New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models

The Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation (NAO) are the dominant modes of oceanic and atmospheric variability in the North Atlantic, respectively, and are key sources of predictability from seasonal to decadal timescales. However, the physical processes and feedback mechanisms linking the AMV and NAO, and the role of diabatic processes in these feedbacks, remain debated. We present a data-driven dynamical modelling framework which captures coupled decadal variability in AMV, NAO, and North Atlantic precipitation. Applying equation discovery methods to observational data, we identify deterministic low-order dynamical models consisting of three coupled ordinary differential equations. These models reproduce observed North Atlantic decadal variability and show robust out-of-sample predictive skill on multi-annual to decadal lead times. The resulting model dynamics include a distinct quasi-periodic 20-year oscillation consistent with a damped oceanic mode of variability. Notably, precipitation-related terms feature prominently in the low-order models, suggesting an important role for latent heat release and freshwater fluxes in mediating ocean–atmosphere interactions. We propose new feedback mechanisms between North Atlantic sea surface temperature and the NAO, with precipitation acting as a dynamical bridge. By linearising the low-order models and computing finite-time Lyapunov exponents, we find that North Atlantic precipitation is more predictable in a positive AMV phase. We then analyse several decadal prediction ensemble experiments based on initialised hindcasts and find comparable state-dependent predictability of precipitation. Overall, these results illustrate how data-driven equation discovery can provide mechanistic hypotheses and new insight beyond conventional analyses of observations and climate model simulations.