Elevation Correction and Interpolation Bias in Regional Climate Model Data Analysis

The analysis of regional climate model (RCM) outputs frequently requires spatial interpolation of the data from the model's native grid to another set of locations: a different grid for intermodel comparison, a set of station locations for modeling of dependent processes or comparison with raw observations, specific points of interest for impacts studies, and so on. Elevation is sometimes neglected during interpolation, even though it has a major influence on climate, with an increase of 1000 meters in altitude giving temperature changes equivalent to a shift in latitude of around 7.5 degrees poleward. The spatial scale over which elevation varies significantly is often much smaller than the scale at which RCMs are typically run (tens of kilometers), and thus the difference in elevation from one set of locations to another can be quite large, even if the locations come from two grids with comparable resolutions. Consequently, the results obtained by interpolation can, in principle, be significantly different depending on whether or not one corrects for elevation. We examine the relevance and characteristics of changes due to elevation correction to determine whether it is important to users of NARCCAP data. Elevation correction in this case is performed by interpolating the data using a thin plate spline algorithm (a type of kriging) with elevation provided as a covariate field. We compare the bias against observations of data regridded with and without elevation correction for the six NARCCAP RCMs using gridded observational datasets with comparable spatial resolution (CRU and UDEL, at 1/2-degree grid spacing) and at much higher resolution (PRISM, at 4-km grid spacing). We also consider the spatial distribution of bias and changes in bias due to elevation correction, the statistical characteristics of bias reduction, and the relationships of bias to elevation and to the observables in question. These results are evaluated in the context of intermodel comparison, impacts modeling and analysis, and other uses popular in the NARCCAP community.