Main characteristics of seasonal forecast
Seasonal forecasting attempts to provide useful information about the "climate" that can be expected in the coming months. The seasonal forecast is not a weather forecast: weather can be considered as a snapshot of continually changing atmospheric conditions, whereas climate is better considered as the statistical summary of the weather events occurring in a given season.
Despite the chaotic nature of the atmosphere, long term predictions are possible to some degree thanks to a number of components which themselves show variations on long time scales (seasons and years) and, to a certain extent, are predictable. The most important of these components is the ENSO (El Nino Southern Oscillation) cycle, which refers to the coherent, large-scale fluctuation of ocean temperatures, rainfall, atmospheric circulation, vertical motion and air pressure across the tropical Pacific. El Niño episodes (also called Pacific warm episodes) and La Niña episodes (also called Pacific cold episodes) represent opposite extremes of the ENSO cycle. The ENSO cycle is the largest known source of year-to-year climate variability.
Changes in Pacific sea surface temperature (SST) are not the only cause of predictable changes in the weather patterns. There are other causes of seasonal climate variability. Unusually warm or cold sea surface temperatures in the tropical Atlantic or Indian Ocean can cause major shifts in seasonal climate in nearby continents.
In addition to the tropical oceans, other factors that may influence seasonal climate are snow cover and soil wetness. All these factors affecting the atmospheric circulation constitute the basis of long-term predictions.
Overall, seasonal forecasting is justified by the long predictability of the oceanic circulation (of the order of several months) and by the fact that the variability in tropical SSTs has a significant global impact on the atmospheric circulation.
Seasonal forecasts provide a range of possible climate changes that are likely to occur in the season ahead. It is important to bear in mind that, because of the chaotic nature of the atmospheric circulation, it is not possible to predict the daily weather variations at a specific location months in advance. It is not even possible to predict exactly the average weather, such as the average temperature for a given month.
The European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System (S4) is based on a global model which, since the oceanic circulation is a major source of predictability in the seasonal scale, is based on coupled ocean-atmosphere integrations.
The S4 has a surface grid with 80 km spacing representing large scale weather patterns. Local weather and climate is much influenced by features too small to be included in the relatively low-resolution model (hills, coastlines, land surface properties). Thus, trying to read off local values from the maps could be very misleading.
The seasonal forecasts consist of a 51-member ensemble. The ensemble is constructed by combining the 5-member ensemble ocean analysis with SST perturbations and the activation of stochastic physics. The forecasts have an initial date of the 1st of each month, and run for 7 months.
Every seasonal forecast model suffers from bias - i.e., the climate of the model forecasts differs to a greater or lesser extent from the observed climate. Since shifts in predicted seasonal climate are often small, this bias needs to be taken into account, and must be estimated from a previous set of model integrations. Forecast monthly mean anomalies (of temperature and rain) are calculated relative to a climate mean formed from the appropriate 1981-2010 re-forecasts. The set of re-forecasts (otherwise known as hindcasts or back integrations) are made starting on the 1st of every month for the years 1981-2010. They are identical to the real-time forecasts in every way, except that the ensemble size is only 15 rather than 51.
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