长序列北极海冰覆盖数据集对比分析

国家卫星气象中心使用2011年至今的风云三号卫星数据开发了一套基于Nasa Team2（NT2）算法的北极海冰密集度数据集,并可实时业务更新。将该数据集与其他国家不同机构业务运行并实时更新多种同类型数据集进行横向对比分析,其中包括:（1）美国冰雪中心基于Nasa Team（NT）算法以及SSM/I、SSMIS数据制作的1978年至今25 km分辨率全球极区海冰覆盖数据集;（2）美国冰雪中心基于Boot Strap（BS）算法以及SSM/I、SSMIS数据制作的1978年至今25 km分辨率全球极区海冰覆盖数据集;（3）美国NOAA基于多种卫星资料、地面观测数据以及海冰模型制作的2004年至今4 km分辨率北半球海冰覆盖数据集（IMS）。对比表明,上述数据集在北极地区不同的时空范围内存在一定的偏差。以分辨率较高的IMS数据集为基准,对其他3种长序列数据集进行初步评价,总体最大偏差超过100×104 km2,其中,NT2数据集过估较明显。经过与IMS数据集多年各月监测最大值的对比订正,NT2数据集过估情况得到改善。在此基础上的分析结果表明,NT、BS、NT2等3种数据集与IMS数据集相比,过估区域主要分布在海岸线附近,夏季过估比冬季更加明显,少估区域与算法、月份相关性明显,夏季少估面积也较冬季更大。NT、BS、NT2等3种数据集之中,NT2数据集与IMS数据集偏差最小,NT数据集次之,BS数据集与IMS数据集偏差最大。结果表明使用风云三号卫星数据的北极海冰覆盖数据集精度与国外3种同类型数据集相当。     

国内外7种常用南极被动微波海冰密集度产品的比较与评估

围绕国内外机构发布的南极被动微波海冰密集度产品（PM-SIC）的差异和精度问题,应用MODIS和Sentinel-1反演的海冰密集度,对德国不莱梅大学（产品UB-AMSR2/ASI）、美国冰雪数据中心（产品NSIDC-SSMIS/NT、NSIDC-SSMIS/CDR、NSIDC-AMSR2/NT2）、欧洲气象卫星应用组织海洋与海冰卫星应用中心（产品OSI-SAF/BR-BST）、国家卫星海洋应用中心（产品NSOAS-SMR/NT）和国家卫星气象中心（产品NSMC-MWRI/NT2）发布的7种南极海冰密集度产品进行比较与评估。结果表明：（1）NSIDC-SSMIS/NT与NSIDC-SSMIS/CDR海冰密集度具有较高的一致性（平均偏差为−0.08%,相关系数为0.99）,NSOAS-SMR/NT与NSIDC-AMSR2/NT2间的差异最大（平均偏差为−14.41%,相关系数为0.81）;（2）7种PM-SIC的变化趋势一致,NSOAS-SMR/NT和NSMC-MWRI/NT2与其他PM-SIC的偏差具有明显的季节性差异;（3）NSOAS-SMR/NT和NSMC-MWRI/NT2与其他PM-SIC均在印度洋扇区、别林斯高晋海和阿蒙森海扇区绝对偏差较大,在罗斯海扇区差异最小。偏差较大的区域主要分布在海冰边缘区及近陆地海域,在高密集度区域差异较小;（4）应用MODIS与Sentinel-1反演的海冰密集度对7种PM-SIC验证表明,NSMC-MWRI/NT2与验证数据的一致性最高。NSOAS-SMR/NT、UB-AMSR2/ASI和OSI-SAF/BR-BST海冰密集度偏低,而NSMC-MWRI/NT2、NSIDC-AMSR2/NT2、NSIDC-SSMIS/CDR和NSIDC-SSMIS/NT海冰密集度偏高。不同海冰密集度产品的比较与评估可为发展遥感反演算法、研制和应用高质量的海冰密集度产品,更好地监测南极海冰动态变化提供依据和参考。     

北极海冰密集度数值预报及验证评估研究

本文系统地评估了国家海洋环境预报中心于我国第七次北极科学考察期间开展的北极海冰密集度数值预报结果。该预报系统基于麻省理工大学通用环流模式,并采用牛顿松弛逼近（Nudging）资料同化方法,计算输出未来1~5 d的北极海冰密集度预报产品。本文将数值预报结果同卫星观测的海冰密集度、再分析资料和"雪龙"号第七次北极考察期间观测的海冰密集度数据进行了对比分析。结果表明,预报的北极海冰密集度小于卫星观测值,24 h、72 h和120 h预报结果的偏差分别为-2.7%、-3.1%和-3.2%;数值产品的预报技巧好于气候态结果和惯性预报,但是在海冰出现快速融化或冻结时,基于Nudging同化的数值预报技巧仍有不足。另外,相比船测数据,数值预报结果在海冰边缘区的偏差相对较大,24 h、72 h和120 h预报结果的偏差分别为8.8%、12.0%和14.5%。     

