Improving Uncertain Climate Forecasts Using a New Minimum Mean Square Error Estimator for the Mean of the Normal Distribution

2009/12/22 by Stephen Jewson, Ed Hawkins, Jewson, Stephen +1
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Atmospheric and Oceanic Physics (physics.ao-ph) #Climate variability and models #FOS: Physical sciences #Hydrology and Drought Analysis #Meteorological Phenomena and Simulations #physics.ao-ph

paper · pdf · doi:10.48550/arxiv.0912.4395

arxiv created 2009/12/22 · openalex publication_date 2009/12/22 · arxiv updated 2010/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

When climate forecasts are highly uncertain, the optimal mean squared error strategy is to ignore them. When climate forecasts are highly certain, the optimal mean squared error strategy is to use them as is. In between these two extremes there are climate forecasts with an intermediate level of uncertainty for which the optimal mean squared error strategy is to make a compromise forecast. We present two new methods for making such compromise forecasts, and show, using simulations, that they improve on previously published methods.

Citations

• Improving the expected accuracy of forecasts of future climate using a simple bias-variance tradeoff
• CMIP3 ensemble spread, model similarity, and climate prediction uncertainty

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