气候变化领域集成服务门户(CCPortal): Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting

CCPortal

DOI	10.1007/s00382-018-4459-x
	Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting
	Comeau D.; Giannakis D.; Zhao Z.; Majda A.J.
发表日期	2019
ISSN	0930-7575
起始页码	5507
结束页码	5525
卷号	52 期号:2020-09-10
英文摘要	Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. Here an ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. We discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
语种	英语
scopus关键词	analog model; annual variation; forecasting method; prediction; regional climate; sea ice; sea surface temperature; seasonal variation; Arctic
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146346
作者单位	Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States; Climate, Ocean, and Sea Ice Modeling Group, Computational Physics and Methods Group (CCS-2), Los Alamos National Laboratory, Los Alamos, NM, United States; Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, United States
推荐引用方式 GB/T 7714	Comeau D.,Giannakis D.,Zhao Z.,et al. Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting[J],2019,52(2020-09-10).
APA	Comeau D.,Giannakis D.,Zhao Z.,&Majda A.J..(2019).Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting.Climate Dynamics,52(2020-09-10).
MLA	Comeau D.,et al."Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting".Climate Dynamics 52.2020-09-10(2019).