Arctic sea ice long-term dynamics according to the satellite microwave data

The main results of processing the long-term satellite data in different spectral ranges and with different spatial resolutions used to map Arctic sea ice parameters are presented. The advantages of the methods developed in order to estimate these parameters on the basis of microwave sensor measurements are indicated. The specific features of the long-term dynamics of the main sea ice geophysical parameters (the sea ice area and distribution during the summer minimum, ice age structure and thickness, multiyear ice concentration, times of ice melt and freeze onset, etc.) are presented. The effect of changes in sheet ice on the status of large Arctic mammals is estimated.     

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。     

Nudging资料同化对北极海冰密集度预报的改进

北极夏季海冰的快速减少使得北极航道提前开通成为可能。为了给北极冰区船运活动提供及时可靠有效的海冰预报保障,急需提高海冰预报水平。本文基于麻省理工大学通用环流模式(MITgcm),使用牛顿松弛逼近(Nudging)资料同化方法将德国不莱梅大学的第二代先进微波辐射成像仪(AMSR2)海冰密集度资料同化到模式中,建立了北极海冰数值预报系统。设计试验对比3种不同Nudging系数计算方案的改进效果,结果表明选择合适参数后,不同方案均能显著改进海冰密集度初始场。通过设计有无Nudging同化的两组预报试验,结合卫星遥感海冰密集度及中国第五次北极科学考察期间"雪龙"船的走航海冰密集度观测数据,定量分析了Nudging同化方案对北极海冰密集度的24~120 h预报结果的改进效果。结果表明,Nudging同化对120 h内全北极海冰密集度的空间分布和移动单点目标的海冰密集度预报结果均有显著改善;但在海冰变化很小的情况下,Nudging同化试验的24~120 h预报结果均劣于惯性预报结果,说明基于Nudging同化的数值预报系统还需进一步提高预报技巧。     

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.     

北极中央区海冰低密集度现象研究

近年,北极中央密集冰区出现海冰低密集度的异常现象。为了探讨这一现象的成因,本文使用ERA-Interim再分析资料,定义了北极中央区海冰低密集度（LCCA）指数,研究了2009-2016年的6-9月北极中央区发生的海冰低密集度现象。分析表明,研究时段内在北极中央区发生了6次明显的海冰低密集度（LCCA峰值）过程。在这些过程中,局地气温异常并不是导致海冰低密集度现象发生最主要的因素;海冰低密集度区域的形态及冰速场分布均与大气环流场相对应;在LCCA指数峰值发生前均有气旋中心出现在北冰洋70°N以北并伴随向北移动,气旋引起海冰辐散,同时所携带的较低纬度的热量导致海冰迅速融化。在6次过程中,有3次为气旋影响配合北极偶极子（DA）型环流。LCCA指数与84°N平均向北温度平流和北极中央区海冰速度散度呈正相关。在LCCA指数峰值前,温度平流对海冰低密集度区域形成的影响大于海冰辐散的影响。     

全球热带海洋海表温度场异常对北极海冰的影响

本文利用1951−2021年哈德莱中心提供的海冰和海温最新资料以及美国国家海洋和大气管理局气候预报中心提供的NCEP/NCAR再分析资料,分析探讨了北极海冰70余年的长期变化特征,进而研究了其快速减少与热带海温场异常变化之间的联系,揭示了在全球热带海洋海温场变化与北极海冰之间存在密切联系的事实。结果表明,北极海冰异常变化最显著区域出现在格陵兰海、卡拉海和巴伦支海。热带不同海区对北极海冰的影响存在明显时滞时间和强度差异,热带大西洋的影响相比偏早,印度洋次之,太平洋偏晚。热带大西洋、印度洋和中东太平洋海温异常影响北极海冰的最佳时间分别是后者滞后26个月、30个月和34个月,全球热带海洋影响北极海冰的时滞时间为33个月。印度洋SST对北极海冰的影响程度最强,其次是太平洋,最弱是大西洋。全球热带海洋对北极海冰的影响过程中,热带东太平洋和印度洋起主导作用。当全球热带海洋SST出现正（负）距平时,北极海冰会出现偏少（多）的趋势,而AO、PNA、NAO对北极海冰变化起重要作用,是热带海洋与北极海冰相系数的重要“纽带”。而AO、PNA和NAO不仅受热带海洋SST的影响,同时也受太平洋年代际振荡PDO和大西洋多年代际AMO的影响,这一研究为未来北极海冰快速减少和全球气候变暖机理的深入研究提供理论支撑。     

