论文标题
结合CMIP5中相互依存的气候模型输出：一种空间贝叶斯方法
Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach
论文作者
论文摘要
未来气候变化的预测在很大程度上依赖于气候模型，并且通过多模型合奏组合气候模型既比单个气候模型更准确，又对不确定性量化很有价值。但是，贝叶斯对多模型合奏的方法因对偏见和可变性做出过分简化的假设而受到批评，并将不同的模型视为统计上的独立。本文扩展了Sansom等人的贝叶斯分层方法。 （2017）通过明确考虑空间变异性和模型间依赖性。我们提出了一个贝叶斯分层模型，该模型说明气候模型与观察，空间和模型依赖性，历史和未来时期之间的紧急关系以及自然变异性之间的偏见。广泛的模拟表明，与常用的简单模型均值相比，我们的模型提供了更好的估计和不确定性定量。使用CMIP5模型存档的数据来说明这些结果。作为例子，对于中部北美，我们的2070---2100的预计平均温度比简单模型平均值低约0.8 k，而对于东亚来说，它高约0.5 k；但是，在这两种情况下，90％可信间隔的宽度均为3--6 K的命令，因此不确定性压倒了预计平均温度的相对较小的差异。
Projections of future climate change rely heavily on climate models, and combining climate models through a multi-model ensemble is both more accurate than a single climate model and valuable for uncertainty quantification. However, Bayesian approaches to multi-model ensembles have been criticized for making oversimplified assumptions about bias and variability, as well as treating different models as statistically independent. This paper extends the Bayesian hierarchical approach of Sansom et al. (2017) by explicitly accounting for spatial variability and inter-model dependence. We propose a Bayesian hierarchical model that accounts for bias between climate models and observations, spatial and inter-model dependence, the emergent relationship between historical and future periods, and natural variability. Extensive simulations show that our model provides better estimates and uncertainty quantification than the commonly used simple model mean. These results are illustrated using data from the CMIP5 model archive. As examples, for Central North America our projected mean temperature for 2070--2100 is about 0.8 K lower than the simple model mean, while for East Asia it is about 0.5 K higher; however, in both cases, the widths of the 90% credible intervals are of the order 3--6 K, so the uncertainties overwhelm the relatively small differences in projected mean temperatures.
