Statistical Combination of Climate Models

• Activity Number: 633
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Yi Fang Chen *+ and Paul Switzer
• Companies: AT&T Labs Research and Stanford University
• Keywords: AOGCM ; Weight ; Trend ; Optimized ; Model ; Extrapolated

Abstract

Atmosphere-ocean general circulation models (GCMs) are the primary tool to study how climate responds to increases in the concentration of greenhouse gases in the atmosphere. Outputs from different AOGCM's have been combined using weighting schemes related to how well they reproduce the historical data. Earlier approaches have inferred model weights in the context of a Bayes hierarchical scheme that treats both the historical record and the several model outputs as independent random samples from a distribution of possible weather data centered around the true climate. However, recent work points to evident correlations among model outputs. Our approach is based on optimizing the fit of model output combinations to historical data, allowing for weighting that is location specific with smoothing of both space-time temperature trends and model weight coefficients. Estimated model weights are extrapolated and applied to `future' AOGCM output to produce predictions of future space-time temperature trends. The approach is illustrated using observed summer temperature data for central North America for the 50-year time period 1940-1989, together with corresponding output from two GCMs.