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

Abstract. Decadal predictions can skillfully forecast the upper ocean temperature in many regions of world. The North Atlantic in particular shows promising results when it comes to high predictive skill of Ocean Heat Content (OHC). Nevertheless, important regional differences exist across Decadal Prediction Systems, which are explored in this multi-model analysis. Differences are also found in their respective uninitialized historical ensembles, which points to large uncertainties in the externally forced signals. We analyze eight CMIP6 climate models with comparable ensembles of decadal predictions and historical simulations to document their differences in upper OHC skill, and to investigate if intrinsic model characteristics, such as key mean state biases in the local forcing from the atmosphere or the local stratification, can influence the relative predictive role of external forcings and internal variability. Particular attention has been given to the Labrador Sea and its surroundings, since this is found to be a region where upper OHC has low observational uncertainties, yet high inter-model spread in the upper OHC prediction skill of decadal predictions and historical experiments. Benchmarking mean state properties of the local surface fluxes and stratification against observations, both strongly linked with the simulated upper OHC skill for the historical ensembles, suggests that their multi-model mean provides the most realistic estimate of the true forced signal