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Article(id=1200468071296127773, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1200468070721508125, articleNumber=null, orderNo=null, doi=10.12284/hyxb2024077, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1711468800000, receivedDateStr=2024-03-27, revisedDate=1717689600000, revisedDateStr=2024-06-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1764143491815, onlineDateStr=2025-11-26, pubDate=1725033600000, pubDateStr=2024-08-31, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764143491815, onlineIssueDateStr=2025-11-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764143491815, creator=13701087609, updateTime=1764143491815, updator=13701087609, issue=Issue{id=1200468070721508125, tenantId=1146029695717560320, journalId=1149651085930835976, year='2024', volume='46', issue='8', pageStart='1', pageEnd='142', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764143491678, creator=13701087609, updateTime=1764143656095, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1200468760399303166, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1200468070721508125, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1200468760399303167, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1200468070721508125, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=89, endPage=107, ext={EN=ArticleExt(id=1200468071623283488, articleId=1200468071296127773, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Simulation of the impact of black carbon on snow and sea ice in the Arctic Ocean, columnId=1194652705852465724, journalTitle=Haiyang Xuebao, columnName=Article, runingTitle=null, highlight=null, articleAbstract=

When black carbon deposits on snow/ice surface, it can reduce the albedo and increase the absorption of shortwave radiation. The changes in black carbon and their impact on the sea ice melting process are worth investigating. Study of the influence of black carbon in the Arctic Ocean was conducted using the CICE sea ice model. The results indicates that under the impact of black carbon deposition from different sources, from 1980 to 2014, the simulated summer albedo of the Arctic Ocean decreased by 0.82% to 1.71%, ultimately causing a decrease in sea ice extent by 0.97%−1.93%. In the Barents Sea, Kara Sea, and Laptev Sea, the summer sea ice area reduction caused by black carbon is approximately 2–3 times greater than the overall reduction in the Arctic Ocean. The simulation results under different black carbon deposition all show that from 1980 to 1995, the impact of black carbon on albedo in the Arctic exhibited a decreasing trend. However, from 1996 to 2014, the black carbon effect shifted to an increasing trend. In low-latitude regions, due to the retreat of sea ice, the effect of black carbon showed a decreasing trend, while in high-latitude regions, due to the cumulative effect of black carbon in multi-year ice, the radiative impact of black carbon showed an enhancing effect.

, correspAuthors=Jie Su, authorNote=null, correspAuthorsNote=null, copyrightStatement=Haiyang Xuebao, 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=Yu Wang, Jie Su), CN=ArticleExt(id=1200468074706097038, articleId=1200468071296127773, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=黑碳对北冰洋积雪和海冰影响的模拟研究, columnId=1149698756456657529, journalTitle=海洋学报, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

当黑碳沉降到冰雪表面时，可使冰雪表面反照率降低，对短波辐射的吸收增加，黑碳的变化对海冰融化过程的影响值得研究。本文利用CICE 海冰模式进行数值模拟，并定量分析北冰洋冰雪中黑碳造成的影响。研究表明，在不同黑碳数据源的强迫下，1980−2014 年间，模拟结果给出的夏季北冰洋反照率平均下降为0.82%～1.71%，最终造成海冰面积下降了0.97%～1.93%，而在巴伦支海、喀拉海以及拉普捷夫海，夏季黑碳造成的海冰面积下降约为北冰洋整体的2−3 倍。不同黑碳沉降强迫下的模拟结果均显示，1980−1995 年，北冰洋区域黑碳对反照率的影响呈现减小趋势，但在1996−2014 年，黑碳影响转为增加趋势。在低纬度海区，由于海冰的消退，黑碳的辐射效应呈现减小趋势，而在高纬度海区，由于多年冰内黑碳的累积效应，黑碳的辐射影响呈现增强效应。

, correspAuthors=苏洁, authorNote=null, correspAuthorsNote=
*苏洁（1966—），女，山东省青岛市人，博士，教授, 主要从事海冰数值模拟和遥感反演方面的研究。E-mail：
, copyrightStatement=版权所有©《海洋学报》编辑部 2024
王煜，苏洁. 黑碳对北冰洋积雪和海冰影响的模拟研究[J]. 海洋学报，2024，46(8)：89–107Wang Yu，Su Jie. Simulation of the impact of black carbon on snow and sea ice in the Arctic Ocean[J]. Haiyang Xuebao，2024, 46(8)：89–107
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王煜（1998—），男，江苏省南通市人，主要从事极地海冰热力学方面的研究。E-mail：

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王煜（1998—），男，江苏省南通市人，主要从事极地海冰热力学方面的研究。E-mail：

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王煜（1998—），男，江苏省南通市人，主要从事极地海冰热力学方面的研究。E-mail：

