Addressing interdependency in a multimodel ensemble by interpolation of model properties
Addressing interdependency in a multimodel ensemble by interpolation of model properties
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DOI：
10.1175/jcli-d-14-00361.1
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发表时间：
2015-07
影响因子：
4.9
通讯作者：
B. Sanderson;R. Knutti;P. Caldwell
中科院分区：
文献类型：
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作者：
B. Sanderson;R. Knutti;P. Caldwell
摘要 用于进行 CMIP5 集合的不同地球系统模型可以部分采样未来气候预测的不确定性。然而，由于不同群体开发的模型共享想法和代码，因此存在偏见，因此将这些预测结合起来会变得很复杂。作者提出了一种将模型结果组合成单变量或多变量分布的方法，该方法对于包含高度相互依赖的模型更加稳健。本研究使用当今气候学的多变量指标来评估最近两个模型比较（CMIP3 和 CMIP5）中的模型性能和相似性。可以对模型特征进行插值，然后在由独立气候属性定义的空间中重新采样。可以通过在靠近预测观测值的空间区域进行更密集的采样来应用某种形式的加权，从而同时考虑模型性能和相互依赖性。抽样分布参数的选择是...
AbstractThe diverse set of Earth system models used to conduct the CMIP5 ensemble can partly sample the uncertainties in future climate projections. However, combining those projections is complicated by the fact that models developed by different groups share ideas and code and therefore biases. The authors propose a method for combining model results into single or multivariate distributions that are more robust to the inclusion of models with a large degree of interdependency. This study uses a multivariate metric of present-day climatology to assess both model performance and similarity in two recent model intercomparisons, CMIP3 and CMIP5. Model characteristics can be interpolated and then resampled in a space defined by independent climate properties. A form of weighting can be applied by sampling more densely in the region of the space close to the projected observations, thus taking into account both model performance and interdependence. The choice of the sampling distribution’s parameters is a s...