Trends, variability and predictive skill of the ocean heat content in North Atlantic: An analysis with the EC-Earth3 model

As the global climate is warming, with important regional differences, there is a growing need to (i) better understand whether and how internal variability controls the regional warming trends, and (ii) to identify the regions in which both the trends and the superimposed interannual variability are predictable. In this study we investigate trends, variability and predictive skill of the upper ocean heat content in the North Atlantic basin. This region is a source of decadal variability, in which internal ocean processes can locally modulate the global warming trends and add additional prediction skill. The analysis is focused on the period 1970-2014, and combines the study of an ensemble of ocean reanalyses, with two sets of CMIP6 experiments performed with the Earth system model EC-Earth3: (i) a 10-member historical ensemble; and (ii) an initialized 10-member retrospective decadal prediction system. External forcings are found to be important for the development of the regional trends, but on their own are unable to reproduce the exact geographical pattern. Our results also show that not all regions in the North Atlantic are equally predictable, which is explained by different contributions of the forcings and internal climate variability. While high levels of predictive skill in regions like the Eastern Subpolar North Atlantic, or the Irminger and Iceland Seas are clearly enabled by initialization, with a negligible influence of the external forcings, skill in others areas like the Subtropical North Atlantic, or the Gulf Stream extension mostly comes from the externally forced trends. The Labrador Sea is a particular case where predictive skill has both an external and internal origin. Large observational and modeling uncertainties affect the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain its very poor predictive skill. We would like to acknowledge the financial support from FCT through projects FCT-UIDB/50019/2020 and PD/BD/142785/2018.