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Laboratory for Ocean Dynamics and Climate，Qingdao Marine Science and Technology Center, Qingdao 266100, China), AuthorCompanyExt(id=1200860815638261809, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, companyId=1200860815625678895, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.青岛海洋科技中心 海洋动力过程与气候功能实验室，山东 青岛 266100)])], figs=[ArticleFig(id=1200860817454395527, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 1, caption=Distribution, seasonal and interannual variation of black carbon deposition data from CMIP6 and MERRA2 (north of 70°N, Arctic Ocean), figureFileSmall=imLTWvRg4VYzveZXRgukrw==, figureFileBig=Blj693i+i4cFvn1nct0sCQ==, tableContent=null), ArticleFig(id=1200860817588613262, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图1, caption=CMIP6及MERRA2黑碳沉降数据分布、季节变化以及年际变化（70°N以北，北冰洋）, figureFileSmall=imLTWvRg4VYzveZXRgukrw==, figureFileBig=Blj693i+i4cFvn1nct0sCQ==, tableContent=null), ArticleFig(id=1200860817693470867, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 2, caption=Comparison of observed black carbon deposition data at various Arctic sites with the black carbon deposition forcing data used in simulations, figureFileSmall=rgsJVuX1sN09pOhDewpqSw==, figureFileBig=otZKnEslTvE8OQRxV3aiQA==, tableContent=null), ArticleFig(id=1200860817844465814, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图2, caption=北极各站点黑碳沉降观测数据与模拟所用黑碳沉降强迫数据对比, figureFileSmall=rgsJVuX1sN09pOhDewpqSw==, figureFileBig=otZKnEslTvE8OQRxV3aiQA==, tableContent=null), ArticleFig(id=1200860817924157594, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 3, caption=Spatial distribution of Arctic albedo simulated by APP-X and CICE 1982−2014 climatology, as well as seasonal and interannual variations, figureFileSmall=OnYY82I1ZSYlj2vC8HBZpw==, figureFileBig=EHNmr4ekWOoGX5RQKr83VA==, tableContent=null), ArticleFig(id=1200860817991266461, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图3, caption=APP-X及CICE模拟的北极地区反照率的空间分布（1982−2014年气候态），以及季节及年际变化, figureFileSmall=OnYY82I1ZSYlj2vC8HBZpw==, figureFileBig=EHNmr4ekWOoGX5RQKr83VA==, tableContent=null), ArticleFig(id=1200860818104512672, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 4, caption=Spatial distribution of the difference in Arctic albedo during the melt season (May-August) simulated by CICE under the influence of black carbon compared to the no black carbon scenario (E0) (climatological average for 1982−2014), figureFileSmall=oWkoxjAnrp8+Y4mCYka+gw==, figureFileBig=qlIWxux+EOV8erhyIhFMZA==, tableContent=null), ArticleFig(id=1200860818226147495, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图4, caption=黑碳影响下，融化季（5−8月）CICE模拟的北极地区反照率相较于无黑碳情形时（E0）的空间差异分布（1982−2014年气候态平均）, figureFileSmall=oWkoxjAnrp8+Y4mCYka+gw==, figureFileBig=qlIWxux+EOV8erhyIhFMZA==, tableContent=null), ArticleFig(id=1200860818343588009, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 5, caption=Interannual variation of May-August average sea ice albedo in the Arctic (north of 70°N) simulated by APP-X and CICE, figureFileSmall=dHJaqQ6TZcDwliEwsvxVFA==, figureFileBig=cNEcxmfEZeoII1MZntGksA==, tableContent=null), ArticleFig(id=1200860818440057002, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图5, caption=APP-X及CICE模拟的5−8月北极（70°N以北）平均海冰反照率年际变化, figureFileSmall=dHJaqQ6TZcDwliEwsvxVFA==, figureFileBig=cNEcxmfEZeoII1MZntGksA==, tableContent=null), ArticleFig(id=1200860818536525996, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 6, caption=Spatial distribution of Arctic sea ice concentration from SSM/I and CICE (1980−2014 climatology), as well as seasonal and interannual variations, figureFileSmall=uwpKl9EWIN1AZ/2pxehK7g==, figureFileBig=w/bI1b2IyMLNT+VU5Mw+AA==, tableContent=null), ArticleFig(id=1200860818670743729, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图6, caption=SSM/I及CICE模拟的北极地区海冰密集度空间分布（1980−2014年气候态）,以及季节及年际变化, figureFileSmall=uwpKl9EWIN1AZ/2pxehK7g==, figureFileBig=w/bI1b2IyMLNT+VU5Mw+AA==, tableContent=null), ArticleFig(id=1200860818750435509, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 7, caption=Spatial distribution of the difference in Arctic sea ice concentration during the melt season (May-August) simulated by CICE under the influence of black carbon compared to the no black carbon scenario (climatological average for 1980−2014), figureFileSmall=GwXdhqhWbkfU1Y067WeRHQ==, figureFileBig=6/MZJpB0EIAO+gdXJuhS5A==, tableContent=null), ArticleFig(id=1200860819874508983, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图7, caption=黑碳影响下，融化季（5−8月）CICE模拟的北极地区海冰密集度相较于无黑碳情形时的空间差异分布（1980−2014年气候态平均）, figureFileSmall=GwXdhqhWbkfU1Y067WeRHQ==, figureFileBig=6/MZJpB0EIAO+gdXJuhS5A==, tableContent=null), ArticleFig(id=1200860820008726714, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 8, caption=Seasonal variation of Arctic sea ice extent simulated by CICE, figureFileSmall=CMcGWiCSxg0EsllK/oKuqw==, figureFileBig=A++psZZIPWMVCqo3GTXDPA==, tableContent=null), ArticleFig(id=1200860820117778622, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图8, caption=CICE模拟的北极地区海冰面积季节变化, figureFileSmall=CMcGWiCSxg0EsllK/oKuqw==, figureFileBig=A++psZZIPWMVCqo3GTXDPA==, tableContent=null), ArticleFig(id=1200860820243607744, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 9, caption=Interannual variation of Arctic sea ice extent simulated by CICE, figureFileSmall=ak22Woy3XiuwLHVz46uvRg==, figureFileBig=vpDexZkFFC9Qi/Mk9oQusw==, tableContent=null), ArticleFig(id=1200860820344271045, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图9, caption=CICE模拟的北极地区海冰面积年际变化, figureFileSmall=ak22Woy3XiuwLHVz46uvRg==, figureFileBig=vpDexZkFFC9Qi/Mk9oQusw==, tableContent=null), ArticleFig(id=1200860820415574212, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 10, caption=Trends in black carbon deposition flux (a−b), albedo differences (c−d) and sea ice extent differences (e−f) in the Arctic Ocean for the periods of 1980−1995 and 1996−2014, figureFileSmall=/0uShMSPT85zBHilXLSoEw==, figureFileBig=hQ+Bo1boEzYW5sImVL1fbQ==, tableContent=null), ArticleFig(id=1200860820533014728, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图10, caption=1980−1995和1996−2014年北冰洋黑碳沉降通量趋势（a−b）、反照率差异趋势（c−d）以及海冰面积差异趋势（e−f）, figureFileSmall=/0uShMSPT85zBHilXLSoEw==, figureFileBig=hQ+Bo1boEzYW5sImVL1fbQ==, tableContent=null), ArticleFig(id=1200860820629483723, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 11, caption=Interannual variation of Arctic black carbon deposition rate and snow and ice black carbon column content for experiments E1 and E2, figureFileSmall=rQ4EsYE70z4lXXVm3PBVsA==, figureFileBig=ep2rW+2z4TKKXfx5bFnO9g==, tableContent=null), ArticleFig(id=1200860820784672974, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图11, caption=E1和E2试验北极黑碳沉降速率及冰雪黑碳柱含量年际变化, figureFileSmall=rQ4EsYE70z4lXXVm3PBVsA==, figureFileBig=ep2rW+2z4TKKXfx5bFnO9g==, tableContent=null), ArticleFig(id=1200860820902113489, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 12, caption=Distribution of observation sites for Arctic black carbon deposition flux and atmospheric black carbon concentration, figureFileSmall=KQ2Jz44ORuiowCEkDEMagg==, figureFileBig=nOGwhmQyrnwA7bkXtSf+6w==, tableContent=null), ArticleFig(id=1200860821015359701, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图12, caption=北极黑碳沉降通量及黑碳大气浓度观测站点的位置分布, figureFileSmall=KQ2Jz44ORuiowCEkDEMagg==, figureFileBig=nOGwhmQyrnwA7bkXtSf+6w==, tableContent=null), ArticleFig(id=1200860821141188825, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Fig. 13, caption=Comparison of simulated and observed black carbon deposition and sea ice extent in the Arctic, figureFileSmall=4Ui/WH4cefkiGxfB02fW+A==, figureFileBig=E3cdyzYWq7oY6LTnOdrdVA==, tableContent=null), ArticleFig(id=1200860821287989469, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=图13, caption=北极地区黑碳沉降以及海冰面积的模拟与观测对比, figureFileSmall=4Ui/WH4cefkiGxfB02fW+A==, figureFileBig=E3cdyzYWq7oY6LTnOdrdVA==, tableContent=null), ArticleFig(id=1200860821371875552, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Table 1, caption=

Black carbon deposition data from the CMIP6 coupled model results and MERRA2 reanalysis data used in this study

, figureFileSmall=null, figureFileBig=null, tableContent=
数据研究单位平均分辨率/km参考文献
MERRA2NASA50Shen等[15]
CESM2-FV2NCAR250Danabasoglu等[16]
CESM2-WACCM-FV2NCAR250Danabasoglu等[16]
CESM2-WACCMNCAR100Danabasoglu等[16]
CESM2NCAR100Danabasoglu等[16]
CanESM5CCCma500Swart等[17]
CanESM5-1CCCma500Swart等[17]
MIROCMIROC6250Kataoka等[18]
MRI-ESM2-0MRI100Yukimoto等[19]
INM-CM4-8INM100Volodin[20]
INM-CM5-0INM100Volodin[20]
IPSL-CM5A2-INCAIPSL500Boucher等[21]
IPSL-CM6A-LR-INCAIPSL250Boucher等[21]
NorESM2-LMNCC250Seland等[22]
EC-Earth3-AerChemEC-Earth100Döscher等[23]
GFDL-ESM4NOAA-GFDL100Dunne等[24]
), ArticleFig(id=1200860821459955938, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=表1, caption=

