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首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment
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An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

机译:在欧洲的统计缩小方法的大型集合的相互熟练:价值完美预测器交叉验证实验的结果

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摘要

>VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. >Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). > Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken.
机译: >价值是一个开放的欧洲协作,以实现气候变化研究的缩减方法,专注于不同的验证方面(边缘,颞,极端,空间,基于过程, 等等。)。在这里,我们描述了参与的方法和第一个实验中的第一个结果,使用“完美”再分析(和Reanalysis驱动的区域气候模型(RCM))预测因子,以评估缩小降水和温度的固有性能,而不是一套86代表欧洲主要气候区域的电台。本研究构成了统计较低的方法最大,最全面的迄今为止的统计信息竞争方法,涵盖了三种常见的次要方法(完善预后,模型输出统计数据包括偏差校正和天气发电机),共有超过50种最多的次要方法常用技术。 >总体而言,大多数次要方法大大改善了(再分析或RCM)原始模型偏差,没有方法或技术似乎通常是优越的,因为存在大的方法 - 方法可变性。大多数影响结果的主要因素是方法的季节性校准(例如,使用移动窗口)及其随机性质。在可能的情况下,所使用的特定预测变量也在验证结果和预测器 - 预测和链路的强度方面发挥重要作用,这表明局部可变性解释。然而,本研究不能为模拟区域未来气候的方法的技能提供结论性评估,并且在欧洲德克克克特倡议的框架内将很快进行进一步的实验(其中价值活动合并并遵循)。<最后,研究透明度和再现性是一直是一个主要的关注和实质性步骤。

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  • 作者单位

    Meteorology Group Instituto de Física de CantabriaCSIC‐University of CantabriaSantander Spain;

    Wegener Center for Climate and Global ChangeUniversity of GrazGraz Austria;

    School of Geography Earth and Environmental SciencesUniversity of BirminghamBirmingham UK;

    Department of Physical Geography and Geoecology Faculty of ScienceCharles UniversityPrague Czech Republic;

    Institute of GeographyUniversity of AugsburgAugsburg Germany;

    The Norwegian Meteorological InstituteOsla Norway;

    Department of Geography/Oeschger Centre for Climate Change ResearchUniversity of BernBern Switzerland;

    Department of Meteorology and ClimatologyUniversity of LodzLodz Poland;

    Rossby CentreSwedish Meteorological and Hydrological InstituteNorrk?ping Sweden;

    Federal Office of Meteorology and Climatology MeteoSwissZurich Switzerland;

    Meteorology Group Instituto de Física de CantabriaCSIC‐University of CantabriaSantander Spain;

    Meteorology Group Departamento de Matemática Aplicada y ComputaciónUniversity of CantabriaSantander Spain;

    Meteorology Group Instituto de Física de CantabriaCSIC‐University of CantabriaSantander Spain;

    Meteorology Group Departamento de Matemática Aplicada y ComputaciónUniversity of CantabriaSantander Spain;

    Meteorology Group Instituto de Física de CantabriaCSIC‐University of CantabriaSantander Spain;

    Meteorology Group Instituto de Física de CantabriaCSIC‐University of CantabriaSantander Spain;

    Laboratoire des Sciences du Climat et de l'Environnement (LSCE‐IPSL/CNRS)Paris France;

    Institute of Atmospheric PhysicsCzech Academy of SciencesPrague Czech Republic;

    Fundación Para la Investigación del Clima (FIC)Madrid Spain;

    Fundación Para la Investigación del Clima (FIC)Madrid Spain;

    University of Helsinki (UHEL)Helsinki Finland;

    University of Helsinki (UHEL)Helsinki Finland;

    Université Grenoble Alpes CNRS IRD Grenoble INP IGEGrenoble France;

    Université Grenoble Alpes CNRS IRD Grenoble INP IGEGrenoble France;

    Agencia Estatal de Meteorología (AEMET)Madrid Spain;

    Agencia Estatal de Meteorología (AEMET)Madrid Spain;

    Meteorological InstituteUniversity of BonnBonn Germany;

    Department of Applied PhysicsUniversity of BarcelonaBarcelona Spain;

    Swedish Meteorological and Hydrological Institute (SMHI)Norrk?ping Sweden;

    Global Change Research Institute Czech Academy of SciencesBrno Czech Republic;

    E?tv?s Loránd University (ELU)Budapest Hungary;

    E?tv?s Loránd University (ELU)Budapest Hungary;

    Federal Office of Meteorology and Climatology MeteoSwissZurich Switzerland;

    Federal Office of Meteorology and Climatology MeteoSwissZurich Switzerland;

    Instituto Dom LuizFaculdade de Ciências Universidade de Lisboa (IDL)Lisboa Portugal;

    Instituto Dom LuizFaculdade de Ciências Universidade de Lisboa (IDL)Lisboa Portugal;

    Adam Mickiewicz UniversityPoznań Poland;

    CECI Université de Toulouse CNRS CerfacsToulouse France;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 气候学;
  • 关键词

    bias adjustment; CORDEX; downscaling; model output statistics; perfect prognosis; reproducibility; validation; weather generators;

    机译:偏见调整;CORDEX;缩小;模型输出统计;完全预后;再现性;验证;天气发生器;

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