DOI: 10.1175/JCLI-D-12-00177.1
Scopus记录号: 2-s2.0-84872956299
论文题名: Short-term climate extremes: Prediction skill and predictability
作者: Becker E.J. ; Van Den Dool H. ; Peña M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2013
卷: 26, 期: 2 起始页码: 512
结束页码: 531
语种: 英语
Scopus关键词: Atlantic hurricane
; Climate forecasts
; Contingency table
; Global climate model
; Permutation tests
; Precipitation extremes
; Precipitation forecast
; Prediction capability
; Root-mean-square errors
; Sample sizes
; Sea surface temperature (SST)
; Signal to noise
; South America
; Surface temperatures
; Hurricanes
; Nickel compounds
; Weather forecasting
; Atmospheric temperature
; climate prediction
; extreme event
; hurricane
; precipitation (climatology)
; sea surface temperature
; weather forecasting
; North America
; South America
英文摘要: Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982-2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables. The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space-time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not. © 2013 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52061
Appears in Collections: 气候变化事实与影响
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作者单位: Climate Prediction Center, NOAA/NWS/NCEP, College Park, MD, United States; IMSG at Environmental Modeling Center, NOAA/NWS/NCEP, College Park, MD, United States
Recommended Citation:
Becker E.J.,Van Den Dool H.,Peña M.. Short-term climate extremes: Prediction skill and predictability[J]. Journal of Climate,2013-01-01,26(2)