本文所使用CMIP耦合模式结果及再分析黑碳沉降数据

, figureFileSmall=null, figureFileBig=null, tableContent=
数据研究单位平均分辨率/km参考文献
MERRA2NASA50Shen等[15]
CESM2-FV2NCAR250Danabasoglu等[16]
CESM2-WACCM-FV2NCAR250Danabasoglu等[16]
CESM2-WACCMNCAR100Danabasoglu等[16]
CESM2NCAR100Danabasoglu等[16]
CanESM5CCCma500Swart等[17]
CanESM5-1CCCma500Swart等[17]
MIROCMIROC6250Kataoka等[18]
MRI-ESM2-0MRI100Yukimoto等[19]
INM-CM4-8INM100Volodin[20]
INM-CM5-0INM100Volodin[20]
IPSL-CM5A2-INCAIPSL500Boucher等[21]
IPSL-CM6A-LR-INCAIPSL250Boucher等[21]
NorESM2-LMNCC250Seland等[22]
EC-Earth3-AerChemEC-Earth100Döscher等[23]
GFDL-ESM4NOAA-GFDL100Dunne等[24]
), ArticleFig(id=1200860821548036325, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Table 2, caption=

Simulation setup for the impact of black carbon on the Arctic sea ice and snow

, figureFileSmall=null, figureFileBig=null, tableContent=
试验
名称		有无黑
碳强迫		黑碳沉降强迫试验描述及模拟时间范围
E0无无无黑碳情形，1975−2020
E1有CMIP6 MEM (11.89)标准试验，1975−2014
E2有MERRA2 (17.09)再分析强迫，1975−2020
E3有EC-Earth3 (19.84)CMIP6最大黑碳沉降，1975−2014
E4有CESM2-WACCM-
FV2 (8.29)		CMIP6最小黑碳沉降，1975−2014
), ArticleFig(id=1200860821631922408, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=表2, caption=

北冰洋冰雪表面黑碳影响的模拟试验设置

, figureFileSmall=null, figureFileBig=null, tableContent=
试验
名称		有无黑
碳强迫		黑碳沉降强迫试验描述及模拟时间范围
E0无无无黑碳情形，1975−2020
E1有CMIP6 MEM (11.89)标准试验，1975−2014
E2有MERRA2 (17.09)再分析强迫，1975−2020
E3有EC-Earth3 (19.84)CMIP6最大黑碳沉降，1975−2014
E4有CESM2-WACCM-
FV2 (8.29)		CMIP6最小黑碳沉降，1975−2014
), ArticleFig(id=1200860821745168619, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Table 3, caption=

Comparison of observed and simulated snow black carbon content in the Arctic Ocean (values in parentheses represent the mean of observations and simulations)

, figureFileSmall=null, figureFileBig=null, tableContent=
序号纬度经度时间观测值/(ng·g−1)模拟值/(ng·g−1)参考文献
北极中央区（6.28, 9.32～28.32）
184.182°N47.507°E2008.56.713.1～35.3Doherty等[35]
284.7°N63.48°W2008.4−2008.565.8～12.3Doherty等[35]
386.142°N45.342°E2008.47.113.2～34.5Doherty等[35]
488.133°N90.717°W2007春季4.66.6～26.5Doherty等[35]
582.88°N154.45°W2006.4−2006.577.9～33NPEO (Field campaign)
波弗特海 (8.445, 12.3～28.3)
673°N145°W2007.48.89 ± 0.0815～32.3Doherty等[35]
775.34°N135.657°W2007.489.6～24.3APLIS/SEDNA campaign
加拿大北极 (7.56, 8.73～26.9)
867.878°N76.47°W2009春季5.23 ± 0.3316.4～40.9Doherty等[35]
969.28°N77.046°W2009春季5.82 ± 0.227.63～22.63Doherty等[35]
1070.067°N124.973°W2009春季11.57 ± 0.348.1～28.3Doherty等[35]
1170.067°N124.973°W2009春季6.65 ± 0.438.1～28.3Doherty等[35]
1275.497°N96.145°W2009春季9.51 ± 0.045.9～19.5Doherty等[35]
1376.555°N104.732°W2009春季8.78 ± 0.048.1～23.9Doherty等[35]
1476.633°N96.212°W2009春季5.37 ± 0.56.9～24.5Doherty等[35]
阿拉斯加 (9, 11.2～23.6)
1571.325°N156.433°W2009春季911.2～23.6Doherty等[35]
东西伯利亚 (15.012, 10.22～37.8)
1674.065°N128.872°E2008春季23.42 ± 0.8316.7～43.4Doherty等[35]
1769.33°N170.856°E2008春季14.55 ± 0.248.6～36.4Doherty等[35]
1869.119°N170.858°E2008春季11.59 ± 0.198.6～36.4Doherty等[35]
1969.022°N170.918°E2008春季13.26 ± 0.798.6～36.4Doherty等[35]
2069.195°N170.946°E2008春季12.24 ± 0.088.6～36.4Doherty等[35]
斯瓦尔巴群岛 (6.5, 2.85～15.95)
2178.91°N11.72°E2007.3−2007.43.23.1～18.1Forsstrom等[36]
2277.897°N18.302°E2007.39.82.6～13.8Forsstrom等[36]
), ArticleFig(id=1200860821887774958, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=表3, caption=

北冰洋区域积雪黑碳含量观测数据与模式模拟值对比（括号内为观测与模拟的平均值）

, figureFileSmall=null, figureFileBig=null, tableContent=
序号纬度经度时间观测值/(ng·g−1)模拟值/(ng·g−1)参考文献
北极中央区（6.28, 9.32～28.32）
184.182°N47.507°E2008.56.713.1～35.3Doherty等[35]
284.7°N63.48°W2008.4−2008.565.8～12.3Doherty等[35]
386.142°N45.342°E2008.47.113.2～34.5Doherty等[35]
488.133°N90.717°W2007春季4.66.6～26.5Doherty等[35]
582.88°N154.45°W2006.4−2006.577.9～33NPEO (Field campaign)
波弗特海 (8.445, 12.3～28.3)
673°N145°W2007.48.89 ± 0.0815～32.3Doherty等[35]
775.34°N135.657°W2007.489.6～24.3APLIS/SEDNA campaign
加拿大北极 (7.56, 8.73～26.9)
867.878°N76.47°W2009春季5.23 ± 0.3316.4～40.9Doherty等[35]
969.28°N77.046°W2009春季5.82 ± 0.227.63～22.63Doherty等[35]
1070.067°N124.973°W2009春季11.57 ± 0.348.1～28.3Doherty等[35]
1170.067°N124.973°W2009春季6.65 ± 0.438.1～28.3Doherty等[35]
1275.497°N96.145°W2009春季9.51 ± 0.045.9～19.5Doherty等[35]
1376.555°N104.732°W2009春季8.78 ± 0.048.1～23.9Doherty等[35]
1476.633°N96.212°W2009春季5.37 ± 0.56.9～24.5Doherty等[35]
阿拉斯加 (9, 11.2～23.6)
1571.325°N156.433°W2009春季911.2～23.6Doherty等[35]
东西伯利亚 (15.012, 10.22～37.8)
1674.065°N128.872°E2008春季23.42 ± 0.8316.7～43.4Doherty等[35]
1769.33°N170.856°E2008春季14.55 ± 0.248.6～36.4Doherty等[35]
1869.119°N170.858°E2008春季11.59 ± 0.198.6～36.4Doherty等[35]
1969.022°N170.918°E2008春季13.26 ± 0.798.6～36.4Doherty等[35]
2069.195°N170.946°E2008春季12.24 ± 0.088.6～36.4Doherty等[35]
斯瓦尔巴群岛 (6.5, 2.85～15.95)
2178.91°N11.72°E2007.3−2007.43.23.1～18.1Forsstrom等[36]
2277.897°N18.302°E2007.39.82.6～13.8Forsstrom等[36]
), ArticleFig(id=1200860822026186993, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=EN, label=Table 4, caption=

