Seasonal forecasting

What is a seasonal forecast?

Seasonal forecasts provide information on how weather, averaged over the next few months, is expected to vary from normal, e.g. "Are UK rainfall totals this winter likely to be above or below the long-term average?". The UK/Europe forecasts relate to the conventional seasons — winter, spring, summer and autumn. For other parts of the world the period of the forecast may vary, e.g. the

North Atlantic tropical storms forecast refers to the June to November season. Seasonal forecasts are indications of an overall picture, as it is impossible to forecast individual events so far ahead; the short-range forecasts are where the details begin to appear.

Because of uncertainty in forecasting at long range, seasonal forecasts are generally expressed in terms of probabilities. For example, our forecast for mean UK temperatures for the winter of 2007/8 gave probabilities for a relatively warm winter, an average winter and a relatively cold winter, as 50%, 30% and 20% respectively.

Long-range forecasts are part of the advice provided to the public on prospects over a range of time scales, and can help government agencies and companies with their long-term strategic planning. The forecasts have global coverage and are used in areas like Africa to help plan for year-to-year variability in rainy seasons.

How are seasonal forecasts possible?

Slowly varying aspects of the Earth's climate, in particular fluctuations in the surface temperature of the global oceans, can influence patterns in the weather. These influences are not easily noticed in day-to-day weather events but become evident in long-term weather averages.

The slow fluctuations of sea-surface temperature (SST) can be predicted, to some extent, at least up to six months ahead. The links between SST and weather can be represented in computer models of the atmosphere and ocean. Computer models developed at the Met Office, like those used in making both daily forecasts and long-term climate change predictions, form the basis of our seasonal prediction systems.

The strongest links between SST patterns and seasonal weather conditions are found in tropical regions, and it is here that seasonal forecasting is most successful. The best known links are those associated with sustained large-scale warming (or cooling) of SST in the tropical Pacific known as El Niño (or La Niña) events. These events can disrupt the normal pattern of weather around the globe, bringing, for example, large changes in seasonal rainfall that lead to droughts in some regions and floods in others.

Although the strongest links between SST and seasonal weather are found in the tropics, there is good evidence that similar, if weaker, links are present in other parts of the globe. The computer model forecasts can thus provide the best available guidance on likely seasonal conditions in many parts of the world, including Europe.

Because the link between weather and SST is best detected in long-term weather averages, and because the uncertainty in forecasts generally rises as the forecast range increases, seasonal forecasts look rather different in format compared to the familiar daily forecasts. The two key differences are:

• forecasts are for conditions averaged over three-month periods
• forecasts are stated in terms of probability

How are the forecasts produced?

The same computer models of the atmosphere that are used to make the daily weather forecasts are used, with some differences:

• they are run forward in time up to many months ahead, rather than just for a few days
• active oceanic, as well as atmospheric, components are included
• they are run many times, with slight variations to represent uncertainties in the forecast process

We occasionally use statistical forecasting methods on the seasonal timescale — in winter and summer for UK and Europe. This is done where physical relationships between weather and the state of the oceans have been found, but where models do not yet show sufficient skill to pick up these particular relationships. This gives rise to a mixed statistical and physical model forecast process.

We also use this mixture of methods for forecasting the mean global mean surface temperature for a year ahead. However, on even longer time scales, such as a century ahead, only physical models are used, as no more skilful statistical approach has been found.