Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

Sea ice concentration (SIC) is one of the most important indicators when monitoring climate changes in the polar region. With the development of the Chinese satellite technology, the FengYun (FY) series has been applied to retrieve the sea ice parameters in the polar region. In this paper, to improve the SIC retrieval accuracy from the passive microwave (PM) data of the Microwave Radiation Imager (MWRI) aboard on the FengYun-3B (FY-3B) Satellite, the dynamic tie-point (DT) Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) (DT-ASI) SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a (from November 18, 2010 to August 19, 2019). Also, by applying a land spillover correction scheme, the erroneous sea ice along coastlines in melt season is removed. The results of FY-3B/DT-ASI are obviously improved compared to that of FY-3B/NT2 (NASA-Team2) in both SIC and sea ice extent (SIE), and are highly consistent with the results of similar products of AMSR2 (Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI. Compared with the annual average SIC of FY-3B/NT2, our result is reduced by 2.31％. The annual average SIE difference between the two FY- 3Bs is 1.65×106 km2, of which the DT-ASI algorithm contributes 87.9％ and the land spillover method contributes 12.1％. We further select 58 MODIS (Moderate-resolution Imaging Spectroradiometer) cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products. The root mean square difference (RMSD) and mean absolute difference (MAD) of the FY-3B/DT-ASI and MODIS results are 17.2％ and 12.7％, which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8％ and 7.2％ compared with FY-3B/NT2. Further, FY-3B/DT-ASI has the most significant improvement where the SIC is lower than 60％. A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of FengYun Satellite.     

渤海冬季海冰反照率变化

渤海海冰对于大尺度气候变化比较敏感,基于CLARA-A1-SAL数据分析了1992~2008年冬季（12、1、2月）渤海海冰区域反照率的时空变化,同时分析了海冰密集度、海冰外延线面积和海水表面温度的变化与海冰反照率的相互关系。渤海海冰区域反照率随时间波动变化且变化趋势不明显,趋势线斜率仅为0.0388%。年际变化在9.93%~14.5%之间,平均值为11.79%。海冰反照率在1999,2000和2005等重冰年的值明显高于其他年份,在1994,1998,2001和2006等轻冰年的值较低。从单个月份反照率来看,12月海冰反照率的增加趋势（趋势线斜率0.0988%）明显高于1月和2月,1月的海冰反照率平均值（12.9%）高于另外两个月份。海冰反照率和海冰密集度呈明显的正相关关系;和海表面温度呈负相关关系（显著性水平90%）。     

波弗特海2019年夏季海冰极端低值成因研究

波弗特海海冰的剧烈变化对区域内生态系统以及经济活动具有重要影响。基于美国国家冰雪数据中心发布的海冰密集度数据,本文对2019年波弗特海夏季海冰面积出现极端低值的机制进行了探讨。2019年融冰季（5–9月）海冰覆盖面积为1.38×105 km2,远低于1998–2020年平均面积2.28×105 km2,统计2019年前秋（2018年10–12月）和前冬季节（2019年1–4月）海冰覆盖面积,发现其与1998–2019年多年平均结果无显著差异;先前季节的海冰冰况不是造成极端低值事件的主要原因。综合海冰漂移场、海冰厚度、10 m风场以及海表面净热通量数据发现,2019年5月份海冰面积减小2.33×105 km2,是1998年以来5月海冰损失量最大的年份,占融冰季节海冰面积减小量的62%。与1998年、2008年、2012年以及2016年波弗特海夏季发生海冰覆盖面积极端低值现象的机制不同,不断减小的海冰厚度以及2019年5月异常强的风场,促使海冰快速向外输出,波弗特海南部5月16日就形成开阔水域;伴随着异常高的海表面净热通量使得海冰更多地融化,造成了2019年夏季海冰的异常现象。随着海冰厚度的不断变薄,海冰对风场的响应越来越强,海冰消退时间不断提前,波弗特海夏季海冰的极端低值现象可能更为频繁地出现。     

Spatiotemporal characteristics of sea ice transport in the Baffin Bay and its association with atmospheric variability