Reduction in sea ice extent for each experiment in 2014 compared to 1980

, figureFileSmall=null, figureFileBig=null, tableContent=
试验名称黑碳造成的海冰面积减小/km2总海冰面积减小/ km2黑碳贡献占比/ %
E10.038 × 1060.700 × 1065.43
E20.047 × 1060.705 × 1066.67
E30.058 × 1060.688 × 1068.43
E40.029 × 1060.704 × 1064.12
融化季（5−8月）
E10.058× 1060.609× 1069.52
E20.072× 1060.610× 10611.80
E30.087× 1060.586× 10614.85
E40.045× 1060.617× 1067.29
), ArticleFig(id=1200860822256873716, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200468071296127773, language=CN, label=表4, caption=

2014年相较于1980年各试验海冰面积减小值

, figureFileSmall=null, figureFileBig=null, tableContent=
试验名称黑碳造成的海冰面积减小/km2总海冰面积减小/ km2黑碳贡献占比/ %
E10.038 × 1060.700 × 1065.43
E20.047 × 1060.705 × 1066.67
E30.058 × 1060.688 × 1068.43
E40.029 × 1060.704 × 1064.12
融化季（5−8月）
E10.058× 1060.609× 1069.52
E20.072× 1060.610× 10611.80
E30.087× 1060.586× 10614.85
E40.045× 1060.617× 1067.29
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黑碳对北冰洋积雪和海冰影响的模拟研究
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王煜 1 , 苏洁 1, 2, 3, 4, *
海洋学报 | 论文 2024,46(8): 89-107
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海洋学报 | 论文 2024, 46(8): 89-107
黑碳对北冰洋积雪和海冰影响的模拟研究
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王煜1 , 苏洁1, 2, 3, 4, *
作者信息
  • 1.中国海洋大学 海洋与大气学院，山东 青岛，266100
  • 2.中国海洋大学 深海圈层与地球系统前沿科学中心/物理海洋教育部重点实验室，山东 青岛 266100
  • 3.中国高校极地联合研究中心，北京 100875
  • 4.青岛海洋科技中心 海洋动力过程与气候功能实验室，山东 青岛 266100

    • 王煜（1998—），男，江苏省南通市人，主要从事极地海冰热力学方面的研究。E-mail：

通讯作者:

*苏洁（1966—），女，山东省青岛市人，博士，教授, 主要从事海冰数值模拟和遥感反演方面的研究。E-mail：
Simulation of the impact of black carbon on snow and sea ice in the Arctic Ocean
Yu Wang1 , Jie Su1, 2, 3, 4, *
Affiliations
  • 1. College of Oceanography and Atmosphere, Ocean University of China, Qingdao 266100, China
  • 2. Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, MOE. Ocean University of China, Qingdao 266100, China
  • 3. Joint Center for Polar Research of Chinese Universities, Beijing 100875, China
  • 4. Laboratory for Ocean Dynamics and Climate，Qingdao Marine Science and Technology Center, Qingdao 266100, China
出版时间: 2024-08-31 doi: 10.12284/hyxb2024077
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摘要
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当黑碳沉降到冰雪表面时，可使冰雪表面反照率降低，对短波辐射的吸收增加，黑碳的变化对海冰融化过程的影响值得研究。本文利用CICE 海冰模式进行数值模拟，并定量分析北冰洋冰雪中黑碳造成的影响。研究表明，在不同黑碳数据源的强迫下，1980−2014 年间，模拟结果给出的夏季北冰洋反照率平均下降为0.82%～1.71%，最终造成海冰面积下降了0.97%～1.93%，而在巴伦支海、喀拉海以及拉普捷夫海，夏季黑碳造成的海冰面积下降约为北冰洋整体的2−3 倍。不同黑碳沉降强迫下的模拟结果均显示，1980−1995 年，北冰洋区域黑碳对反照率的影响呈现减小趋势，但在1996−2014 年，黑碳影响转为增加趋势。在低纬度海区，由于海冰的消退，黑碳的辐射效应呈现减小趋势，而在高纬度海区，由于多年冰内黑碳的累积效应，黑碳的辐射影响呈现增强效应。

黑碳  /  北冰洋  /  CICE海冰模式  /  反照率  /  海冰面积

When black carbon deposits on snow/ice surface, it can reduce the albedo and increase the absorption of shortwave radiation. The changes in black carbon and their impact on the sea ice melting process are worth investigating. Study of the influence of black carbon in the Arctic Ocean was conducted using the CICE sea ice model. The results indicates that under the impact of black carbon deposition from different sources, from 1980 to 2014, the simulated summer albedo of the Arctic Ocean decreased by 0.82% to 1.71%, ultimately causing a decrease in sea ice extent by 0.97%−1.93%. In the Barents Sea, Kara Sea, and Laptev Sea, the summer sea ice area reduction caused by black carbon is approximately 2–3 times greater than the overall reduction in the Arctic Ocean. The simulation results under different black carbon deposition all show that from 1980 to 1995, the impact of black carbon on albedo in the Arctic exhibited a decreasing trend. However, from 1996 to 2014, the black carbon effect shifted to an increasing trend. In low-latitude regions, due to the retreat of sea ice, the effect of black carbon showed a decreasing trend, while in high-latitude regions, due to the cumulative effect of black carbon in multi-year ice, the radiative impact of black carbon showed an enhancing effect.

