[14-Dec-2018] Ren, L., Xie, P., Xue, Y., Kumar, A., Boyer, T., and Bayler, E.J.
Presented at the 2018 AGU Fall MeetingA analysis of observation-based global oceanic data, comprising pentad (5-day mean) data sets of global sea-surface salinity (SSS) and associated freshwater fluxes (precipitation (P) and evaporation (E)), is developed at the NOAA Climate Prediction Center (CPC) for real-time monitoring of sub-seasonal variations. SSS analysis, at pentad temporal resolution, is developed through blending in situ measurements from the NOAA National Center for Environmental Information (NCEI), retrievals from the European Space Agency's (ESA) Soil Moisture - Ocean Salinity (SMOS) mission, the joint U.S. and Argentinian Aquarius mission, and the National Aeronautics and Space Administration's (NASA) Soil Moisture Active-Passive (SMAP) mission. The blending is performed in two steps: 1) removing the bias in the satellite data through matching the Probability Density Function (PDF) against co-located in situ measurements, and 2) combining the bias-corrected satellite data with the in situ measurements through Optimal Interpolation (OI). The oceanic evaporation analysis is defined by matching the NOAA Climate Forecast System Reanalysis (CFSR) evaporation fields with respect to the Woods Hole Oceanographic Institution's Objectively Analyzed air-sea Fluxes (OAFlux). For real-time updates when no concurrent OAFlux is available, the matching factor is estimated as a combination of climatological values and the most-recent factor computed from concurrent OAFlux data. The oceanic precipitation fields, meanwhile, are constructed using bias-corrected NOAA Climate Prediction Center morphing (CMORPH) integrated satellite estimates. The pentad fields of SSS, E, P, and E-P are produced on a 1olatitude/longitude grid over the global ocean and updated with a latency of 2 days. Using these data sets, spatial maps of pentad anomalies, Hovmöller diagrams of equatorial mean variations, as well as time series of SSS and fresh water flux over several equatorial oceanic regions, are generated as part of a real time monitoring effort. A simple budget analysis of SSS provides attribution insights for the variations of SSS in the different regions.