气候变化领域集成服务门户(CCPortal): Potential for western US seasonal snowpack prediction

CCPortal

DOI	10.1073/pnas.1716760115
	Potential for western US seasonal snowpack prediction
	Kapnick S.B.; Yang X.; Vecchi G.A.; Delworth T.L.; Gudgel R.; Malyshev S.; Milly P.C.D.; Shevliakova E.; Underwood S.; Margulis S.A.
发表日期	2018
ISSN	0027-8424
起始页码	1180
结束页码	1185
卷号	115 期号:6
英文摘要	Western US snowpack-snow that accumulates on the ground in the mountains-plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.
英文关键词	Climate; Cryosphere; Seasonal prediction; Snowpack; Water
语种	英语
scopus关键词	Article; cold climate; environmental factor; environmental monitoring; environmental temperature; geographic distribution; prediction and forecasting; priority journal; runoff; seasonal variation; United States; water supply; weather
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/158885
作者单位	Kapnick, S.B., Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ 08540, United States; Yang, X., Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ 08540, United States, Cooperative Programs for the Advancement of Earth System Science, University Corporation for Atmospheric Research, Boulder, CO 80307, United States; Vecchi, G.A., Geosciences, Princeton University, Princeton, NJ 08544, United States, Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, United States; Delworth, T.L., Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ 08540, United States; Gudgel, R., Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ 08540, United States; Malyshev, S., Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ 08540, United States...
推荐引用方式 GB/T 7714	Kapnick S.B.,Yang X.,Vecchi G.A.,et al. Potential for western US seasonal snowpack prediction[J],2018,115(6).
APA	Kapnick S.B..,Yang X..,Vecchi G.A..,Delworth T.L..,Gudgel R..,...&Margulis S.A..(2018).Potential for western US seasonal snowpack prediction.Proceedings of the National Academy of Sciences of the United States of America,115(6).
MLA	Kapnick S.B.,et al."Potential for western US seasonal snowpack prediction".Proceedings of the National Academy of Sciences of the United States of America 115.6(2018).