black carbon  /  Arctic Ocean  /  CICE model  /  albedo  /  sea ice area
王煜, 苏洁. 黑碳对北冰洋积雪和海冰影响的模拟研究. 海洋学报, 2024 , 46 (8) : 89 -107 . DOI: 10.12284/hyxb2024077
Yu Wang, Jie Su. Simulation of the impact of black carbon on snow and sea ice in the Arctic Ocean[J]. Haiyang Xuebao, 2024 , 46 (8) : 89 -107 . DOI: 10.12284/hyxb2024077
正文
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1 引言
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黑碳（black carbon，简称BC）是生物质和化石燃料不完全燃烧的产物，是大气颗粒物中吸光性最强的物质[1]。随着北极变暖，北极地区野火频率的增加，相应黑碳的排放也逐渐增加[2]，黑碳对于北极环境的影响也越来越为人们所重视[3]。以往研究认为，黑碳对海冰融化的影响主要是通过降低积雪和海冰的反照率来实现的[4–6]，即当黑碳沉降到冰雪表面时，可以使冰雪表面变“黑”，从而降低冰雪表面的反照率，最终加强冰雪对短波辐射的吸收，促进海冰融化，海冰面积减小。
现场观测数据是研究北极区域黑碳长期变化趋势的重要资料。Sharma等[7]对北极Alert站（Nunavut, Canada）以及Barrow站（Alaska, USA）大气黑碳浓度的长期观测数据进行分析，指出1989−2003年间，两个站点的大气黑碳浓度分别下降了54%和27%；而在2000年后，两个站点的黑碳浓度都存在一个上升趋势。进一步的研究中，Sharma等[8]对更长时段（1990−2009）的站点观测数据进行分析，并通过与NIES（National Institute for Environmental Studies）大气传输模式模拟的大气黑碳浓度进行对比，在确认1990−2000年下降趋势的同时，指出北极大气黑碳浓度的大幅度下降发生在20世纪90年代初，下降幅度可达40%。Ruppel等[9]在俄罗斯北极地区进行黑碳采样，并利用同位素分析法直接推算出1800−2014年间观测站点的年平均黑碳沉降通量，结果显示4个观测站点中除一个站点在20世纪90年代初的黑碳沉降通量下降，黑碳沉降通量在1980年之后总体呈现上升趋势，这与前人研究存在明显矛盾，且生物质源的黑碳比例明显增加，这可能与俄罗斯北极地区野火频率的增加有关。对观测数据以及再分析数据的综合评估显示，2003−2019年间，北极夏季生物质源的气溶胶光学厚度存在一个显著的上升趋势，主要体现在北美和欧亚大陆北部，侧面印证了这些区域黑碳的上升趋势[10]。尽管存在以上研究，北冰洋区域的黑碳观测数据仍十分稀少，长期观测数据局限于少量陆上站点，且绝大多数观测数据为大气黑碳浓度数据，大气黑碳沉降或冰雪黑碳含量数据极少，这限制了对冰雪黑碳含量及其影响的大面积、长期定量研究的开展。
由于黑碳实测数据时空覆盖率有限且种类不足，因此，人们使用数值模拟的方法开展对冰雪中黑碳影响的模拟研究。通过模拟研究，前人对北极地区黑碳影响的时空特征有了更加准确的认识。Dou等[11]将GISS-E2-PUCCINI模式模拟的积雪黑碳含量与实测数据对比，表明目前模式模拟的北极黑碳空间分布在北极中央区能够较好地符合观测结果，但在俄罗斯北极地区，模式模拟值要远小于观测值，该研究进一步将GISS-E2-PUCCINI模拟得到的黑碳沉降数据与CICE4海冰模式相结合，并进行了分区讨论，结果表明俄罗斯北极地区黑碳造成的反照率降低约为1.25%，远高于其他区域（0.39%～0.64%）。其他研究也表明北极冰雪黑碳分布类似的特征[12]，且特别强调陆地黑碳沉降要大于北冰洋区域，陆地上黑碳造成的辐射强迫同样要大于后者，同时沉降在陆地上的黑碳也可以通过使得大气变暖，从而间接影响海冰的融化过程。而Li和Flanner[13]则重点关注格陵兰岛黑碳对积雪融化的影响，黑碳对积雪融化的影响随黑碳沉降的增加而增加，由于积雪融化，即使考虑黑碳在表面的“储存”，第一年沉降的黑碳也很难对第二年的积雪产生影响，即一年时间段的模拟就足以捕捉该年积雪中黑碳的气候效应。最新的研究对CMIP6历史试验中全球黑碳模拟结果进行了分区讨论[14]，北极地区黑碳造成的反照率降低约为0.5%。尽管CMIP6历史试验的时间跨度为1850−2014年，但该研究却是将北极作为一个整体，我们对北极内部的时空差异仍然认识不足。
目前，北冰洋区域的黑碳正经历一个快速变化过程，黑碳对北冰洋海冰的影响存在较大不确定性。尽管前人对北冰洋区域黑碳的气候效应进行了较多研究，但不同研究侧重点不同，模拟时的试验设置（海洋与大气强迫、时间段等）也存在很大差异，对北冰洋区域黑碳效应的长期时空变化特征缺乏全面的量化评估，对不同研究中黑碳效应的共性特征缺少认识。本文利用CICE6海冰模式，以ERA5大气再分析数据作为大气强迫，利用耦合模式比较计划（CMIP6）中黑碳沉降的历史试验结果以及MERRA2黑碳沉降数据，对整个北冰洋区域黑碳造成的影响进行模拟研究，研究黑碳对北极海冰融化的影响，包括其对反照率、海冰面积的降低作用，分析在不同的黑碳沉降条件下，模式模拟的北冰洋区域黑碳对冰雪融化的影响的共性时空特征。
2 数据
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2.1 黑碳沉降数据
本文所用黑碳沉降数据主要来自CMIP6项目，包括15个气候模式的历史模拟结果（表1）。CMIP6历史试验数据的时间长度为1850−2014年。本文选取1980−2014年全球逐月黑碳沉降数据作为CICE海冰模式的黑碳沉降外强迫。
此外，本文还使用了美国航空航天局（NASA）提供的MERRA2黑碳沉降再分析数据，该数据的时间范围为1980年至今，空间分辨率为0.625° × 0.5°。本文将其与CMIP6的黑碳沉降数据结合，来大致评估北极黑碳的沉降情况。
2.2 ERA5再分析数据
本文模拟采用的大气强迫数据来自欧洲数值预报中心（European Centre for Medium-Range Weather Forecasts，简称“ECMWF”）提供的ERA5全球大气再分析数据集[25]，时间范围为1951年至今，空间分辨率为0.25° × 0.25°。本文使用了包括2 m温度、2 m露点温度、气压、风速、降雪率以及云量数据。本文所用数据时间长度为1980年至2014年。对于长、短波辐射，由CICE根据ERA5提供的云量、湿度等再分析数据计算得出，空间分辨率插值至CICE模式1°分辨率极地网格。
2.3 其他数据
为了对模式模拟结果进行验证，本文采用了以下卫星遥感数据。
逐日APP-X蓝空反照率数据集[26]，隶属于美国国家海洋和大气管理局(NOAA)发展的气候数据记录（Climate Data Record, CDR）项目，时间为1982年至今，空间分辨率为25 km。逐日SSM/I海冰密集度产品[27]，来自美国国家冰雪数据中心(NSIDC)，时间为1979年至今，空间分辨率为25 km。
以上数据皆选取与模式模拟的公共时段，并插值至CICE模式1°分辨率极地网格来与模式模拟结果进行比对。
3 黑碳参数化及试验设置
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本文所用模式为美国Los Alamos国家实验室开发的CICE海冰模式[28]，能够模拟包括海冰反照率、海冰密集度以及冰雪厚度等参数，是目前大型气候模式中最常采用的海冰模块。
3.1 黑碳辐射影响参数化
