OS039-08
Effect of Rain-adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

Monday, 14 December 2020: 10:21
Virtual
Eric C Hackert, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, United States, Santha Akella, Global Modeling and Assimilation Office (GMAO), NASA Goddard Space Flight Center, Greenbelt, MD, United States, Robin M Kovach, NASA, GSFC, Greenbelt, MD, United States, Kazumi Nakada, SSAI/GMAO NASA Goddard, Greenbelt, United States, Anna Borovikov, Sugar Land, TX, United States, Andrea Molod, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Kyla Drushka, University of Washington, Applied Physics Laboratory, Seattle, WA, United States and Maria Marta Jacob, CoNAE, Cordoba, Argentina
Abstract:
The El Niño/Southern Oscillation (ENSO) phenomenon has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help to improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved short-term forecasts of the coupled system. So far, few studies (e.g. Martin et al., 2019, Tranchant et al., 2018) have focused on improving the near-surface density and mixing through assimilation of satellite sea surface salinity (SSS).

For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer (typically 5 m) instead of at the surface (i.e. top 1 cm). In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at 5 m. The Rain Impact Model (RIM – Santos-Garcia et al., 2014) uses a simple diffusion model (Asher et al., 2014) to determine the near surface salinity gradient (i.e. 1 cm to 5 m). The Aquarius (V5) satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at 5 m (we call this Aquarius@5m)

We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). For all reanalysis experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius SSS data as if it were 5 m data (as before) for Sep. 2011 to Jun. 2015. An additional reanalysis is performed assimilating the Aquarius@5m data.

Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the Aquarius@5m. We also compare results of coupled forecasts that are initialized from these reanalyses for the big 2015 El Niño. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

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