A multi-model analysis of the decadal prediction skill for the North Atlantic ocean heat content

Abstract. Decadal predictions can skilfully forecast upper-ocean temperatures in many regions worldwide. The North Atlantic, in particular, shows high predictive skill for the ocean heat content (OHC). This multi-model study analyses eight CMIP6 climate models with comparable decadal prediction (Decadal Climate Prediction Project, DCPP) and historical (HIST) ensembles to document differences in North Atlantic (NA) upper-OHC skill and investigates the underlying causes. The decadal predictions consistently identify two main regions with high predictive capacity and added value of initialization: the Labrador Sea (LS) and the eastern North Atlantic. A region east of the Grand Banks (EGB) is also found to exhibit negative skill scores, with its extent and location varying widely across models, possibly due in part to observational uncertainties affecting both forecast verification and local initialization. Special attention is given to the Labrador Sea and its surroundings, a region characterized by high inter-model spread in OHC prediction skill in both DCPP and HIST experiments. These differences hinder the identification of the relative contributions of external forcings and internal variability to local OHC predictability. To address this, we explore the relationship between the local OHC skill in the HIST ensemble and various mean-state properties in the Labrador Sea, revealing a strong link between the skill in those experiments and both the mean local surface fluxes and density stratification. Benchmarking these mean-state properties against observations and reanalyses suggests that the multi-model mean likely offers the most realistic estimate of the forced signal, accounting for approximately 16 % of the total OHC variance in the Labrador Sea. These findings underscore the critical role of stratification and atmospheric forcing biases in shaping predictive skill and highlight the potential of multi-model ensembles to advance our understanding of decadal predictability.