Sea ice export through the Baffin Bay plays a vital role in modulating the sea ice cover variability in the Labrador Sea.In this study,satellite-derived sea ice products are used to obtain the sea ice area flux (SIAF) through the three passages in the Baffin Bay (referred to as A,B,and C for the north,middle,and south passages,respectively).The spatial variability of the monthly sea ice drift in the Baffin Bay is presented.The interannual variability and trends in SIAF via the three passages are outlined.The connection to several large-scale atmospheric circulation modes is assessed.Over the period of 1988–2015,the average annual (October to the following September) SIAF amounts to 555×10~3 km~2,642×10~3 km~2,and 551×10~3 km~2 through Passages A,B,and C,respectively.These quantities are less than that observed through the Fram Strait (FS,707×10~3 km~2) of the corresponding period.The positive trends in annual SIAF,on the order of 53.1×10~3 km~2/(10 a) and 43.2×10~3 km~2/(10 a)(significant at the 95%confidence level),are identified at Passages A and B,respectively.The trend of the south passage (C),however,is slightly negative (–13.3×10~3 km~2/(10 a),not statistically significant).The positive trends in annual SIAF through the Passages A and B are primarily attributable to the significant increases after 2000.The connection between the Baffin Bay sea ice export and the North Atlantic Oscillation is not significant over the studied period.By contrast,the association with the cross-gate sea level pressure difference is robust in the Baffin Bay (R equals 0.69 to 0.71,depending on the passages considered),but relatively weaker than that over FS (R=0.74).     

误差订正对2018年夏季次季节尺度海冰预测的作用

北极海冰次季节尺度预测在针对破冰船和商船的实际服务中十分重要，但常常受制于气候模拟的模拟能力。本研究提出了一种误差订正方法并分别应用到两个气候模式：海洋一所地球系统模式（FIOESM）和美国国家环境预报中心（NCEP）的气候预报系统（CFS），来改善北极海冰60天尺度的预测。本研究的预测工作是中国第9次北极科学考察和2018年夏季中远集团北极商业航行的业务化海冰服务保障的重要部分。模式起报时间分别是2018年7月1日、8月1日和9月1日，预报时效均是60天。结果显示，FIOESM整体上低估了海冰密集度的数值，平均偏差可达30%。误差订正对海冰密集度（SIC）的均方根偏差（RMSE）的改进比例可达27%，对海冰外缘线（SIE）的整体偏差（IIEE）的改进比例为10%。而对于CFS，SIE在边缘区域的过高估计是其主要特点。误差订正导致了SIC的RMSE改进了7%，而对SIE的IIEE改进了17%。在海冰范围预测方面，FIOESM预测的最小范围数值和时间点都和观测接近，而CFS的预测结果偏差较大。另外和其他S2S模式的结果比较发现，本研究提出的误差订正方法对存在较大偏差的预测结果改进更为有效。     

北极秋季海冰减少与亚洲大陆冬季温度异常

本文使用SVD等诊断分析方法探讨北极秋季海冰密集度与亚洲冬季温度异常之间的关系。结果表明,近30余年来,北极秋季海冰减少伴随着亚洲大陆冬季温度降低,但青藏高原地区、北冰洋和北太平洋沿岸除外。北极秋季海冰密集度减小激发欧亚大陆和北冰洋北部两个区域位势高度的改变,这种异常的变化模态从秋季持续到冬季。位势高度异常的负值中心位于巴伦支海和喀拉海。位势高度异常的正值中心位于蒙古区域。与重力位势高度异常伴随的风场异常为亚洲冬季温度降低提供自北向南的冷气流。随着北极海冰的不断减少,其与亚洲大陆冬季温度降低之间的关系将为气候长期预测提供参考。     

