Journal Article

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs

Crouch, Feifei Liu, Stein, Michael L., Moyer, Elisabeth J., Jacob, Robert L., McInerney, David J., Castruccio, Stefano

2014

Overview

The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO₂ concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.

• Publisher: American Meteorological Society
• Subject: Anomalies / Carbon dioxide / Carbon dioxide concentration / Climate / Climate change / Climate change policy / Climate models / Climatic analysis / Climatic zones / Climatology. Bioclimatology. Climate change / Colleges & universities / Earth, ocean, space / ENVIRONMENTAL SCIENCES / Exact sciences and technology / External geophysics / General circulation models / Geophysics. Techniques, methods, instrumentation and models / Global climate models / Global warming / Mathematical models / Meteorology / Model output statistics / Modelling / Oceans / Precipitation / Scaling / Statistical models / Statistics / Trajectories