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Coastal waters of Kerala, which form an integral part of the Malabar upwelling zone off the southwest coast of India, constitute an important fishing region for small pelagics. Satellite remote sensing data from 1998−2014 were used to test the hypothesis that fluctuations in the landings of Sardinella longiceps, the major pelagic fish landed in the area designated as the South Eastern Arabian Sea (SEAS), are influenced by seasonal variability in phytoplankton biomass (measured as chlorophyll a [chl a] concentration), under the changing strength of physical para - meters such as sea surface temperature (SST), alongshore wind stress, Ekman mass transport, sea level anomaly (SLA) and Kerala rainfall. Multiple linear regression analysis (MLRA) was used to assess the influence of physical forcing mechanisms on chl a concentration on monthly and seasonal scales. We found that SLA, alongshore wind stress, SST and rainfall were ranked 1 to 4, respectively, and the first 3 factors significantly influenced the chl a concentration of SEAS. Pearson’s correlation analysis between monthly chl a and sardine landing (with chl a leading) showed a maximum positive correlation (+0.26) at 2 and 3 mo lags, emphasizing that the influence of chl a on the fishery of S. longiceps is seasonal (r = 0.35 for seasonal lead−lag correlation) in the coastal waters of SEAS. Variation in phytoplankton biomass, as evidenced by chl a fluctuations, seems to have a decisive role in regulating the physiological condition of larvae spawned during the southwest monsoon season, their juveniles and finally the adults that are recruited into the fishery in the next season. Using the quantity of phytoplankton as a predictive tool will exploit the presumptive trophic link to aid understanding of sardine fishery dynamics in upwelling zones.

Issue sup1: Copernicus Marine Service Ocean State Report, Issue 5 Supplement.-- Karina von Schuckmann (Editor), Pierre-Yves Le Traon (Editor), Neville Smith (Chair) (Review Editor), Ananda Pascual (Review Editor), Samuel Djavidnia (Review Editor), Jean-Pierre Gattuso (Review Editor), Marilaure Grégoire (Review Editor), The work was supported by the Copernicus Marine Environment Monitoring Service (CMEMS) LATEMAR project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union. This work has also been conducted as part of the bilateral project EOLO-1 (“Extreme Oceanic waves during tropical, tropical-like, and bomb cyclones”) between CNR-ISMAR and the University of Tokyo (Japan; Prof. Takuji Waseda). Antonio Ricchi funding have been provided via the PON “AIM” - Attraction and international mobility program AIM1858058. Joanna Staneva acknowledges H2020 IMMERSE Project, Arno Behrens was supported by CMEMS Waveflow project and G. Gayer by ESA Black Sea and Danube Regional Initiative (EO4BS).

In his 1953 paper, Harald Sverdrup argued that the development of a spring bloom in the ocean depends on the juxtaposition of two depth horizons: the mixed-layer depth and the critical depth. Mixed-layer depth shallower than the critical depth favours phytoplankton growth in the layer and vice versa. However, mathematically, Sverdrup left the problem unsolved in the form of a transcendental equation. In spite of the high number of citations that this paper has garnered, the solution to this equation has not been found, until now. In this work, we present an analytical solution for the critical depth, as originally defined by Sverdrup. The paper opens with the definition of the critical depth and the description of the Lambert W function. The analytical solution for critical depth follows. Sverdrup’s original model is extended to include the effect of light attenuation by phytoplankton and the analytical solution for steady-state biomass in the mixed layer is derived. The expression for mixed-layer production at steady state is also presented. Two novel variants of the critical depth are defined: the optically uncoupled critical depth and the optically coupled critical depth. It is demonstrated that at steady state the optically coupled critical depth equals the mixed-layer depth and that the irradiance at the base of the mixed layer equals the irradiance at the optically uncoupled critical depth. Competitive exclusion is demonstrated to hold and the optically uncoupled critical depth is linked to the critical light intensity in multi-species competition. Finally, a conservation principle for the critical depth is found.