2017年夏季北极中央航道海冰观测特征及海冰密集度遥感产品评估

2017年夏季中国第八次北极科学考察期间，"雪龙"号极地考察船首次成功穿越北极中央航道，期间全程开展了海冰要素的人工观测。中央航道走航期间的平均海冰密集度和平均冰厚分别为0.64和1.5 m，海冰密集度时空变化大且以厚当年冰为主，高纬密集冰区的浮冰大小显著高于海冰边缘区。基于"雪龙"号的船基走航观测海冰密集度评估比较了国际上常用的5种常用的微波遥感反演海冰密集度产品，同走航目测海冰密集度点对点的比较，误差最大的为德国不来梅大学AMSR2基于Bootstrap算法的产品，平均误差和均方根误差分别为0.19和0.28；误差最小的为欧洲气象卫星应用组织基于AMSR2数据和OSHD和TUD两种不同算法的产品，平均误差分别为-0.02和0.01，均方根误差均为0.20。从日平均比较来看，AMSR2基于Bootstrap算法的误差最大，平均误差和均方根误差分别为0.15和0.20；AMSR2/OSI SAF（TUD）的误差最小，平均误差和均方根误差分别为0.0和0.11，OSI SAF产品更接近人工观测结果。     

Role of sea ice in formation of wintertime arctic temperature anomalies

Numerical experiments with the ECHAM5 atmospheric general circulation model (AGCM) using the empirical HadISST1.1 data on sea surface temperature (SST) and sea ice concentration (SIC) in the 20th century as boundary conditions are analyzed. The experiments show that the model correctly reproduces the wintertime Arctic warming in the last 30 years of the 20th century but is unable to reproduce mid-20th century warming. Because the wintertime Arctic surface air temperature changes are closely related to SIC anomalies, it is assumed that one reason for this discrepancy is the lack of a negative SIC anomaly in the prescribed boundary conditions during a mid-20th century warm period. It is also shown that the model with-out prescribed ice cover changes does not reproduce a temperature trend in the Arctic in recent 30 years of the 20th century. The experimental results indicate that the mid-20th century warming was accompanied by a significant negative anomaly of the wintertime Arctic sea ice extent comparable to current trends and also point to a considerable contribution of natural variability to modern climate changes.     

大气环流优势模态对北极海冰变化的响应Ⅰ.北极涛动

利用美国冰雪中心海冰密集度数据,分析了1979-2012年北极海冰面积的时间变化特征,发现北极海冰具有显著的年代际变化特征,分别在1997和2007年前后存在两次年代际转型突变点,相应的大气环流优势模态——北极涛动(AO)也存在显著的时空变化。1979-1996年阶段海冰下降趋势较弱并以较强的年际振荡为主,AO模态较强且显示出低频振荡特征;1997-2006年阶段北极海冰快速减退趋势占优,同时伴随着较弱的年际振荡,AO模态减弱且振荡周期缩短;2007-2012年阶段海冰范围较快下降同时具有极强的年际振荡,方差变化是前两个阶段的2~3倍,AO不仅强度加强,空间结构也发生了变化,极涡中心分别向格陵兰岛和白令海峡一侧延伸,这种结构有利于极地冷空气入侵欧洲和北美。利用ECHAM5大气模式进行的数值试验结果也证实了较强振荡的海冰强迫对AO模态的改变具有决定作用。     

Quantifying the contribution of natural variability to September Arctic sea ice decline

Arctic sea ice extent has been declining in recent decades. There is ongoing debate on the contribution of natural internal variability to recent and future Arctic sea ice changes. In this study, we contrast the trends in the forced and unforced simulations of carefully selected global climate models with the extended observed Arctic sea ice records. The results suggest that the natural variability explains no more than 42.3% of the observed September sea ice extent trend during 35 a(1979–2013) satellite observations, which is comparable to the results of the observed sea ice record extended back to 1953(61 a, less than 48.5% natural variability). This reinforces the evidence that anthropogenic forcing plays a substantial role in the observed decline of September Arctic sea ice in recent decades. The magnitude of both positive and negative trends induced by the natural variability in the unforced simulations is slightly enlarged in the context of increasing greenhouse gases in the 21st century.However, the ratio between the realizations of positive and negative trends change has remained steady, which enforces the standpoint that external forcing will remain the principal determiner of the decreasing Arctic sea ice extent trend in the future.     

