How seasonal forecast skill varies between the tropics and Europe
Why seasonal predictability varies by region, season and variable, what makes Europe challenging, and why verification matters more than forecast colours.
Skill is not uniform
Seasonal skill varies with model, region, variable, season, lead and verification metric. Tropical Pacific SST associated with ENSO is a prominent source of predictability. European atmospheric forecasts are often more challenging, but a blanket “high tropics, low Europe” ranking hides substantial variation. Assess the relevant hindcasts against observations and a stated reference forecast.
Why the tropics are predictable
Tropical weather is tightly coupled to sea-surface temperature. Warm water drives convection, convection drives circulation, and the ocean changes slowly. Predicting the ocean can therefore help predict seasonal atmospheric statistics. It does not determine every tropical region’s weather; model errors and internal variability still matter. Strong anomalies or high tercile probabilities alone do not demonstrate skill.
Why Europe is hard
Mid-latitude weather is dominated by the internal variability of the jet stream, which is chaotic and only weakly tied to slow boundary conditions. The North Atlantic Oscillation (NAO) is a major influence on European winters. Some seasonal hindcast studies demonstrate NAO skill, yet models can underestimate its predictable signal relative to noise. This “signal-to-noise paradox” helps explain why a weak model signal need not imply no skill. It is not a verified ranking of the five current systems shown here.
Season and variable matter
- Winter: large-scale circulation, ENSO and stratospheric influences can provide useful predictability, which varies across Europe and between systems.
- Summer: temperature can carry information from warming trends and land-surface conditions. Skill for rainfall must be assessed regionally; there is no universal zero-skill rule.
- Variable and metric: temperature, precipitation and SST have different sources of predictability. Their relative skill depends on where, when and how it is measured.
What this means for reading the maps
Anomaly magnitude and summary colours cannot tell you local skill. Model agreement and run consistency provide context; neither replaces verification. A neutral summary simply means no outer category passes its display rule. This site does not yet publish verification, so consult the skill maps on the Copernicus site for the model and season you care about, and read why a probability is not forecast skill.
Sources and method
These references explain the underlying science and provider conventions. Descriptions of this site’s calculations, colours and coverage were checked against its processing code and imported data.
- C3S seasonal verification plots
- Scaife and Smith (2018): a signal-to-noise paradox in climate science
- Met Office: introduction to seasonal forecasting