The biological dynamics of pelagic marine ecosystems are strongly influenced by the size structure and ecological succession of phytoplankton, which in turn modifies photosynthetic efficiency. Variability in photosynthetic rates is closely coupled with changes in community structure, but it is difficult to obtain coincident data at high enough resolution to characterise these changes. In this study, we employ hierarchical cluster analysis on chlorophyll-normalised high performance liquid chromatography (HPLC) pigment concentrations from the North West Atlantic, to identify seasonal successional trends amongst phytoplankton populations. Changes in phytoplankton community were also analysed as a function of mean equivalent spherical diameter (MESD) derived from absorption measurements, photosynthetic rates, water-column stratification and temperature. Well-mixed conditions in spring to early summer were associated with populations of large cells containing high concentrations of fucoxanthin, chlorophyll- c 1 and chlorophyll- c 2 relative to chlorophyll- a (Chl a ). As stratification increased over the course of the summer, these cells were replaced by populations dominated by chlorophyll- b , 19'-hexanoyloxyfucoxanthin, 19'-butanoyloxyfucoxanthin and divinyl chlorophyll- a, indicative of small picophytoplankton. As stratification decreased in autumn, MESD and alloxanthin increased, suggesting the presence of cryptophytes. Positive relationships were found between MESD and the quantum yield of photosynthesis ( φ m ) for 7 out of the 8 phytoplankton clusters identified, while negative relationships between mean mixed layer photosynthetically active radiation and φ m and the light limited slope of photosynthesis ( α B ) were observed for 4 clusters, as a result of nutrient limitation and photo-protection. The highest photosynthetic rates were associated with a pico & nanophytoplankton communities, which increased from spring to late summer as stratification intensified. By contrast, diatom communities had the lowest photosynthetic rates throughout the year. These successional patterns in the dominant phytoplankton size-class and phenology support Margalef's mandala in terms of the relationship between turbulence and community structure. The study sheds new light on assemblages dominated by smaller cells, under warm, stratified conditions, having higher photosynthetic efficiencies, which has implications for the carbon flux in the NW Atlantic.

Harmful algal blooms (HABs) present a potential danger for human health and commercial activities, especially in coastal regions. Observing systems increasingly rely on remote sensors to monitor and possibly predict the locations and intensity of such blooms. Here we present a novel approach for detecting HABs of Alexandrium fundyense in the Bay of Fundy, Canada. A. fundyense is considered toxic for individuals who consume shellfish when cell abundances adjacent to shellfish harvesting areas are as low as 200 cells·L−1, making it difficult to use direct remote sensing techniques to assess the threat in the early stages of the development of the bloom. Using in situ A. fundyense cell abundance measurements, together with satellite observations of sea-surface temperature and the occurrence of diatom-dominated phytoplankton populations, a warning system was developed based on three levels of alerts: green (low abundance of A. fundyense), orange (possible threat of A. fundyense) and red (high probability of A. fundyense concentrations that would result in shellfish toxicity above safe levels for human consumption). Combined information on diatom phenology and variations in sea-surface temperature are key to the timing of A. fundyense blooms: our data reveal that the termination of the diatom spring bloom, associated with the warming of the water, can trigger an increase in A. fundyense cell abundance. The objective criteria for a HAB warning system was developed and tested in the Bay of Fundy using two different datasets: one to develop the algorithm (data collected between 1998 and 2007) and one to assess its performance (data collected in 2011). The warning system is based on the cautionary principle that a false negative (warning not issued when it should have been) is far more serious than a false positive (warning issued when it should not have been). The overall success of the algorithm when tested on the validation dataset is about 70% using a threshold of 150 A. fundyense cells·L−1, with a low occurrence of false negative red alerts (

Ocean-colour radiometry is recognised as an Essential Climate Variable (ECV) according to the Global Climate Observing System (GCOS), because of its capability to observe significant properties of the marine ecosystem at synoptic to global scales. Yet the value of ocean colour for climate-change studies depends to a large extent not only on the decidedly important quality of the data per se, but also on the qualities of the algorithms used to convert the multi-spectral radiance values detected by the ocean-colour satellite into relevant ecological, bio-optical and biogeochemical variables or properties of the ocean. The algorithms selected from the pool of available algorithms have to be fit for purpose: detection of marine ecosystem responses to climate change. Marine ecosystems might respond in a variety of ways to changing climate, including perturbations to regional distributions in the quantity and in the type of phytoplankton present, their locations and in their seasonal dynamics. The ideal algorithms would be capable of distinguishing between abundance and type, and would not mistake one for the other. They would be robust to changes in climate, and would not rely on assumptions that might be valid only under current climatic conditions. Based on such considerations, we identify a series of ideal qualitative traits that algorithms for climate-change studies would possess. Necessarily, such traits would have to complement the quantitative requirements for precision, accuracy and stability in the data over long time scales. We examine the extent to which available algorithms meet the criteria, according to the work carried out in the Ocean Colour Climate Change Initiative, and where improvements are still needed.

