Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical model
Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical model
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DOI：
10.1111/j.1467-9876.2011.01010.x
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发表时间：
2012-01-01
影响因子：
1.6
通讯作者：
Sain, Stephan R.
中科院分区：
文献类型：
--
作者：
Kang, Emily L.;Cressie, Noel;Sain, Stephan R.
。本文研究了北美区域气候变化评估计划第一阶段6个区域气候模式（RCMs）集合产生的20年平均北方冬季气温。我们使用长期平均值（20年积分）来平滑变率，并从RCM输出中获取气候特性。我们发现，尽管rcm类似地捕获了从海岸到海岸和从南到北的大尺度气候变化，但它们的输出在某些地区可能存在很大差异。我们提出了一个贝叶斯层次模型来综合来自RCM集合的信息，并构建了一个共识气候信号，每个RCM根据其自身的变率参数对共识做出贡献。贝叶斯方法使我们能够对所有未知数进行后验推断，包括共识气候信号和每个RCM中的大规模固定效应和小规模随机效应。通过后验均值、后验方差和后验空间分位数对共识气候和rcm输出的联合分布进行了研究。我们在贝叶斯层次模型中使用空间随机效应模型，因此，我们可以处理来自所有rcm的精细分辨率输出的大型数据集。此外，我们的模型允许灵活的空间协方差结构，而无需假设平稳性或各向同性。
. We investigate the 20-year-average boreal winter temperatures generated by an ensemble of six regional climate models (RCMs) in phase I of the North American Regional Climate Change Assessment Program. We use the long-run average (20-year integration) to smooth out variability and to capture the climate properties from the RCM outputs. We find that, although the RCMs capture the large-scale climate variation from coast to coast and from south to north similarly, their outputs can differ substantially in some regions. We propose a Bayesian hierarchical model to synthesize information from the ensemble of RCMs, and we construct a consensus climate signal with each RCM contributing to the consensus according to its own variability parameter. The Bayesian methodology enables us to make posterior inference on all the unknowns, including the large-scale fixed effects and the small-scale random effects in the consensus climate signal and in each RCM. The joint distributions of the consensus climate and the outputs from the RCMs are also investigated through posterior means, posterior variances and posterior spatial quantiles. We use a spatial random-effects model in the Bayesian hierarchical model and, consequently, we can deal with the large data sets of fine resolution outputs from all the RCMs. Additionally, our model allows a flexible spatial covariance structure without assuming stationarity or isotropy.