Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles
Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles
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DOI：
10.1175/jcli3363.1
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发表时间：
2005-05
影响因子：
4.9
通讯作者：
C. Tebaldi;Richard L. Smith;D. Nychka;L. Mearns
中科院分区：
文献类型：
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作者：
C. Tebaldi;Richard L. Smith;D. Nychka;L. Mearns
摘要提出了一个贝叶斯统计模型，它结合了大气-海洋环流模式（AOGCMs）和观测的多模式集合的信息，以确定区域尺度上未来温度变化的概率分布。来自统计假设的后验分布纳入了标准的偏差和收敛的相对权重隐含分配给合奏成员。这种方法可以被认为是可靠性总体平均方法的扩展和细化。为了说明问题，作者考虑了在《排放情景综合报告》（SRES）的A2排放情景下运行的9个AOGCM的平均表面温度输出，用于北方冬季和夏季，汇总了22个陆地区域，并分为两个代表当前和未来气候条件的30年平均值。温度变化的最终概率密度函数的形状变化很大，从单峰曲线的区域，…
Abstract A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where...