The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state

Abstract

Salinity is a key ocean state property, changes in which reveal the variation of the water cycle and the ocean thermohaline circulation. However, prior to this century, in situ salinity observations were extremely sparse, which decreased the reliability of simulations of ocean general circulation by ocean and climate models. In 2009, sea surface salinity (SSS) observations covered the global ocean via the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission, and several versions of global SSS products were subsequently released. How can these data benefit model performance? Previous studies found contradictory results. In this work, we assimilated SMOS-SSS data into the LASG/IAP Climate system Ocean Model (LICOM) using the Ensemble Optimum Interpolation (EnOI) assimilation scheme. To assess and quantify the contribution of SMOS-SSS data to model performance, several tests were conducted. The results indicate that the CECOS/CATDS 2010.V02 SMOS-SSS product can significantly improve model simulations of sea surface/subsurface salinity fields. This study provides the basis for the future assimilation of SMOS-SSS data for short-range climate forecasting. Key Points: SMOS sea surface salinity observations were assimilated into the LICOM ocean model SMOS-SSS data play a complementary role in model salinity simulations Observation error covariances and the choice of SMOS product products are important (2016), The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state,

Importance of the Equatorial Undercurrent on the sea surface salinity in the eastern equatorial Atlantic in boreal spring

“…Progress have been recently made in SSS observation, thanks to the new satellite SSS measurements provided by the European Space Agency Soil Moisture and Ocean Salinity (SMOS mission led by ESA in collaboration with CNES and CDTI) [Reul et al, 2012] and by the Aquarius/SACD mission [Lagerlof, 2012] that are available from 2010 to present and 2011 to 2015, respectively. Satellite measurements offer the opportunity to observe the SSS with an unprecedented resolution about 50-150 km, 3-7 days [Lee et al, 2012[Lee et al, , 2014Hernandez et al, 2014;Reul et al, 2014aReul et al, ,2014bLu et al, 2016]. In the tropical Atlantic, satellite observations allow to monitor the seasonal SSS variability [Tzortzi et al, 2013] and to detect tropical instability waves (TIWs) [Lee et al, 2014].…”

Assimilation of Satellite Salinity for Modelling the Congo River Plume

“…Previous studies assimilating SMOS [18,21] have used an observation error of between 0.1-0.4 PSU. However, these studies did not focus on river plumes.…”

“…While the more traditional Argo floats provide relatively accurate in-situ salinity profiles, their lack of spatial coverage (typically 3 • by 3 • ) is a significant limitation. In 2009 the European Space Agency (ESA) launched the first satellite to monitor surface sea salinity under the Soil-Moisture-Ocean Salinity (SMOS) mission [18] providing global coverage of surface salinity. Köhl et al [19] assimilated SMOS but found no benefits to the global model salinity.…”

“…Köhl et al [19] assimilated SMOS but found no benefits to the global model salinity. Conversely, Lu et al [18] found that SMOS data plays a complementary role in model salinity simulations with an Ensemble Optimal Interpolation DA scheme (EnOI). A new version of SMOS that dramatically improved the land/sea interference and coastal coverage has been available from 2017 [20].…”

Halosteric Sea Level Changes during the Argo Era

“…This highlights the possibility of accurately re-assessing historical HSLA; (2) nowadays, we still have more observations in the upper layer, since sea surface salinity can be observed from satellites (such as the Soil Moisture Ocean Salinity (SMOS) mission by the European Space Agency and the NASA Aquarius mission). Assimilating the upper ocean surface salinity into ocean models will have the potential to significantly improve the model simulation [46,47].…”