CMIP5气候模式模拟的北极海冰特征及其季节变化

This paper is focused on the seasonality change of Arctic sea ice extent(SIE) from 1979 to 2100 using newly available simulations from the Coupled Model Intercomparison Project Phase 5(CMIP5).A new approach to compare the simulation metric of Arctic SIE between observation and 31 CMIP5 models was established.The approach is based on four factors including the climatological average,linear trend of SIE,span of melting season and annual range of SIE.It is more objective and can be popularized to other comparison of models.Six good models(GFDL-CM3,CESM1-BGC,MPI-ESM-LR,ACCESS-1.0,Had GEM2-CC,and Had GEM2-AO in turn) are found which meet the criterion closely based on above approach.Based on ensemble mean of the six models,we found that the Arctic sea ice will continue declining in each season and firstly drop below 1 million km~2(defined as the ice-free state) in September 2065 under RCP4.5 scenario and in September 2053 under RCP8.5 scenario.We also study the seasonal cycle of the Arctic SIE and find out the duration of Arctic summer(melting season) will increase by about 100 days under RCP4.5 scenario and about 200 days under RCP8.5 scenario relative to current circumstance by the end of the 21 st century.Asymmetry of the Arctic SIE seasonal cycle with later freezing in fall and early melting in spring,would be more apparent in the future when the Arctic climate approaches to "tipping point",or when the ice-free Arctic Ocean appears.Annual range of SIE(seasonal melting ice extent) will increase almost linearly in the near future 30–40 years before the Arctic appears ice-free ocean,indicating the more ice melting in summer,the more ice freezing in winter,which may cause more extreme weather events in both winter and summer in the future years.     

北极海冰的年代际转型与中国冻雨年代际变化的关系

基于1961-2013年HadISST海冰密集度资料,定义了北极海冰的季节性融冰指数,结果显示近几十年来北极季节性融冰范围呈显著的上升趋势,并分别在20世纪70年代末和90年代中期存在显著的年代际转型,相应地,中国冻雨发生频数总体上呈现出显著的减少趋势,但也存在显著的年代际转型。在20世纪70年代末之前,北极季节性融冰范围较小但显著增长,中国冻雨频数年际变化振幅较大,且主要受巴伦支海、喀拉海海冰的影响;20世纪70年代末至90年代中期北极季节性融冰范围维持振荡特征,没有显著的线性趋势,中国冻雨频数变化振幅减小,与北极海冰相关较弱,主要相关因子为北大西洋及北太平洋海表温度变化;而90年代中期以后,北极海冰融化加快,特别是2007年以后,季节性融冰范围大大增加,而中国冻雨频数处于低发时段,其变化与太平洋扇区海冰及堪察加半岛附近海温呈显著负相关,季节性融冰的显著区域也从东西伯利亚海逆时针旋转向波弗特海-加拿大群岛北部扩张,同时向北极中央区扩张。不同年代影响冻雨的海温或海冰关键海区不同,产生特定的大气环流异常响应,进而影响到我国冻雨。     

北极高密集度冰区海冰的多时间尺度变化特征及其极端低值事件分析

利用美国冰雪中心(NSIDC)高分辨率海冰密集度等多种数据,定义了北极高密集度冰区(High concentration ice region:HCIR)海冰变化指数,在此基础上研究了1989—2017年HCIR海冰多尺度变化特征及其极端低值事件的可能形成原因。结果表明:北极HCIR海冰密集度具有显著的单峰型季节变化特征,4月密集度最高,9月密集度最低,年较差达17.70%,兼有夏季融冰期短、冬季结冰期长且持续稳定的特点。HCIR海冰存在显著的年际年代际变化,在2007年发生了年代际转折以后,海冰变化指数的年际变化幅度和频次明显加强,且在2016、2012、2007、2011、2008和2010年依次出现海冰密集度极端降低事件;2016年9月初HCIR海冰密集度达到历史最低值,接近50%。对HCIR海冰密集度极端低值事件的统计研究表明,29年间共出现874天(次)极端低值事件,约占总频次的8%;空间上海冰密集度的降低主要出现在沿HCIR边界线一带,存在巴伦支海-喀拉海北缘的斯瓦尔巴群岛-北地群岛和东西伯利亚-波弗特海两个中心区域,该空间分布与气旋式大气环流引起的北冰洋Ekman漂流的辐散分布相一致。这表明HCIR海冰密集度的极端降低与极涡的动力作用有关,同时风场对海冰的动力辐散作用还会引起HCIR开阔水域的扩大,进一步加强海冰反照率的正反馈机制,使得热力和动力作用耦合起来共同影响HCIR海冰的加速融化。     

