Multi-Model Forecast Quality Assessment of CMIP6 Decadal Predictions

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). Decadal climate predictions are a relatively new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and forecast periods) with skill that can be used to develop climate services to inform users in several sectors and define benchmarks for improvements in forecast systems. This work evaluates the quality of multi-model forecasts of near-surface air temperature, precipitation, Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available retrospective decadal predictions contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The predictions generally show high skill in predicting temperature, AMV, and GSAT, while the skill is more limited for precipitation. Different approaches for generating a multi-model forecast are compared, finding small differences between them. The multi-model ensemble is also compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the historical simulations to estimate the impact of initialization. An added value is found for several ocean and land regions for temperature, AMV, and GSAT, while it is more reduced for precipitation. Moreover, the full ensemble is compared to a sub-ensemble to measure the impact of the ensemble size. Finally, the implications of these results in a climate services context, which requires predictions issued in near real-time, are discussed. This study has been performed in the framework of the C3S_34c contract (ECMWF/ COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the European Commission H2020 EUCP project (Grant 776613). CDT thanks the funding by the Spanish Ministry for Science and Innovation (FPI PRE2019-088646). MGD is grateful for funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. BSM acknowledges financial support from the Marie Sklodowska-Curie fellowship (Grant 713673) and from a fellowship of La Caixa Foundation (ID 100010434).