The standing stock of phytoplankton carbon is a fundamental property of oceanic ecosystems, and of critical importance to the development of Earth System models for assessing global carbon pools and cycles. Some methods to estimate phytoplankton carbon at large scales from ocean colour data rely on the parameterisation of carbon-to-chlorophyll ratio, which is known to depend on factors such as the phytoplankton community structure, whereas other methods are based on the estimation of total particulate organic carbon (POC), and rely on the assumption that a known fraction of POC is made up of phytoplankton carbon. The carbon-to-chlorophyll ratio is also used in marine ecosystem models to convert between carbon and chlorophyll, a common requirement. In this paper we present a novel bio-optical algorithm to estimate the carbon-to-chlorophyll ratio, and the standing stocks of phytoplankton carbon partitioned into various size classes, from ocean colour. The approach combines empirical allometric relationships of phytoplankton size structure with an absorption-based algorithm for estimating phytoplankton size spectra developed earlier. Applying the new algorithm to satellite ocean colour data from September 1997 to December 2013, the spatio-temporal variations of carbon-to-chlorophyll ratio and phytoplankton carbon across various size classes are computed on a global scale. The average annual stock of phytoplankton carbon, integrated over the oceanic mixed-layer depth, is estimated to be ∼0.26 gigatonnes, with the size-partitioned stocks of 0.14 gigatonnes for picoplankton, 0.08 gigatonnes for nanoplankton and 0.04 gigatonnes for microplankton. The root-mean-square error and the bias in the satellite-derived estimates of picoplankton carbon, when compared with corresponding in situ data, are found to be 36.23 mgC m −3 and −13.53 mgC m −3, respectively, on individual pixels. The regional uncertainties in the estimates of phytoplankton carbon are calculated to be less than the relative uncertainties in other satellite-derived products, for most parts of the global ocean, and can amplify only for certain oceanographic regions. Although the new estimates of phytoplankton are of the same order of magnitude as those based on existing models, our study suggests that a consensus is yet to be built on the accurate sizes of the phytoplankton carbon pools; improved satellite chlorophyll products, and better estimates of inherent optical properties would be essential pre-requisites to minimising the uncertainties.

Special Issue Feature Paper Special Issue on Ocean Remote Sensing.-- 27 pages, 8 figures, 2 tables, 2 appendixes.-- Correction: Kulk et al. Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sens. 2020, 12, 826. doi: 10.3390/rs13173462, Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr −1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive, This research was funded by the European Space Agency (ESA) Living Planet Fellowship programme (PICCOLO, G.K.), the Simons Foundation grant Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES, number 549947, S.S.) and the UK Natural Environment Research Council National Capability funding for the Atlantic Meridional Transect (AMT, G.H.T.). This paper is a contribution to the Ocean Colour Climate Change Initiative (OC-CCI) and Biological Pump and Carbon Exchange Processes (BICEP) projects of ESA. Additional support from the National Centre for Earth Observations (UK) is also gratefully acknowledge, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

We seek to understand, in mathematical terms, the causes of stability in marine phytoplankton biomass. The stability of a simple, mixed-layer-phytoplankton-nutrient model is analysed. Primary production is modelled as a non-linear function of nutrient concentration and light. The steady state of the model system is demonstrated to be stable with a linear relation between steady state biomass and nutrients. The causes of stability are shown to be shading and nutrient limitation. When only light limitation and shading are taken into account, the steady state is a sink node. However, when nutrient limitation is taken into account, without shading, the steady state can be either a sink node or a spiral sink. The transition from a sink node to a spiral sink occurs when normalized mixed layer production becomes larger than the equivalent influx rate of nutrients into the mixed layer, demonstrating that nutrient limitation of production is a necessary, but not a sufficient condition for oscillatory solutions. In both cases, the characteristic return times are derived explicitly. The effect of shading is found to cause the depression of the steady state towards lower biomass than would otherwise be attainable. The influence of mixed-layer depth variation on stability is also analysed.

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

Seasonality in carbon chemistry of Cochin backwaters, Southern India, was investigated between 2018 and 2019. Dissolved inorganic carbon (DIC) showed strong seasonal variations. Lowest DIC was observed during the Southwest Monsoon (SWM), in conjunction with low salinity in surface waters, suggesting strong freshwater influence. The maximum concentration of partial pressure of carbon dioxide in water (pCO2w) was recorded from polluted waters of Vembanad Lake ( ∼ 16,000 μ atm). Excluding the SWM, the inner most stations (freshwater) showed lower pCO2w levels compared with the outermost (estuarine) ones. With regard to sampling stations, all the environmental properties, except silicate and phosphate, exhibited significant variation, pointing to large spatial heterogeneity across the stations. Salinity showed a strong correlation with dissolved inorganic carbon within the Cochin backwaters. High pH and low pCO2w observed in some of the inner most stations indicates role of pH in carbonate speciation. Our study indicates large seasonal fluctuation in biogeochemical parameters and strong heterogeneity between individual stations which therefore necessitates development of local biogeochemical models for better understanding of carbon budget in these waters.

