Introduction to seasonal forecasting

What is a seasonal forecast?

Weather forecasts provide information about the weather expected over the next few days. While it is generally not possible to predict these day-to-day changes in detail beyond about a week ahead, it is possible to say something about likely conditions averaged over the next few months. Seasonal forecasts provide information about these long-term averages.

Why are seasonal forecasts possible?

Conditions at the Earth's surface, in particular slow 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, up to about 6 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 the familiar daily forecasts and for long-term climate change prediction, form the basis of our seasonal prediction systems.

The strongest links between SST patterns and seasonal weather trends are found in tropical regions, and it is here that seasonal forecasting is most successful. The best known links are those associated with the El Niño phenomenon, which is a warming of SST in the tropical Pacific that occurs on average every three or four years. El Niño 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.

Our forecasts provide this information in the form of geographical maps of these probabilities for seasonally-averaged temperature and rainfall.

How are the forecasts produced?

The same computer models of the atmosphere that are used to make the familiar daily weather forecasts also lie at the heart of seasonal forecasts. Three additional features of the method are worth mentioning:

• the models are run forward in time to a range of 6 months ahead, rather than just a few days.
• the models have active oceanic as well as atmospheric components, to represent important ocean-atmosphere interactions.
• the forecast models are run not once but many times, with slight variations to represent uncertainties in the forecast process.