Designing an optimal strategy for GMAO S2S ensemble forecast.

Anna Borovikov, Science Systems and Applications, Inc., NASA/GMAO, Lanham, MD, United States, Robin M Kovach, NASA, GSFC, Greenbelt, MD, United States, Siegfried D Schubert, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Young-Kwon Lim, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, United States and Andrea Molod, Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, United States
Abstract:
GMAO Sub/Seasonal prediction system (S2S) is being readied for a major upgrade to GEOS S2S Version 3. An important factor in successful extended range forecast is the definition of an ensemble. For initialization of the ensemble we propose a combination of lagged and burst initial conditions. We plan to run a relatively large ensemble of 40 members for sub-seasonal forecast (up to 3 months), at which point we sub-sample the ensemble, and continue the forecast with 10 members (up to 12 months). Here we present the results of the extensive testing of various ways to generate the perturbations to the initial conditions and the validation of the stratified sampling strategy we chose.

To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states separated by 1-10 days, randomly chosen from a corresponding season. We considered perturbing separately only the atmospheric fields or only the ocean or both of the forecast initial conditions. Considering varying separation times between the analysis states, we were able to produce perturbations sampling various modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread.

Our ensemble size for sub-seasonal forecasts was determined as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes, NAO, PNA and AO. It is not feasible to run equally large ensemble for seasonal forecasts. Using a stratified sampling procedure we can identify the emerging directions of error growth. By comparing the stratified ensemble with randomly sampled ensemble of the same size, we were able to show that the former better estimates the mean of the original large ensemble.

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