Phenology relates to the study of timing of periodic events in the life cycle of plants or animals as influenced by environmental conditions and climatic forcing. Phenological metrics provide information essential to quantify variations in the life cycle of these organisms. The metrics also allow us to estimate the speed at which living organisms respond to environmental changes. At the surface of the oceans, microscopic plant cells, so-called phytoplankton, grow and sometimes form blooms, with concentrations reaching up to 100 million cells per litre and extending over many square kilometres. These blooms can have a huge collective impact on ocean colour, because they contain chlorophyll and other auxiliary pigments, making them visible from space. Phytoplankton populations have a high turnover rate and can respond within hours to days to environmental perturbations. This makes them ideal indicators to study the first-level biological response to environmental changes. In the Earth’s climate system, the El Niño–Southern Oscillation (ENSO) dominates large-scale inter-annual variations in environmental conditions. It serves as a natural experiment to study and understand how phytoplankton in the ocean (and hence the organisms at higher trophic levels) respond to climate variability. Here, the ENSO influence on phytoplankton is estimated through variations in chlorophyll concentration, primary production and timings of initiation, peak, termination and duration of the growing period. The phenological variabilities are used to characterise phytoplankton responses to changes in some physical variables: sea surface temperature, sea surface height and wind. It is reported that in oceanic regions experiencing high annual variations in the solar cycle, such as in high latitudes, the influence of ENSO may be readily measured using annual mean anomalies of physical variables. In contrast, in oceanic regions where ENSO modulates a climate system characterised by a seasonal reversal of the wind forcing, such as the monsoon system in the Indian Ocean, phenology-based mean anomalies of physical variables help refine evaluation of the mechanisms driving the biological responses and provide a more comprehensive understanding of the integrated processes.

Photosynthesis parameters are routinely estimated from in vitro measurements of primary production under constant light reaching each incubation bottle, by fitting a photosynthesis- irradiance function to the measurements. Here we take one such function and integrate it in time for variable light input, similar to natural conditions, to obtain the analytical solution for the vertical profile of daily phytoplankton production in the field. This solution is then fitted to in situ measurements of primary production profiles in the same manner as a photosynthesis-irradiance function is fitted to in vitro measurements under controlled and constant light conditions to retrieve the photosynthesis-irradiance parameters. The method is tested on the Hawaii Ocean Time-series data set. The solution explained 97.88% of the variance in measured normalized production at individual depths. The recovered parameters were then used to model the normalized daily water-column production. The model explained 99.21% of variance in normalized watercolumn production of the entire data set. The seasonal cycle of the photosynthesis parameters recovered with the analytical solution was further studied for the Hawaii Ocean Time-series. With respect to the photosynthesis parameter determination, the solution bridges the gap between classical photosynthesis-irradiance measurements under controlled light conditions and in situ measurements which are made under natural, variable light conditions. It presents a new tool for the estimation of photosynthesis parameters from in situ measurements of primary production.

Coastal waters are optically diverse; studying their optical characteristics is an important application of satellite oceanography. In coastal ecosystems of the northern Indian Ocean, optical diversity has been little studied, except for the global analysis by Me´ lin and Vantrepotte (2015). This paper is a contribution towards identification and characterisation of optical classes in the coastal regions of the northern Indian Ocean. The study identified eight optical classes using the monthly climatological datasets of remote sensing reflectance for the 1998-2013 period from the Ocean Colour Climate Change Initiative (OC-CCI, www.oceancolour.org). The optical classification we adopted uses the fuzzy logic method, based on Moore et al. (2009). The seasonal variations of the eight resultant optical classes of the coastal waters of the northern Indian Ocean were explored. From the mean reflectance spectral signals obtained, it appears that classes 1 to 6 belong to Case-1 waters and classes 7 and 8 correspond to Case-2 waters. Classes 1 to 2 appear in deeper oligotrophic waters; classes 3 to 6 are present in intermediate depths; classes 7 and 8 are mostly found within inshore eutrophic regions with high chlorophyll concentrations, sediments from river plumes and land runoffs. The optical variability between seasons (the summer and winter monsoon and the intermonsoon seasons) are influenced by variations in physical forcing, such as surface winds, ocean currents, precipitation and sediment influx from rivers and land runoff. Optical diversity index ranged from around 0.3 to 1.36. High diversity indices ranging between 1 and 1.36 were found in areas dominated by classes 1 to 4, whereas low diversity indices 0.3 occurred in areas where classes 7 and 8 dominated. The variations in the dominant optical classes are shown to be related to changes in chlorophyll concentration and suspended sediment load, as indicated by remote sensing reflectance at 670 nm. On the other hand, optical diversity appears to be high in zones of transition between dominant optical classes. Keywords: Coastal ecosystems, Satellite Ocean colour, Classification, Remote sensing reflectance, ecosystem management.

