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Representing Model Uncertainty in Multiannual Predictions
Author(s) -
Befort Daniel J.,
O'Reilly Christopher H.,
Weisheimer Antje
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl090059
Subject(s) - reliability (semiconductor) , climatology , environmental science , stochastic modelling , climate model , meteorology , numerical weather prediction , econometrics , climate change , computer science , mathematics , statistics , geology , physics , oceanography , power (physics) , quantum mechanics
The most prominent way to account for model uncertainty is through the pragmatic combination of simulations from individual climate models into a multimodel ensemble (MME). However, alternative approaches to represent intrinsic model errors within single‐model ensembles (SMEs) using stochastic parameterizations have proven beneficial in numerical weather prediction. Nevertheless, stochastic parameterizations are not included in most current decadal prediction systems. Here, the effect of the stochastically perturbed physical tendency (SPPT) scheme 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 sea surface temperature 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 multimodel combination to improve the reliability of climate predictions on multiannual time scales.