气候变化领域集成服务门户(CCPortal): S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts

CCPortal

DOI	10.1002/wcc.567
	S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts
	Cohen J.; Coumou D.; Hwang J.; Mackey L.; Orenstein P.; Totz S.; Tziperman E.
发表日期	2019
ISSN	1757-7780
EISSN	1757-7779
卷号	10 期号:2
英文摘要	The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models. © 2018 The Authors. WIREs Climate Change published by Wiley Periodicals, Inc.
英文关键词	climate prediction; machine learning; polar vortex; unsupervised learning
语种	英语
scopus关键词	Artificial intelligence; Climate models; Climatology; Dynamical systems; Learning systems; Statistics; Unsupervised learning; Vortex flow; Climate prediction; Knowledge generations; Machine learning methods; Polar vortex; Seasonal climate prediction; Seasonal forecasts; Seasonal prediction; Statistical techniques; Weather forecasting; accuracy assessment; climate modeling; climate prediction; machine learning; polar vortex; seasonal variation; unsupervised classification; weather forecasting; Arctic
来源期刊	Wiley Interdisciplinary Reviews: Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129924
作者单位	Atmospheric and Environmental Research, Inc., Lexington, MA, United States; Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, United States; Department of Water and Climate Risk, Institute for Environmental Studies, VU University Amsterdam, Amsterdam, Netherlands; Potsdam Institute for Climate Impact Research, Potsdam, Germany; Stanford University, Stanford, CA, United States; Microsoft Research New England, Cambridge, MA, United States; Department of Earth and Planetary Sciences, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
推荐引用方式 GB/T 7714	Cohen J.,Coumou D.,Hwang J.,et al. S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts[J],2019,10(2).
APA	Cohen J..,Coumou D..,Hwang J..,Mackey L..,Orenstein P..,...&Tziperman E..(2019).S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts.Wiley Interdisciplinary Reviews: Climate Change,10(2).
MLA	Cohen J.,et al."S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts".Wiley Interdisciplinary Reviews: Climate Change 10.2(2019).