In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth than the estimates based on linear extrapolation. Subsequently, water column production is calculated, and here, the model predicted on average 26% lower water column production. With the recovered parameters and the known magnitude of the overestimation, the time-series of water column production is then re-established with the non-linearly-corrected data. During this 55-year period, distinct regimes were observed, which were classified with a regime shift detection method. It is then demonstrated how the recovered parameters can be used in a remote sensing application. A seasonal cycle of the recovered assimilation number is constructed along with the seasonal cycle of remotely-sensed chlorophyll. The two are then used to model the seasonal cycle of water column production. An upper and a lower bound on the seasonal cycle of water column production based on remotely-sensed chlorophyll data are then presented. Measured water column production was found to be well within the range of remotely-sensed estimates. With this work, the utility of in situ measurements as a means of providing information on the assimilation number is presented and its application as a reference for remote sensing models highlighted.

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 differences among phytoplankton carbon (Cphy) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in Cphy. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m−3), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m−3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (bbp) retrieval.

Oceanic phytoplankton respond rapidly to a complex spectrum of climate-driven perturbations, confounding attempts to isolate the principal causes of observed changes. A dominant mode of variability in the Earth-climate system is that generated by the El Niño phenomenon. Marked variations are observed in the centroid of anomalous warming in the Equatorial Pacific under El Niño, associated with quite different alterations in environmental and biological properties. Here, using observational and reanalysis datasets, we differentiate the regional physical forcing mechanisms, and compile a global atlas of associated impacts on oceanic phytoplankton caused by two extreme types of El Niño. We find robust evidence that during Eastern Pacific (EP) and Central Pacific (CP) types of El Niño, impacts on phytoplankton can be felt everywhere, but tend to be greatest in the tropics and subtropics, encompassing up to 67% of the total affected areas, with the remaining 33% being areas located in high-latitudes. Our analysis also highlights considerable and sometimes opposing regional effects. During EP El Niño, we estimate decreases of −56 TgC/y in the tropical eastern Pacific Ocean, and −82 TgC/y in the western Indian Ocean, and increase of +13 TgC/y in eastern Indian Ocean, whereas during CP El Niño, we estimate decreases −68 TgC/y in the tropical western Pacific Ocean and −10 TgC/y in the central Atlantic Ocean. We advocate that analysis of the dominant mechanisms forcing the biophysical under El Niño variability may provide a useful guide to improve our understanding of projected changes in the marine ecosystem in a warming climate and support development of adaptation and mitigation plans.

The effect of biomass dynamics on the estimation of watercolumn primary production is analyzed, by coupling a primary production model to a simple growth equation for phytoplankton. The production model is formulated with depth- and time-resolved biomass, and placed in the context of earlier models, with emphasis on the canonical solution for watercolumn production. A relation between the canonical solution and the general solution for the case of an arbitrary depth-dependent biomass profile was derived, together with an analytical solution for watercolumn production in case of a depth dependent biomass profile described with the shifted Gaussian function. The analysis was further extended to the case of a time-dependent, mixed-layer biomass, and two additional analytical solutions to this problem were derived, the first in case of increasing mixed-layer biomass and the second in case of declining biomass. The solutions were tested with Hawaii Ocean Time-series data. The canonical solution for mixed-layer production has proven to be a good model for this data set. The shifted Gaussian function was demonstrated to be an accurate model for the measured biomass profiles and the shifted Gaussian parameters extracted from the measured profiles were further used in the analytical solution for watercolumn production and results compared with data. The influence of time-dependent biomass on mixed-layer production was studied through analytical solutions. Re-examining the Critical Depth Hypothesis we derived an expression for the daily increase in mixed-layer biomass. Finally, the work was placed in a remote sensing context and the time-dependent model for biomass related to the remotely sensed-biomass.