2013年北极最小海冰范围比2012年增加的原因分析

北极海冰范围从1979年有卫星观测资料以来呈现明显下降趋势,尤其是9月份。2012年9月北极海冰范围达到有观测记录以来的最小值,而2013年9月比2012年同期增加了60%。增加的区域主要在东西伯利亚海区、楚科奇海和波弗特海区。本文应用距平和经验模态分解方法,分析了美国国家冰雪数据中心的北极海冰卫星数据、欧洲预报中心的夏季底层大气环流数据和上层海洋的温度,指出2013年北极最小海冰范围比2012年在北冰洋太平洋扇区增加的原因,是由于表面气温(SAT)降低、海平面气压(SLP)升高、气旋式风场异常、表面空气中水汽含量(SH)降低以及海表面温度(SST)降低5个条件形成的冰-SAT、冰-SST和冰-汽(SH)3个正反馈机制共同作用造成的。     

基于海冰密集度的消退起始时间判别方法改进研究与应用

海冰融化过程以正反馈的形式影响着海洋的热量吸收,对北极生态环境的变化和经济活动的开展起着重要作用。基于1979–2018年北冰洋逐日海冰密集度数据,本文综合考虑不同海域海冰冰况等因素,对北冰洋边缘海海冰消退起始时间的判别方法进行了改进。通过不同的方案对比分析表明,改进后的方法能够反映不同海域、不同年份冰情的变化;并且可消除一些天气扰动现象的干扰,避免过早地判别消退起始时间。应用本方法分析发现北冰洋各边缘海消退起始时间存在提前的趋势,与融化起始时间的提前趋势较为一致。但是不同海域提前程度存在明显差异,喀拉海和楚科奇海提前消退的趋势最强,达到了9 d/(10 a),而东西伯利亚海消退提前趋势最弱,只有4 d/(10 a),区域间的差异逐渐增大。海冰消退起始时间存在显著的年际差异,各边缘海的标准差均在15 d左右,近10年中消退最早与最晚之间的差值最大可达50 d,出现在波弗特海。     

一个区域海洋——海冰——大气耦合模式中的北极气候偏差：年结果检验

The Coupling of three model components, WRF/PCE （polar climate extension version of weather research and forecasting model （WRF））, ROMS （regional ocean modeling system）, and CICE （community ice code）, has been implemented, and the regional atmosphere-ocean-sea ice coupled model named WRF/PCE- ROMS-CICE has been validated against ERA-interim reanalysis data sets for 1989. To better understand the reasons that generate model biases, the WRF/PCE-ROMS-CICE results were compared with those of its components, the WRF/PCE and the ROMS-CICE. There are cold biases in surface air temperature （SAT） over the Arctic Ocean, which contribute to the sea ice concentration （SIC） and sea surface temperature （SST） biases in the results of the WRF/PCE-ROMS-CICE. The cold SAT biases also appear in results of the atmo- spheric component with a mild temperature in winter and similar temperature in summer. Compared to results from the WRF/PCE, due to influences of different distributions of the SIC and the SST and inclusion of interactions of air-sea-sea ice in the WRF/PCE-ROMS-CICE, the simulated SAT has new features. These influences also lead to apparent differences at higher levels of the atmosphere, which can be thought as responses to biases in the SST and sea ice extent. There are similar atmospheric responses in feature of distribution to sea ice biases at 700 and 500 hPa, and the strength of responses weakens when the pressure decreases in January. The atmospheric responses in July reach up to 200 hPa. There are surplus sea ice ex- tents in the Greenland Sea, the Barents Sea, the Davis Strait and the Chukchi Sea in winter and in the Beau- fort Sea, the Chukchi Sea, the East Siberian Sea and the Laptev Sea in summer in the ROMS-CICE. These differences in the SIC distribution can all be explained by those in the SST distributions. These features in the simulated SST and SIC from ROMS-CICE also appear in the WRF/PCE-ROMS-CICE. It is shown that the performance of the WRF/PCE-ROMS-CICE is determined to a l     

Characteristics of Winter Surface Air Temperature Anomalies in Moscow in 1970–2016 under Conditions of Reduced Sea Ice Area in the Barents Sea

