北极海冰范围时空变化及其与海温气温间的数值分析

本文利用美国国家冰雪中心提供的1989-2014年海冰范围资料,分析了北极海冰范围的年际变化和季节变化规律。分析发现,北极海冰范围呈减少趋势,每年减小\begin{array}{c}5.91\times {10}^4\mathit{k}{m}^2\end{array},夏季减少趋势显著,冬季减少趋势弱。北极海冰范围显现相对稳定的季节变化规律,海冰的结冰和融化主要发生在各个边缘海,夏季期间的海冰具有融化快、冻结快的特征。结合海温、气温数据,进行北极海冰范围与海温、气温间的数值分析,结果表明北极海冰范围变化通过影响北极海温变化进而影响北极气温变化。海冰范围的季节变化滞后于海温和气温的季节变化。基于北极考察走航海温气温数据,进行楚科奇海海冰范围线与海温气温间的数值分析,发现楚科奇海海冰范围线所在区域的海温、气温与纬度高低、离陆地远近有关。     

东北典型林区雪深反演算法的验证与分析

积雪对自然环境和人类活动都有极其重要的影响。积雪参数（雪面积、雪深和雪水当量）反演对水文模型和气候变化研究有着实际的意义。然而,目前森林区的雪深遥感反演精度一直有待于进一步提高。东北地区是我国最大的天然林区和重要的季节性积雪区之一,本文利用FY3B卫星微波成像仪（MWRI）L1级亮温数据和L2级雪水当量数据,以及东北典型林区野外实测雪深数据,对Chang算法、NASA 96算法和FY3B雪深业务化反演算法进行了验证与分析。结果表明：在东北典型林区的雪深反演中,Chang算法和NASA 96算法反演的雪深波动都比较大,当森林覆盖度f≤0.6时,NASA 96算法表现比较好,均方根误差值在3种算法中较小,但当f >0.6时,NASA 96算法失真严重。当考虑纯森林像元（f=1）时,Chang算法低估了雪深47%。当f≤0.3时,FY3B业务化算法始终优于Chang算法。整体上,FY3B业务化算法相对稳定,具有较高的精度。     

利用ICESat/GLAS数据获取北极海冰出水高度的研究

利用ICESat激光测高卫星数据获取2003-10～2008-03北冰洋秋季与冬季海冰出水高度,结果与国外相关研究基本一致。出水高度数据的拟合结果表明,北极海冰出水高度以每年约2.3 cm的速度递减,该速度比此前的相关研究结果更快。在不同作者利用ICESat数据计算的北极海冰出水高度的对比中,不同方法得到的结果之间存在明显的系统误差。对引起系统误差的原因进行深入分析表明,高程滤波的窗口长度、确定海面高的范围以及海面观测值的选取方式都会使出水高度的计算产生cm级的误差。     

光学与微波遥感的新疆积雪覆盖变化分析

利用2002-2013年冬季的MODIS光学遥感数据,以及AMSR-E、AMSR2与MWRI被动微波遥感数据,建立了新疆地区冬季每日积雪分布遥感反演模型。首先,将Terra与Aqua双星MODIS的积雪产品融合,初步去云并最大化积雪信息;然后,利用AMSR-E/AMSR2和MWRI被动微波数据进行每日雪盖提取;最后,利用被动微波遥感数据反演得到的每日雪盖结果对双星融合后依然有云的像元进行替换,得到每日积雪分布情况。据此模型提取了11年间冬季的积雪天数信息,结合气象台站观测数据,分析了新疆冬季积雪的年内和年际变化规律。结果表明,新疆地区积雪主要分布在北部新疆,积雪天数与地形关系密切,山区积雪天数较多,盆地及城市区积雪天数较少;积雪天数年内变化是从11月到次年1月随温度降低逐渐增加,从1月到3月积雪天数则逐渐减少。新疆地区积雪天数在这11年中存在一定的波动,积雪天数与该年的平均气温,以及月低于0℃的天数存在显著相关性,与降雪量关系不明显。新疆地区近年来积雪天数重心有向西向南移动的趋势,这可能与全球气候变暖导致多年积雪融化有关。     

