Dynamical seasonal prediction

Dynamical Seasonal Prediction (DSP) is an informally coordinated multi-inst itution research project to investigate the predictability of seasonal mean atmospheric circulation and rainfall. The basic idea is to test the feasib ility of extending the technology of routine numerical weather prediction b eyond the inherent limit of deterministic predictability of weather to prod uce numerical climate predictions using state-of-the-art global atmospheric models. Atmospheric general circulation models (AGCMs) either forced by pr edicted sea surface temperature (SST) or as part of a coupled forecast syst em have shown in the past that certain regions of the extratropics, in part icular, the Pacific-North America (PNA) region during Northern Hemisphere w inter, can be predicted with significant skill especially during years of l arge tropical SST anomalies. However, there is still a great deal of uncert ainty about how much the details of various AGCMs impact conclusions about extratropical seasonal prediction and predictability. DSP is designed to compare seasonal simulation and prediction results from five state-of-the-art U.S. modeling groups (NCAR, COLA, GSFC, GFDL, NCEP) i n order to assess which aspects of the results are robust and which are mod el dependent. The initial emphasis is on the predictability of seasonal ano malies over the PNA region. This paper also includes results from the ECMWF model, and historical forecast skill over both the PNA region and the Euro pean region is presented for all six models. It is found that with specified SST boundary conditions, all models show th at the winter season mean circulation anomalies over the Pacific-North Amer ican region are highly predictable during years of large tropical sea surfa ce temperature anomalies. The influence of large anomalous boundary conditi ons is so strong and so reproducible that the seasonal mean forecasts can b e given with a high degree of confidence. However, the degree of reproducib ility is highly variable from one model to the other, and quantities such a s the PNA region signal to noise ratio are found to vary significantly betw een the different AGCMs. It would not be possible to make reliable estimate s of predictability of the seasonal mean atmosphere circulation unless caus es for such large differences among models are understood.