Seasonal Climate Prediction and Variability in the North Atlantic

The North Atlantic region exerts a dominant influence on seasonal climate variability across Europe, eastern North America and beyond. Central to this variability is the North Atlantic Oscillation (NAO), a pressure‐driven seesaw that modulates wind patterns, storm tracks, temperature and precipitation. Predictive skill on seasonal timescales arises from ocean–atmosphere coupling, the persistence of autumn sea-ice and sea‐surface temperature anomalies, and stratospheric circulation anomalies that propagate downward into the troposphere. Improvements in dynamical and empirical forecasting systems, alongside more comprehensive ensemble strategies, have begun to reveal windows of opportunity in which confidence in regional extremes can be raised. At the same time, uncertainty remains substantial, owing to internal variability, model biases in water vapour and ocean currents, and the so-called signal-to-noise paradox, in which models underestimate the amplitude of predictable signals. Advances in emergent constraint techniques, targeted perturbation experiments and enhanced monitoring promise to refine seasonal forecasts and support early warnings for floods, storms and energy demand.

Research from Nature Portfolio

Recent multi‐model analyses have identified climatological water-vapour errors as a key source of uncertainty in projections of the multi-decadal NAO. By applying an emergent constraint linking observed volcanic and greenhouse-gas signals to model spread, researchers have shown that, under high-emissions scenarios, the NAO may reach unprecedented magnitudes, with serious implications for flood risk and storm damage—outcomes that could be mitigated by substantial emissions reductions. Complementary work on the Summer NAO has uncovered a surprising stratospheric influence: the strength of the lower-stratospheric polar vortex in late spring propagates downward to shape summer circulation, yielding skillful seasonal forecasts in years of anomalous vortex behaviour. Finally, a foundational empirical model has demonstrated robust winter NAO predictability by regressing autumn sea-ice concentration, stratospheric circulation indices and sea-surface temperature anomalies, delivering skilful outlooks for winter temperature and precipitation across Eurasia and North America.

Seasonal Climate Prediction and Variability in the North Atlantic publication trend

The graph below shows the total number of articles in seasonal climate prediction and variability in the north atlantic across all publications each year (not limited to Nature Index journals).

Technical terms

North Atlantic Oscillation (NAO): A leading mode of atmospheric variability in the North Atlantic, defined by the pressure difference between the Azores high and the Icelandic low, influencing westerly winds and storm tracks.

Stratospheric polar vortex: A circumpolar band of strong westerly winds in the stratosphere whose strength and variability can modulate tropospheric circulation patterns.

Emergent constraint: A technique that uses observed climate relationships to reduce uncertainty in future projections by linking model biases to predictable signals.

Signal-to-noise paradox: The discrepancy whereby climate models exhibit weaker predictable signal amplitude than implied by their correlation skill with observations.

References

1. Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022. Nature Communications (2023).
2. Mitigation needed to avoid unprecedented multi-decadal North Atlantic Oscillation magnitude. Nature Climate Change (2025).
3. Skilful predictions of the Summer North Atlantic Oscillation. Communications Earth & Environment (2023).
4. A robust empirical seasonal prediction of winter NAO and surface climate. Scientific Reports (2017).
5. Skillful prediction of UK seasonal energy consumption based on surface climate information. Environmental Research Letters (2023).
6. Understanding winter windstorm predictability over Europe. Weather and Climate Dynamics (2024).
7. A signal-to-noise paradox in climate science. npj Climate and Atmospheric Science (2018).