Presented at the 95th AMS MeetingRecently available satellite sea-surface salinity (SSS) fields provide an important new global data stream for assimilation into ocean forecast systems. In this study, we present results from assimilating satellite SSS fields from NASA's Aquarius mission into the National Oceanic and Atmospheric Administration's (NOAA) operational Modular Ocean Model version 4 (MOM4), the oceanic component of NOAA's operational seasonal-interannual Climate Forecast System (CFS). Experiments on the sensitivity of the ocean's overall state to different relaxation time periods were run to evaluate the importance of assimilating high-frequency (daily to mesoscale) and low-frequency (seasonal) SSS variability. All runs are of three-year duration, September 2011 through August 2014. Aquarius SSS data from Aquarius Data Processing System (ADPS), version 3.0, mapped daily fields at 1-degree spatial resolution, were used. NOAA's MOM4, spanning 810S-900N with 0.5-degree meridional resolution and varying zonal resolution of 0.5 degrees or less, provides a fast and robust platform for analyzing ocean dynamics coarser than eddy-resolving scales. Four model simulations were started from the same initial ocean condition and forced with NOAA's daily Climate Forecast System Reanalysis (CFSR) fluxes, using a relaxation technique to assimilate daily satellite sea surface temperature (SST) fields and selected SSS fields, where, except as noted, a 30-day relaxation period is used. The simulations are: (1) WOAMC, the reference case and similar to the operational setup, assimilating monthly climatological SSS from the 2009 NOAA World Ocean Atlas; (2) AQ_D, assimilating daily Aquarius SSS; (3) AQ_M, assimilating monthly Aquarius SSS; and (4) AQ_D10, assimilating daily Aquarius SSS, but using a 10-day relaxation period. The analysis focuses on the tropical Pacific Ocean, where the salinity dynamics are intense and dominated by El Niño interannual variability in the cold tongue region and by high-frequency precipitation events in the western Pacific warm pool region. To assess the robustness of results and conclusions, we also examine the results for the tropical Atlantic and Indian Oceans. Assimilating satellite SSS fields at the sea surface constrains surface prognostic variables, inducing changes in baroclinic pressure gradients and, thus, three-dimensional circulation patterns and the oceanic state. Preliminary validation studies are conducted using observations, such as satellite sea-surface height (SSH) fields and in situ Argo buoy vertical profiles of temperature and salinity, to demonstrate that SSS data assimilation improves ocean state representation of the following variables: ocean heat content (0-300m), dynamic height (0-1000m), mixed-layer depth, and sea surface height. Further, an Empirical Orthogonal Function (EOF) analysis is conducted of the restoring fluxes generated by the assimilation experiments to examine implied biases and errors in the surface buoyancy fluxes.