基于ICESat/GLAS的山东省SRTM与ASTER GDEM高程精度评价与修正

SRTM3和ASTER GDEM V2数据具有较高的空间分辨率和广泛的覆盖范围,对于地学研究具有重要意义;但在不同地形复杂度和地面覆盖物区域,两类数据的误差分布并不均匀。SRTM3和ASTER GDEM V2数据自公布以来,其精度修正一直是研究热点。然而大范围区域精度验证缺乏有效手段,传统方法可靠性差且数据获取成本较高。自ICESat-1数据公开以来,它们已成为SRTM3和ASTER GDEM V2精度评定的主要检核点。为此,本文以山东省为研究区域,借助ICESat-1评估了SRTM3和ASTER GDEM V2的高程精度,并根据插值误差曲面对两种DEM进行了修正。分析表明,原始SRTM和ASTER高程中误差分别为5.57 m和7.20 m,均高于标称精度;随着坡度的增大,高程精度呈降低的趋势。通过分析土地覆盖类型与误差分布关系表明:农田、灌丛土地类型精度较高;森林、湿地精度较低。分别采用反距离加权、普通克里金、地形转栅格和自然邻域插值方法构建误差曲面。结果表明:不同的插值方法构建的误差曲面的特征和精度也不同。其中,反距离加权修正的效果最佳,其次是地形转栅格和自然邻域,而普通克里金修正的效果最差。     

基于NSIDC海冰产品的FY北极海冰数据集优化

北极海冰对全球气候起着非常重要的调制作用,海冰范围是海冰监测的基本参数。近40年,北极地区持续变暖,北极海冰显著减少,进而引发北极自然环境恶化、北半球极端天气频发、全球海平面上升等一系列环境和气候问题。准确获取北极海冰范围及其演变趋势,确定海冰变化对全球气候系统的响应,是研究和预测全球气候变化趋势的关键之一。HasISST和OISST海冰数据集在海冰监测中应用最为广泛,可为北极地区长时间序列海冰变化研究提供基础数据,但这2套数据集空间分辨率相对较低,应用于北极关键区对中国气候响应研究方面存在很大的局限,为解决这一问题和弥补国内海冰监测微波遥感数据的空白,2011年6月27日,国家卫星气象中心（National Satellite Meteorological Center, NSMC）发布了FY（Fengyun, FY）北极海冰数据集,该数据集利用搭载在FY卫星上的微波成像仪（Microwave Radiation Imager, MWRI）数据,使用Enhance NASA Team算法制作,该算法利用前向辐射传输模型模拟北极地区4种海表类型（海水、新生冰、一年冰和多年冰）在不同大气条件下MWRI辐射亮温,进而得到每种大气条件下0~100%的海冰覆盖度查找表（海冰覆盖度每次增加1%）,通过观测值与模拟值的比对得到海冰覆盖度,由该数据集计算得到的北极海冰范围在大部分区域与实际情况相符。该产品虽已进行通道间匹配误差修正和定位精度偏差订正,但由于其搭载的微波成像仪（Microwave Radiation Imager, MWRI）天线长度有限,造成传感器探测到的地物回波信号相对较弱,难以区分海冰和近岸附近的陆地,影响了该数据集的精度和应用。为解决这一问题,本文基于美国冰雪中心（National Snow and Ice Data Center, NSIDC）发布的海冰产品对FY海冰数据集进行优化,NSIDC产品利用判断矩阵对海岸线附近的像元进行识别,并对误差像元进行不同程度的修正,由NSIDC产品计算得到的北极海冰范围与实际情况更为符合。数据集优化大大提高了FY海冰数据集的精度,研究结果表明,优化后FY海冰数据集与NSIDC产品相关系数高达0.9997,且二者日、月、年平均最大海冰范围偏差仅为3.5%、1.9%、0.9%,且FY海冰数据集优化过程对其较好的空间分异特征无明显影响。该数据集可正确地反映北极海冰范围及其变化情况,且海岸线附近海冰的分布情况更准确,可为北极海冰变化研究提供可靠的基础数据。     

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。     

Definition of Arctic and Antarctic Sea Ice Variation Index

1　Introduction Itiswellknownthatseaiceinthepolarregionplaysanimportantroleintheglobal climatechangesasapartofclimatesystem(Carleton1989;YuanandMartinson2000, 2001;ChengandBian2002;LiuandMartinson2002;LiuandZhang2004;Gigorand Wallace2002etal).Infact,numerousmodelingstudiessuggestanimportantinfluence throughtheseaicefieldsalone(Grumbine1994,Meehl1990,Rindetal.1995).Inor dertounderstandthevariabilityofArcticandAntarcticseaicealongwiththepossiblecon nectionswithclimaticanomaliesindetail…     

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.     

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。     

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.     

Improvements in Long-Lead Prediction of Early-Summer Subtropical Frontal Rainfall Based on Arctic Sea Ice

Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal rainfall(SFR) during early summer(May-June mean, MJ) by considering Arctic sea ice(ASI) variability as a new potential predictor. A MJ SFR index(SFRI), the leading principle component of the empirical orthogonal function(EOF) analysis applied to the MJ precipitation anomaly over EA, is defined as the predictand. Analysis of 38-year observations(1979-2016) revealed three physically consequential predictors. A stronger SFRI is preceded by dipolar ASI anomaly in the previous autumn, a sea level pressure(SLP) dipole in the Eurasian continent, and a sea surface temperature anomaly tripole pattern in the tropical Pacific in the previous winter. These precursors foreshadow an enhanced Okhotsk High, lower local SLP over EA, and a strengthened western Pacific subtropical high. These factors are controlling circulation features for a positive SFRI. A physical-empirical model was established to predict SFRI by combining the three predictors. Hindcasting was performed for the 1979-2016 period, which showed a hindcast prediction skill that was, unexpectedly, substantially higher than that of a four-dynamical models’ ensemble prediction for the 1979-2010 period(0.72 versus 0.47). Note that ASI variation is a new predictor compared with signals originating from the tropics to mid-latitudes. The long-lead hindcast skill was notably lower without the ASI signals included, implying the high practical value of ASI variation in terms of long-lead seasonal prediction of MJ EA rainfall.     

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.     

顾及数据配准的江西省SRTM DEM精度评价和修正

目前,ICESat/GLAS是大尺度SRTM DEM精度评价的主要数据源.然而,现有的精度评价方法均忽略了2组数据的有效配准.为此,本文分析了数据配准前、后SRTM DEM整体精度差异,以及不同地形因子和土地利用类型对SRTM DEM影响程度.在此基础上,充分考虑SRTM DEM精度影响因素,分别借助多元线性回归(ML...