Abstract: Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the New NASA/GMAO Seasonal Forecast System (Ocean Sciences Meeting 2020)

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the New NASA/GMAO Seasonal Forecast System

Eric C Hackert, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, United States, Robin M Kovach, NASA, GSFC, Greenbelt, MD, United States, Andrea Molod, Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, United States, Anna Borovikov, Sugar Land, TX, United States and Jelena Marshak, NASA Goddard Space Flight Center, GMAO, Greenbelt, MD, United States

Abstract:

We assess the impact of satellite sea surface salinity (SSS) observations for improving near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts. NASA’s Global Modeling and Assimilation Office (GMAO) has developed a new Subseasonal to Seasonal Prediction system (GEOS-S2S) Version 3 that, for the first time, routinely assimilates all available satellite sea surface salinity (SSS) observations. Other improvements for the GEOS-S2S Version 3 system include a higher resolution MOM5 (Griffies et al., 2005) ocean model (now 0.25^o x 0.25^o x 50 layers) and an improved atmospheric/ocean interface layer (Akella and Suarez, 2018). Atmospheric forcing is provided by the NASA MERRA-2 reanalysis (Gelaro et al., 2017).

For all initialization experiments, all available along-track absolute dynamic topography and in situ temperature and salinity observations are assimilated using the LETKF scheme similar to Penny et al., 2013. In addition, all available along-track satellite SSS (Aquarius – Lilly and Lagerloef, 2008, SMAP – Fore et al., 2016 and SMOS – Boutin et al., 2018) are routinely assimilated into the ocean reanalysis. A separate reanalysis withholds along-track SSS data.

In this presentation, we highlight the impact of satellite SSS on the ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus one that withholds SSS. For recent case studies, we find that for the big 2015 El Niño, the 2017 La Niña, and the 2018 weak El Niño, assimilation of satellite SSS improves ENSO forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. Finally, we compare 9-month seasonal forecasts initialized from these two reanalyses (i.e. with versus without SSS assimilation) for the tropical Pacific NINO3.4 region over the period, 1981-present.

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