Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small
Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small
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DOI：
10.1029/2011wr011524
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发表时间：
2012-09-01
影响因子：
5.4
通讯作者：
Pegram, Geoffrey
中科院分区：
文献类型：
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作者：
Bardossy, Andres;Pegram, Geoffrey
在此之前，Bardossy和Pegram(2011)根据环流模式对莱茵河流域172个地区25公里尺度上的区域气候模式(RCM)降雨量进行了缩减。由于担心RCM降水量的空间统计特性，我们计算了同一集合日降水量的空间相关系数(CCCS)。我们发现，RCM降水的CCCS明显低于观测值。基于CP的缩小尺度导致CCCS增加，但仍低于观测到的CCCS。这种对空间相关性的低估，因此观察到的集群，对基于RCM输出的大范围洪水计算具有潜在的有害后果，即使在25公里尺度上完全消除基于CP的偏差之后也是如此。因此，在本文中，我们描述了两种新的记录相关方法，它们被设计用于在进行最终的分位数-分位数变换之前将RCM估计的CCCS校正回观测集合的CCCS。我们使用两种记录相关的方法：矩阵方法和序贯回归方法。它们都产生了类似的结果，而且都很成功，因为它们几乎准确地捕捉到了观测到的CCCS，解决了干旱天数比例高带来的问题。尽管记录相关技术取得了很大的成功(当比较处理前后的空间相关性时)，但该方法并没有完全解决重建问题：(1)大区域的极端日降雨量没有被完全重新捕获；(2)在不重叠的三重站点上用熵计算的聚类行为证实，二维协方差度量虽然非常有效，但并不能捕获在自然降雨中观察到的所有聚类。
Previously, Bardossy and Pegram (2011) achieved downscaling of regional climate model (RCM) rainfall, dependent on circulation patterns (CPs), over 172 areas of the Rhine basin at the 25 km scale. Uneasy about the spatial statistics of the downscaled RCM rainfall, we calculated the spatial cross-correlation coefficients (CCCs) of daily rainfalls of the same set. We found that the CCCs of the RCM precipitations were significantly lower than those of the observations. CP-based downscaling led to an increase of the CCCs which still remained below the observed CCCs. This underestimation of spatial correlation, hence observed clustering, has potentially deleterious consequences for flood calculations over large areas based on RCM outputs, even after full CP-based bias elimination at the 25 km scale. In this paper we therefore describe two novel recorrelation methods designed to correct the CCCs of the RCM estimates back to those of the observed set before undertaking the final quantile-quantile transform. We use two methods of recorrelation: matrix methods and sequential regression. They both produced similar results and were successful in that they captured the observed CCCs almost exactly, coping with problems presented by the high proportion of dry days. In spite of the complete success of the recorrelation techniques (when comparing spatial correlations before and after treatment) the methodology does not solve the reconstitution problem fully: (1) extreme daily rainfall totals on large areas are not recaptured completely and (2) clustering behavior, as computed by entropy on nonoverlapping triple sites, confirms that the two-dimensional covariance dependence measure, although very effective, does not capture all of the clustering observed in natural rainfall.