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The element carbon plays a central role in climate and life on Earth. It is capable of moving among the geosphere, cryosphere, atmosphere, biosphere and hydrosphere. This flow of carbon is referred to as the Earth's carbon cycle. It is also intimately linked to the cycling of other elements and compounds. The ocean plays a fundamental role in Earth's carbon cycle, helping to regulate atmospheric CO2 concentration. The ocean biological carbon pump (OBCP), defined as a set of processes that transfer organic carbon from the surface to the deep ocean, is at the heart of the ocean carbon cycle. Monitoring the OBCP is critical to understanding how the Earth's carbon cycle is changing. At present, satellite remote sensing is the only tool available for viewing the entire surface ocean at high temporal and spatial scales. In this paper, we review methods for monitoring the OBCP with a focus on satellites. We begin by providing an overview of the OBCP, defining and describing the pools of carbon in the ocean, and the processes controlling fluxes of carbon between the pools, from the surface to the deep ocean, and among ocean, land and atmosphere. We then examine how field measurements, from ship and autonomous platforms, complement satellite observations, provide validation points for satellite products and lead to a more complete view of the OBCP than would be possible from satellite observations alone. A thorough analysis is then provided on methods used for monitoring the OBCP from satellite platforms, covering current capabilities, concepts and gaps, and the requirement for uncertainties in satellite products. We finish by discussing the potential for producing a satellite-based carbon budget for the oceans, the advantages of integrating satellite-based observations with ecosystem models and field measurements, and future opportunities in space, all with a view towards bringing satellite observations into the limelight of ocean carbon research.

Phytoplankton patterns, tightly linked to the dynamics of the ocean surface layer and its atmospheric forcing, have major impacts on ecosystem functioning and are valuable indicators of its response to environmental variability and change. Phytoplankton phenology and its underlying drivers are spatially variable, and the study of its patterns, particularly over heterogeneous regions, benefits from a delineation of regions with specific phenological properties, or phenoregions. The area Southwest off the Iberian Peninsula (SWIP, NE Atlantic) integrates a highly complex set of coastal and ocean domains that collectively challenge the understanding of regional phytoplankton phenology and related forcing mechanisms. This study aims to evaluate phytoplankton phenology patterns over the SWIP area, during an 18-year period (September 1997 – August 2015), using an objective, unsupervised partition strategy (Hierarchical Agglomerative Clustering – HAC) based on phenological indices derived from satellite ocean colour data. The partition is then used to describe region-specific phytoplankton phenological patterns related to bloom magnitude, frequency, duration and timing. Region-specific variability patterns in phenological indices and their linkages with environmental determinants, including local ocean physical-chemical variables, hydrodynamic variables and large scale climate indices, were explored using Generalized Additive Models (GAM). HAC analyses identified five coherent phenoregions over SWIP, with distinctive phytoplankton phenological properties: two open ocean and three coastal regions. Over the open ocean, a single, low magnitude and long bloom event per year, was regularly observed. Coastal phenoregions exhibited up to six short bloom events per year, and higher intra-annual and variability. GAM models explained 50–90% of the variance of all phenological indices except bloom initiation timing, and revealed that interannual patterns in phytoplankton phenology and their environmental drivers varied markedly among the five phenoregions. Over the oceanic phenoregions, large-scale climate indices (Eastern Atlantic Pattern, Atlantic Meridional Oscillation), mixed layer depth (MLD) and nitrate concentration preceding primary bloom events were influential predictors, reflecting the relevance of nutrient limitation. For the Coastal-Slope, a relatively more light-limited phenoregion, North Atlantic Oscillation and wind speed were more relevant, and bloom magnitude was also positively influenced by riverine discharge. This variable was a significant predictor of bloom frequency, magnitude and duration over the Riverine-influenced region. Over the Upwelling-influenced region, upwelling intensity and mean annual MLD showed stronger partial effects on phytoplankton phenology. Overall, our phenology-based unsupervised approach produced a biologically-relevant SWIP partition, providing an evaluation of the complexity of interactions between phytoplankton and multiple environmental forcing, particularly over coastal areas. info:eu-repo/semantics/publishedVersion