[1] Phytoplankton are an important component of the oceanic carbon cycle. Yet, due to methodological constraints, the carbon biomass of phytoplankton is poorly characterized. To address this limitation, we have explored the bio-optical relationship between in situ measurements of the particle backscattering coefficient at 470 nm, bbp(470), and the phytoplankton carbon concentration for cells with diameter less than 20 µm (Cf). We found a significant relationship between bbp(470) and Cf for Atlantic oceanic waters with chlorophyll-a concentrations less than 0.4 mg m−3 (or bbp(470)

The Gulf of Aden, situated in the northwest Arabian Sea and linked to the Red Sea, is a relatively unexplored ecosystem. Understanding of large-scale biological dynamics is limited by the lack of adequate datasets. In this study, we analyse 15 years of remotely-sensed chlorophyll-a data (Chl- a , an index of phytoplankton biomass) acquired from the Ocean Colour Climate Change Initiative (OC-CCI) of the European Space Agency (ESA). The improved spatial coverage of OC-CCI data in the Gulf of Aden allows, for the first time, an investigation into the full seasonal succession of phytoplankton biomass. Analysis of indices of phytoplankton phenology (bloom timing) reveals distinct phytoplankton growth periods in different parts of the gulf: a large peak during August (mid-summer) in the western part of the gulf, and a smaller peak during November (mid-autumn) in the lower central gulf and along the southern coastline. The summer bloom develops rapidly at the beginning of July, and its peak is approximately three times higher than that of the autumnal bloom. Remotely-sensed sea-surface temperature (SST), wind-stress curl, vertical nutrient profiles and geostrophic currents inferred from the sea-level anomaly, were analysed to examine the underlying physical mechanisms that control phytoplankton growth. During summer, the prevailing southwesterlies cause upwelling along the northern coastline of the gulf (Yemen), leading to an increase in nutrient availability and enhancing phytoplankton growth along the coastline and in the western part of the gulf. In contrast, in the central region of the gulf, lowest concentrations of Chl- a are observed during summer, due to strong downwelling caused by a mesoscale anticyclonic eddy. During autumn, the prevailing northeasterlies enable upwelling along the southern coastline (Somalia) causing local nutrient enrichment in the euphotic zone, leading to higher levels of phytoplankton biomass along the coastline and in the lower central gulf. The monsoon wind reversal is shown to play a key role in controlling phytoplankton growth in different regions of the Gulf of Aden.

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

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (< R m), nano- (2-20 mu m) and micro-phytoplankton (>20 mu m)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.

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 rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of photosynthesis irradiance functions and parameters for modeling in situ production profiles. In light of the results obtained in this work we argue that the choice of the primary production model should reflect the available data and these models should be data driven regarding parameter estimation.

The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean.

Phytoplankton phenology and primary production were examined in the Iceland–Faroe region through synthesis of all available data, both in situ and remotely sensed. In the Arctic water, the early onset of stratification in spring gave rise to the rapid shallowing of the mixed layer and triggered the earlier spring bloom north of Iceland, whereas the weakly stratified water-column in the Atlantic water and associated deep mixed layer delayed the spring bloom south of Iceland. The protocol (Nearest Neighbor Method, NNM) developed by Platt et al. (2008) was used to estimate the daily, water-column primary production from ocean color data. The key element of the procedure is an archived database, including (in this implementation) 505 sets of parameters of photosynthetic–light curves and 197 vertical profiles of chlorophyll around Iceland–Faroe region. The spatial structure in the climatology of annual primary production determined in this way was consistent with observations made by the simulated in situ method using ships as a platform, but, inevitably, the fields produced from the remotely sensed data were smoother. The annual primary production estimated by the NNM method overestimates the (much more sparse) data for in situ production by 50% on average. We examined the relative errors in the estimation of primary production that would arise from ignorance of the non-uniformity in the biomass profile. The vertically uniform model tended to underestimate the annual primary production by about 36% compared with the non-uniform model in a spectral calculation.