在CICE模式中，黑碳的辐射影响过程体现在Delta-Eddington短波辐射方案[29]中。对于冰雪中黑碳的影响，通常将黑碳作为雪粒固有光学特性的一个扰动，根据黑碳的固有光学特性来计算其对反照率的影响，进而影响辐射的吸收，固有光学特性由消光系数、单次散射反照率，以及不对称参数3部分组成。将不同种类黑碳（亲/疏性黑碳）的固有光学特性代入到Delta-Eddington方案中进行计算，从而计算出冰雪反照率，其具体计算过程见Delta-Eddington方案[29–30]，在此不再详述。
3.2 CICE北极冰雪中黑碳的影响试验设置
本文利用CICE海冰模式对北极地区冰雪中的黑碳对表面反照率及海冰面积的影响进行模拟研究。以ERA5再分析数据为大气强迫，利用MERRA2（由于该数据更长的时段，为了在更长时间范围内研究黑碳对北极海冰的影响，模拟时段为1980−2020年）以及CMIP6中的最大、最小及平均（EC-Earth3、CESM2-WACCM-FV2和CMIP-MEM，模拟时段为1980−2014年）4套黑碳沉降数据作为外强迫来进行模拟，研究冰雪中的黑碳分布及其对冰雪参量的影响。模拟从1975年开始，前5年为预热，从1980年开始对模拟结果进行分析。模拟的空间分辨率为1°，时间分辨率为每小时。试验设置如表2所示。
在CMIP6历史试验数据集以及MERRA2再分析数据中，黑碳沉降是按照干/湿沉降来进行划分，而非CICE模式中的亲/疏水性。Flanner等[31]在其研究中指出亲水性黑碳由湿沉降提供，而疏水性黑碳则主要由干沉降提供。因此，本文将干沉降黑碳作为模式疏水性黑碳，而将湿沉降黑碳作为亲水性黑碳输入，由此作为CICE海冰模式的黑碳强迫数据。
4 结果分析
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4.1 北冰洋黑碳沉降时空变化特征
冰雪中的黑碳主要来自大气沉降过程，本文首先对模拟采用的黑碳沉降强迫数据的分布变化特征进行分析。沉降数据主要来自MERRA2及CMIP6的模拟结果。如图1a所示，1980−2014年间，CMIP6各个模式模拟的黑碳沉降数据在具体量级上存在较大差异，最大的总沉降模拟结果（EC-Earth3）能达到最小模拟结果（CESM2-WACCM-FV2）的2−3倍，而MERRA2黑碳沉降再分析数据尽管在中位数上小于EC-Earth3模式，但其最大沉降数值要高于所有CMIP6的结果，达到了2.78 × 10−13 kg/(m2·s)，具有更显著的年际变化特征。
季节变化上（图1b）, CMIP6模式模拟的黑碳总沉降具有双峰特征，而MERRA2数据为显著单峰特征，夏季的黑碳总沉降远高于其他月份，也高于CMIP的结果，约为其冬季总沉降的3倍。而在年际变化上（图1c），CMIP6数据与MERRA2数据表现出截然相反的趋势，后者在1980−2014年间的增强趋势显著，而CMIP6数据则总体呈现减少趋势。具体看，CMIP6各数据在1980−1995年呈现下降趋势，在1996−2014年则呈现上升趋势，而MERRA2再分析数据在20世纪90年代初期存在一个明显的下降，而后呈现上升趋势。总体而言，不同源的黑碳沉降数据在年际变化趋势上都存在一个先下降再上升的特征。
空间分布上（图1d），几套数据均呈现出巴伦支海至喀拉海沿岸是黑碳沉降的高值区，黑碳沉降总体上呈现从低纬向高纬减少的特征，与Goldenson等[12]的黑碳沉降特征相似。量值上，MERRA2数据高于CMIP-MEM，MEAAR2的年平均黑碳沉降约为CMIP6-MEM的1.4倍，部分海区甚至能达到2倍以上。
本文将1980−2014年各模式及再分析数据的黑碳沉降情况与基于同位素方法测定的北极黑碳年平均沉降观测数据进行对比[9]，结果表明在现场观测的4个湖区（俄罗斯北极西部），模式及再分析数据皆远远低估了黑碳沉降量，观测数据比模式模拟及再分析数据高了约1−2个数量级。Ruppel等[9]同样利用模式模拟的黑碳沉降与观测值进行对比，其模拟结果的量级与CMIP6及MERRA2相近，且趋势上在1980−2000年间呈现下降趋势，与观测数据的总体上升趋势存在较大差别。对于模拟与观测的差别，一方面是由于观测值并不绝对来自大气沉降，还有来自其他区域的涌入量[32]（例如河流涌入等），另一方面则是目前气候模式对于黑碳沉降的普遍低估，这种情况在北极地区尤甚。
基于北极站点的大气黑碳浓度观测数据，本文利用Jurado等[33–34]提供的参数化方案，结合ERA5降水数据，将大气黑碳浓度观测数据转化为黑碳沉降数据，由此对选取的4套黑碳沉降强迫数据进行评估。如图2所示，尽管本文采用的黑碳沉降强迫场相较于观测存在明显差异，但是CMIP6及MERRA2黑碳沉降能够提供更长时间序列，在各个站点黑碳沉降通量的相对大小上，以及某些站点的年际变化趋势上，能够在一定程度上反映规律。
4.2 北冰洋冰雪黑碳含量评估
黑碳沉降到冰雪表面后，由CICE海冰模式计算冰雪黑碳含量，进而计算冰雪中黑碳的辐射影响，本节对CICE模拟的黑碳含量结果进行评估。本文选取了北冰洋区域积雪黑碳含量观测数据的部分观测点[11]，与CICE模式的最近网格点的模拟值进行对比，评估CICE海冰模式积雪黑碳含量的模拟结果。如表3所示，总体而言，CICE海冰模式模拟的积雪黑碳含量在量级上与观测接近。不同黑碳沉降强迫下模拟得到的积雪黑碳含量差异很大，最高值甚至能达到最低值的4倍。
俄罗斯北极区域的黑碳含量要大大高于其他区域，但加拿大和阿拉斯加北极区域的积雪黑碳含量要略低于北极中央区，本文模式模拟的结果与基于观测数据得到的北极积雪黑碳含量的空间分布特征类似[35]，俄罗斯北极>加拿大和阿拉斯加北极>北冰洋。由于北冰洋区域冰雪黑碳含量观测数据稀少，采样条件、测量方法等的差异，使得观测数据本身存在很大不确定性[11]，尤其是黑碳含量差别并不显著的海区（如北极中央区和加拿大北极区域），观测得到的黑碳空间分布特征并不绝对可靠。此外，由于北极地区黑碳排放以及运输过程的显著空间差异性，使得目前模式模拟的黑碳沉降很难准确反映黑碳沉降的季节和年际变化，通常会造成积雪中黑碳含量低估2−3倍[36]。本文模拟得到的春季冰雪黑碳含量在量值上与观测数据接近，且很好地再现了俄罗斯北极地区高冰雪黑碳含量的特征，一定程度上验证了模式对冰雪黑碳含量的模拟能力。
4.3 黑碳对北极冰雪模拟的影响
正如图1所示，不同源的北冰洋黑碳沉降通量在时空分布以及量级上的差异明显，本文利用CICE海冰模式，结合不同数据源的沉降通量研究黑碳对北冰洋的表面反照率以及海冰面积的影响。由于黑碳的影响主要集中在融化季[30，35]，因此本节讨论也集中在5−8月份。
首先是未考虑黑碳效应时，CICE海冰模式对北冰洋反照率的模拟。如图3所示，未考虑黑碳影响时，CICE模拟的北极冰雪反照率与APP-X反照率遥感结果相比总体高估，融化季反照率相较于APP-X数据高估约8.44%。在5/6月二者的偏差存在空间不一致性，主要特征体现为大西洋扇区的高估和太平洋扇区的低估，高估区主要集中在西格陵兰海至拉普捷夫海沿岸，该特征与以往的研究结果相似[39]。尽管模拟的反照率要高于遥感数据，但二者在1982−2014年间的年际变化相关性为0.77；趋势上都表现出明显的下降趋势，与观测相比，模拟反照率的下降趋势相对较弱（图3e）。
考虑黑碳后，CICE海冰模式模拟的反照率有所降低，融化季5−8月黑碳的影响递增，反照率相对变化的峰值出现在8月，在整个融化季，与E0试验结果相比，E1−E4的模拟结果反照率分别下降了2.13%，2.54%，3.48%以及1.67%，黑碳对反照率的影响随沉降强迫的增加而增大，其中6−8月反照率差异更明显（图3f）。具体到空间分布上(图4)，5−8月黑碳对全北冰洋区域的反照率的影响逐渐增强，值得注意的是8月，相较于前几个月，该月黑碳造成的反照率差异有更明显的空间分布特征，尤其是E3试验更为显著（图4c4），黑碳造成的反照率差异主要集中在大西洋扇区，尤其是巴伦支海至拉普捷夫海，该区域反照率下降的绝对值能达到5%以上，该区域同时也是模拟相较于遥感观测的高估区（图3），考虑黑碳后，该区域的模拟反照相较于观测的误差降低，模拟结果得到改善。