基于CryoSat-2数据反演2011～2017年波弗特海海冰厚度变化

利用CryoSat-2卫星测高数据反演波弗特海的海冰厚度,并利用2010~2013年10月份仰视声呐(ULS)和2011年冰桥计划（IceBridge）数据对结果进行精度评估。结果表明,测高反演的海冰吃水深度与ULS吃水深度差值的最大值和标准差分别为14 cm和4 cm;测高反演的海冰厚度与冰桥计划海冰厚度差值的平均值和标准差分别为2.7 cm和65.7 cm,优于Laxon(2013)研究结果（分别优化2.1 cm和6.6 cm）。在此基础上,研究2011~2017年波弗特海夏冬两季的海冰厚度变化,发现二者具有类似的分布特征,且冬季3月海冰覆盖范围更广,厚度更大;进一步分析2011~2017年3月份冬季海冰厚度年际变化,发现其呈整体下降趋势,且2012年最小,2014年最大。     

陕西省地(市)级遥感应用系统的设计与实现

从拓宽地（市）局遥感应用服务产品的领域、改善服务产品的形式、提高服务产品水平的角度,提出了适合地（市）级遥感应用系统的建立方法和资料处理流程,使地、市遥感应用进入定量分析的阶段.开发的基于局地数据集的地（市）级遥感应用处理软件, 可同时处理EOS和NOAA、FY卫星接收的数据资料,并可进行植被长势、沙尘暴、干旱等监测.通过配发的遥感数据和应用处理软件将为我省各地（市）初步建立起遥感应用系统.     

利用MODIS影像提取火烧迹地方法的研究

火烧迹地监测不仅可以反映火灾对生态系统的影响情况及损失信息,还能为全球碳循环研究提供重要的数据支持。本文利用MODIS地表反射率产品（MOD09A1）的近红外和短波红外波段构建的归一化燃烧率指数（NBR）,计算前后2期影像的NBR差值,并在光谱指数差分法的基础上,结合MODIS植被数据产品（MOD44B）提供的植被覆盖度信息,设置规则提取火烧迹地。本文选择西伯利亚地区东南部的林地、草地、农田等不同生态系统的交界地带作为实验区,利用本文算法提取该区域的火烧迹地。实验结果表明：（1）本文算法的火烧迹地提取效果较好,优于MODIS火烧迹地产品（MCD45A1）,kappa系数由0.70提高到0.75;（2）利用林木覆盖度、草本覆盖度数据,可以减少误判,提高火烧迹地提取的精度,kappa系数分别由0.69、0.73都提高到0.75。     

2001-2013年洞庭湖流域植被覆盖度时空变化特征

本文基于2001-2013年MODIS NDVI多时序数据,采用像元二分模型估算了洞庭湖流域植被覆盖度,分析了区域近13年来植被覆盖度的变化特征及趋势,并结合同期气象数据,阐明了植被覆盖度变化对气候因素的响应。结果表明：（1）近13年洞庭湖流域植被覆盖度的整体变化较为稳定,呈微弱减少趋势,速率为-0.3%/10a。（2）洞庭湖流域绝大部分区域植被覆盖状况良好,植被覆盖度呈自西向东递减趋势,高植被覆盖度及中高植被覆盖度占整个流域面积的88.63%,水体或低植被覆盖度及中低植被覆盖度仅占2.57%。（3）洞庭湖流域植被覆盖度变化趋势为北部强于南部、东部强于西部。流域内植被覆盖度极显著与显著减少的面积比例为5.30%、增加面积的比例为4.29%,植被覆盖度变化不显著占90.40%。该区域植被覆盖度变化受人为因素影响更大。     

地表反射率遥感数据支持的火烧迹地面积提取

生物质燃烧排放大量烟雾和温室气体对于全球气候变化有显著影响，而准确及时地提取火烧迹地面积对于火灾补救、植被恢复、估算大气排放至关重要。中分辨率成像光谱仪MODIS较高的时间分辨率可以快速获取全球每日的火烧迹地产品，但对于小型和破碎度高的火烧迹地的遗漏率比较高。据此，本研究融合MODIS与Landsat-8 OLI(Operational Land Imager陆地成像仪)的时空优势，提出了基于地表反射率数据集支持的火烧面积提取算法。首先，使用MODIS地表反射率产品MOD09GA构建燃烧日期前后在红、绿、蓝、近红外和短波红外的先验地表反射率数据集。然后，采用自适应遥感图像时空融合算法(Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM)以及线性拟合的方法对MODIS与Landsat-8地表反射率数据进行空间和光谱一致化处理。最后，运用自动阈值的方法厘定火烧区域的最佳阈值。此外，通过选取4个不同的燃烧规模样地/样区验证了该算法的火烧迹地面积提取准确率在75%以上。本研究将MODIS的高时间分辨率和Landsat-8...     