In recent years, phytoplankton phenology has been proposed as an indicator to monitor systematically the state of the pelagic ecosystem and to detect changes triggered by perturbation of the environmental conditions. Here we describe the phenology of phytoplankton growth for the world ocean using remotesensing ocean colour data, and analyse its variability between 1998 and 2007. Generally, the tropics and subtropics present long growing period (≈15–20 weeks) of low amplitude (

Computing the vertical structure of primary production in ocean ecosystem models requires information about the vertical distribution of available light, chlorophyll concentration and photosynthesis response parameters. Conversely, given information on vertical structure of chlorophyll and light, we can extract photosynthesis parameters from vertical profiles of primary production measured at sea, as we illustrate here for the Bermuda Atlantic Time-Series Study. The procedure is based on a model of the production profile, which itself depends on the underwater light field. To model the light field, attenuation coefficients were estimated from measured optical profiles using a simple model of exponential decay of photosynthetically-available irradiance with depth, which accounted for 97% of the variance in the measured optical data. With the underwater light climate known, an analytical solution for the production profile was employed to recover photosynthesis parameters by minimizing the residual model error. The recovered parameters were used to model normalized production profiles and normalized watercolumn production. The model explained 95% of the variance in the measured normalized production at depth and 97% of the variance in measured normalized watercolumn production. A shifted Gaussian function was used to model biomass profiles and accounted for 93% of the variance in measured biomass at depth. An analytical solution for watercolumn production with the shifted Gaussian biomass was also tested. With the recovered photosynthesis parameters, maximum instantaneous growth rates were estimated by using a literature value for the carbon-to-chlorophyll ratio in this region of the Atlantic. An exact relationship between the maximum instantaneous growth rate and the daily growth rate in the ocean was derived. It was shown that calculating the growth rate by dividing the production by the carbon-to- chlorophyll ratio is equivalent to calculating it from the ratio of the final to the initial biomass, even when production is time dependent. Finally, the seasonal cycle of the recovered assimilation number at the Bermuda Station was constructed and analysed. The presented approach enables the estimation of photosynthesis parameters and growth rates from measured production profiles with only a few model assumptions, and increases the utility of in situ primary production measurements. The retrieved parameters have direct applications in satellite-based estimates of primary production from ocean-colour data, of which we give an example.

The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical gyres, which are considered to be the largest biomes on earth. In this study, the spatial and temporal variability in the global distribution of Prochlorococcus was estimated in the Atlantic Ocean using an empirical model based on data from 13 Atlantic Meridional Transect cruises. Our model uses satellite-derived sea surface temperature (SST), remote-sensing reflectance at 443 and 488 nm, and the water temperature at a depth of 200 m from Argo data. The model divides the population of Prochlorococcus into two groups: ProI, which dominates under high-light conditions associated with the surface, and ProII, which favors low light found near the deep chlorophyll maximum. ProI and ProII are then summed to provide vertical profiles of the concentration of Prochlorococcus cells. This model predicts that Prochlorococcus cells contribute 32 Mt of carbon biomass (7.4 × 1026 cells) to the Atlantic Ocean, concentrated mainly within the subtropical gyres (35%) and areas near the Equatorial Convergence Zone (30%). When projected globally, 3.4 × 1027 Prochlorococcus cells represent 171 Mt of carbon biomass, with 43% of this global biomass allocated to the upper ocean (0–45 m depth). Annual cell standing stocks were relatively stable between the years 2003 and 2014, and the contribution of the gyres varies seasonally as gyres expand and contract, tracking changes in light and temperature, with lowest cell abundances during the boreal and austral winter (1.4 × 1013 cells m−2), when surface cell concentrations were highest (9.8 × 104 cells mL−1), whereas the opposite scenario was observed in spring–summer (2 × 1013 cells m−2). This model provides a three-dimensional view of the abundance of Prochlorococcus cells, revealing that Prochlorococcus contributes significantly to total phytoplankton biomass in the Atlantic Ocean, and can be applied using either in situ measurements at the sea surface (r2 = 0.83) or remote-sensing observables (r2 = 0.58).

The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producing essential living marine resources for the populations of Western Africa. In this region, the Ivorian continental shelf is under pressure from various anthropogenic sources, which have put the regional fish stocks, especially Sardinella aurita, the dominant pelagic species in Ivorian industrial fishery landings, under threat from overfishing. Here, we combine in situ observations of Sardinella aurita catch, temperature, and nutrient profiles, with remote-sensing ocean-color observations, and reanalysis data of wind and sea surface temperature, to investigate relationships between Sardinella aurita catch and oceanic primary producers (including biomass and phenology of phytoplankton), and between Sardinella aurita catch and environmental conditions (including upwelling index, and turbulent mixing). We show that variations in Sardinella aurita catch in the following year may be predicted, with a confidence of 78%, based on a bilinear model using only physical variables, and with a confidence of 40% when using only biological variables. However, the physics-based model alone is not sufficient to explain the mechanism driving the year-to-year variations in Sardinella aurita catch. Based on the analysis of the relationships between biological variables, we demonstrate that in the Ivorian continental shelf, during the study period 1998–2014, population dynamics of Sardinella aurita, and oceanic primary producers, may be controlled, mainly by top-down trophic interactions. Finally, based on the predictive models constructed here, we discuss how they can provide powerful tools to support evaluation and monitoring of fishing activity, which may help towards the development of a Fisheries Information and Management System.