图5为北冰洋融化季（5−8月）平均反照率的年际变化。在年际变化上（图5a1−d1），虽然各个月份的模拟反照率均明显大于观测，但模拟值与观测值的年际变化基本一致，相关性均高于0.6。对于黑碳造成的反照率差异（图5a2−d2），CMIP6各个黑碳沉降强迫下模拟的反照率差异的年际变化总体均呈现下降特征，而MERRA2沉降条件下，黑碳的影响则逐渐增强，其中8月黑碳影响的增强趋势最为明显，达到了0.586%dec−1。黑碳有无情况下的反照率差异的年际变化与黑碳沉降的年际变化情况基本一致，但同时也受到海冰密集度变化，黑碳在多年冰上的累积等的影响，例如8月，E3试验造成的反照率差异在2000年后存在一个显著上升趋势，但该上升趋势在E3试验8月的黑碳沉降中并没有明显的体现，关于黑碳效应的年际变化趋势将在4.4节进行进一步的讨论。
黑碳造成北极反照率降低，并促进海冰–反照率反馈，海冰密集度和面积的变化便是反照率反馈的体现之一。图6为CICE模拟的海冰密集度和面积与SSM/I遥感数据的对比，CICE模拟的海冰密集度在北极中央区与观测基本符合，但除了8月的大西洋扇区外在其他海区总体明显高估，模式的高估部分原因是由于CICE海冰模式本身参数化的影响[35]。E0情况下，CICE对海冰面积的高估在融化季明显，高估了约20%（图6d）。而考虑黑碳效应后（图6f），黑碳对海冰面积的影响在1−4月几乎可以忽略不计，进入融化季后，黑碳造成的海冰面积减小的峰值出现在8月，该月E1−E4海冰面积在减小1.5 × 105～3.1 × 105 km2之间，融化季结束后的海冰增长和冻结期，各试验中海冰面积迅速恢复至接近无黑碳情形，黑碳的影响并未延续至第二年。而在年际变化上（图6e），1980−2014年间，海冰面积的模拟与遥感数据的相关性达到了0.95，相较于遥感结果，CICE模拟的海冰面积减小趋势较弱，减小速度约为0.299 × 106 km2/dec，约为遥感结果的一半。在考虑黑碳效应后（图6g），黑碳对海冰面积的影响仅为模式与遥感差别的4.5%，对模拟海冰面积的年际变化趋势几乎无影响。相较于未考虑黑碳情形（E0），CMIP6黑碳强迫下的各试验北极海冰面积差异均随时间呈现减小趋势，MERRA2黑碳沉降条件下（E2）的北极整体海冰面积差异呈现增加趋势，黑碳影响逐渐增加。
考虑黑碳效应后，密集度分布与未考虑黑碳情形下（E0）的空间差异见图7，与反照率类似的是，5月海冰密集度的降低并不显著，6−7月黑碳的影响逐渐增加，到了8月，黑碳在边缘海区造成的密集度下降非常明显，密集度下降超过了5%。与未考虑黑碳情形（E0）相比，黑碳的效应主要集中在巴伦支海、喀拉海以及拉普捷夫海，该区域同时也是CICE模拟海冰密集度相较于遥感的高估区，在引入黑碳效应后，可以改善该区域的高估现象，但同时需要指出的是，8月该区域黑碳效应的引入使得原本模拟就低估的海冰密集度更加低估，相较于观测的误差反而增大。而在广大的北极中央区，黑碳对海冰密集度的影响几乎为0，甚至密集度略有上升，后者可能是由于该区域的动力过程造成的。
北冰洋区域黑碳对北极海冰面积下降的贡献如表4所示，相较于1980年，各试验2014年黑碳造成的海冰面积下降仅占总海冰面积下降的4.12%～8.43%，平均占比为6.15%，但在融化季，各试验黑碳造成的海冰面积减小献大幅增加，约为全年平均值的1.75倍。
尽管整体上黑碳对北极海冰面积的模拟影响很小，但大西洋扇区的几个边缘海区黑碳对海冰面积的影响约为北极整体的2−3倍，不容忽视。例如黑碳影响最为显著的巴伦支海、喀拉海以及拉普捷夫海区域，在季节变化上（图8a1−c1），3个海区的模拟海冰面积相较于SSM/I遥感数据均高估，不同在于巴伦支海的高估主要出现在冬季，而另外两个海区的高估主要出现在融化季。对于黑碳造成的海冰面积差异（图8a2−c2），E1情形下，夏季（6−8月）黑碳造成的海冰面积下降3个海区分别达到了3.39%，4.22%以及1.94%，北极整体平均下降约为1.18%，而在极端的E3情形下，海冰面积下降3个海区分别达到了5.34%，6.23%以及3.26%，北极平均值约为1.93%。
年际变化上（图9a1−c1），在这3个海区，黑碳对海冰面积的影响相对于模拟海冰面积与遥感的差别而言仍然是较小，黑碳对海冰的年际变化趋势几乎无影响。具体到与无黑碳时海冰面积（E0）的差异而言，各试验模拟的海冰面积的差异都呈现减小趋势（图9a2−c2），E2试验也是如此，E2试验巴伦支海面积差异的减小趋势可以归因为MERRA2沉降数据在该海区的减小趋势，但在喀拉海以及拉普捷夫海，黑碳沉降仍为增加趋势，但其对海冰面积的影响却呈现减小趋势，主要原因是这两个海区海冰的消退现象，使得黑碳对海冰面积的绝对影响减弱，海冰面积差异呈现减小趋势。
本文模拟得到的北冰洋区域黑碳的影响在量级上与以往研究接近。以反照率为例，本文各试验在春季造成北冰洋反照率下降了0.16%～0.42%，而在夏季下降了0.82%～1.71%，基于北冰洋上冰雪黑碳的零星观测数据估算得出黑碳在春季造成北冰洋反照率下降了约0.2%，而在夏季下降了约1.5%，黑碳浓度高值区甚至能达到3.8%[35]。
4.4 北冰洋区域黑碳影响的趋势
黑碳辐射强迫的变化趋势大致主要受黑碳沉降本身的变化趋势的影响，但也不尽然。由于各试验黑碳沉降场以及辐射强迫场趋势转变时间节点不一致，大致选择20世纪90年代中期分两段进行分析。如图10a−b所示， 对于CMIP6北冰洋区域的黑碳沉降通量，在1980−1995年期间，各试验基本都呈现下降趋势，1996−2014年，各试验北冰洋的黑碳沉降出现明显的上升；而MERRA2（E2）体现了空间不一致，前一个时段在北美北极地区存在一个上升区。后一个时段在北冰洋北部存在一个下降趋势，但未通过95%显著性检验，这种空间不一致性主要是时间节点的选择造成的。
黑碳沉降趋势的转变直接影响反照率差异的年际变化趋势，总体而言，1980−1995年间北冰洋区域黑碳的影响呈现减小趋势，以反照率为例，约为−0.07%dec−1至−0.2%dec−1，而在1996−2014年间呈现增加趋势，约为0.01%dec−1至0.07%dec−1。但反应在空间上却有明显的不一致性（图10c−d）。1980−1995年，在北极中央区，尽管CMIP6及MERRA2的黑碳沉降通量呈现下降趋势，但各试验计算的反照率差异在北极中央区存在一个明显增加的趋势，而在低纬度海区，如大西洋扇区，反照率差异趋势与黑碳沉降的趋势一致；而在1996−2014年，北极中央区的黑碳沉降通量总体呈现上升趋势，反照率差异也呈现上升趋势，且相比于1980−1995年的增强趋势要更强，但在低纬度海区，尽管黑碳沉降总体也呈现上升趋势，但相应反照率差异却呈现下降趋势。对于海冰密集度差异的年际变化趋势（图10e−f），其与黑碳沉降也存在类似的空间不一致性。
上述空间不一致性反应了北极海冰变化对黑碳效应的影响。对于高纬度海区，在1980−1995年间，尽管高纬度区域的黑碳沉降总体呈现下降趋势（图11），但由于高纬度海区以多年冰为主，黑碳更易存贮在海冰当中，使得高纬度海区黑碳的影响呈现一个上升趋势；而对于低纬度海区，由于海冰密集度较低以及海冰的消退，海域面积增加，黑碳沉降到海冰上的部分减少，造成在1996−2014年间，即使低纬度黑碳沉降呈现增加趋势，但黑碳造成的影响仍然减少。
5 讨论
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本文有关冰雪中的黑碳及对海冰影响模拟结果的主要不确定性来自于黑碳沉降外强迫。简便起见，只是采用了不同模式（源）的黑碳沉降数据进行模拟。对于北极地区黑碳的沉降通量，不同数据源（MERRA2及CMIP6各个气候模式模拟）在量级上存在较大差别，这主要取决于不同大气模式的黑碳排放清单、干湿沉降过程以及远程输运过程等因素。观测数据显示，在俄罗斯北极地区（陆地区域），实际黑碳沉降比模式模拟均值要高1−2个数量级[9]，在北欧的几个湖泊点，观测的黑碳沉降则比模式模拟高了约10倍[40]，北极地区模式模拟的黑碳沉降总体上是低估的（图12a−c），使得最终黑碳对以上海区冰雪融化的影响也被低估，对海冰的季节变化刻画不够准确也有影响。
