Statistical tools for climate science

为了制定适应气候变化的计划，重要的是开发量化工具，以应对全球、区域和地方各级气候预测的不确定性。在这项研究中，我们开发了统计工具，让决策者对如何解释预测有更清晰的看法。例如，我们预测(伴随着不确定性)未观测站点的温度，假设观测站点达到了网络的最大或最小。本研究使用的技术工具包括非平稳和相依观测的经验过程模型。数据将包括全球温度重建、瑞典、挪威和美国的温度和降水站网络、CMIP5全球模式运行以及CORDEX和其他储存库的区域气候模型运行。我们还将研究极值理论中的非平稳工具，包括空间和时空情况，并实施可视化方法，将气候模型集合的输出与数据进行比较。

In order to plan for adaptation to climate change, it is important to develop quantitative tools for the uncertainty of climate projections at global, region, and local levels. In this research we develop statistical tools that will let decision-makers get a clearer perspective on how to interpret the projections. For example, we predict (with attendant uncertainty) temperatures at unobserved sites, given that an observed site achieved the network maximum or minimum. The technical tools used in this research include empirical process models for nonstationary and dependent observations. Data will include global temperature reconstructions, networks of temperature and precipitation stations from Sweden, Norway and the United States, and CMIP5 global model runs as well as regional climate model runs from CORDEX and other repositories. We will also study nonstationary tools in extreme value theory, including spatial and spatio-temporal situations, and implement visualization methods for comparing output from ensembles of climate models to data.