ENSEMBLES: A new multi-model ensemble for seasonal-to-annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

Details ENSEMBLES: A new multi-model ensemble for seasonal-to-annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs ENSEMBLES: A new multi-model ensemble for seasonal-to-annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

Nov 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009georl..3621711w&link_type=abstract

Geophysical Research Letters, Volume 36, Issue 21, CiteID L21711

Statistics

Computation

20

Atmospheric Processes: Global Climate Models (1626, 4928), Mathematical Geophysics: Probabilistic Forecasting (3238), Mathematical Geophysics: Prediction (3245, 4263), Mathematical Geophysics: Uncertainty Quantification (1873, 1990), Computational Geophysics: Model Verification And Validation

Scientific paper

A new 46-year hindcast dataset for seasonal-to-annual ensemble predictions has been created using a multi-model ensemble of 5 state-of-the-art coupled atmosphere-ocean circulation models. The multi-model outperforms any of the single-models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead-times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi-model ensemble than for DEMETER in the 4-6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi-model ensemble does not improve the forecast skill further. Annual-range hindcasts show anomaly correlation skill of ˜0.5 up to 14 months ahead. A wide range of output from the multi-model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data.

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