为研究黑碳沉降量级上的不确定性会给模拟结果带来多大影响，评估本文结果的局限性，本文结合前文所讲的在俄罗斯北极地区、北欧地区以及斯尔瓦巴群岛的观测数据，在E1试验的基础上，对北极不同分区的黑碳沉降进行加倍，进行试验E5，加倍的倍数根据分区附近的观测数据大致确定（图12d）。
如图13所示，在E5强迫下，模拟的北极海冰面积下降了约1.14%（5−8月下降了2.41%），在1980−2014年间的模拟与SSM/I观测数据更加接近，而在巴伦支海、喀拉海以及拉普捷夫海，全年海冰面积分别下降3.2%，7.5%以及5.4%，5−8月则分别下降了9.5%，15.6%以及10.4%，在黑碳加倍海区，尤其是喀拉海与拉普捷夫海，5−8月模拟的海冰面积与观测的绝对误差分别下降了70%与40%。除喀拉海外，E5与E1模拟的海冰面积年际变化非常一致，前者略小于后者，与实测更为接近。而喀拉海在2000年之前有8%的减小，与实际观测的误差明显减小，而在这之后，无论E1还是E5，模拟的海冰面积与观测的差距都逐渐增大。值得注意的是，在黑碳加倍的几个海区，增强的黑碳效应主要改善了8、9两个月的海冰面积的模拟，但对于5−7月，黑碳加倍的效果并不显著，这可能是融化季前期黑碳主要用来促进积雪融化导致的。
除黑碳沉降的量级外，沉降本身的趋势也存在很大不确定性，不同源的黑碳沉降甚至出现相反的年际变化趋势（图1），但这些黑碳沉降都存在一个先减少再增加的年际变化趋势。对于1995年前后黑碳沉降趋势的转变，可能与北极地区黑碳排放变化有关。对北极地区大气黑碳的分析表明[7]，在20世纪90年代初，北极大气黑碳浓度发生骤降，1990−2009年下降了约40%，主要原因是来自俄罗斯（苏联）北极地区的黑碳大幅减少，使得北极黑碳浓度大幅降低，而在2000年之后，其研究指出来自欧洲北极源区的黑碳逐步增加，这与本文黑碳沉降的变化趋势特征一致。另外，随着工业由高纬度发达国家向中低纬度转移，客观上减少了输送至北极的黑碳，造成90年代之前大气黑碳浓度的下降，但随着发展中国家如东亚地区经济的发展，黑碳排放逐渐增加，造成2000年后欧亚北极黑碳沉降的增加[8，42]。另外，北极降水变化趋势也是北极黑碳沉降增加趋势的一个重要原因，黑碳湿沉降与降水量正相关[32]，2000年以来，北极地区降水逐渐增加，进而造成黑碳湿沉降增加，而黑碳湿沉降是冰雪黑碳的主要来源，最终造成黑碳总沉降的增加趋势。
最后， 由于本文模拟是基于单一海冰模式进行的，一些相互作用并未在模拟结果中体现。例如反照率反馈，当黑碳造成海冰面积减小后，海表面吸收的短波辐射增加，使得近地面大气升温进而促进海冰进一步融化，该反馈过程使得沉降在陆地区域的黑碳（沉降量也更高）能够远距离影响北冰洋区域的海冰融化，前者造成的辐射强迫甚至要高于后者[31]。此外，由于大气黑碳的云−黑碳−辐射反馈作用，使得到达表面的短波辐射减少，长波辐射增加，在某些区域与沉降到冰雪中的黑碳存在相反的辐射效应[43–44]。由于单一海冰模式中没有考虑这些相互作用的效应，本文得到的北冰洋区域黑碳对海冰反照率及海冰面积的影响存在一定程度的低估，未来还需要在耦合模式中，进一步考虑黑碳在北冰洋造成的影响。
6 结论
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黑碳是吸光性最强的颗粒，对北冰洋区域的冰雪融化有着较为明显的影响，北冰洋区域黑碳影响的量化评估对深入了解近年来北极海冰的快速变化过程有着重要作用。本文基于CICE海冰模式，结合CMIP6及MERRA2黑碳再分析数据，量化了1980−2014年北冰洋区域黑碳本身及其对冰雪影响的时空变化趋势，并分析了共性特征。
（1）CMIP6和MERRA2的黑碳沉降数据在趋势和量级上差异明显，1980−2014年间，CMIP6各黑碳沉降数据总体呈现下降趋势，约为−1.2 × 10−14 kg/(m2·s·dec)，但MERRA2呈现上升趋势，约为2.56 × 10−14 kg/(m2·s·dec)，最大的沉降模拟结果能达到最小模拟结果的2～3倍。但各黑碳沉降模拟数据均显示在20世纪90年代中期，黑碳沉降趋势发生一个转变，由下降趋势转为增加趋势。
（2）在CMIP6及MERRA2黑碳沉降强迫下，1980−2014年，黑碳造成北冰洋夏季平均反照率下降了0.82%～1.71%，使得融化加剧，最终造成海冰面积下降了0.97%～1.93%；虽然黑碳对北冰洋整体海冰的影响较小，但在大西洋扇区，尤其是在巴伦支海、喀拉海以及拉普捷夫海，夏季黑碳造成的海冰面积下降百分比约为北冰洋整体的2−3倍。
（3）各模拟结果均显示黑碳对北极海冰的影响在上世纪90年代中期存在一个突变，1980−1995年，黑碳对反照率和海冰面积的影响总体呈现下降趋势，下降趋势主要来自边缘海区；但在1996−2014年，黑碳影响转为增加趋势，增加趋势主要来自北极中央区。对于低纬度海区，由于边缘海区海冰的消退，直接沉降到海冰上的黑碳减少，即使黑碳沉降呈现增加趋势，但相应黑碳效应却减弱；而对于高纬度海区，黑碳更容易储存在多年冰当中，使得高纬度区域黑碳效应呈现增加趋势。
本文的研究有助于理解黑碳在北极气候变化过程中的作用。目前条件下主要建立在模拟得到的黑碳沉降数据上，从结果可以看出，黑碳沉降场的差异对模拟结果有着较大影响，野火、火山喷发等极端事件都会对区域黑碳沉降造成重大影响，而现有大气模式模拟的黑碳沉降通量对这些事件并没有很好的考虑，使得区域黑碳沉降相较于实际要大大低估，这些特殊事件的持续性效应都有待进一步研究。未来，更准确的黑碳沉降数据是量化评估黑碳气候效应的关键，这需要更多观测数据的支持。
致谢：感谢中国科学院大学的窦挺峰教授和中国海洋大学的张扬老师提供的部分北极黑碳数据和相关研究资料，为本文的研究提供了帮助。感谢尹豪和曹淑涛博士在CICE海冰模式的运行等方面提供的前期基础及帮助。
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2024年第46卷第8期
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doi: 10.12284/hyxb2024077
  • 接收时间：2024-03-27
  • 首发时间：2025-11-26
  • 出版时间：2024-08-31
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  • 收稿日期：2024-03-27
  • 修回日期：2024-06-07
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国家自然科学面上基金(42076228)
崂山实验室科技创新项目(LSKJ202202301)
作者信息
    1.中国海洋大学 海洋与大气学院，山东 青岛，266100
    2.中国海洋大学 深海圈层与地球系统前沿科学中心/物理海洋教育部重点实验室，山东 青岛 266100
    3.中国高校极地联合研究中心，北京 100875
    4.青岛海洋科技中心 海洋动力过程与气候功能实验室，山东 青岛 266100

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*苏洁（1966—），女，山东省青岛市人，博士，教授, 主要从事海冰数值模拟和遥感反演方面的研究。E-mail：
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