Each forecast starts from the observed state of the ocean, land and atmosphere - the 'initial conditions'. Unfortunately, the initial conditions are not known precisely (one reason is that observing stations/instruments are sparsely distributed in some regions), and individual forecasts can be very sensitive to small uncertainties in the initial conditions. The sensitivity to initial conditions is taken into account by making a number of predictions each with different starting conditions which reflect our uncertainty in the initial state.
In addition to forecast sensitivity to starting conditions, forecasts can also be sensitive to the way we represent the real climate system in the computer models so-called sensitivities to model formulation. We address these uncertainties by using a random parameter perturbation scheme (Bowler et al, 2008) to simulate, using a single model framework, a range of representations of physical processes, thus sampling a large part of model uncertainty.
Such techniques generate an 'ensemble' of forecasts, with each individual forecast referred to as an ensemble 'member'. The forecast products are created by analysing the output from all the ensemble members.
Bowler, N.E., Arribas, A., Mylne, K.R., Robertson, K.B. and Beare, S.E., 2008: The MOGREPS short-range ensemble prediction system. QJR Meteorol Soc, 134, 703-722.
With default settings of the menu-driven map selection, the forecast products displayed are from the Met Office global seasonal prediction system, version 4, referred to as 'GloSea4'. An extensive set of performance validation information is provided for the GloSea4 system from analysis of retrospective forecasts over the period 1989-2002, updated each month.
The products are based on the output from forecasts made using a coupled ocean-atmosphere General Circulation Model (GCM). This is is a version of the HadGEM3 climate model, which uses the UM (Met Office Unified Model) atmosphere, NEMO (Nucleus for European Modelling of the Ocean) ocean and MOSES (Met Office Surface Exchange Scheme) land surface. The model resolution, as used in GloSea4, is N96 (1.25° in latitude and 1.875° in longitude) - approx 120 km in mid-latitudes, in the horizontal - and 38 levels in the vertical for the atmosphere, and the ORCA1 grid (1° ocean with 1/3° refinement between 20° S and 20° N) and 42 levels in the vertical for the ocean. The model contains no flux corrections or relaxations to climatology. Climate forcings (aerosols, methane and CO2 concentrations, etc.) are set to observed values for the period 1960-2000 and follow the scenario A1B afterwards. Ozone is fixed to observed climatological values and includes a seasonal cycle.
Each forecast requires initial ocean, land and atmosphere conditions. The land and atmosphere conditions are specified from atmospheric analyses that are produced separately for weather prediction purposes. The ocean initial conditions are taken from ocean analyses generated specifically for seasonal forecasting, using the ocean GCM component of GloSea4. The ocean GCM is run using surface fluxes of momentum, heat and water prescribed from atmospheric analyses, while assimilating sub-surface ocean observational data, with temperatures in the top layer(s) constrained to be close to surface observations.
Each week forecasts are run with starting conditions corresponding to the beginning of the week, to create a 14-member 'perturbed physics' ensemble: the different ensemble members are created by randomly perturbing parameters in physical parametrisations. By pooling three weeks' perturbed-physics ensembles together, a larger, lagged-start ensemble is created, which samples both initial-condition and model uncertainties.
In parallel to the forecast, four times a month (on the 1st, 9th, 17th and 25th) a three-member hindcast is generated in the same way, for the 14 years 1989-2002. The hindcasts are used to calibrate the ensemble forecast, which is then used to generate the seasonal products.
In addition to seasonal forecasts of temperature and precipitation, GloSea4, is used to forecast the number of tropical storms forming in the North Atlantic out to six months ahead.
The predictions are based on GloSea4 representation of dynamical and physical processes characteristic of tropical storms, and are achieved by counting the frequency of tropical storms in the model forecasts, using a technique similar to that described by Vitart and Stockdale (2001).
Vitart, F. and Stockdale, T.N., 2001: Seasonal forecasting of tropical storms using coupled GCM integrations, Mon Weather Rev, 129, 2,521-2,537.