Abstract

Abstract. Monitoring sea surface salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, mostly closer to 5 m depth than the surface, were available to estimate the SSS. The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made it possible for the first time to measure SSS from space and can bring a valuable additional constraint to control the model salinity. Nevertheless, satellite SSS still contains some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study is to estimate the SSS bias and a suitable observation error for the data assimilation system. It was made possible by modifying a 3D-Var bias correction scheme and by using the analysis of the residuals and errors with an adapted statistical technique. This article presents the design and the analysis of an observing system experiment (OSE) conducted with the 0.25∘ resolution Mercator Ocean global analysis and forecasting system during the El Niño 2015/16 event. The SSS data assimilation constrains the model to be closer to the near-surface salinity observations in a coherent way with the other data sets already routinely assimilated in an operational context. This also shows that the overestimation of E–P is corrected by data assimilation through salting in regions where precipitations are higher. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 m. Comparisons to independent salinity data sets show a small but positive impact and corroborate the fact that the impact of SMOS SSS assimilation is larger in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) regions. There is little impact on the sea surface temperature (SST) and sea surface height (SSH) error statistics. Nevertheless, the SSH seems to be impacted by the tropical instability wave (TIW) propagation, itself linked to changes in barrier layer thickness (BLT). Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS data assimilation performance.

Highlights

  • Recent progress in data treatment of sea surface salinity (SSS) from space makes possible its assimilation in ocean analysis systems (Boutin et al, 2017)

  • Assimilation of in situ temperature and salinity profiles from this database is mostly from ARGO floats; expendable bathythermograph (XBT); conductivity, temperature, and depth measurements(CTDs); moorings; gliders; and sea mammals. The assimilation of those routine observations in the observing system experiment (OSE) provides a realistic context for the global ocean observing system so that the experiments address the complementarity of the different data sets with satellite SSS

  • While the impact of SSS assimilation is neutral on the other variables in terms of data assimilation statistics (RMSE averaged in different areas), it is not the case when we look at the time evolution of model fields

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Summary

Introduction

Recent progress in data treatment of sea surface salinity (SSS) from space makes possible its assimilation in ocean analysis systems (Boutin et al, 2017). We present the impact of assimilating SSS observations from space into the global 0.25◦ Mercator Ocean operational system (see Lellouche et al, 2013) evaluated in the SMOS Niño 2015 project Org/projects/smos-Nino, last access: 18 April 2019) are helping to prepare the assimilation of space SSS data and allow testing their impact on short-term ocean forecast and analysis. Experiments conducted within the SMOS Niño project to test the impact of the satellite SSS data were carefully designed and analyzed to ensure robust conclusions on the impact of SSS measurements on ocean analysis. The structure of this article is as follows: after a description of the OSE where the operational system, the bias correction, the SSS observation error and the presentation of the experimental design are described, the effect of the SMOS SSS data assimilation is presented, while discussions and conclusions are provided in Sect.

OSE approach
Ocean model and configuration
Regular observation data
SSS from space
Data assimilation scheme
Background error covariances
Observation error covariances
Bias correction scheme
Bias correction scheme for large-scale SSS
SSS observation error
OSE design
OSE analysis
Assimilation diagnostics
Evaluation of the analysis toward independent observations
Comparisons to TAO mooring
Comparisons to ship SSS
Findings
Discussion and conclusions
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