卫星气候数据集的应用研究与发展分析

卫星气候数据集是卫星气候研究的基础。在规范卫星气候数据集基本概念的基础上,针对现有基本气候数据集（FCDR）和专题气候数据集（TCDR）的分类方式,无法反映卫星气候数据特点的问题,认为应将专题气候数据集进一步划分为单一遥感仪器专题气候数据集、多种遥感仪器融合专题气候数据集及卫星与多源资料融合专题气候数据集等几类。这种分类方法便于用户更好地了解和使用卫星气候数据。然后,重点围绕基本气候变量和基本卫星气候变量含义、卫星气候数据集生产规范、国内外主要卫星气候数据生产计划等方面,综述了卫星气候数据集建设及规范化生产已取得的最新研究进展。在此基础上,分析了卫星气候数据集建设和应用中存在的主要问题,展望了卫星气候数据集发展,同时对我国卫星气候数据集建设提出具体建议。     

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.     

Spatio-temporal Variation of Arctic Sea Ice in Summer from 2003 to 2013

The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer(AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 10~5 km~2/yr, twice as fast as the rate between 1979 and 2006, and slightly slower than from 2002 to 2011. Area with 90% sea-ice concentration decreased by 1.32 × 10~7 km~2/yr, while locations with 50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 10~5 km~2 annually over the past 11 years.     

Impacts of SIS and CICE as Sea Ice Components in BCC_CSM on the Simulation of the Arctic Climate

Sea ice is a sensitive indicator of climate change and an important component of climate system models. The Los Alamos Sea Ice Model 5.0(CICE5.0) was introduced to the Beijing Climate Center Climate System Model(BCC_CSM) as a new alternative to the Sea Ice Simulator(SIS). The principal purpose of this paper is to analyze the impacts of these two sea ice components on simulations of basic Arctic sea ice, atmosphere, and ocean states. Two sets of experiments were conducted with the same configurations except for the sea ice component used, i.e., SIS and CICE. The distributions of sea ice concentration and thickness reproduced by the CICE simulations in both March and September were closer to actual observations than those reproduced by SIS simulations, which presented a very thin sea ice cover in September. Changes in sea ice conditions also brought about corresponding modifications to the atmosphere and ocean circulation. CICE simulations showed higher agreement with the reference datasets than did SIS simulations for surface air temperature, sea level pressure, and sea surface temperature in most parts of the Arctic Ocean. More importantly, compared with simulations with SIS, BCC_CSM with CICE revealed stronger Atlantic meridional overturning circulation(AMOC), which is more consistent with actual observations. Thus, CICE shows better performance than SIS in BCC_ CSM. However, both components demonstrate a number of common weaknesses, such as overestimation of the sea ice cover in winter, especially in the Nordic Sea and the Sea of Okhotsk. Additional studies and improvements are necessary to develop these components further.     

Status of the Recent Declining of Arctic Sea Ice Studies

In the past 30 years, a large-scale change occurred in the Arctic climatic system, which had never been observed before 1980s. At the same time, the Arctic sea ice experienced a special evolution with more and more rapidly dramatic declining. In this circumstance, the Arctic sea ice became a new focus of the Arctic research. The recent advancements about abrupt change of the Arctic sea ice are reviewed in this paper .The previous analyses have demonstrated the accelerated declining trend of Arctic sea ice extent in the past 30 years, based on in-situ and satellite-based observations of atmosphere, as well as the results of global and regional climate simulations. Especially in summer, the rate of decrease for the ice extents was above 10% per decade. In present paper, the evolution characteristics of the arctic sea ice and its possible cause are discussed in three aspects, i.e. the sea ice physical properties, the interaction process of sea ice, ocean and atmosphere and its response and feedback mechanism to global and arctic climate system.     

