Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections

Lauren Paige Seaby, Jens Christian Refsgaard, Torben Sonnenborg, Simon Stisen, Jens Hesselbjerg Christensen, Karsten Høgh Jensen

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    Resumé

    An ensemble of 11 regional climate model (RCM) projections are analysed for Denmark from a hydrological modelling inputs perspective. Two bias correction approaches are applied: a relatively simple monthly delta change (DC) method and a more complex daily distribution-based scaling (DBS) method. Differences in the strength and direction of climate change signals are compared across models and between bias correction methods, the statistical significance of climate change is tested as it evolves over the 21st century, and the impact of choice of reference and change period lengths is analysed as it relates to assumptions of stationary in current climate and change signals. Both DC and DBS methods are able to capture mean monthly and seasonal climate characteristics in temperature (T), precipitation (P), and potential evapotranspiration (ETpot). For P, which is comparatively more variable by day, the DC approach is insufficient at recreating projected regimes while the DBS correction method can transfer changes in the mean as well as the variance, improving the characterisation of temporal dynamics as well as heavy precipitation events. Climate change signals in the near-future (2011–2040) are hidden by natural variability and are therefore not significant, in the mid-future (2041–2070) the significance of climate change signals depend on the choice of climate model, and in the far future (2071–2100) climate change signals are strong across all models and variables. Some models already display significant differences in climate variables within the past timeframe for Denmark. Current climate characteristics are not necessarily stationary and the temporal positioning of a reference period might impact the magnitude of relative climate change. Reference and change period lengths over 15 years are adequate in size to overcome natural variability and still have stationarity in the climate change signal within the periods.
    OriginalsprogEngelsk
    TidsskriftJournal of Hydrology
    Vol/bind486
    Sider (fra-til)479-493
    Antal sider15
    ISSN0022-1694
    DOI
    StatusUdgivet - 12 apr. 2013

    Citer dette

    Seaby, Lauren Paige ; Refsgaard, Jens Christian ; Sonnenborg, Torben ; Stisen, Simon ; Christensen, Jens Hesselbjerg ; Jensen, Karsten Høgh. / Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections. I: Journal of Hydrology. 2013 ; Bind 486. s. 479-493.
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    abstract = "An ensemble of 11 regional climate model (RCM) projections are analysed for Denmark from a hydrological modelling inputs perspective. Two bias correction approaches are applied: a relatively simple monthly delta change (DC) method and a more complex daily distribution-based scaling (DBS) method. Differences in the strength and direction of climate change signals are compared across models and between bias correction methods, the statistical significance of climate change is tested as it evolves over the 21st century, and the impact of choice of reference and change period lengths is analysed as it relates to assumptions of stationary in current climate and change signals. Both DC and DBS methods are able to capture mean monthly and seasonal climate characteristics in temperature (T), precipitation (P), and potential evapotranspiration (ETpot). For P, which is comparatively more variable by day, the DC approach is insufficient at recreating projected regimes while the DBS correction method can transfer changes in the mean as well as the variance, improving the characterisation of temporal dynamics as well as heavy precipitation events. Climate change signals in the near-future (2011–2040) are hidden by natural variability and are therefore not significant, in the mid-future (2041–2070) the significance of climate change signals depend on the choice of climate model, and in the far future (2071–2100) climate change signals are strong across all models and variables. Some models already display significant differences in climate variables within the past timeframe for Denmark. Current climate characteristics are not necessarily stationary and the temporal positioning of a reference period might impact the magnitude of relative climate change. Reference and change period lengths over 15 years are adequate in size to overcome natural variability and still have stationarity in the climate change signal within the periods.",
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    Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections. / Seaby, Lauren Paige; Refsgaard, Jens Christian; Sonnenborg, Torben; Stisen, Simon; Christensen, Jens Hesselbjerg; Jensen, Karsten Høgh.

    I: Journal of Hydrology, Bind 486, 12.04.2013, s. 479-493.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections

    AU - Seaby, Lauren Paige

    AU - Refsgaard, Jens Christian

    AU - Sonnenborg, Torben

    AU - Stisen, Simon

    AU - Christensen, Jens Hesselbjerg

    AU - Jensen, Karsten Høgh

    PY - 2013/4/12

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    N2 - An ensemble of 11 regional climate model (RCM) projections are analysed for Denmark from a hydrological modelling inputs perspective. Two bias correction approaches are applied: a relatively simple monthly delta change (DC) method and a more complex daily distribution-based scaling (DBS) method. Differences in the strength and direction of climate change signals are compared across models and between bias correction methods, the statistical significance of climate change is tested as it evolves over the 21st century, and the impact of choice of reference and change period lengths is analysed as it relates to assumptions of stationary in current climate and change signals. Both DC and DBS methods are able to capture mean monthly and seasonal climate characteristics in temperature (T), precipitation (P), and potential evapotranspiration (ETpot). For P, which is comparatively more variable by day, the DC approach is insufficient at recreating projected regimes while the DBS correction method can transfer changes in the mean as well as the variance, improving the characterisation of temporal dynamics as well as heavy precipitation events. Climate change signals in the near-future (2011–2040) are hidden by natural variability and are therefore not significant, in the mid-future (2041–2070) the significance of climate change signals depend on the choice of climate model, and in the far future (2071–2100) climate change signals are strong across all models and variables. Some models already display significant differences in climate variables within the past timeframe for Denmark. Current climate characteristics are not necessarily stationary and the temporal positioning of a reference period might impact the magnitude of relative climate change. Reference and change period lengths over 15 years are adequate in size to overcome natural variability and still have stationarity in the climate change signal within the periods.

    AB - An ensemble of 11 regional climate model (RCM) projections are analysed for Denmark from a hydrological modelling inputs perspective. Two bias correction approaches are applied: a relatively simple monthly delta change (DC) method and a more complex daily distribution-based scaling (DBS) method. Differences in the strength and direction of climate change signals are compared across models and between bias correction methods, the statistical significance of climate change is tested as it evolves over the 21st century, and the impact of choice of reference and change period lengths is analysed as it relates to assumptions of stationary in current climate and change signals. Both DC and DBS methods are able to capture mean monthly and seasonal climate characteristics in temperature (T), precipitation (P), and potential evapotranspiration (ETpot). For P, which is comparatively more variable by day, the DC approach is insufficient at recreating projected regimes while the DBS correction method can transfer changes in the mean as well as the variance, improving the characterisation of temporal dynamics as well as heavy precipitation events. Climate change signals in the near-future (2011–2040) are hidden by natural variability and are therefore not significant, in the mid-future (2041–2070) the significance of climate change signals depend on the choice of climate model, and in the far future (2071–2100) climate change signals are strong across all models and variables. Some models already display significant differences in climate variables within the past timeframe for Denmark. Current climate characteristics are not necessarily stationary and the temporal positioning of a reference period might impact the magnitude of relative climate change. Reference and change period lengths over 15 years are adequate in size to overcome natural variability and still have stationarity in the climate change signal within the periods.

    KW - climate Change

    KW - bias correction

    KW - ENSEMBLES

    KW - delta change

    KW - distribution-based scaling

    U2 - 10.1016/j.jhydrol.2013.02.015

    DO - 10.1016/j.jhydrol.2013.02.015

    M3 - Journal article

    VL - 486

    SP - 479

    EP - 493

    JO - Journal of Hydrology

    JF - Journal of Hydrology

    SN - 0022-1694

    ER -