Brown Bag - Identifying Trends in the Spatial Errors of a Regional Climate Model via Clustering

12/10/2014 - 12:15pm

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

Veronica Berrocal, University of Michigan

Wednesday, December 10, 2014

Time: 12:15 pm

Location: Mesa Laboratory, Chapman Room

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 since 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.
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 and seasonal maximum 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.
On the first level of the hierarchy, the model specifies a normal distribution for seasonal average temperature and a continuous GEV spatial process for seasonal maximum temperature. We apply our modeling framework to data from Southern Sweden spanning the period December 1, 1962 to November 30, 2007. Our analysis reveals intriguing tendencies with respect to the RCM spatial errors relative to seasonal average temperature; on the other hand, no systematic spatial error is detected for seasonal maximum temperature during the period 1963-2007.