Transition Periods Between Sea Ice Concentration and Sea Surface Air Temperature in the Arctic Revealed by an Abnormal Running Correlation

This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration(SIC) and sea surface air temperature(SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea(BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.     

北极熊栖息地稳定性的遥感评估方法

北极熊是北极最重要的哺乳动物之一,近年来数量却在减少。海冰作为北极熊狩猎、活动和繁殖的平台,是其栖息地的重要组成部分。因此其种群栖息地变化主要依赖于海冰变化。本文基于美国雪冰中心的海冰密集度和NOAA提供的ETOPO1基岩数据,分析了北极海冰密集度、开阔水域面积、海冰消退时间、海冰出现时间、开阔水域季节长度的年际变化,进而评价北极熊栖息地的稳定性。结果表明,海冰密集度呈现降低的趋势,开阔水域面积增大,多年冰数量减少,大多变为一年冰。海冰消退时间提前,海冰出现时间延后,开阔水域季节长度大幅增加,与1992年相比增加了72 d。19个栖息地中,巴伦支海是开阔水域面积和季节长度变化贡献最大的海域,增加速度分别为9.71×10³ km²/a和71.69 d/10a。以开阔水域季节长度变化率为依据,将北极熊栖息地划分为稳定、次稳定和不稳定3个等级。总共有3个稳定栖息地,包括分布在相对其他栖息地而言纬度较低的楚科奇海、西哈得孙湾和南哈得孙湾。13个次稳定栖息地,包括拉普捷夫海、喀拉海、东格陵兰、巴芬湾、戴维斯海峡、福克斯湾、布西亚湾、麦克林托克海峡、梅尔维尔子爵海峡、挪威湾、北波弗特、南波弗特和兰开斯特海峡。3个不稳定栖息地,均位于70°N以北,包括北极盆地、巴伦支海和凯恩盆地。稳定区主要位于低纬度,不稳定区全部位于高纬度。该分级结果表明高纬度地区虽然海冰覆盖多,但是年际变化十分显著,不稳定的3个区域内北极熊对海冰变化适应时间更少,年际迁移变化大,对北极熊的生存发展更为不利。     

Polar Sea Ice Identification and Classification Based on HY-2A/SCAT Data

In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.     

Response of the Dominant Modes of Atmospheric Circulation in the Northern Hemisphere to a Projected Arctic Sea Ice Loss in 2007

This study revisits the Arctic sea ice extent(SIE) for the extended period of 1979-2015 based on satellite measurements and finds that the Arctic SIE experienced three different periods: a moderate sea ice decline period for 1979-1996, an accelerated sea ice decline period from 1997 to 2006, and large interannual variation period after 2007, when Arctic sea ice reached its tipping point reported by Livina and Lenton(2013). To address the response of atmospheric circulation to the lowest sea ice conditions with a large interannual variation, we investigated the dominant modes for large atmospheric circulation responses to the projected 2007 Arctic sea ice loss using an atmospheric general circulation model(ECHAM5). The response was obtained from two 50-yr simulations: one with a repeating seasonal cycle of specified sea ice concentration for the period of 1979-1996 and one with that of sea ice conditions in 2007. The results suggest more occurrences of a negative Arctic Oscillation(AO) response to the 2007 Arctic sea ice conditions, accompanied by an North Atlantic Oscillation(NAO)-type atmospheric circulation response under the largest sea ice loss, and more occurrences of the positive Arctic Dipole(AD) mode under the 2007 sea ice conditions, with an across-Arctic wave train pattern response to the largest sea ice loss in the Arctic. This study offers a new perspective for addressing the response of atmospheric circulation to sea ice changes after the Arctic reached the tipping point in 2007.     

CryoSat-2卫星海冰区域波形识别及海冰干舷高确定

利用40%阈值法对CryoSat-2卫星波形数据进行重跟踪,将波形特征参数和海冰浓度相结合,对海冰和Lead(浮冰之间的开阔水域)进行有效识别。利用沿轨前后搜索算法计算海冰干舷高,并引用AWI结果,绘制2011~2013年北冰洋多年冰区域和一年冰区域平均海冰干舷高变化趋势图。比较本文结果与AWI结果的各年同期数据,验证本文结果的可靠性。