On the basis of observational data on daily mean surface air temperature (SAT) and sea ice concentration (SIC) in the Barents Sea (BS), the characteristics of strong positive and negative winter SAT anomalies in Moscow have been studied in comparison with BS SIC data obtained in 1949–2016. An analysis of surface backward trajectories of air-particle motions has revealed the most probable paths of both cold and warm air invasions into Moscow and located regions that mostly affect strong winter SAT anomalies in Moscow. Atmospheric circulation anomalies that cause strong winter SAT anomalies in Moscow have been revealed. Changes in the ways of both cold and warm air invasions have been found, as well as an increase in the frequency of blocking anticyclones in 2005–2016 when compared to 1970–1999. The results suggest that a winter SIC decrease in the BS in 2005–2016 affects strong winter SAT anomalies in Moscow due to an increase in the frequency of occurrence of blocking anticyclones to the south of and over the BS.     

渤海AVHRR多通道海冰密集度反演算法试验研究

为了得到更精确的渤海海冰密集度反演参数,采用辽东湾不同类型海冰ASD实测数据,在分析光谱特征的基础上,针对NOAA/AVHRR数据确定出合适海冰密集度反演算法阈值。继而,基于线性光谱混合模型的多通道反演算法进行了一系列算法试验。同时实现了基于LandSat5-TM数据的渤海海冰密集度场反演,并利用该结果与AVHRR单通道和多通道算法得到的海冰密集度反演结果进行比对分析。定量误差分析结果表明,当单通道算法或组合算法中包含1通道时,与Landsat5-TM反演结果的平均误差为正值,包含2通道且不包含1通道时,平均误差为负值;同时使用这两个通道较只包含其一的各种组合算法的平均误差明显偏小;在各种组合算法中,1245四个通道组合反演的海冰密集度结果误差最小,可应用于渤海AVHRR数据海冰密集度反演。     

Mechanisms for the link between onset and duration of open water in the Kara Sea

The sea ice conditions in the Kara Sea have important impacts on Arctic shipping, oil and gas production, and marine environmental changes. In this study, sea ice coverage (CR) less than 30％ is considered as open water, its onset and end dates are defined as Topen and Tclose, respectively. The sea ice melt onset (Tmelt) is defined as the date when ice-sea freshwater flux initially changes from ice into the ocean. Satellite-based sea ice concentration (SIC) from 1989 to 2019 shows a negative correlation between Topen and Tclose (r = –0.77, p < 0.01) in the Kara Sea. This phenomenon is also obtained through analyzing the hindcast simulation from 1994 to 2015 by a coupled ocean and sea-ice model (NAPA1/4). The model results reveal that thermodynamics dominate the sea ice variations, and ice basal melt is greater than the ice surface melt. Heat budget estimation suggests that the heat flux is significant correlated with Topen (r = –0.95, p < 0.01) during the melt period (the duration of multi-year averaged Tmelt to Topen) influenced by the sea ice conditions. Additionally, this heat flux is also suggested to dominate the interannual variation of the heat input during the whole heat absorption process (r = 0.81, p < 0.01). The more heat input during this process leads to later Tclose (r = 0.77, p < 0.01). This is the physical basis of the negative correlation between Topen and Tclose. Therefore, the duration of open water can be predicted by Topen and thence support earlier planning of marine activities.     

北极海冰变率的独特模式及其与大气强迫的关系

The spatial structure of the Arctic sea ice concentration(SIC) variability and the connection to atmospheric as well as radiative forcing during winter and summer for the 1979–2017 period are investigated. The interannual variability with different spatial characteristics of SIC in summer and winter is extracted using the empirical orthogonal function(EOF) analysis. The present study confirms that the atmospheric circulation has a strong influence on the SIC through both dynamic and thermodynamic processes, as the heat flux anomalies in summer are radiatively forced while those in winter contain both radiative and "circulation-induced" components. Thus,atmospheric fluctuations have an explicit and extensive influence to the SIC through complex mechanisms during both seasons. Moreover, analysis of a variety of atmospheric variables indicates that the primary mechanism about specific regional SIC patterns in Arctic marginal seas are different with special characteristics.