Samples from a monthly monitoring programme in the Cantabrian Sea were analysed with a FlowCAM-based automated technique. The estimates of abundance, biomass size spectra and taxonomic diversity of nano- and microplankton communities were compared with those obtained by traditional microscopical analysis of the same samples. The structure and abundance of a preserved plankton sample determined using FlowCAM showed minimal differences compared with traditional microscopical estimates. The effects of sample preservation and inaccuracies in the automatic classification are the main causes of discrepancies in the size structure determination between the two approaches. However, the synoptic understanding of the seasonal variation in the abundance, biomass and diversity obtained from the two methods is similar. Our results suggest that the natural variations in the community attributes explored are of greater magnitude than the error introduced by the methods and that the fully automatic method is adequate to explore these variations.

Any technique developed to enumerate plankton must take into account the size structure of the plankton community. Automatic sampling devices must be capable of analysing a minimum number of cells of the largest size to cover the whole size range intended to be sampled effectively. The Flow Cytometer And Microscope (FlowCAM^®) has been used in the last decade to estimate the size structure of the plankton community. Few attempts, however, have been made to compare FlowCAM measurements with the results provided by traditional microscopy methods for size-structure estimations. FlowCAM can operate in three working modes: autoimage, fluorescence triggered and side-scatter triggered. Autoimage and fluorescence triggered cannot only count accurately a mono-specific suspension of cells, but they are also useful to estimate the size structure of natural samples. The side-scatter-triggered mode is not effective to estimate the size structure of natural samples, although it can count a sparse mono-specific solution accurately. The analysis of natural samples with FlowCAM requires a planned pre-processing of the samples to adjust the density of triggering particles (concentrating or diluting the sample) and to pre-filtrate the sample to avoid cell clumping or obstruction of the flow chamber. The size structure obtained with FlowCAM and with microscopy counts on preserved samples are comparable. Sample preservation, however, alters the size structure of the sample, which suggests that results based on preserved samples must be taken with caution. Automatic sampling devices like FlowCAM could provide a more precise analysis of plankton communities, increasing the resolution of surveys and avoiding the effects of preservation and sample storage.

The most commonly used biomass estimate for microalgae is obtained from cell biovolume, usually calculated from microscopically measured linear dimensions. Although reliable, this is a highly time-consuming and specialized technique. Automated sampling devices that acquire images of cells and use pattern recognition techniques to identify the images have been developed as an alternative to microscopy-based methods. There are some aspects of automatic sampling and classification methods, however, which can be improved for the analysis of field samples including living and non-living particles. In this work, we demonstrate how the accuracy of a state-of the-art technique for plankton classification (Support Vector Machine) can be improved up to 86% if a previous automated step designed to remove non-living images is included. There is a tendency with the currently applied automatic methods to misestimate cell biovolume due to the two-dimensionality of the images. Here, we present a data set of more than 500 samples to show that the greatest effect is caused by the incorrect estimation of biovolume of the chain-forming diatoms. This results in an overestimate of biomass of between 20 and 100% where chain-forming diatoms represent more than the 20% of the biomass of the sample. We show how the classification method can be adapted to provide not only taxonomic but also the morphological classification of cells in order to obtain a more reliable estimate of biovolume according to the predicted cell shape, in a way comparable with microscopy-based estimates.

The Mediterranean Forecasting System produces operational analyses and reanalyses and 10 d forecasts for many essential ocean variables (EOVs), from currents, temperature, salinity, and sea level to wind waves and pelagic biogeochemistry. The products are available at a horizontal resolution of $\mathrm{1}/\mathrm{24}$ ^∘ (approximately 4 km) and with 141 unevenly spaced vertical levels. The core of the Mediterranean Forecasting System is constituted by the physical (PHY), the biogeochemical (BIO), and the wave (WAV) components, consisting of both numerical models and data assimilation modules. The three components together constitute the so-called Mediterranean Monitoring and Forecasting Center (Med-MFC) of the Copernicus Marine Service. Daily 10 d forecasts and analyses are produced by the PHY, BIO, and WAV operational systems, while reanalyses are produced every ∼ 3 years for the past 30 years and are extended (yearly). The modelling systems, their coupling strategy, and their evolutions are illustrated in detail. For the first time, the quality of the products is documented in terms of skill metrics evaluated over a common 3-year period (2018–2020), giving the first complete assessment of uncertainties for all the Mediterranean environmental variable analyses.

In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation‐by‐sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates.

Chromophoric dissolved organic matter (CDOM) significantly contributes to the non-water absorption budget in the Mediterranean Sea. The absorption coefficient of CDOM, a_CDOM(λ), is measurable in situ and can be retrieved remotely, although ocean-colour algorithms do not distinguish it from the absorption of detritus. These observations can be used as indicators for the concentration of other relevant biogeochemical variables in the ocean, e.g. dissolved organic carbon. However, our ability to model the biogeochemical processes that determine CDOM concentrations is still limited. Here we propose a novel parameterization of the CDOM cycle that accounts for the interplay between the light- and nutrient-dependent dynamics of local CDOM production and degradation, as well as its vertical transport. The parameterization is included in a one-dimensional (1D) configuration of the Biogeochemical Flux Model (BFM), which is here coupled to the General Ocean Turbulence Model (GOTM) through the Framework for Aquatic Biogeochemical Models (FABM). Here the BFM is augmented with a bio-optical component that resolves spectrally the underwater light transmission. We run this new GOTM-(FABM)-BFM configuration to simulate the seasonal a_CDOM(λ) cycle at the deep-water site of the Bouée pour l'acquisition de Séries Optiques à Long Terme (BOUSSOLE) project in the northwestern Mediterranean Sea. Our results show that accounting for both nutrient and light dependence of CDOM production improves the simulation of the seasonal and vertical dynamics of a_CDOM(λ), including a subsurface maximum that forms in spring and progressively intensifies in summer. Furthermore, the model consistently reproduces the higher-than-average concentrations of CDOM per unit chlorophyll concentration observed at BOUSSOLE. The configuration, outputs, and sensitivity analyses from this 1D model application will be instrumental for future applications of BFM to the entire Mediterranean Sea in a three-dimensional configuration.