A dependent multimodel approach to climate prediction with Gaussian processes
A dependent multimodel approach to climate prediction with Gaussian processes
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利用高斯过程进行气候预测的相关多模型方法
DOI：
10.1017/eds.2022.24
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Chatterjee, Snigdhansu
中科院分区：
文献类型：
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作者：
Thompson, Marten;Braverman, Amy;Chatterjee, Snigdhansu
对未来气候的模拟包含了由许多来源引起的可变性，包括内部随机性和外部强迫。然而，尽我们所能，气候模型和真实观测到的气候依赖于相同的潜在物理过程。在本文中，我们同时研究多个气候模拟模式和观测数据的输出，并试图利用它们的平均结构以及可能反映气候对共同强迫的响应的相互依赖关系。贝叶斯模型为多种气候模拟的微妙组合提供了丰硕的基础。我们介绍了一种这样的方法，即使用高斯过程来表示所有模拟和观察到的气候共同的平均函数。相关随机效应对包含在多个气候模型输出和观测气候数据内和之间的可能信息进行编码。我们提出了一种经验贝叶斯方法来分析这类模型的计算效率。这种方法适用于CMIP6模式集合，我们展示了它在预报全球近地表平均气温方面的有效性。结果表明，该模型及其扩展可为气候预测和不确定性量化提供参考价值。
Simulations of future climate contain variability arising from a number of sources, including internal stochasticity and external forcings. However, to the best of our abilities climate models and the true observed climate depend on the same underlying physical processes. In this paper, we simultaneously study the outputs of multiple climate simulation models and observed data, and we seek to leverage their mean structure as well as interdependencies that may reflect the climate’s response to shared forcings. Bayesian modeling provides a fruitful ground for the nuanced combination of multiple climate simulations. We introduce one such approach whereby a Gaussian process is used to represent a mean function common to all simulated and observed climates. Dependent random effects encode possible information contained within and between the plurality of climate model outputs and observed climate data. We propose an empirical Bayes approach to analyze such models in a computationally efficient way. This methodology is amenable to the CMIP6 model ensemble, and we demonstrate its efficacy at forecasting global average near-surface air temperature. Results suggest that this model and the extensions it engenders may provide value to climate prediction and uncertainty quantification.