Abstract:

Statistical downscaling is a useful technique to localize global or regional climate model projections to assess the potential impact of climate changes. It requires quantifying a relationship between climate model output and local observations from the past, but the two sets of measurements are not necessarily taken simultaneously, so the usual regression techniques are not applicable. In the case of univariate downscaling, a simple quantile-matching approach with asynchronous measurements often works well, but challenges remain for downscaling bivariate or multivariate data. In this talk, we discuss a bivariate downscaling method for asynchronous measurements based on a notion of bivariate ranks or positions. The proposed method is preferable to univariate downscaling, because it is able to preserve general forms of association between two variables, such as temperature and precipitation, in statistical downscaling. This talk will focus on an idea that might lead to a useful notion of multivariate quantiles, as well as its specific application to climate projection.