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    Short-Term Climate Extremes: Prediction Skill and Predictability

    Source: Journal of Climate:;2012:;volume( 026 ):;issue: 002::page 512
    Author:
    Becker, Emily J.
    ,
    van den Dool, Huug
    ,
    Peña, Malaquias
    DOI: 10.1175/JCLI-D-12-00177.1
    Publisher: American Meteorological Society
    Abstract: orecasts 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.
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      Short-Term Climate Extremes: Prediction Skill and Predictability

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    contributor authorBecker, Emily J.
    contributor authorvan den Dool, Huug
    contributor authorPeña, Malaquias
    date accessioned2017-06-09T17:06:18Z
    date available2017-06-09T17:06:18Z
    date copyright2013/01/01
    date issued2012
    identifier issn0894-8755
    identifier otherams-79454.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4222236
    description abstractorecasts 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.
    publisherAmerican Meteorological Society
    titleShort-Term Climate Extremes: Prediction Skill and Predictability
    typeJournal Paper
    journal volume26
    journal issue2
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-12-00177.1
    journal fristpage512
    journal lastpage531
    treeJournal of Climate:;2012:;volume( 026 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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