Guide to reading seasonal forecasts

Seasonal forecasts are probabilistic, model-relative and easy to over-read. These short explainers cover what each map on this site shows, how it was derived and where its limits are.

1. What a seasonal forecast is, and how it differs from a weather forecast

   Seasonal forecasts predict the odds of a warmer, cooler, wetter or drier season months ahead. Why they are probabilistic, what they can and cannot tell you.

2. How to read seasonal forecast maps

   What the colours, units and legends on a seasonal forecast map mean, and how to use point inspection to read a value across several models.

3. What an ensemble-mean anomaly shows

   The ensemble mean anomaly is the average departure of all forecast members from the model climate. What it can and cannot tell you about the coming season.

4. Tercile and quintile probabilities explained

   How below-normal, near-normal and above-normal probabilities are derived from ensemble members and hindcasts, and how to read the tercile summary map.

5. Why seasonal anomalies use a 1993–2016 hindcast climatology

   Seasonal forecast anomalies are measured against each model’s own hindcast climate, not observations. Why the reference period matters when comparing models.

6. Lead time, initialization and valid period

   How seasonal forecast lead months are counted, why lead 1 is the initialization month, and how to line up different runs on the same valid period.

7. How three-month seasonal means are built

   Three-month means average the monthly forecasts; three-month probabilities are recalculated from member averages. Why the two are not interchangeable.

8. Comparing seasonal models and monthly runs

   Two ways to compare seasonal forecasts: several centres at one initialization, or several monthly runs from one centre. When each comparison is useful.

9. How El Niño and La Niña drive seasonal predictability

   ENSO is the largest source of seasonal predictability. How Pacific sea-surface temperatures shape the coming season, where the effect is strong and where it is weak.

10. Why seasonal forecast skill is high in the tropics and low over Europe

    Seasonal forecast models are far more skilful in the tropics than in mid-latitudes. What makes Europe hard, and which seasons and variables are more predictable.

11. Why a probability is not forecast skill

    A 60% probability of above-normal temperature says nothing about how often the model is right. What skill means for seasonal forecasts and why it is missing here.

12. Seasonal forecast glossary

    Short definitions of the terms used on seasonal forecast maps: anomaly, ensemble, hindcast, tercile, lead time, initialization, valid period, teleconnection and more.