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    Time Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series

    Source: Journal of Climate:;2001:;volume( 014 ):;issue: 020::page 4047
    Author:
    Huth, Radan
    ,
    Kyselý, Jan
    ,
    Dubrovský, Martin
    DOI: 10.1175/1520-0442(2001)014<4047:TSOOGS>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: The time structure of simulated daily maximum and minimum temperature series, produced by several different methods, is compared with observations at six stations in central Europe. The methods are statistical downscaling, stochastic weather generator, and general circulation models (GCMs). Outputs from control runs of two GCMs are examined: ECHAM3 and CCCM2. Four time series are constructed by statistical downscaling using multiple linear regression of 500-hPa heights and 1000-/500-hPa thickness: (i) from observations with variance reproduced by the inflation technique, (ii) from observations with variance reproduced by adding a white noise process, and (iii) from the two GCMs. Two runs of the weather generator were performed, one considering and one neglecting the annual cycle of lag-0 and lag-1 correlations among daily weather characteristics. Standard deviation and skewness of day-to-day temperature changes and lag-1 autocorrelations are examined. For heat and cold waves, the occurrence frequency, mean duration, peak temperature, and mean position within the year are studied. Possible causes of discrepancies between the simulated and observed time series are discussed and identified. They are shown to stem, among others, from (i) the absence of physics in downscaled and stochastically generated series, (ii) inadequacies of treatment of physical processes in GCMs, (iii) assumptions of linearity in downscaling equations, and (iv) properties of the underlying statistical model of the weather generator. In downscaling, variance inflation is preferable to the white noise addition in most aspects as the latter results in highly overestimated day-to-day variability. The inclusion of the annual cycle of correlations into the weather generator does not lead to an overall improvement of the temperature series produced. None of the methods appears to be able to reproduce all the characteristics of time structure correctly.
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      Time Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4199578
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    contributor authorHuth, Radan
    contributor authorKyselý, Jan
    contributor authorDubrovský, Martin
    date accessioned2017-06-09T16:01:29Z
    date available2017-06-09T16:01:29Z
    date copyright2001/10/01
    date issued2001
    identifier issn0894-8755
    identifier otherams-5906.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4199578
    description abstractThe time structure of simulated daily maximum and minimum temperature series, produced by several different methods, is compared with observations at six stations in central Europe. The methods are statistical downscaling, stochastic weather generator, and general circulation models (GCMs). Outputs from control runs of two GCMs are examined: ECHAM3 and CCCM2. Four time series are constructed by statistical downscaling using multiple linear regression of 500-hPa heights and 1000-/500-hPa thickness: (i) from observations with variance reproduced by the inflation technique, (ii) from observations with variance reproduced by adding a white noise process, and (iii) from the two GCMs. Two runs of the weather generator were performed, one considering and one neglecting the annual cycle of lag-0 and lag-1 correlations among daily weather characteristics. Standard deviation and skewness of day-to-day temperature changes and lag-1 autocorrelations are examined. For heat and cold waves, the occurrence frequency, mean duration, peak temperature, and mean position within the year are studied. Possible causes of discrepancies between the simulated and observed time series are discussed and identified. They are shown to stem, among others, from (i) the absence of physics in downscaled and stochastically generated series, (ii) inadequacies of treatment of physical processes in GCMs, (iii) assumptions of linearity in downscaling equations, and (iv) properties of the underlying statistical model of the weather generator. In downscaling, variance inflation is preferable to the white noise addition in most aspects as the latter results in highly overestimated day-to-day variability. The inclusion of the annual cycle of correlations into the weather generator does not lead to an overall improvement of the temperature series produced. None of the methods appears to be able to reproduce all the characteristics of time structure correctly.
    publisherAmerican Meteorological Society
    titleTime Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series
    typeJournal Paper
    journal volume14
    journal issue20
    journal titleJournal of Climate
    identifier doi10.1175/1520-0442(2001)014<4047:TSOOGS>2.0.CO;2
    journal fristpage4047
    journal lastpage4061
    treeJournal of Climate:;2001:;volume( 014 ):;issue: 020
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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