This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.
Special Issue Paper
Regional climate model assessment using statistical upscaling and downscaling techniques†
Article first published online: 18 APR 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Advances in Statistical Methods for Climate Analysis
Volume 23, Issue 5, pages 482–492, August 2012
How to Cite
Berrocal, V. J., Craigmile, P. F. and Guttorp, P. (2012), Regional climate model assessment using statistical upscaling and downscaling techniques. Environmetrics, 23: 482–492. doi: 10.1002/env.2145
- Issue published online: 25 JUL 2012
- Article first published online: 18 APR 2012
- Manuscript Accepted: 16 MAR 2012
- Manuscript Revised: 12 MAR 2012
- Manuscript Received: 30 SEP 2011
- Gaussian processes;
- point and areal prediction;
- regional climate models;
- space–time statistical modeling;
Climate models are mathematical models that describe the temporal evolution of climate, oceans, atmosphere, ice, and land-use processes, across a spatial domain via systems of partial differential equations. Because these models cannot be solved analytically, the model output is generated numerically over grid boxes. Regional climate models (RCMs), or the dynamic downscaling of global climate models to regional scales, are often used for planning purposes, and it is important to assess carefully the uncertainty of such models. We evaluate the Swedish Meteorological and Hydrological Institute (SMHI) RCM by comparing its model output at the grid box level, with the predictions obtained from two observation-driven spatio-temporal statistical models. The “downscaling model” combines the spatially and temporally smoothed climate model output with temperature observations at synoptic stations in a spatio-temporal linear statistical model. The “upscaling model” describes the observational temperature alone at the daily scale, via a spatio-temporal model that includes a wavelet-based trend, spatially varying seasonality, along with volatility and long-range dependence terms. Both statistical models have the ability to make predictions at a seasonal scale, both at point and grid box level. In the years 1962–2007 in South Central Sweden, we show that the climate model performs well in predicting the annual and seasonal average temperature at three reserved stations, but there are interesting differences among the model output and the statistical model-based predictions at the grid box level. Copyright © 2012 John Wiley & Sons, Ltd.