Over 1993–2016, studies have shown that the observed global mean sea level (GMSL) budget is closed within the current data uncertainties. However, non‐closure of the budget was recently reported when using Jason‐3, Argo and GRACE/GRACE Follow‐On data after 2016. This non‐closure may result from errors in the data sets used to estimate the GMSL and its components. Here, we investigate possible sources of errors affecting Jason‐3 and Argo data. Comparisons of Jason‐3 GMSL trends with other altimetry missions show good agreement within 0.4 mm/yr over 2016–present. Besides, the wet tropospheric correction uncertainty from the Jason‐3 radiometer contributes to up to 0.2 mm/yr. Therefore, altimetry alone cannot explain the misfit in the GMSL budget observed after 2016. Argo‐based salinity products display strong discrepancies since 2016, attributed to instrumental problems and data editing issues. Reassessment of the sea level budget with the thermosteric component provides about 40% improvement in the budget closure.

The Sentinel-3 Mission Performance Centre (S3MPC) is tasked by the European Space Agency (ESA) to monitor the health of the Copernicus Sentinel-3 satellites and ensure a high data quality to the users. This paper deals exclusively with the effort devoted to the altimeter and microwave radiometer, both components of the Surface Topography Mission (STM). The altimeters on Sentinel-3A and -3B are the first to operate in delay-Doppler or SAR mode over all Earth surfaces, which enables better spatial resolution of the signal in the along-track direction and improved noise reduction through multi-looking, whilst the radiometer is a two-channel nadir-viewing system. There are regular routine assessments of the instruments through investigation of telemetered housekeeping data, calibrations over selected sites and comparisons of geophysical retrievals with models, in situ data and other satellite systems. These are performed both to monitor the daily production, assessing the uncertainties and errors on the estimates, and also to characterize the long-term performance for climate science applications. This is critical because an undetected drift in performance could be misconstrued as a climate variation. As the data are used by the Copernicus Services (e.g., CMEMS, Global Land Monitoring Services) and by the research community over open ocean, coastal waters, sea ice, land ice, rivers and lakes, the validation activities encompass all these domains, with regular reports openly available. The S3MPC is also in charge of preparing improvements to the processing, and of the development and tuning of algorithms to improve their accuracy. This paper is thus the first refereed publication to bring together the analysis of SAR altimetry across all these different domains to highlight the benefits and existing challenges.

Copernicus Sentinel-3 Surface Topography Mission embarks a two-channel microwave radiometer combined with the altimeter in order to correct the altimeter range for the excess path delay resulting from the presence of water vapour in the troposphere. The in-flight calibration of a single instrument is the critical point to achieve the expected performances. In the context of a constellation, the inter-calibration is even more important. After a presentation of the instrument design, we present the diagnoses used for the calibration of Sentinel-3A, using vicarious calibration over specific areas and double difference methods. The inter-calibration of Sentinel-3B with Sentinel-3A is performed during the tandem phase, using the residual differences of co-located measurements. Finally performances are assessed at crossover points with two parameters, first the wet troposphere correction by comparison with Jason-3; secondly on the Sea Surface Height by difference of variance. Analysis results have shown that Sentinel-3A is well calibrated, consistent with other instruments, and that Sentinel-3B is calibrated within 0.4 K with Sentinel-3A as a reference. Performances and stability fulfill the requirements for both missions.

The long-term stability of microwave radiometers (MWR) on-board altimetry missions is critical to reduce the uncertainty on the global mean sea level estimate. Harmonization and homogenization steps are applied to MWR observations in that perspective. The Sentinel-3 tandem phase provides a unique opportunity to quantify the uncertainties on the “zero-bias line” homogenization approach defined by Bennartz et al. (2020). Initially developed to improve the performance of the wet tropospheric correction retrieval, it is used here to provide a common reference for the inter-calibration between Sentinel-3A and Sentinel-3B MWR. A simplified version of the “zero-bias line” approach, a linear correction depending on brightness temperatures, allows to strongly reduce the bias between the two radiometers for both channels (about 0.5 K) and the standard deviation of the difference (0.3 K). The full version of the approach adding a dependency on wind speed has improved the quality of the WTC retrieval (Bennartz et al. 2020) but degrades the performance of the homogenization. It is thus recommended to apply the simplified version of this approach in the processing of fundamental data record. The quantification of the uncertainties on the homogenization approach is only possible due to the ideal configuration of the Sentinel-3 tandem phase. The same dataset and the same metrics could be used to assess other approaches.

