Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling
12 October 2020

The Effect of Geographic Sampling on Evaluation of Extreme Precipitation in High-Resolution Climate Models

Science

A novel approach is developed to quantify the impact of geographic sampling of weather stations on the relative performance of high-resolution climate model representations of precipitation extremes. Our focus is on the evaluation of global high-resolution models with a horizontal resolution of tens of kilometers since for these models there are many cases where the physical output of a climate model would be compared with a statistically interpolated quantity instead of actual measurements of the climate system.

Impact

We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance, although ignoring geographic sampling does not systematically change model assessment in a consistent way across models. We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets. The effect is significant for the high-resolution models of the HighResMIP subproject of the CMIP6 experiment and we recommend that geographic sampling be implemented at the PCMDI when these models are formally evaluated.

Summary

Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

Contact
William D. Collins
Lawrence Berkeley National Laboratory
Publications
Risser, M, and M Wehner.  2020.  "The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models."  Advances in Statistical Climatology, Meteorology and Oceanography 6(2): 115-139.  https://doi.org/10.5194/ascmo-6-115-2020.