Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data
Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data
复制标题
结合 CMIP5 气候模型和观测数据对平衡气候敏感性进行贝叶斯层次推断
DOI：
10.1007/s10584-018-2232-0
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发表时间：
2018
期刊：
影响因子：
4.8
通讯作者：
B. Nadiga
中科院分区：
文献类型：
--
作者：
A. Jonko;N. Urban;B. Nadiga
尽管经过了几十年的研究，但从最先进的地球系统模型（ESM）推断的地球平衡气候对二氧化碳强迫的敏感性仍然存在很大的多模型不确定性。多模型不确定性的统计处理通常限于简单的ESM平均方法。有时，模型的权重取决于它们再现历史气候观测的程度。在这里，我们提出了一种新的方法来多模型组合和不确定性量化。我们的方法不是平均一组离散的模型，而是从简单模型参数的缩减空间上的连续分布中采样。我们适合的自由参数的降阶气候模式的输出的多模式集合的每个成员。然后使用分层贝叶斯统计模型组合降阶参数估计值。其结果是简化模型参数的多模型分布，包括气候敏感性。实际上，ESM集合内的多模型不确定性问题被转换为简化模型内的参数不确定性问题。然后，可以用观测数据更新多模型分布，结合两条独立的证据线。我们将这种方法应用于24个全球表面温度和大气层顶部净辐射对二氧化碳突然翻两番的响应的模型模拟，以及四个历史温度数据集。我们的降阶模型是一个2层能量平衡模型。我们提出的概率分布的气候敏感性的基础上（1）多模式集合单独和（2）多模式集合和观测。
Despite decades of research, large multi-model uncertainty remains about the Earth’s equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen
影响因子：
4.9
作者：
D. Klocke;R. Pincus;J. Quaas
通讯作者：
D. Klocke;R. Pincus;J. Quaas