NOAA’s Seasonal Forecast System (SFS) Development Plan: A Community Modeling Approach to Increase S2S Forecast Skill

NOAA has initiated the development of a new Seasonal Forecast System (SFS) to be implemented into National Weather Service (NWS) operations. Through the SFS, the NWS will provide enhanced seasonal predictions for precipitation, drought, temperature, tropical cyclone frequency, and their extremes, for decision makers across industries in the public and private sectors.

Using the community-based Unified Forecast System (UFS), the SFS will build upon and extend the capabilities of the sub-seasonal Global Ensemble Forecast System (GEFS). Accurate SFS prediction requires improved physical descriptions of slowly changing processes on the land, in the oceans, for sea ice, and for atmospheric composition. Data assimilation improvements are also required to improve initial states for the land, ocean and sea ice in SFS component models that provide the long-term memory of the Earth System. A historical reanalysis and reforecast will also be performed for model calibration and to further improve seasonal forecast outlooks along with post-processing methods.

The NWS Office of Science and Technology Integration (OSTI) Modeling Program and the OAR Weather Program Office (WPO) Subseasonal-to-Seasonal Program are jointly supporting the SFS development project. A SFS development plan has been drafted with a goal to build SFS v1 as a replacement of the more than one decade-old Climate Forecast System version 2 (CFSv2). We will present the development plan, which includes a public release of the coupled SFS and reanalysis-reforecast data sets to the community, along with target configurations and experiments to resolve systematic biases and improve forecasts of climate variability for a variety of phenomena such as MJO and ENSO. The SFS project strongly encourages feedback and collaboration on diagnosing and understanding of model deficiencies in representing physical processes, reducing model systematic biases, and identifying key sources of seasonal predictability.