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论文标题：Climatologies at high resolution for the earth’s land surface areas

期刊：

作者：Dirk Nikolaus Karger, Olaf Conrad, Jürgen Böhner, Tobias Kawohl, Holger Kreft, Rodrigo Wilber Soria-Auza, Niklaus E. Zimmermann, H. Peter Linder & Michael Kessler

发表时间：2017/09/05

数字识别码：10.1038/sdata.2017.122

原文链接：

有关气候条件的高分辨率信息对于环境和生态明升体育app中的许多应用来说至关重要。在《明升体育app数据》发表的Climatologies at high resolution for the earth’s land surface areas一文中，来自瑞士苏黎世大学的Dirk Nikolaus Karger及同事以CHELSA（地球陆地表面区域的高分辨率气候学）数据的形式呈现了ERA-Interim（ERA-Interim是1979年以来的全球大气再分析，不断实时更新）再分析经降尺度模型产生的温度和降水量预计值，分辨率高达30弧秒。温度算法主要基于对大气温度进行统计学降尺度。降水算法则结合了地形预测因素，包括风场，山谷分布和边界层高度，并对结果进行了偏差校准。由此作者得到了1979-2013年期间的每月的温度和降水信息。作者将来自CHELSA算法的数据与其他标准化产品及全球历史气候网络的下属气象台数据进行了比较，并且还检验了新的气候学数据在物种分布模型中的应用情况，发现能够提高物种分布预测的准确性。作者发现在温度方面，CHELSA数据与其他产品准确度类似，但其对降水类型的预测明显更好。

图1：基于WorldClim和CHELSA的气候数据及紫云英物种分布比较图。左：气候数据，右：物种分布，上：WorldClim，下：CHELSA。

摘要：High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

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期刊介绍： () is a peer-reviewed, open-access journal for descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data. Scientific Data welcomes submissions from a broad range of research disciplines, including descriptions of big or small datasets, from major consortiums to single research groups. Scientific Data primarily publishes Data Descriptors, a new type of publication that focuses on helping others reuse data, and crediting those who share.

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