GEOS Subseasonal to Seasonal (S2S) System undergoes a major upgrade
10.31.17
At the end of October 2017, the GMAO's subseasonal-to-seasonal forecasting system, GEOS-S2S, underwent a major upgrade. Products from this new system, GEOS-S2S-v2_1, will replace GMAO contributions with the prior system (GEOS-S2S-V1: Borovikov et al., 2017) to a number of national projects, beginning with the November delivery cycle. The seasonal forecast anomalies from the new system are based on the system’s performance in a suite of retrospective forecasts (hindcasts), which are used to calculate the baseline climatology and drift. GEOS-S2S products are contributed to:
- The National Multi-Model Ensemble (NMME) suite of seasonal forecasts, coordinated and led by NOAA’s Climate Prediction Center http://www.cpc.ncep.noaa.gov/products/NMME/
- The Sea-Ice Outlook, coordinated by the Sea-ice Prediction Network (https://www.arcus.org/sipn/)
- The NMME subseasonal predictions (http://cola.gmu.edu/kpegion/subx/), for which GEOS-S2S-v2_1 has been in used since August 2017
All components of the coupled GEOS model and analysis system have undergone substantial changes compared to the older system. Much newer versions of all GEOS model components are used. For the atmosphere, this includes a change from a latitude-longitude model running at 1° resolution to a version running on a cubed sphere grid (Putman and Lin, 2007) with a c180 resolution, corresponding to about 0.5° or 50km. The atmospheric model has undergone numerous updates to the physical processes, including the use of a two-moment cloud microphysics scheme (Barahona et al., 2014). The ocean model has been updated from MOM4 to MOM5, both of which are configured to run on the same tripolar grid at approximately 0.5° resolution with 40 layers. The representation of the cryosphere has been substantially enhanced in the new system, building on the work of Cullather et al. (2014).
Atmospheric and land initial states in GEOS-S2S-v2_1 are based on the MERRA-2 reanalysis (Gelaro et al., 2017). The ocean analysis in the new system abandons the technique described in Borovikov et al. (2017) in favor of a new formulation based on the code of Penny (2014), but using a static representation of background error covariances. GEOS-S2S-v2_1 assimilates along-track altimetry observations from NASA’s Jason satellites and includes a nudging to MERRA-2 SST and sea ice boundary conditions.
ENSO forecasts have comparable skill in the new system as in the old one, with a slight degradation of quality of precipitation forecasts over the tropical Pacific. More positively, the overall representation of precipitation in the new system is improved over storm tracks and near topography. The new system also outperforms the old one in many aspects of the atmospheric and oceanic circulations, in net heat flux at the surface, and in metrics related to bias and anomaly correlation in particular over land.
References:
Barahona, D., A. Molod, J. Bacmeister, A. Nenes, A. Gettelman, H. Morrison, V. Phillips, and A. Eichmann, 2014: Development of two-moment cloud microphysics for liquid and ice within the NASA Goddard Earth Observing System Model (GEOS-5). Geosci. Model Dev, 7, 1733-1766. doi: 10.5194/gmd-7-1733-2014.
Borovikov, A. Y., R. I. Cullather, R. M. Kovach, J. Marshak, G. Vernieres, Y.V. Vikhliaev, B. Zhao, and Z. Li, 2017: GEOS-5 seasonal forecast system. Clim. Dyn., in press.
Cullather, R. L., S. M. J. Nowicki, B. Zhao, and M. J. Suarez, 2014: Evaluation of the surface representation of the Greenland ice sheet in a general circulation model. J. Climate, 27, 13, 4835-4856. doi: 10.1175/JCLI-D-13-00635.1.
Gelaro, R., et al., 2017: The Modern-Era Retrospective Analysis for Research and Applications, Version-2 (MERRA-2). J. Climate, 30, 5419-5454. DOI 10.1175/JCLI-D-16-0758.1.
Griffies, S. M., et al., 2011: GFDL’s CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. J. Climate, 24, 3520-3544.
Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2004: A Technical Guide to MOM4, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, USA, 337 pp.
Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2007: Ocean modelling with MOM. Clivar Exchanges, 12(3), 3-5, 13.
Penny, S., 2014: The Hybrid Local Ensemble Transform Kalman Filter. Monthly Weather Review 142 (6), 2139-2149.
Putman, W.M., and S.J. Lin, 2007: Finite-Volume Transport on Various Cubed-Sphere Grids. J. Comp. Phys. 227, 55-78.
