Deficient ocean–atmosphere feedbacks constrain seasonal NAO prediction

Abstract. As the North Atlantic Oscillation (NAO) accounts for a dominant share of wintertime weather variability across the North Atlantic basin, it is a coveted target for seasonal prediction. Yet dynamical forecast systems continue to exhibit limited skill, in part due to deficiencies in representing ocean–atmosphere feedbacks. Here, mediation analysis – a statistical framework from causal inference – is applied to identify and quantify feedback pathways linking late-autumn North Atlantic sea surface temperature (SST) anomalies to the subsequent winter NAO. This approach is attractive because it is straightforward to apply, easy to interpret, and can be used directly on observations-derived data like reanalyses without requiring idealised model perturbation experiments.

The analysis reveals a physically coherent feedback sequence. Anomalous November SST patterns promote the gradual formation of a surface-pressure dipole rotated clockwise relative to the canonical NAO structure. This dipole induces advection anomalies in the western North Atlantic, which in turn modulate surface fluxes in the Subpolar Gyre and lower-tropospheric baroclinicity in the storm-track entry region east of Newfoundland. These changes nudge the NAO, which, once established, feeds back onto the fluxes and baroclinicity, reinforcing the anomaly and sustaining the circulation pattern.

A central finding is that a state-of-the-art seasonal prediction system fails to capture these feedback mechanisms. The baroclinicity pathway, the process through which changes in eddy growth reinforce the circulation anomaly, is particularly deficient, accounting for only 2 % of the lagged SST–NAO correlation in SEAS5 compared with 44 % in the ERA5 reanalysis. This misrepresentation likely represents a fundamental barrier to improved NAO forecast skill.

More broadly, the results demonstrate the potential of mediation analysis as a diagnostic tool for disentangling coupled feedbacks directly from observations, evaluating their representation in models, and guiding targeted improvements that could enhance seasonal prediction of the NAO.