Accounting for Skill in Nonlinear Trend, Variability, and Autocorrelation Facilitates Better Multi-Model Projections
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We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both non-linear trend and first-order autocorrelated variability, and to make weighted probabilistic projections. In validation experiments the method tends to exhibit superior skill over a trend-only weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean temperature change over Korea by the end of the 21st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.
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