Multi-model spread and probabilistic seasonal forecasts in PROVOST

The skill of the PROVOST (PRediction Of climate Variations On Seasonal to i nterannual Time-scales) long-range multi-model ensemble integrations is ana lysed. The ensemble PROVOST forecasts result from integrating three differe nt models over the period 1979-93 using analysed sea surface temperatures. For each model, a set of nine-member ensembles have been run from consecuti ve European Centre for Medium-Range Weather Forecasts re-analyses. Using th e full set of models, a large multi-model ensemble has been constructed and verified. Positive skill is found for forecasts of geopotential at 500 hPa, temperatu re at 850 hPa and precipitation. Skilful forecasts tend to occur at the sam e time in most of the models when skill is computed over large areas; the E uropean region is poorly forecast. The skill commonality may be due to the use of either similar initial conditions or boundary conditions. Skill is s hown to be at a maximum in late winter and early spring in mid latitudes. N o means have been found for linearly predicting the skill of the ensemble m ean using the ensemble spread. The multi-model ensemble improves the skill of the individual models only marginally when verifying the ensemble mean. However, when using the full ensemble in a probabilistic formulation, the m ulti-model approach offers a systematic improvement. The improvement arises both from the use of different models in the ensemble and from the higher ensemble size obtained by combining all of the models for building the mult i-model ensemble. It is shown that a part of the skill improvement in the t ropics is due to the multi-model approach, mainly in spring and summer. On the other hand, most of the gain in the extratropics comes from the increas e in ensemble size.