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

Journal article

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. Copyright 2009 by the American Geophysical Union.

Authors: Weisheimer, A; Doblas-Reyes, F; Palmer, T; Alessandri, A; Arribas, A

• Publication status: Published
• Journal: GEOPHYSICAL RESEARCH LETTERS
• Volume: 36
• Issue: 21
• Publication date: 2009-11-12
• DOI: 10.1029/2009GL040896
• ISSN: 0094-8276
• Language: English