Type 1: Collaborative Research: Bayesian Hierarchical Climate Prediction LO2170174

该项目的重点是通过应用贝叶斯推理开发气候和气候影响预测框架。贝叶斯分析是一种基于理论的技术，用于组合信息，从数据中学习，然后在这些步骤中表示不确定性的同时形成预测。现代层次贝叶斯模型已被证明在复杂环境下是非常有效的。该策略涉及对各种观测数据源的概率分布（即数据模型）进行规范。此外，概率分布，被称为先验过程模型，被表述为未知过程在不同时空尺度上相互作用，以及未知参数。通过贝叶斯定理将过程的先验分布与数据模型相结合，得到未知量的后验概率分布。这个分布可以作为预测的基础。对概率的依赖意味着该方法内置了不确定性管理。我们方法的关键是将全球和区域尺度上的气候模型输出结合起来，就好像它们是观测一样。然后将这些数据与相对简单的、基于物理的先前模型结合起来。预测和不确定性量化所需的相应计算比与大尺度气候系统模式相关的计算要容易几个数量级。概率论提供了使用分析产生预测和相关影响不确定性的技术。这些结果然后作为决策支持的基础。当前气候研究的一个关键挑战是在年代际尺度上预测区域气候行为。在年代际尺度上对气候的可靠预测是决策者选择适应战略以应对气候变化影响的决策支持基础。关于气候的大量信息是由气候系统模式产生的。然而，模型结果受到各种不确定性的影响。此外，这些模型在计算上要求很高，并产生大量数据集，因此我们量化与基于模型的预测相关的不确定性的能力受到严重限制。为了应对这一挑战，我们开发了用于预测分析的贝叶斯技术，该技术以有效的方式使用气候模型输出，并处理不确定性。这种大尺度气候系统模式、区域气候模式和简单气候模式的结合使用，在气候科学中创造了一个协同的环境。其次，因为我们的策略依赖于相对简单的计算，我们提供技术来产生特定于影响和本地或区域特定的预测信息，以响应具有各种责任的各种决策者的需求。

Berliner, 1049064Wikle, 1049093 The focus of this project is the development of a climate and climate-impact prediction framework by applying Bayesian reasoning. Bayesian analysis is a theoretically grounded technique for combining information, learning from data and then forming predictions while representing uncertainties in these steps. Modern hierarchical Bayesian modeling has proved very effective in complicated settings. The strategy involves the specification of probability distributions, known as data models, for a variety of observational data sources. In addition, probability distributions, known as prior process models, are formulated for unknown processes interacting with each other on various space-time scales, as well as unknown parameters. The prior distributions for the processes are combined with the data models via Bayes' Theorem to produce a posterior probability distribution for the unknown quantities. This distribution serves as the basis for prediction. The reliance on probability means that uncertainty management is built-in to the approach. The key to our approach is to incorporate climate model output, at both global and regional scales, as if they are observations. These data are then combined with relatively simple, though physically based, prior models. The corresponding computations needed for prediction and uncertainty quantification are orders of magnitude easier than those associated with large-scale climate system models. Probability theory provides techniques for using the analyses to produce predictions and associated uncertainties of impacts. These results then serve as the basis for decision support. A critical challenge in current climate research is the prediction of regional climate behavior on decadal time scales. Reliable predictions of climate on decadal scales are the cornerstone of decision support for policy makers in their selection of adaptation strategies to address the impacts of climate change. Substantial information regarding climate is produced by climate system models. However, model results are subject to a variety of uncertainties. Further, these models are very demanding computationally and produce massive datasets, so there are severe limitations on our ability to quantify the uncertainties associated with model-based predictions. To respond to the challenge, we develop Bayesian techniques for predictive analyses that use climate model output in an efficient fashion and also deal with uncertainty. This combined use of large-scale climate system models, regional climate models, and simple climate models creates a synergistic environment in climate science. Next, because our strategy relies on comparatively simple calculations, we provide techniques for producing impact-specific and local or regional-specific predictive information in response to the needs of a variety of decision makers having a variety of responsibilities.