Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature
Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature
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区域气候模型评估背景下绩效测量的现场意义第 1 部分：温度
DOI：
10.1007/s00704-017-2100-2
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发表时间：
2018
影响因子：
3.4
通讯作者：
Wulfmeyer
中科院分区：
文献类型：
--
作者：
Ivanov;Warrach-Sagi;Wulfmeyer
介绍了一种对区域气候模式（RCM）模拟的降尺度性能进行严格空间分析的新方法。它基于网格单元的局部测试的多重比较，也称为“现场”或“全局”显著性。新的性能措施，估计相对于大尺度强迫场的降尺度数据的附加值。该方法是典型的应用到一个标准的欧洲CORDEX后报模拟与天气研究和预报（WRF）模式，再加上陆面模式NOAH在0.11毫米网格分辨率。通过与德国气象局高分辨率网格观测数据的比较，分析了德国冬季和夏季1990-2009年期间的月温度气候学。场显著性检验以有意义的方式控制了错误拒绝的局部检验的比例，并且对空间依赖性具有鲁棒性。因此，统计上显著的局部检验的空间模式也是有意义的。我们从过程导向的角度来解释它们。在冬季和夏季的大部分地区，降尺度分布与观测分布在统计上是不可区分的。由于高估了海拔，在深河河谷中出现了系统性的夏季冷偏差，在沿海地区可能是由于海风环流增强，在大型湖泊中则是由于水温插值。由于强烈的热岛效应，凹型地形的城市地区有一个温暖的夏季偏差，没有反映在观测中。WRF-NOAH在复杂地形区域的月温度场中产生适当的细尺度特征，但在空间均匀的区域，即使是很小的偏差也会导致相对于驾驶再分析的显着恶化。由于全球气候模式（GCM）驱动的模拟的附加值不能小于这个完美的边界估计，这项工作以严格的方式证明了全球气候模拟动力降尺度的明显附加价值。评价方法具有广泛的适用性，因为它是分布自由，强大的空间依赖性，并占时间序列结构。
A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as “field” or “global” significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11∘grid resolution. Monthly temperature climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.