The real transfer function and the phase shift between sea surface height (SSH) and sea surface buoyancy (SSB) were estimated from the output of a realistic eddy-resolving model of the Mediterranean Sea circulation. The analysis of their temporal evolution unveiled the existence of a clear seasonal cycle closely related to that of the mixed layer depth. The phase shifts between SSH and SSB attain their minimum for deep mixed layers, which is different from zero. Besides, the spectral slope of the transfer function at scales shorter than 100 km fluctuates between k−1 and k−2. For deep mixed layers, it is close to k−1, as predicted by the surface quasigeostrophic (SQG) solution. At longer wavelengths, it is approximately constant under the different environmental conditions in all of the subbasins analyzed with the exception of the Gulf of Lions. The capability to observe sea surface temperature (SST) from satellites motivated the extension of this analysis to SST and SSH. Results showed a similar qualitative behavior but with larger phase shifts. In spite of the presence of a phase shift, even for deep mixed layers, results revealed that it is still possible to reconstruct surface dynamics from SST using a transfer function, provided that the mixed layer is deep enough. For the present study, a threshold value of 70 m was enough to identify the appropriate environmental conditions. In addition, the results revealed that a precise estimation of the transfer function significantly improves the reconstruction of the flow in comparison with the application of the classical SQG solution.

Ocean currents are a key component to understanding many oceanic and climatic phenomena and knowledge of them is crucial for both navigation and operational applications. Currently, they are derived from Sea Surface Height (SSH) measurements provided by altimeters. However, distances between tracks and the limited number of available altimeters lead to errors in the accurate location of oceanic currents. In this study, we investigate the capability of Sea Surface Temperature (SST) observations to reconstruct surface currents at a global scale. The methodology we use consists of estimating the stream function by taking the phase from SST and the spectrum of SSH and then comparing it with altimetric measurements. Results reveal that SST provided by microwave radiometers can be used to retrieve ocean currents during winter near the major extratropical current systems, which are characterized by an intense mesoscale activity and the presence of strong thermal gradients. We have also found that surface ocean current reconstruction based on Surface Quasi-Geostrophic approach can be improved if the information about the energy spectrum provided by altimeters is used. This points to the development of a new method of reconstructing ocean currents based on the combination of the phase of SST images with the energy spectrum derived from along-track altimetric measurements.

Abstract. After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of BEC global SMOS SSS product v2.0, as well as an extensive quality assessment. Three SMOS SSS fields are distributed: a high-resolution level-3 product (with DOI https://doi.org/10.20350/digitalCSIC/12601, Olmedo et al., 2020a) consisting of binned SSS in 9 d maps at 0.25∘×0.25∘; low-resolution level-3 SSS computed from the binned salinity by applying a smoothing spatial window of 50 km radius; and level-4 SSS (with DOI https://doi.org/10.20350/digitalCSIC/12600, Olmedo et al., 2020b) consisting of daily 0.05∘×0.05∘ maps that are computed by multifractal fusion with sea surface temperature maps. For the validation of BEC SSS products, we have applied a battery of tests aimed at the assessment of quality of the products both in value and in structure. First, we have compared BEC SSS products with near-to-surface salinity measurements provided by Argo floats. Secondly, we have assessed the geophysical consistency of the products characterized by singularity analysis, and the effective spatial resolutions are also estimated by means of power density spectra and singularity density spectra. Finally, we have calculated full maps of SSS errors by using correlated triple collocation. We have compared the performance of the BEC SMOS product with other satellite SSS and reanalysis products. The main outcomes of this quality assessment are as follows. (i) The bias between BEC SMOS and Argo salinity is lower than 0.02 psu at a global scale, while the standard deviation of their difference is lower than 0.34 and 0.27 psu for the high- and low-resolution level-3 fields (respectively) and 0.24 psu for the level-4 salinity. (ii) The effective spatial resolution is around 40 km for all SSS products and regions. (iii) The results from triple collocation show the BEC SMOS level-4 product as the product with the lowest estimated salinity error in most of the global ocean and the BEC SMOS high-resolution level-3 as the one with the lowest estimated salinity error in regions strongly affected by rainfall and continental freshwater discharge.

Abstract. Measuring salinity from space is challenging since the sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) is low (about 0.5 K psu−1), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2–3 K). Moreover, the sensitivity of the TB to SSS at cold waters is even lower (0.3 K psu−1), making the retrieval of the SSS in the cold waters even more challenging. Due to this limitation, the ESA launched a specific initiative in 2019, the Arctic+Salinity project (AO/1-9158/18/I-BG), to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product (Martínez et al., 2019) . The product consists of 9 d averaged maps in an EASE 2.0 grid of 25 km. The product is freely distributed from the Barcelona Expert Center (BEC, http://bec.icm.csic.es/, last access: 25 January 2022) with the DOI number https://doi.org/10.20350/digitalCSIC/12620 (Martínez et al., 2019). The major change in this new product is its improvement of the effective spatial resolution that permits better monitoring of the mesoscale structures (larger than 50 km), which benefits the river discharge monitoring.

The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.