Complete spatial model calibration

Abstract

Computer simulation models are central to environmental science. These mathematical models are used to understand complex weather and climate patterns and to predict the climate’s response to different forcings. Climate models are of course not perfect reflections of reality, and so comparison with observed data is needed to quantify and to correct for biases and other deficiencies. We propose a new method to calibrate model output using observed data. Our approach not only matches the marginal distributions of the model output and gridded observed data, but it simultaneously postprocesses the model output to have the same spatial correlation as the observed data. This comprehensive calibration method permits realistic spatial simulations for regional impact studies. We apply the proposed method to global climate model output in North America and show that it successfully calibrates the model output for temperature and precipitation.