A new scheme for retrieving ocean surface salinity from simulated multi-angular SMOS brightness temperature

The European Space Agency will launch the first salinity satellite for remotely sensing the global soil moisture and ocean salinity (SMOS) at a sun-synchronous orbit in 2009. One of the payloads on the satellite is a synthetic aperture microwave radiometer (MIRAS), which is an innovative instrument designed as a two-dimensional (2D) interferometer for acquiring brightness temperature (TB) at L-band (1.4 GHz). MIRAS allows measuring TB at a series of incidences for full polarizations. As the satellite travels, a given location within the 2D field of view is observed from different incidence angles. The authors develop a new scheme to retrieve the sea-surface salinity (SSS) from SMOS’s TB at multi-incidence angles in a pixel, utilizing the properties of emissivity changing with incidence angles. All measurements of a given Stokes parameter in a pixel are first fitted to incidence angles in three order polynomial, and then the smoothed data are used for retrieving the SSS. The procedure will remove the random noise in TB greatly. Furthermore, the new method shows that the error in retrieved SSS is very sensitive to the system biases in the calibrated TB of the sensor, but the error in the retrieval is also a system bias, which can be corrected by post-launch validation. Therefore, this method may also serve as a means to evaluate the calibration precision in TB.     

HY-2卫星扫描微波辐射计数据反演北极海冰漂移速度

本文基于最大互相关法,利用海洋二号（HY-2）卫星扫描微波辐射计37 GHz通道多时相垂直极化亮温数据,获取了北极海冰漂移速度。采用2012年和2013年国际北极浮标计划海冰现场观测数据,对利用微波辐射计亮温资料反演的冬季北极海冰漂移速度进行了定量验证,结果表明:流速和流向均方根误差分别为1.12 cm/s和16.37°,从一定程度上说明了HY-2卫星扫描微波辐射计亮温数据反演海冰漂移速度的可行性。此外,使用美国国防气象卫星F-17搭载的专用微波成像仪91 GHz通道垂直极化亮温,采用高斯拉普拉斯滤波方法进行处理,结合最大互相关法反演的海冰漂移速度,优于法国海洋开发研究院海冰漂移速度产品。     

北极秋季海冰密集度与中国初冬降雨之间的关系

本文通过对中国地区实测降水及北极海冰卫星数据的分析,研究了北极秋季海冰密集度与中国初冬降雨的关系。合成分析的研究结果表明2000年之前中国南方和北方冬季降水偏少,中部降水偏多,这之后中国南方和北方冬季降水增加,中部降水减少。SVD研究结果显示,北极海冰减少使得近三十年来中国南方和北方冬季降雨呈现逐渐增多,中部地区(从青藏高原向东北方向至日本)降雨逐步减少的趋势。随着北极海冰的进一步减少,如遇合适的气候条件,南方冻雨出现的概率会加大。北极秋季海冰异常的回复过程加之冬季海冰异常的延续信号在中国、蒙古及日本北部激发一个阻塞高压,以巴伦支海/卡拉海为中心激发一个异常低压。这使得来自北冰洋大西洋扇区的冷空气南下至欧洲大陆和亚洲北部,在阻塞高压的影响下,冷空气进一步南下,进入东亚地区。这不仅使得亚洲冬季温度降低,也为中国北部降水增加提供条件。     

北极中央区海冰密集度与云量相关性分析

本文使用海冰密集度以及低云、中云、高云的日平均数据,借助滑动相关分析方法,研究了北极中央区海冰密集度与云量之间的相关性,分析了海冰与云的相互作用机制。研究表明,在春季海冰融化季节(4、5月)、秋季海冰冻结季节(10、11月),低云与海冰密集度之间表现为较好的负相关,表明在这段时间内冰区海面蒸发强烈,对低云的形成有重要贡献。在10月和11月,中云与海冰密集度也有很好的负相关,表明秋季低云可以通过抬升形成中云。高云与海冰密集度之间并没有明显的相关性,可能原因:一方面海冰的空间分布对高云无影响,另一方面,高云主要影响到达的短波辐射,从而影响海冰的融化和冻结速度,与海冰厚度有直接显著的关系,而与海冰密集度的关系不明显。此外,在海冰密集度与低云存在较好负相关的情况下会出现某些年份相关性不好的情况,我们的研究发现这是北极中央区与周边海区发生了海冰交换或云交换的结果。     

