GSTDTAP  > 气候变化
DOI10.1175/JCLI-D-18-0765.1
Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe
Kamarainen, Matti1; Uotila, Petteri2; Karpechko, Alexey Yu.3; Hyvarinen, Otto1; Lehtonen, Ilari1; Raisanen, Jouni2
2019-09-01
发表期刊JOURNAL OF CLIMATE
ISSN0894-8755
EISSN1520-0442
出版年2019
卷号32期号:17页码:5363-5379
文章类型Article
语种英语
国家Finland
英文摘要

A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons.


英文关键词Europe Principal components analysis Forecast verification skill Seasonal forecasting Statistical forecasting Decadal variability
领域气候变化
收录类别SCI-E
WOS记录号WOS:000477657300002
WOS关键词NORTH-ATLANTIC OSCILLATION ; EURASIAN SNOW COVER ; SEA-ICE ; SUMMER TEMPERATURE ; CLIMATE ; PREDICTION ; WINTER ; VARIABILITY ; WEATHER ; MODES
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/186746
专题气候变化
作者单位1.Finnish Meteorol Inst, Weather & Climate Change Impact Res, Helsinki, Finland;
2.Univ Helsinki, Inst Atmospher & Earth Syst Res, Helsinki, Finland;
3.Finnish Meteorol Inst, Meteorol Res, Helsinki, Finland
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GB/T 7714
Kamarainen, Matti,Uotila, Petteri,Karpechko, Alexey Yu.,et al. Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe[J]. JOURNAL OF CLIMATE,2019,32(17):5363-5379.
APA Kamarainen, Matti,Uotila, Petteri,Karpechko, Alexey Yu.,Hyvarinen, Otto,Lehtonen, Ilari,&Raisanen, Jouni.(2019).Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe.JOURNAL OF CLIMATE,32(17),5363-5379.
MLA Kamarainen, Matti,et al."Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe".JOURNAL OF CLIMATE 32.17(2019):5363-5379.
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