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首页> 外文期刊>Journal of Climate >Reassessing statistical downscaling techniques for their robust application under climate change conditions.
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Reassessing statistical downscaling techniques for their robust application under climate change conditions.

机译：重新评估统计缩减技术以在气候变化条件下稳健地应用。

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The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5-Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1 degrees C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-11-00687.1
机译：鉴于其在气候变化研究中的强大适用性，对统计缩减（SD）技术的性能进行了严格的重新评估。为此，除了标准的准确度测度和分布相似性得分外，作者还估计了在气候变暖和异常温暖的历史时期下该方法的鲁棒性。该验证框架适用于相互比较12种不同SD方法（来自模拟，天气类型和回归族）的性能，以降低西班牙的最低和最高温度。首先，根据地理域和预测变量集对这些方法进行校准。结果高度依赖于后者，最佳的预测变量集包括近地表温度数据（尤其是2 m温度），可以适当地区分与对流层低层温度反转有关的寒冷天气。尽管回归方法在相关性方面表现最佳，但模拟和天气生成器方法更适合于再现观测到的分布，尤其是在冬季最低温度的情况下。但是，后两个家庭大大低估了这项工作中考虑的暖期的温度异常。当考虑全球气候模型[ECHAM5-Max Planck Institute（MPI）模型]给出的21世纪末的变暖信号时，发现这一低估至关重要。在这种情况下，与鲁棒性有意义的值一致，不同的降尺度方法提供的升温值的差值在1摄氏度的范围内。因此，拟议的测试是一种有前途的技术，可用于检测在气候变化研究中使用的统计缩减方法中缺乏鲁棒性的技术。数字对象标识符http://dx.doi.org/10.1175/JCLI-D-11-00687.1

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