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。
英文摘要:
Arctic sea ice plays a very important role in the modulation of global climate and sea ice extent is a basic parameter for sea ice monitoring. In recent 40 years, a series of environmental and climatic issues such as degradation of Arctic natural environment, frequent extreme weather in the Northern Hemisphere and global sea-level rise are caused by continuous warming and apparent sea ice decrease in Arctic. So it's important to know the extent, variation, trend of Arctic sea ice and its response to global climate change. The most commonly used datasets such as HadISST and OISST sea ice dataset provided long time series of changes in sea ice of the Arctic regions. However, the spatial resolution of these datasets is relatively low. There are some limits in the study of response of sea ice change in Arctic key regions to weather and climate in China. To overcome these problems and to make up the lack of passive microwave sea ice dataset provided by China, FY (Feng Yun) sea ice dataset is developed by NSMC (National Satellite Meteorological Center) on June 27th, 2011. In this dataset, the Enhanced NASA Team (NT2) algorithm is used based on the data of MWRI (Microwave Radiation Imager) sensor carried on FY satellite. In this algorithm, direct radiative transfer model is used to model MWRI brightness temperature for four surface types (ice-free ocean, new-formed ice, one-year ice and multi-years ice) and for different atmospheric conditions. Then, sea ice coverage lookup table (0% to 100% in 1% increments) is obtained based on modeled brightness temperature considering different atmospheric conditions. Sea ice coverage is confirmed by comparing observed value with modeled value. Sea ice extent is consistent with the actual situation in most Arctic regions. Although matching errors between channels and positioning errors have been corrected in FY dataset, the received echo signal is relatively weak due to the shorter antenna on MWRI. The weak echo signal makes it difficult to correctly differentiate the boundary between sea ice and near sea shore land, which greatly impact the total accuracy of the dataset and its application. In order to solve this problem, this study introduces a method of optimizing FY Arctic sea ice dataset based on NSIDC (National Snow and Ice Data Center) sea ice product. In NSIDC product, judgment matrix was created covering the entire grid and identifying each pixel as land, shore, near-shore, offshore or ocean as determined by the land/sea mask. Then, these different pixels are corrected in different degrees, respectively. Sea ice extent calculated from NSIDC product is strongly consistent to the actual situation. The accuracy of FY dataset is greatly improved. The analysis results indicated an extremely significantly positive correlation with the NSIDC product (R~2 = 0.9997) during June 27th, 2011-December 31st, 2015. The maximum deviation percent of daily, monthly and annually sea ice extent is 3.5%, 1.9% and 0.9%, respectively. Also, the optimization process of FY dataset has no obvious influence on the spatial stratified heterogeneity of the dataset. The optimized FY dataset can correctly reflect Arctic sea ice extent and its variation, especially in coastline regions. It can provide reliable basic data for the study of Arctic sea ice change.