气候变化领域集成服务门户(CCPortal): Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies

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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 P.; Yang Z.-L.; Wei J.; Dickinson R.E.; Zhang Y.; Zhao L.
发表日期	2020
ISSN	17489318
卷号	15 期号:6
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. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	Asian monsoon; dynamical seasonal forecast; GRACE; MODIS; multi-satellite snow data assimilation
语种	英语
scopus关键词	Atmospheric thermodynamics; Digital storage; Forecasting; Geodetic satellites; Radiometers; Spectroscopy; Climate forecasting; Coupling strengths; Gravity recovery and climate experiments; Monsoon circulations; Satellite data assimilation; Seasonal forecasts; Simulation accuracy; Terrestrial water storage; Snow
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153980
作者单位	Jackson School of Geosciences, University of Texas at Austin, Austin, TX, United States; Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China; Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States; Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, China; Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; University of California, Los Angeles, CA, United States
推荐引用方式 GB/T 7714	Lin P.,Yang Z.-L.,Wei J.,et al. Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies[J],2020,15(6).
APA	Lin P.,Yang Z.-L.,Wei J.,Dickinson R.E.,Zhang Y.,&Zhao L..(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 P.,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).