Diatoms are among the most efficient marine organisms for primary production and carbon sequestration, absorbing at least 10 billion tonnes of carbon dioxide every year. Yet, the spatial distributions of these planktonic organisms remain puzzling and the underlying physical processes poorly known. Here we investigate what dynamical conditions are conductive to episodic diatom blooms in oligotrophic waters based on Lagrangian diagnosis and satellite-derived phytoplankton functional types and ocean currents. The Lagrangian coherence of the flow is diagnosed in space and time simultaneously to identify which structures favor diatom growth. Observations evidence that flow structures with a high degree of coherence (40 days or longer) in high turbulent kinetic energy and vorticity sustain high concentrations of diatoms in the sunlite layers. Our findings show that the integration of Eulerian kinematic variables into a Lagrangian frame allows revealing new dynamical aspects of geophysical turbulence and unveil transport properties having large biological impacts.

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. 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. They display large year-to-year seasonal variations over the 2010-2019 decade, sometimes by more than +/-0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI dataset excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original dataset. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

The Arctic Ocean contains only a 1% of the world’s ocean water, but the rivers that flow out into it account for the 10% of the volume world’s rivers freshwater. The upper layer of fresher water facilitates the creation of sea ice and plays an important role in the position of the jet stream and storms over the northern hemisphere [ISBN, 978-82-7971-097-4]. Remote sensing measurements are of special importance in the Arctic since in situ data is very scarce there. SMOS and SMAP are currently providing sea surface salinity (SSS) measures, but only the product provided by Barcelona Expert Center (BEC) is a dedicated product for the Arctic region. The product that we present in this work is an improvement of the BEC Arctic v2.0. The new version 3.0 has as the primary objective the describing better the river discharges. The spatial grid used is WGS84/NSIDC EASE-Grid 2.0 North for the all stages of the processing chain. This procedure avoids spatial interpolation, favoring the definition of river mouths. The salinity retrieval is based on the Debiased non-bayesian method [doi:10.1016/j.rse.2017.02.023] and similarly to what is done in the processing of altimetric data, SMOS salinity is corrected using a reference calculated from the own SMOS data for each latitude, longitude, pass orientation and antenna measuring position. Arctic v3.0 differs from current method [doi:10.3390/rs10111772] in two important points: the reference is computed for brightness temperature instead of SSS and the antenna has been divided in a more homogeneous grid. Other improvements concern to data filtering and propagation of the radiometric errors to SSS. All these improvements provide level 3 maps less noisy, increasing the effective resolution of salinity gradients. Freshwater gradients are much better resolved than in previous version (Fig. 1). Comparison with JPL SMAP product is also planned as a first step to generate a combined product. This work is funded by ESA Arctic + project and also includes the assimilation of the resulting SSS product in the ocean-sea ice data assimilation system TOPAZ as the next version TOPAZ5. A preliminary study [doi:10.5194/os-2018-163] has been performed concluding that BEC product could be a good candidate to be assimilated by TOPAZ. Moreover, some preliminary tests with a pre-release v3.0 version will start shortly.

The contribution of ocean fronts to the properties and temporal evolution of Sea Surface Temperature (SST) structure functions have been investigated using a numerical model of the California Current system. First, the intensity of fronts have been quantified by using singularity exponents. Then, leaning on the multifractal theory of turbulence, we show that the departure of the scaling of the structure functions from a straight line, known as anomalous scaling, depends on the intensity of the strongest fronts. These fronts, at their turn, are closely related to the seasonal change of intensity of the coastal upwelling characteristics of this area. Our study points to the need to correctly reproduce the intensity of the strongest fronts and, consequently, properly model processes such as coastal upwelling in order to reproduce SST statistics in ocean models.