Identifying trends in the spatial errors of a regional climate model via clustering

Abstract

Since their introduction in 1990, regional climate models (RCMs) have been widely used to study the impact of climate change on human health, ecology, and epidemiology. To ensure that the conclusions of impact studies are well founded, it is necessary to assess the uncertainty in RCMs. This is not an easy task because two major sources of uncertainties can undermine an RCM: uncertainty in the boundary conditions needed to initialize the model and uncertainty in the model itself. Using climate data for Southern Sweden over 45 years, in this paper, we present a statistical modeling framework to assess an RCM driven by analyses. More specifically, our scientific interest here is determining whether there exist time periods during which the RCM in consideration displays the same type of spatial discrepancies from the observations. The proposed model can be seen as an exploratory tool for atmospheric modelers to identify time periods that require a further in-depth examination. Focusing on seasonal average temperature, our model relates the corresponding observed seasonal fields to the RCM output via a hierarchical Bayesian statistical model that includes a spatio-temporal calibration term. The latter, which represents the spatial error of the RCM, is in turn provided with a Dirichlet process prior, enabling clustering of the errors in time. We apply our modeling framework to data from Southern Sweden spanning the period 1 December 1962 to 30 November 2007, revealing intriguing tendencies with respect to the RCM spatial errors of seasonal average temperature.