Catherine Schmechtig

Sensitivity of the Sandblasting Flux Calculations to the Soil Size Distribution Accuracy

Abstract Atmospheric dust concentrations are very sensitive to the dust surface emissions that are mainly controlled by saltation and sandblasting processes. Thus, a correct modeling of concentrations directly depends on mass flux parameterization accuracy. In 2001, Alfaro and Gomes proposed a whole set of parameterizations linking the dust flux to surface wind speed and soil characteristics. Their formulation is based on the integration of elementary fluxes, discretized along a soil size distribution. But, because the sandblasting is a threshold process, this discretization must be as fine as possible when the threshold acts. And because this threshold depends on dynamic parameters, it is necessary to always integrate fluxes with a high resolution. This leads to large numerical simulations. In this paper it is shown that it is possible to estimate fluxes with a good accuracy by adding equations that are dedicated to better describing the sensitive parts of the emission scheme.

Seasonal variability of nutrient concentrations in the Mediterranean Sea: Contribution of Bio‐Argo floats

In 2013, as part of the French NAOS (Novel Argo Oceanic observing System) program, five profiling floats equipped with nitrate sensors (SUNA-V2) together with CTD and bio-optical sensors were deployed in the Mediterranean Sea. At present day, more than 500 profiles of physical and biological parameters were acquired, and significantly increased the number of available nitrate data in the Mediterranean Sea. Results obtained from floats confirm the general view of the basin, and the well-known west-to-east gradient of oligotrophy. At seasonal scale, the north western Mediterranean displays a clear temperate pattern sustained by both deep winter mixed layer and shallow nitracline. The other sampled areas follow a subtropical regime (nitracline depth and mixed layer depth are generally decoupled). Float data also permit to highlight the major contribution of high-frequency processes in controlling the nitrate supply during winter in the north western Mediterranean Sea and in altering the nitrate stock in subsurface in the eastern basin.

A novel near real-time quality-control procedure for radiometric profiles measured by Bio-Argo floats: protocols and performances

AbstractAn array of Bio-Argo floats equipped with radiometric sensors has been recently deployed in various open ocean areas representative of the diversity of trophic and bio-optical conditions prevailing in the so-called case 1 waters. Around solar noon and almost every day, each float acquires 0–250-m vertical profiles of photosynthetically available radiation and downward irradiance at three wavelengths (380, 412, and 490 nm). Up until now, more than 6500 profiles for each radiometric channel have been acquired. As these radiometric data are collected out of an operator’s control and regardless of meteorological conditions, specific and automatic data processing protocols have to be developed. This paper presents a data quality-control procedure aimed at verifying profile shapes and providing near-real-time data distribution. This procedure is specifically developed to 1) identify main issues of measurements (i.e., dark signal, atmospheric clouds, spikes, and wave-focusing occurrences) and 2) validate...

Assessing the Variability in the Relationship Between the Particulate Backscattering Coefficient and the Chlorophyll a Concentration From a Global Biogeochemical‐Argo Database

Characterizing phytoplankton distribution and dynamics in the worlds open oceans requires in situ observations over a broad range of space and time scales. In addition to temperature/salinity measurements, Biogeochemical-Argo (BGC-Argo) profiling floats are capable of autonomously observing at high frequency bio-optical properties such as the chlorophyll fluorescence, a proxy of the chlorophyll a concentration (Chla), the particulate backscattering coefficient (bbp), a proxy of the stock of particulate organic carbon, and the light available for photosynthesis. We analyzed an unprecedented BGC-Argo database of more than 8,500 multi-variable profiles collected in various oceanic conditions, from subpolar waters to subtropical gyres. Our objective is to refine previously established Chla vs bbp relationships and gain insights into the sources of vertical, seasonal and regional variability in this relationship. Despite some nuances in the relationship considering one or another water column layer or region, a general covariation occurs at a global scale. We distinguish two main contrasted situations: (1) concomitant changes in Chla and bbp that correspond to actual variations in phytoplankton biomass, e.g. in subpolar regimes; (2) a decoupling between the two variables attributed to photoacclimation or changes in the relative abundance of non-algal particles, e.g. in subtropical regimes. The variability in the bbp:Chla ratio in the surface layer appears to be essentially influenced by the type of particles and photoacclimation processes. The dense BGC-Argo database helps identifying the spatial and temporal scales at which this ratio is predominantly driven by one or the other of these two factors.

A neural network‐based method for merging ocean color and Argo data to extend surface bio‐optical properties to depth: Retrieval of the particulate backscattering coefficient

The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean.