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The Partnership for Observation of the Global Ocean (POGO) and the Scientific Committee on Oceanic Research (SCOR) have been running a Visiting Fellowship program in ocean observations that has trained 200 fellows since its inception in 2001. We report here on the medium- and long-term impacts of the training, as demonstrated by a combination of quantitative results from evaluation questionnaires and qualitative reports from fellows and supervisors. The key findings are that the fellowships have had positive impacts on the fellows themselves (e.g., participation in new research projects, implementation of new techniques, long-term collaborations), as well as “training the trainers,” with most fellows indicating that they had passed on the knowledge gained. Benefits to the host supervisor and institution are also apparent through continued collaborations (e.g., reciprocal exchange visits, joint publications, joint projects). More widespread impacts are evident, where fellows have become key participants in the international scientific community. 

Information on the intracellular content and functional diversity of phytoplankton pigments can provide valuable insight on the ecophysiological state of primary producers and the flow of energy within aquatic ecosystems. Combined global datasets of analytical flow cytometry (AFC) cell counts and High-Performance Liquid Chromatography (HPLC) pigment concentrations were used to examine vertical and seasonal variability in the ratios of phytoplankton pigments in relation to indices of cellular photoacclimation. Across all open ocean datasets, the weight-to-weight ratio of photoprotective to photosynthetic pigments showed a strong depth dependence that tracked the vertical decline in the relative availability of light. The Bermuda Atlantic Time-series Study (BATS) dataset revealed a general increase in surface values of the relative concentrations of photoprotective carotenoids from the winter-spring phytoplankton communities dominated by low-light acclimated eukaryotic microalgae to the summer and early autumn communities dominated by high-light acclimated picocyanobacteria. InProchlorococcus-dominated waters, the vertical decline in the relative contribution of photoprotective pigments to total pigment concentration could be attributed in large part to changes in the cellular content of photosynthetic pigments (PSP) rather than photoprotective pigments (PPP), as evidenced by a depth-dependent increase of the intracellular concentration of the divinyl chlorophyll-a(DVChl-a) whilst the intracellular concentration of the PPP zeaxanthin remained relatively uniform with depth. The ability ofProchlorococcuscells to adjust their DVChl-acell^-1over a large gradient in light intensity was reflected in more highly variable estimates of carbon-to-Chl-aratio compared to those reported for other phytoplankton groups. This cellular property is likely the combined result of photoacclimatory changes at the cellular level and a shift in dominant ecotypes. Developing a mechanistic understanding of sources of variability in pigmentation of picocyanobacteria is critical if the pigment markers and bio-optical properties of these cells are to be used to map their biogeography and serve as indicators of photoacclimatory state of subtropical phytoplankton communities more broadly. It would also allow better assessment of effects on, and adaptability of phytoplankton communities in the tropical/subtropical ocean due to climate change. 

Abstract. A global in situ data set for validation of ocean colour productsfrom the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented.This version of the compilation, starting in 1997, now extends to 2021,which is important for the validation of the most recent satellite opticalsensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprisesin situ observations of the following variables: spectral remote-sensingreflectance, concentration of chlorophyll-a, spectral inherent opticalproperties, spectral diffuse attenuation coefficient, and total suspendedmatter. Data were obtained from multi-project archives acquired via openinternet services or from individual projects acquired directly from dataproviders. Methodologies were implemented for homogenization, qualitycontrol, and merging of all data. Minimal changes were made on the originaldata, other than conversion to a standard format, elimination of some points,after quality control and averaging of observations that were close in timeand space. The result is a merged table available in text format. Overall,the size of the data set grew with 148 432 rows, with each row representing aunique station in space and time (cf. 136 250 rows in previous version;Valente et al., 2019). Observations of remote-sensing reflectance increasedto 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There wasalso a near tenfold increase in chlorophyll data since 2016. Metadata ofeach in situ measurement (original source, cruise or experiment, principalinvestigator) are included in the final table. By making the metadataavailable, provenance is better documented and it is also possible toanalyse each set of data separately. The compiled data are available athttps://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022). 

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel. 

Abstract. A global compilation of in situ data is useful to evaluate thequality of ocean-colour satellite data records. Here we describe the datacompiled for the validation of the ocean-colour products from the ESA OceanColour Climate Change Initiative (OC-CCI). The data were acquired fromseveral sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018.Observations of the following variables were compiled: spectralremote-sensing reflectances, concentrations of chlorophyll a, spectralinherent optical properties, spectral diffuse attenuation coefficients andtotal suspended matter. The data were from multi-project archives acquiredvia open internet services or from individual projects, acquired directlyfrom data providers. Methodologies were implemented for homogenization,quality control and merging of all data. No changes were made to theoriginal data, other than averaging of observations that were close in timeand space, elimination of some points after quality control and conversionto a standard format. The final result is a merged table designed forvalidation of satellite-derived ocean-colour products and available in textformat. Metadata of each in situ measurement (original source, cruise orexperiment, principal investigator) was propagated throughout the work andmade available in the final table. By making the metadata available,provenance is better documented, and it is also possible to analyse each setof data separately. This paper also describes the changes that were made tothe compilation in relation to the previous version (Valente et al., 2016).The compiled data are available athttps://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).