The ocean surface is organized into regions with distinct properties reflecting the complexity of interactions between environmental forcing and biological responses. The delineation of these functional units, each with unique, homogeneous properties and underlying ecosystem structure and dynamics, can be defined as ocean surface partitioning. The main purposes and applications of ocean partitioning include the evaluation of particular marine environments; generation of more accurate satellite ocean colour products; assimilation of data into biogeochemical and climate models; and establishment of ecosystem-based management practices. This paper reviews the diverse approaches implemented for ocean surface partition into functional units, using ocean colour remote sensing (OCRS) data, including their purposes, criteria, methods and scales. OCRS offers a synoptic, high spatial-temporal resolution, multi-decadal coverage of bio-optical properties, relevant to the applications and value of ocean surface partitioning. In combination with other biotic and/or abiotic data, OCRS-derived data (e.g., chlorophyll-a, optical properties) provide a broad and varied source of information that can be analysed using different delineation methods derived from subjective, expert-based to unsupervised learning approaches (e.g., cluster, fuzzy and empirical orthogonal function analyses). Partition schemes are applied at global to mesoscale spatial coverage, with static (time-invariant) or dynamic (time-varying) representations. A case study, the highly heterogeneous area off SW Iberian Peninsula (NE Atlantic), illustrates how the selection of spatial coverage and temporal representation affects the discrimination of distinct environmental drivers of phytoplankton variability. Advances in operational oceanography and in the subject area of satellite ocean colour, including development of new sensors, algorithms and products, are among the potential benefits from extended use, scope and applications of ocean surface partitioning using OCRS. info:eu-repo/semantics/publishedVersion

Phytoplankton, the dominant marine primary producers, are considered to be highly sensitive indicators of ecosystem condition and change. The southwest area off the Iberian Peninsula (SWIP, NE Atlantic) is located in a biogeographical transition zone between temperate and subtropical waters, and classified as being very vulnerable to climate change. SWIP includes a variety of oceanic and coastal domains, under the influence of topographic irregularities, coastal upwelling and continental freshwater outflows, that collectively challenge the understanding of phytoplankton dynamics and controls. This study aimed to evaluate patterns in seasonal and interannual variability in phytoplankton and underlying environmental determinants within specific regions of SWIP, during a 15-year period (1997–2012), and to assess whether climate variability affects the regions in different ways. Empirical Orthogonal Function (EOF) analysis of satellite-retrieved sea surface chlorophyll-a concentration (Chl-a), acquired from the Ocean Colour Climate Change Initiative (OC-CCI), 4-km, 16-day resolution, was used to regionalize the study area. Region-specific Chl-a variability patterns and their linkages with environmental determinants were explored using Generalized Additive Mixed Models (GAMM). A set of local physical-chemical variables, derived from satellite and model data, and large-scale climate indices, were used as environmental variables. EOF analysis of Chl-a variability over the heterogeneous SWIP area identified nine coherent regions, with distinctive variability patterns (4 coastals, 2 slopes and 3 open-ocean regions). Region specific GAMM models explained between 32% and 82% of Chl-a variance, with higher explanatory power (N61%) for open ocean regions and coastal regions under increased riverine influence. Chl-a model predictors, as well as their effects, varied markedly among SWIP regions. However, climate-sensitive local environmental variables (sea surface temperature – SST and photosynthetically available radiation) emerged as the most influential general predictors overall, and large-scale climate indices showed significant but minor effects. Over oceanic SWIP regions, Chl-a (0.08–1.50 μg L−1) showed a uni-modal annual cycle, with increases during the mixed layer deepening and late-winter maxima, reflecting seasonal changes in SST and ocean stratification, and probably related to increased nutrient availability and/or decreased mortality. Over coastal regions south of 37°N, Chl-a (0.23–10 μg L−1) also benefited from riverine discharges, mostly during winter, and upwelling induced by zonal westerly winds, stronger during summer. Over the Portuguese west coast region, Chl-a (0.26–2.20 μg L−1) showed a uni-modal annual cycle, with summer maxima, associated with the stimulatory effects of meridional northerly winds and coastal upwelling that partially extended into slope waters. Chl-a interannual variability showed zonal differences within SWIP, with significant interannual patterns only for regions south of 37°N. Nonetheless, contrasting trends were detected in coastal (decline) and oceanic (increase) regions, possibly a consequence of between-region differences in the relative roles of nutrient and light limitation, corresponding to significant interannual increases in wind speed and mixed layer depth. Our study used a biologically-relevant objective regionalization of a heterogeneous area, to elucidate phytoplankton dynamics and controls. The region-specific associations observed between phytoplankton and multiple climate-sensitive environmental drivers over the SWIP area reinforce the role of phytoplankton as a strategic element for evaluating ecosystem responses to climate variability and change. info:eu-repo/semantics/publishedVersion

