Sea Surface Salinity and its uncertainty in 2010-2020 CCI version 3 fields
Authors: Boutin Jacqueline , Martin Adrien , Thouvenin-Masson Clovis , Reul Nicolas , Catany Rafael , Consortium Climate Change Initiative Sea Surface Salinity 
Published: March 2022

&lt;p&gt;Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data [&lt;em&gt;Boutin et al.&lt;/em&gt;, 2021]. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale.&lt;/p&gt;&lt;p&gt;In this presentation, we review recent advances and performances of the last (version 3) CCI+SSS product.&lt;/p&gt;&lt;p&gt;The CCI v3 processing has been updated to improve the long-term stability of the SMOS SSS [&lt;em&gt;Perrot et al.&lt;/em&gt;, 2021] and to improve the level 4 SSS uncertainty estimates. A correction for the instantaneous rainfall impact [&lt;em&gt;Supply et al.&lt;/em&gt;, 2020] is applied, so that, in rainy regions the CCI v3 fields are close to bulk salinities. In the level 4 optimal interpolation, a full least square propagation of the errors is implemented, instead of a simplified propagation.&lt;/p&gt;&lt;p&gt;When compared with Argo upper salinities, the robust standard deviation of the pairwise difference is 0.16 pss. However, this number includes a sampling mismatch between the in-situ near-surface salinity done at a single space and time and the two-dimensional satellite SSS. We use a small-scale resolution simulation (1/12&amp;#176; GLORYS) to quantitatively estimate the sampling uncertainty. A quantitative validation of CCI v3 SSS and its associated uncertainties is performed by considering the satellite minus Argo salinity normalized by the sampling and retrieval uncertainties [&lt;em&gt;Merchant et al.&lt;/em&gt;, 2017]. We find that, at global scale, the sampling mismatch contributes to ~20% of the observed differences between Argo and satellite data; in highly variable regions (river plumes, fronts), the sampling mismatch is the dominant term explaining satellite minus Argo salinity differences.&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Boutin, J., et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, &lt;em&gt;JGR-Oceans&lt;/em&gt;, &lt;em&gt;126&lt;/em&gt;(11), doi:10.1029/2021JC017676.&lt;/p&gt;&lt;p&gt;Merchant, C. J., et al. (2017), Uncertainty information in climate data records from Earth observation, &lt;em&gt;Earth Syst. Sci. Data&lt;/em&gt;, doi:10.5194/essd-9-511-2017.&lt;/p&gt;&lt;p&gt;Perrot, X., et al. (2021), CCI+SSS: A New SMOS L2 Reprocessing Reduces Errors on Sea Surface Salinity Time Series, IGARSS proceedings, doi: 10.1109/IGARSS47720.2021.9554451.&lt;/p&gt;&lt;p&gt;Supply, A.et al. (2020), Variability of Satellite Sea Surface Salinity Under Rainfall, in &lt;em&gt;Satellite Precipitation Measurement: Volume 2&lt;/em&gt;, doi:10.1007/978-3-030-35798-6_34.&lt;/p&gt;