Type 1: Collaborative Research: Bayesian Hierarchical Climate Prediction LO2170174

Berliner，1049064 Wikle，1049093 该项目的重点是通过应用贝叶斯推理制定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，然后形成预测，同时表示这些步骤中的不确定性。 现代层次贝叶斯建模在复杂环境中被证明是非常有效的。 该战略涉及为各种观测数据源指定概率分布，称为数据模型。 此外，概率分布，被称为先验过程模型，制定了各种时空尺度上相互作用的未知过程，以及未知参数。 过程的先验分布通过贝叶斯定理与数据模型相结合，以产生未知量的后验概率分布。 该分布用作预测的基础。 对概率的依赖意味着不确定性管理是该方法的内在因素。 我们方法的关键是在全球和区域尺度上纳入气候模型输出，就好像它们是观测一样。 然后将这些数据与相对简单但基于物理的先验模型相结合。 预测和不确定性量化所需的相应计算比大尺度气候系统模型所需的计算要容易几个数量级。 概率论提供了使用分析来预测影响和相关不确定性的技术。 这些结果将作为决策支持的基础。 当前气候研究的一个关键挑战是在十年时间尺度上预测区域气候行为。 对十年尺度气候的可靠预测是决策者在选择应对气候变化影响的适应战略时提供决策支持的基石。 气候系统模式产生了大量关于气候的信息。 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非常高，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，我们开发了贝叶斯技术，用于预测分析，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型、区域气候模型和简单气候模型的结合使用在气候科学中创造了一个协同环境。 其次，由于我们的战略依赖于相对简单的计算，我们提供了技术，以产生特定的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.