层500 m热含量和全水深热含量的长期变化趋势如图13所示。由图可知，1850–1975年间，MME上层500 m和全水深热含量基本保持不变。1975年以后，上层500 m热含量呈明显的上升趋势，到了2075–2100年间，热含量基本保持不变；1975年以后，全水深热含量也呈上升趋势，但相较上层500 m其上升趋势较缓。这可能是因为上层海洋受到二氧化碳增加等因素导致的全球变暖的影响要大于深层海洋。粗蓝线和粗红线分别是历史时期SODA和ECCO2年平均热含量时间序列。在上层500 m，SODA和ECCO2热含量高于MME，增长趋势也大于MME，但对于全水深，SODA和ECCO2热含量则低于MME，在增长趋势方面则无明显差异。
在上层500 m，大部分中国模式热含量和热含量增长趋势均低于MME、ECCO2和SODA，但在全水深情况下，大部分中国模式热含量要高于MME、ECCO2和SODA。其中，中国模式CIESM热含量与其他CMIP6模式和再分析资料相差很大。不同模式热含量差异很大，在全水深情况下，甚至有一个CMIP6模式热含量出现了负值。
3.3 CMIP6模式间不确定性
图14给出模式间热含量偏差的标准差。由图可知，历史和未来情形下模式间热含量偏差的标准差比较相似，模式间热含量变化最大的区域主要出现在北欧海、欧亚海盆和加拿大海盆。在上层500 m，模式间热含量标准差最大值出现在挪威海域，加拿大海盆和欧亚海盆次之，说明模式间热含量变化最大的区域在挪威海域；对于全水深，标准差最大值出现在戴维斯海峡，欧亚海盆较高，加拿大海盆和挪威海次之。
利用模式间EOF(正交函数分解)方法，我们先对29个CMIP6模式处理得到多模式集合平均结果MME，用每个模式与MME做差得到模式与MME偏差的矩阵，然后进行EOF分解，得到的模式间序列即为不同模式对该模态的模拟情况，结果如图15和图16所示。
如图15和图16所示，上层500 m和全水深模式间EOF第一模态（EOF1）表现出整个北冰洋同位相变化特征，且第一模态表现出与热含量水平分布类似的特征。在北冰洋内部的欧亚海盆和加拿大海盆，出现沿着海盆边缘的红线和蓝线，其绝对值大小均在0.5～1之间。出现沿海盆边缘的线说明不同CMIP6模式对于大西洋水在北冰洋内部沿海盆边缘的流动差异较大；从模式间序列来看，上层500 m有16个模式较MME偏冷，13个模式偏暖。6个中国模式偏暖，4个偏冷；全水深有12个模式较MME偏冷，17个模式偏暖。8个中国模式偏暖，2个偏冷。
3.4 CMIP6对未来北冰洋热含量预测
CMIP6模式给出了2015–2100年在不同情景下的预测结果。在未来试验场景SSP126下，6个中国模式提供了结果。为了观察北冰洋热含量在未来的变化情况，我们选取了两个时间段进行对比：2020–2050年和2060–2090年，得到MME热含量水平分布如图17所示。
图17a和图17b是上层500 m热含量水平分布，图17c和图17d是全水深热含量水平分布。整体看来，对于上层500 m和全水深，其热含量水平分布情况与历史情形相似。2060–2090年较2020–2050年均表现出明显的热含量自欧亚海盆向加拿大海盆增加的趋势。这说明在CMIP6模式对于未来情形的模拟中，北冰洋在增暖。
图18给出了6个CMIP6中国模式在未来两个时间段北冰洋上层500 m（图18a）和全水深（图18b）热含量的水平分布情况。由图可知，中国模式的未来热含量水平分布与历史情形相似，但在未来两个时间段，除了FIO-ESM-2-0，其余5个中国模式均没有表现出明显的热含量增加，热含量变化很小，与MME所表现的北冰洋增暖情形相差甚大。对于全水深，中国模式的未来热含量水平分布与历史情形相似，但类似于上层500 m，在未来的两个时间段，只有FIO-ESM-2-0表现出热含量的增加，其余5个中国模式均没有表现出明显的热含量增加，热含量变化很小，模拟情况同MME相差较大。
4 结论