地球系统模式FIO-ESM对北极海冰的模拟和预估

评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于CMIP5(Coupled Model Intercomparison Project Phase 5)的历史实验对北极海冰的模拟能力,分析了该模式基于CMIP5未来情景实验在不同典型浓度路径(RCPs,Representative Concentration Pathways)下对北极海冰的预估情况。通过与卫星观测的海冰覆盖范围资料相比,该模式能够很好地模拟出多年平均海冰覆盖范围的季节变化特征,模拟的气候态月平均海冰覆盖范围均在卫星观测值±15%范围以内。FIO-ESM能够较好地模拟1979-2005年期间北极海冰的衰减趋势,模拟衰减速度为每年减少2.24×104 km2,但仍小于观测衰减速度(每年减少4.72×104 km2)。特别值得注意的是:不同于其他模式所预估的海冰一直衰减,FIO-ESM对21世纪北极海冰预估在不同情景下呈现不同的变化趋势,在RCP2.6和RCP4.5情景下,北极海冰总体呈增加趋势,在RCP6情景下,北极海冰基本维持不变,而在RCP8.5情景下,北极海冰呈现继续衰减趋势。     

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) are assimilated into this system, using the method of localized error subspace transform ensemble Kalman ?lter (LESTKF). Five-year (2014–2018) Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast. Since sea ice thickness in the PIOMAS is updated in time, it is a good choice for data assimilation to improve sea ice prediction skills in the near-real-time Arctic sea ice seasonal prediction.     

热带太平洋海温异常对北极海冰的可能影响

本文利用1950-2015年间Hadley环流中心海冰和海温资料及NCEP/NCAR再分析资料,研究了热带太平洋海温异常对北极海冰的可能影响,并从大气环流和净表面热通量两个角度探讨了可能的物理机制。结果表明,在ENSO事件发展年的夏、秋季节,EP型与CP型El Niño事件与北极海冰异常的联系无明显信号。而La Niña事件期间北极海冰出现显著异常,并且EP型与CP型La Niña之间存在明显差异。EP型La Niña发生时,北极地区巴伦支海、喀拉海关键区海冰异常减少,CP型La Niña事件则对应着东西伯利亚海、楚科奇海地区海冰异常增加。在EP型La Niña发展年的夏、秋季节,热带太平洋海温异常通过遥相关波列,使得巴伦支海、喀拉海海平面气压为负异常并与中纬度气压正异常共同构成类似AO正位相的结构,形成的风场异常有利于北大西洋暖水的输入,同时造成暖平流,偏高的水汽含量进一步加强了净表面热通量收入,使得巴伦支海、喀拉海海冰异常减少。而在CP型La Niña发展年的夏季,东西伯利亚海、楚科奇海关键区受其东侧气旋式环流的影响,以异常北风分量占主导,将海冰从极点附近由北向南输送到关键区,海冰异常增加,而净表面热通量的作用较小。     

Arctic summer sea ice phenology including ponding from 1982 to 2017

Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring. Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming. Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt. Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements. Moreover, the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season. Therefore, four timings, i.e., date of snow and ice surface melt onset (MO), date of pond onset (PO), date of sea ice opening (DOO), and date of sea ice retreat (DOR); and three durations, i.e., melt pond formation period (MPFP, i.e., MO–PO), melt pond extension period (MPEP, i.e., PO–DOR), and seasonal loss of ice period (SLIP, i.e., DOO–DOR), were used. PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results, whereas the pan-Arctic was observed nearly 4 days later in NASA Team results. Significant negative trends were presented in the MPEP in the Hudson Bay, the Baffin Bay, the Greenland Sea, the Kara and Barents seas in both results, indicating that the Arctic sea ice undergoes a quick transition from ice to open water, thereby extending the melt season year to year. The high correlation coefficient between MO and PO, MPFP illustrated that MO predominates the process of pond formation.     

基于卫星高度计的北极海冰厚度变化研究

A modified algorithm taking into account the first year(FY) and multiyear(MY) ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat(2003–2008). Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns(2005–2008) leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.     

欧亚大陆冬季温度对北极海冰减少的敏感性及非线性响应研究

The recent decline in the Arctic sea ice has coincided with more cold winters in Eurasia.It has been hypothesized that the Arctic sea ice loss is causing more mid-latitude cold extremes and cold winters,yet there is lack of consensus in modeling studies on the impact of Arctic sea ice loss.Here we conducted modeling experiments with Community Atmosphere Model Version 5(CAM5) to investigate the sensitivity and linearity of Eurasian winter temperature response to the Atlantic sector and Pacific sector of the Arctic sea ice loss.Our experiments indicate that the Arctic sea ice reduction can significantly affect the atmospheric circulation by strengthening the Siberian High,exciting the stationary Rossby wave train,and weakening the polar jet stream,which in turn induce the cooling in Eurasia.The temperature decreases by more than 1°C in response to the ice loss in the Atlantic sector and the cooling is less and more shifts southward in response to the ice loss in the Pacific sector.More interestingly,sea ice loss in the Atlantic and Pacific sectors together barely induces cold temperatures in Eurasia,suggesting the nonlinearity of the atmospheric response to the Arctic sea ice loss.