利用来自CMIP5历史和AMIP5模拟的33个模型的数据,我们对南亚地区总降水量及其对流和大尺度成分的偏差进行了系统分析。我们已经使用了23年(1983-2005年)的数据,并且计算了关于PERSIANN-CDR降水的模型偏差(对流/大比例比来自TRMM 3A12)。将聚类算法应用于CMIP5模型中出现的总降水,对流降水和大规模降水偏差,以基于全局偏差模式中的相似度对它们进行分组。随后,对AMIP5模型进行了分析,以得出偏差是否主要归因于各个模型的大气成分或海洋成分。我们的分析表明,属于给定组的一组单个模型对用于聚类的变量(总/对流/大规模降水)有些敏感。在南亚地区,一些对流和大规模降水偏差在各组之间是常见的,强调尽管在全球范围内,偏差的模式可能足以将模型分为不同的组,但在地区上可能并不正确。 。一般而言,模型会高估对流成分,而低估南亚区域的大尺度成分,尽管空间大小取决于模型组而有所不同。我们发现对流降水偏差主要受这些模型中使用的对流参数化方案中使用的闭合和触发假设的控制,并在较小程度上了解各个云模型的详细信息。使用两种不同的方法:(i)聚类,(ii)比较CMIP5模型与AMIP5对应模型的偏差模式,我们发现,总体而言,大气成分(而不是通过SSTs和大气-海洋偏差产生的海洋成分)反馈)在确定对流和大规模降水偏差中起着重要作用。然而,已经发现海洋成分对于一个对流群在决定对流降水偏向(在海洋大陆上)方面很重要。大气成分(而不是通过海表温度和大气-海洋反馈偏差产生的海洋成分)在确定对流和大规模降水偏差方面起着重要作用。然而,已经发现海洋成分对于一个对流群在决定对流降水偏向(在海洋大陆上)方面很重要。大气成分(而不是通过海表温度和大气-海洋反馈偏差产生的海洋成分)在确定对流和大规模降水偏差方面起着重要作用。然而,已经发现海洋成分对于一个对流群在决定对流降水偏向(在海洋大陆上)方面很重要。
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Precipitation Biases in CMIP5 Models over the South Asian Region.
Using data from 33 models from the CMIP5 historical and AMIP5 simulations, we have carried out a systematic analysis of biases in total precipitation and its convective and large-scale components over the south Asian region. We have used 23 years (1983–2005) of data, and have computed model biases with respect to the PERSIANN-CDR precipitation (with convective/large-scale ratio derived from TRMM 3A12). A clustering algorithm was applied on the total, convective, and large-scale precipitation biases seen in CMIP5 models to group them based on the degree of similarity in the global bias patterns. Subsequently, AMIP5 models were analyzed to conclude if the biases were primarily due to the atmospheric component or due to the oceanic component of individual models. Our analysis shows that the set of individual models falling in a given group is somewhat sensitive to the variable (total/convective/large-scale precipitation) used for clustering. Over the south Asian region, some of the convective and large-scale precipitation biases are common across groups, emphasizing that although on a global scale the bias patterns may be sufficiently different to cluster the models into different groups, regionally, it may not be true. In general, models tend to overestimate the convective component and underestimate the large-scale component over the south Asian region, although with spatially varying magnitudes depending on the model group. We find that the convective precipitation biases are largely governed by the closure and trigger assumptions used in the convection parameterization schemes used in these models, and to a lesser extent on details of the individual cloud models. Using two different methods: (i) clustering, (ii) comparing the bias patterns of models from CMIP5 with their AMIP5 counterparts, we find that, in general, the atmospheric component (and not the oceanic component through biases in SSTs and atmosphere-ocean feedbacks) plays a major role in deciding the convective and large-scale precipitation biases. However, the oceanic component has been found important for one of the convective groups in deciding the convective precipitation biases (over the maritime continent).