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.