The Ocean Colour Climate Change Initiative intends to provide a long-term time series of ocean colour data and investigate the detectable climate impact. A reliable and stable atmospheric correction procedure is the basis for ocean colour products of the necessary high quality. In order to guarantee an objective selection from a set of four atmospheric correction processors, the common validation strategy of comparisons between in-situ and satellite-derived water leaving reflectance spectra, is extended by a ranking system. In principle, the statistical parameters such as root mean square error, bias, etc. and measures of goodness of fit, are transformed into relative scores, which evaluate the relationship of quality dependent on the algorithms under study. The sensitivity of these scores to the selected database has been assessed by a bootstrapping exercise, which allows identification of the uncertainty in the scoring results. Although the presented methodology is intended to be used in an algorithm selection process, this paper focusses on the scope of the methodology rather than the properties of the individual processors.

In recent years, the global distribution of phytoplankton functional types (PFT) and phytoplankton size classes (PSC) has been determined by remote sensing. Many of these methods rely on interpretation of phytoplankton size or type from pigment data, but independent validation has been difficult due to lack of appropriate in situ data on cell size. This work uses in situ data (photosynthetic pigments concentration and cell abundances) from the north-east Atlantic, along a trophic gradient, sampled from 2005 to 2010, as well as Atlantic Meridional Transect (AMT) data for the same region, to test a previously developed conceptual model, which calculates the fractional contributions of pico-, nano- and micro-plankton to total phytoplankton chlorophyll biomass ( Brewin et al., 2010 ). The application of the model proved to be successful, as shown by low mean absolute error between data and model fit. However, regional values obtained for the model parameters had some effect on the relative distribution of size classes as a function of chlorophyll-a, compared with the results according to the original model. The regional parameterisation yielded a dominance of micro-plankton contribution for chlorophyll-a concentrations greater than 0.5 mg m − 3 , rather than from 1.3 mg m − 3 in the original model. Intracellular chlorophyll-a (Chla) per cell, for each size class, was computed from the cell enumeration results (microscope counts and flow cytometry) and the chlorophyll-a concentration for that size class given by the model. The median intracellular chlorophyll-a values computed were 0.004, 0.224 and 26.78 pg Chla cell − 1 for pico-, nano-, and micro-plankton respectively. This is generally consistent with the literature, thereby providing an indirect validation of the method based on pigments to assign size classes. Using a satellite-derived composite image of chlorophyll-a for the study area, a map of cell abundance was generated based on the computed intracellular chlorophyll-a for each size-class, thus extending the remote-sensing method for mapping size classes of phytoplankton from chlorophyll-a concentration to mapping cell numbers in each class. The map reveals the ubiquitous presence of pico-plankton, and shows that all size classes are more abundant in more productive areas.

A historical data series, based on the identification and enumeration of phytoplankton in weekly samples collected at the Scripps Institution of Oceanography, San Diego, during the period 1920–1939, has been studied using spectral analysis. Considerable structure is present at time-scales less than one year. This is associated with upwelling events induced by fluctuations in the along-shore component of the wind stress.

Ecological dynamics in estuaries can be characterized in terms of spatial and temporal scales of variability. Data from Bedford Basin and other inlets on the Atlantic Coast of Nova Scotia are used to examine the scales of physical forcing and the scales of biological response. We establish temporal scales for which these inlets appear to be autonomous with respect to the adjacent continental shelf (~