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前人对于北极热含量已经进行过一些研究。Serreze等[12]利用PHC资料计算得到北冰洋热含量的季节变化：热含量在9月份最高，4月份最低。本文基于观测和再分析资料，结合CMIP6模式资料，研究发现CMIP6模式模拟的北冰洋热含量也表现出与PHC相同的季节变化，但不同模式的差异非常大；对于北冰洋的热含量长期变化趋势，在1850–1975年间，MME和大部分CMIP6模式的上层500 m热含量基本保持不变，1975年之后热含量呈明显的上升趋势，到了2075–2100年间热含量增加逐渐减少，基本保持不变。全水深热含量的上升趋势相较上层500 m较缓。其中，中国模式CIESM的热含量远高于再分析资料和其他CMIP6模式，模拟较差。
Lique和Steele[15]利用ORCA025研究了1968–2007年间北冰洋热含量的水平分布。研究发现，北大西洋水通过弗拉姆海峡东部和巴伦支海进入北极盆地，并在盆地周围的大陆坡继续流动。本文研究发现在历史情形下（1850–2014年），北冰洋上层500 m热含量在挪威海最大，由弗拉姆海峡至欧亚海盆热含量逐渐降低；全水深热含量的最大值出现在挪威海，极大值出现在北冰洋内部海盆边缘区域，在欧亚海盆和加拿大海盆内部热含量为负。历史情形下，相较观测和再分析资料，MME全水深热含量在几乎所有海域均偏暖。未来情形下（2015–2100年），MME的模拟结果表明北冰洋正在增暖，但大部分中国模式却没有表现出明显的热含量增加现象。通过分析CMIP6模式与PHC年平均热含量之间的空间泰勒图，本文发现：在历史情形下，BCC-CSM2-MR和BCC-ESM1对北冰洋上层500 m和全水深热含量的模拟都比较差，NESM3对上层500 m热含量的模拟较差，MRI-ESM2-0对全水深热含量的模拟较差。CESM2、CESM2-WACCM和中国模式FIO-ESM-2-0对上层500 m热含量的模拟较好，CNRM-CM6-1、CNRM-ESM2-1和Had-GEM3-GC31-LL对全水深热含量的模拟较好。
CMIP6模式对于北冰洋垂直温度剖面的模拟情况与观测和再分析资料差异较大，在加拿大海盆，模拟的温度垂直剖面与观测类似，但整体温度偏高；在欧亚海盆，模拟的温度垂直剖面形状与观测相差较大，没有能较准确地模拟出大西洋水地垂直结构，在1 000 m以深，模拟的温度偏高2℃左右。
本文分析了北冰洋热含量的水平分布、季节变化和长期变化趋势等，但对北冰洋热含量的分析没有深入至其影响机制层面，下一步的工作可以结合北冰洋海表面热通量和海洋热输送等资料深入分析北冰洋热含量的影响因素，同时从模式的动力学框架和对北极极端事件的模拟情况等角度对CMIP6模式进行进一步的评估。
基金
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  • 国家重点研发计划（2017YFA0604602）；江苏省研究生科研与实践创新计划项目（B200203134）；国家自然科学基金面上项目（41676019）
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2021年第43卷第7期
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doi: 10.12284/hyxb2021147
  • 接收时间：2020-12-31
  • 首发时间：2026-02-26
  • 出版时间：2021-07-25
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  • 收稿日期：2020-12-31
  • 修回日期：2021-06-02
基金
国家重点研发计划（2017YFA0604602）；江苏省研究生科研与实践创新计划项目（B200203134）；国家自然科学基金面上项目（41676019）
作者信息
    1河海大学 海洋学院，江苏 南京 210098

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白学志（1969-），男，江苏省连云港市人，主要从事海洋环流、海气相互作用和极地海洋等方面研究。E-mail：
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