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DOI | 10.1088/1748-9326/ab80ef |
Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies | |
Lin, Peirong1,6; Yang, Zong-Liang1,2; Wei, Jiangfeng3; Dickinson, Robert E.1,7; Zhang, Yongfei4; Zhao, Long5 | |
2020-06-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:6 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Peoples R China |
英文摘要 | Properly initializing land snow conditions with multi-satellite data assimilation (DA) may help tackle the long-standing challenge of Asian monsoon seasonal forecasts. However, to what extent can snow DA help resolve the problem remains largely unexplored. Here we establish, for the first time, that improved springtime snow initializations assimilating the Moderate Spectral Imaging Satellite (MODIS) snow cover fraction and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data can improve the simulation accuracy of Asian monsoon seasonal anomalies. Focusing on the western Tibetan Plateau (TP) and mid- to high-latitude Eurasia (EA), two regions where multi-satellite snow DA is critical, we found that DA influences the monsoon circulation at different months depending on the regional snow-atmosphere coupling strengths. For the pre-monsoon season, accurate initialization of the TP snow is key, and assimilating MODIS data slightly outperforms jointly assimilating MODIS and GRACE data. For the peak-monsoon season, accurate initialization of the EA snow is more important due to its long memory, and assimilating GRACE data brings the most pronounced gains. Among all the Asian monsoon subregions, the most robust improvement is seen over central north India, a likely result of the region's strong sensitivity to thermal forcing. While this study highlights complementary snow observations as promising new sources of the monsoon predictability, it also clarifies complexities in translating DA to useful monsoon forecast skill, which may help bridge the gap between land DA and dynamical climate forecasting studies. |
英文关键词 | Asian monsoon dynamical seasonal forecast multi-satellite snow data assimilation GRACE MODIS |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000546800800001 |
WOS关键词 | LAND DATA ASSIMILATION ; INDIAN-SUMMER MONSOON ; SOIL-MOISTURE ; TIBETAN PLATEAU ; STREAMFLOW FORECASTS ; WATER STORAGE ; MODEL ; PREDICTABILITY ; SKILL ; VARIABILITY |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/289336 |
专题 | 气候变化 |
作者单位 | 1.Univ Texas Austin, Jackson Sch Geosci, Austin, TX 78712 USA; 2.Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm Res Temperate East A, Beijing, Peoples R China; 3.Nanjing Univ Informat Sci & Technol, Int Joint Res Lab Climate & Environm Change, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Key Lab Meteorol Disaster,Minist Educ, Nanjing, Peoples R China; 4.Princeton Univ, Program Atmospher & Ocean Sci, Princeton, NJ 08544 USA; 5.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing, Peoples R China; 6.Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA; 7.Univ Calif Los Angeles, Los Angeles, CA USA |
推荐引用方式 GB/T 7714 | Lin, Peirong,Yang, Zong-Liang,Wei, Jiangfeng,et al. Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(6). |
APA | Lin, Peirong,Yang, Zong-Liang,Wei, Jiangfeng,Dickinson, Robert E.,Zhang, Yongfei,&Zhao, Long.(2020).Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies.ENVIRONMENTAL RESEARCH LETTERS,15(6). |
MLA | Lin, Peirong,et al."Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies".ENVIRONMENTAL RESEARCH LETTERS 15.6(2020). |
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