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.

Error-quantified, synoptic-scale relationships between chlorophyll-a (Chl-a) and phytoplankton pigment groups at the sea surface are presented. A total of ten pigment groups were considered to represent three Phytoplankton Size Classes (PSCs, micro-, nano- and picoplankton) and seven Phytoplankton Functional Types (PFTs, i.e. diatoms, dinoflagellates, green algae, prymnesiophytes (haptophytes), pico-eukaryotes, prokaryotes and Prochlorococcus sp.). The observed relationships between Chl-a and PSCs/PFTs were well-defined at the global scale to show that a community shift of phytoplankton at the basin and global scales is reflected by a change in Chl-a of the total community. Thus, Chl-a of the total community can be used as an index of not only phytoplankton biomass but also of their community structure. Within these relationships, we also found non-monotonic variations with Chl-a for certain pico-sized phytoplankton (pico-eukaryotes, Prokaryotes and Prochlorococcus sp.) and nano-sized phytoplankton (Green algae, prymnesiophytes). The relationships were quantified with a least-square fitting approach in order to enable an estimation of the PFTs from Chl-a where PFTs are expressed as a percentage of the total Chl-a. The estimated uncertainty of the relationships depends on both PFT and Chl-a concentration. Maximum uncertainty of 31.8% was found for diatoms at Chl-a = 0.49 mg m⁻³. However, the mean uncertainty of the relationships over all PFTs was 5.9% over the entire Chl-a range observed in situ (0.02 < Chl-a < 4.26 mg m⁻³). The relationships were applied to SeaWiFS satellite Chl-a data from 1998 to 2009 to show the global climatological fields of the surface distribution of PFTs. Results show that microplankton are present in the mid and high latitudes, constituting only ~10.9% of the entire phytoplankton community in the mean field for 1998–2009, in which diatoms explain ~7.5%. Nanoplankton are ubiquitous throughout the global surface oceans, except the subtropical gyres, constituting ~45.5%, of which prymnesiophytes (haptophytes) are the major group explaining ~31.7% while green algae contribute ~13.9%. Picoplankton are dominant in the subtropical gyres, but constitute ~43.6% globally, of which prokaryotes are the major group explaining ~26.5% (Prochlorococcus sp. explaining 22.8%), while pico-eukaryotes explain ~17.2% and are relatively abundant in the South Pacific. These results may be of use to evaluate global marine ecosystem models.

The Red Sea holds one of the most diverse marine ecosystems, primarily due to coral reefs. However, knowledge on large-scale phytoplankton dynamics is limited. Analysis of a 10-year high resolution Chlorophyll-a (Chl-a) dataset, along with remotely-sensed sea surface temperature and wind, provided a detailed description of the spatiotemporal seasonal succession of phytoplankton biomass in the Red Sea. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) data, four distinct Red Sea provinces and seasons are suggested, covering the major patterns of surface phytoplankton production. The Red Sea Chl-a depicts a distinct seasonality with maximum concentrations seen during the winter time (attributed to vertical mixing in the north and wind-induced horizontal intrusion of nutrient-rich water in the south), and minimum concentrations during the summer (associated with strong seasonal stratification). The initiation of the seasonal succession occurs in autumn and lasts until early spring. However, weekly Chl-a seasonal succession data revealed that during the month of June, consistent anti-cyclonic eddies transfer nutrients and/or Chl-a to the open waters of the central Red Sea. This phenomenon occurs during the stratified nutrient depleted season, and thus could provide an important source of nutrients to the open waters. Remotely-sensed synoptic observations highlight that Chl-a does not increase regularly from north to south as previously thought. The Northern part of the Central Red Sea province appears to be the most oligotrophic area (opposed to southern and northern domains). This is likely due to the absence of strong mixing, which is apparent at the northern end of the Red Sea, and low nutrient intrusion in comparison with the southern end. Although the Red Sea is considered an oligotrophic sea, sporadic blooms occur that reach mesotrophic levels. The water temperature and the prevailing winds control the nutrient concentrations within the euphotic zone and enable the horizontal transportation of nutrients.

Penetration of solar radiation in the ocean is determined by the attenuation coefficient (K_d(λ)). Following radiative transfer theory, K_d is a function of angular distribution of incident light and water's absorption and backscattering coefficients. Because these optical products are now generated routinely from satellite measurements, it is logical to evolve the empirical K_d to a semianalytical K_d that is not only spectrally flexible, but also the sun‐angle effect is accounted for explicitly. Here, the semianalytical model developed in Lee et al. (2005b) is revised to account for the shift of phase function between molecular and particulate scattering from the short to long wavelengths. Further, using field data collected independently from oligotrophic ocean to coastal waters covering >99% of the K_d range for the global oceans, the semianalytically derived K_d was evaluated and found to agree with measured data within ∼7–26%. The updated processing system was applied to MODIS measurements to reveal the penetration of UVA‐visible radiation in the global oceans, where an empirical procedure to correct Raman effect was also included. The results indicated that the penetration of the blue‐green radiation for most oceanic waters is ∼30–40% deeper than the commonly used euphotic zone depth; and confirmed that at a depth of 50–70 m there is still ∼10% of the surface UVA radiation (at 360 nm) in most oligotrophic waters. The results suggest a necessity to modify or expand the light attenuation product from satellite ocean‐color measurements in order to be more applicable for studies of ocean physics and biogeochemistry.

Satellite-derived remote-sensing reflectance (Rrs) just above the sea surface can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scales for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper, we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Data set (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, are ranked for spectrally resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking is tested using a Monto-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflect either their immunity to scale errors or instrument noise in Rrs data, or simply that data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggest the performance of some semi-analytical algorithms at retrieving chlorophyll were comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also performed with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.JRC.H.1-Water Resource