Presented at the 2014 Ocean Sciences MeetingRecently available satellite sea-surface salinity (SSS) fields provide an important new global data stream for assimilating into ocean forecast systems. We present results from assimilating SMOS and Aquarius SSS data into NOAA's operational MOM4. A relaxation technique was used for assimilating SST and SSS data. Sensitivity experiments were conducted to explore different relaxation time periods to evaluate the different impacts of assimilating high/low-frequency SSS variability. The control simulation relaxes to monthly mean SSS values. We expect that assimilating satellite SSS fields at the sea surface will constrain surface circulation, inducing changes in baroclinic pressure gradients, thus changing upper-ocean circulation patterns. Focusing on the tropical Pacific Ocean, we examine changes in upper-ocean heat content, mixed-layer depths, and velocity in the water column to the depth of the 20° C isotherm, showing that assimilating surface salinity fields causes significant seasonal-interannual changes in the patterns of mass, momentum, and heat in model results. Preliminary validation studies, examining independent variables such as sea-surface height (SSH) and ocean heat content, demonstrate that satellite SSS data assimilation improves ocean state representation, thus contributing to better model initializations for coupled seasonal and tropical cyclone forecast systems.