气候变化领域集成服务门户(CCPortal): Prediction skill of the 2012 U.S. great plains flash drought in subseasonal experiment (SubX) models

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DOI	10.1175/JCLI-D-19-0863.1
	Prediction skill of the 2012 U.S. great plains flash drought in subseasonal experiment (SubX) models
	Deangelis A.M.; Wang H.; Koster R.D.; Schubert S.D.; Chang Y.; Marshak J.
发表日期	2020
ISSN	0894-8755
起始页码	6229
结束页码	6253
卷号	33 期号:14
英文摘要	Rapid-onset droughts, known as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA's GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable. While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (3-4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary atmospheric circulation anomalies. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Agricultural robots; Drought; Landforms; Mechanical waves; NASA; Soil moisture; Water resources; Atmospheric circulation anomaly; Dry soil conditions; Global forecast systems; Initial conditions; Lead time; Quasi-stationary; Rapid intensification; Rossby wave; Weather forecasting; atmospheric circulation; climate modeling; climate prediction; drought; experiment; seasonal variation; soil moisture; Great Plains
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/171211
作者单位	Science Systems and Applications, Inc., Lanham, MD, United States; Climate Prediction Center, NOAA, NWS, NCEP, College Park, MD, United States; Global Modeling and Assimilation Office, NASA, GSFC, Greenbelt, MD, United States; Goddard Earth Sciences Technology and Research, Morgan State University, Baltimore, MD, United States
推荐引用方式 GB/T 7714	Deangelis A.M.,Wang H.,Koster R.D.,et al. Prediction skill of the 2012 U.S. great plains flash drought in subseasonal experiment (SubX) models[J],2020,33(14).
APA	Deangelis A.M.,Wang H.,Koster R.D.,Schubert S.D.,Chang Y.,&Marshak J..(2020).Prediction skill of the 2012 U.S. great plains flash drought in subseasonal experiment (SubX) models.Journal of Climate,33(14).
MLA	Deangelis A.M.,et al."Prediction skill of the 2012 U.S. great plains flash drought in subseasonal experiment (SubX) models".Journal of Climate 33.14(2020).