The salinity of polar oceans is undergoing significant changes due to sea ice melt and increased continental runoff, which have resulted in a decrease in sea surface salinity (SSS) across most regions of the Arctic Ocean. Similarly, changes in the extent and thickness of Antarctic sea ice are altering SSS in the Southern Ocean, intensifying upper ocean stratification. These shifts profoundly impact ocean circulation, the ocean's capacity to absorb atmospheric heat and carbon, and ultimately, Earth’s climate. Notably, variations in SSS play a crucial role in the potential collapse of the Atlantic Meridional Overturning Circulation, with timing potentially earlier than anticipated by current climate models.Accurate SSS estimates are essential for monitoring freshwater fluxes at ocean boundaries (e.g., sea ice melting and formation, river runoff, and precipitation), surface hydrography variability affecting deep water formation and overturning circulation, and exchanges with other ocean basins—all of which influence global climate. However, current climate models struggle to accurately represent high-latitude water mass properties due to simplistic depictions of processes like lateral mixing, convection, and entrainment, especially in marginal ice zones. These limitations hinder the models’ ability to predict climate change impacts effectively.SSS is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) and an Essential Ocean Variable by the Global Ocean Observing System (GOOS). While current 1.4 GHz (L-band) radiometer missions have revolutionized global SSS measurements at scales of 40–150 km with revisit intervals of 3 to 8 days, their sensitivity to SSS diminishes significantly in cold waters (by a factor of ~3 between 30°C and 0°C), leading to greater uncertainties in polar regions.The CryoRad mission, an ESA Earth Explorer 12 candidate, features a radiometer with an extended frequency range of 0.4–2 GHz, designed to improve SSS measurement accuracy in cold waters by at least a factor of two compared to L-band radiometers.As part of the CNES study on "Salinity Estimation in Cold Seas Using Multiband 0.4–2 GHz" and the ESA CryoRad Phase 0 Science and Requirements Consolidation Study (SciReC), we conducted simulations using a simplified CryoRad instrument model. These simulations demonstrate the mission's potential to enhance SSS retrieval at high latitudes. The uncertainties in SSS retrieval were evaluated, considering various radiometric measurement factors such as sea surface temperature, wind speed, and atmospheric influences, which were modeled using radiative transfer principles validated for L-band and extrapolated to lower frequencies.This simulator was used to perform an initial sensitivity analysis for Level 2 and Level 3 salinity estimates. Our presentation will detail the simulator's implementation, including the direct and inverse models, inversion strategies, and the performance achieved in estimating SSS within the context of an academic case study.

A key correction in altimetry measurements is the wet tropospheric correction (WTC), which accounts for the delay on the measured range predominantly caused by water vapor in the troposphere. The WTC is currently one of the main sources of error affecting the altimeter range over ocean surfaces, with direct impact on estimates of sea surface height and the global mean sea level. Most current altimetry missions rely on onboard passive microwave radiometers that measure the brightness temperature at key frequencies for the spatio-temporal collocated estimation of the WTC. For such missions, the current operational algorithms for the estimation of the WTC can be placed in two categories: those using a neural network and those using a log-linear equation dependent on classes of WTC and windspeed. These two methodologies both follow a semi-empirical approach, but differ significantly in the inputs used, the training databases on which they are developed, and the mathematical approach itself. The objective of this study is to conduct a comprehensive comparison between these two algorithms, focusing solely on the mathematical aspect, in order to determine if the differences in the approaches of the two algorithms have a significant effect on the resulting WTC products, their performance and their limitations. To ensure the identical conditions required to achieve this, we develop the two algorithms so that they take the same inputs, have the same training database, and then we apply them to identical simulated and observed datasets to determine their performance. We use the three-frequency configuration (18.7 GHz−23.8 GHz−34 GHz) of the Advanced Microwave Radiometer for Climate (AMR-C) on Copernicus Sentinel-6 Michael Freilich (Sentinel-6MF), and radiative transfer modelling using the Radiative Transfer for TOVS (RTTOV), to create a simulated database that will serve for both development of the algorithms and performance evaluation. Both algorithms are then applied to the same observations from Copernicus Sentinel-6MF to characterise their observational performance in comparison to both model and operational products, as well as to each other. We find that, despite the significant differences in the two methodologies, when the algorithms are developed and applied to the same databases, they deliver very similar performances both on simulated and observed datasets and face similar limitations. In addition, for both algorithms we find performances similar to or slightly better than that of the operational WTC product for Copernicus Sentinel-6MF.