Weighting climate model ensembles for mean and variance estimates
By Ned Haughton, Gab Abramowitz, Andrew J. Pitman, Steven J. Phipps. Climate Dynamics, 2015
55 citations recorded by the source corpus.
Projections based on climate model ensembles commonly assume that each individual model simulation is of equal value. When combining simulations to estimate the mean and variance of quantities of interest, they are typically unweighted. Exceptions to this approach usually fall into two categories. First, ensembles may be pared down by removing either poorly performing model simulations or model simulations that are perceived to add little additional information, typically where multiple simulations have come from the same model. Second, weighting methodologies, usually based on model performance differences, may be applied, and lead to some improvement in the projected mean. Here we compare the effect of three different weighting techniques—simple averaging, performance based weighting, and weighting that accounts for model dependence—on three ensembles generated by different approaches to model perturbation. We examine the effect of each weighting technique on both the ensemble mean and variance. For comparison, we also consider the effect on the CMIP5 ensemble. While performance weighting is shown to improve the estimate of the mean, it does not appear to improve estimates of ensemble variance, and may in fact degrade them. In contrast, the model independence weighting approach appears to improve both the ensemble mean and the variance in all ensembles.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/df05c3fc2d1742c83dbf2e47d9bc4735d9b6b11a
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In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- How well do crop modeling groups predict wheat phenology, given calibration data from the target population? (2019, background): “In other fields there have been suggestions that the results of different modeling groups should be weighted differently, for example, based on how well they fit the calibration data (Haughton et al., 2015).”