Representing model uncertainty in multi‐annual predictions - Befort, DJ; O'Reilly, CH; Weisheimer, A (Wiley, 2021)
The most prominent way to account for model uncertainty is through the pragmatic combi16 nation of simulations from individual climate models into a multi-model ensemble (MME). However, alternative approaches to represent intrinsic model errors within single-model ensembles (SME) using stochastic parameterisations have proven beneficial in numerical weather prediction. Nevertheless, stochastic parameterisations are not included in most current decadal prediction systems. Here, the effect of the stochastically perturbed physical tendency scheme (SPPT) is examined in 28-month predictions using ECMWF’s forecast model and contrasted with a MME constructed from current decadal prediction systems. Compared to SMEs, SPPT improves the skill and reliability of tropical SST forecasts during the first 18 months (similar to the MME). Thus, stochastic schemes can be an effective and low-cost alternative to be used separately or in conjunction with the multi-model combination to improve the reliability of climate predictions on multi-annual time scales.