气候变化领域集成服务门户(CCPortal): Benchmark seasonal prediction skill estimates based on regional indices

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DOI	10.5194/tc-13-1073-2019
	Benchmark seasonal prediction skill estimates based on regional indices
	Walsh J.E.; Stewart J.S.; Fetterer F.
发表日期	2019
ISSN	19940416
EISSN	13
起始页码	1073
结束页码	1088
卷号	13 期号:4
英文摘要	Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this study we use observational data to evaluate the contribution of the trend to the skill of persistencebased statistical forecasts of monthly and seasonal ice extent on the pan-Arctic and regional scales.We focus on the Beaufort Sea for which the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the winter and summer trends during the 1990s. Persistence-based statistical forecasts of the Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. In only a few regions does September ice extent correlate significantly with antecedent ice anomalies in the same region more than 2 months earlier. The springtime "predictability barrier" in regional forecasts based on persistence of ice extent anomalies is not reduced by the inclusion of several decades of pre-satellite data. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea's ice extent as far back as July explains about 20% of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two. © 2019 Author(s).
学科领域	assessment method; autocorrelation; benchmarking; forecasting method; index method; numerical model; prediction; satellite data; sea ice; seasonal variation; statistical analysis; summer; trend analysis; winter; Arctic Ocean; Beaufort Sea; Chukchi Sea; East Siberian Sea
语种	英语
scopus关键词	assessment method; autocorrelation; benchmarking; forecasting method; index method; numerical model; prediction; satellite data; sea ice; seasonal variation; statistical analysis; summer; trend analysis; winter; Arctic Ocean; Beaufort Sea; Chukchi Sea; East Siberian Sea
来源期刊	The Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/118899
作者单位	Alaska Center for Climate Assessment and Policy, University of Alaska, Fairbanks, AK 99709, United States; National Snow and Ice Data Center, University of Colorado, Boulder, CO 80303, United States
推荐引用方式 GB/T 7714	Walsh J.E.,Stewart J.S.,Fetterer F.. Benchmark seasonal prediction skill estimates based on regional indices[J],2019,13(4).
APA	Walsh J.E.,Stewart J.S.,&Fetterer F..(2019).Benchmark seasonal prediction skill estimates based on regional indices.The Cryosphere,13(4).
MLA	Walsh J.E.,et al."Benchmark seasonal prediction skill estimates based on regional indices".The Cryosphere 13.4(2019).