Stochastic ensemble climate forecast with an analogue model

Pascal Yiou (1, 2) , Céline Déandreis (3)

1 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
2 ESTIMR - Extrèmes : Statistiques, Impacts et Régionalisation
3 IPSL - Institut Pierre-Simon-Laplace

This paper presents a system to perform large-ensemble climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast a North Atlantic Oscillation (NAO) index and the daily average temperature in five European stations. We simulated 100-member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e., from a meteorological to a seasonal forecast. We tested the hindcast simulations with the usual forecast skill scores (CRPS or correlation) against persistence and climatology. We find significantly positive skill scores for all timescales. Although this model cannot out-perform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast.

Domaines: Océan, Atmosphère

Pascal Yiou, Céline Déandreis. Stochastic ensemble climate forecast with an analogue model. Geoscientific Model Development, 2019, 12 (2), pp.723-734. ⟨10.5194/gmd-12-723-2019⟩. ⟨hal-02902932⟩