Ocean colour is the only essential climate variable that targets a biological variable (chlorophyll-a concentration (chl-a)) and is also amenable to remote sensing at the global scale. However, the finite lifetime of individual ocean-colour sensors, and the differences in their characteristics increase the difficulty of creating a long-term, consistent, ocean-colour time series that meets the requirements of climate studies. The Ocean Colour Climate Change Initiative (OC-CCI), a European Space Agency programme, has recently produced a time series of satellite-based ocean-colour products at the global scale, merging data from three sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer on the Aqua Earth Observing System (MODIS-Aqua), and Medium Resolution Imaging Spectrometer (MERIS), while attempting to reduce inter-sensor biases.In this work we present a comparison between the OC-CCI chlorophyll-a product and precursor satellite-derived data sets, from both single missions (SeaWiFS, MODIS-Aqua, and MERIS) and multi-mission products (global ocean colour (GlobColour) and Making Earth Science Data Records for Use in Research Environments (MEaSUREs)). To this end, OC-CCI global monthly composites are compared to the similar products offered by single-mission and multi-mission records. Our results indicate that the OC-CCI product provides a higher number of observations. Comparing the observations that match with precursors, the OC-CCI product was generally most similar to the single-mission products. Relationships between OC-CCI and other precursors did not change significantly during a common and continuous period, and, on average the root-mean-square differences between log-transformed chlorophyll-a concentration are below or equal to 0.11. Further, when considering variability that could arise when merging data from different sources, it is shown that the OC-CCI product is a longer term constant than those from other multi-mission initiatives studied here.

Le Quére, Corinne ... et al.-- 23 pages, 13 figures, 4 tables, supplement https://dx.doi.org/10.5194/bg-13-4111-2016-supplement, Global ocean biogeochemistry models currently employed in climate change projections use highly simplified representations of pelagic food webs. These food webs do not necessarily include critical pathways by which ecosystems interact with ocean biogeochemistry and climate. Here we present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types (PFTs): six types of phytoplankton, three types of zooplankton, and heterotrophic procaryotes. We improved the representation of zooplankton dynamics in our model through (a) the explicit inclusion of large, slow-growing macrozooplankton (e.g. krill), and (b) the introduction of trophic cascades among the three zooplankton types. We use the model to quantitatively assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean high-nutrient lowchlorophyll (HNLC) region during summer. When model simulations do not include macrozooplankton grazing explicitly, they systematically overestimate Southern Ocean chlorophyll biomass during the summer, even when there is no iron deposition from dust. When model simulations include a slow-growing macrozooplankton and trophic cascades among three zooplankton types, the high-chlorophyll summer bias in the Southern Ocean HNLC region largely disappears. Our model results suggest that the observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton community growth rates. This result has implications for the representation of global biogeochemical cycles in models as zooplankton faecal pellets sink rapidly and partly control the carbon export to the intermediate and deep ocean, C. Le Quéré and E. T. Buitenhuis were funded by UK-NERC projects NE/C516079/1 and NE/K001302/1, and European Commission project EMBRACE 282672. R. Moriarty was funded by EU FAASIS project MEST/CT/2004/514159. M. Vogt was funded by the Marie Curie Research and Training Network GREENCYCLES project MC-RTN-512464 and EUR-OCEANS project 282672

We examine a model of the rate of phytoplankton production in the ocean and its dependence on depth. The model is analysed as a function of photosynthesis parameters and it is shown that: (i) production profiles with depth are determined uniquely by the parameter values ; (ii) daily water column production is not uniquely determined by the parameter values ; (iii) a unique combination of parameters exists for which the model best fits a measured production profile. An inverse procedure is developed to recover photosynthesis parameters from measured profiles of primary production, and its performance tested by application to profiles of primary production collected at the Hawaii Ocean Time Series. For each profile tested, the method is successful in recovery of the photosynthesis parameters. The method can be applied to the estimation of photosynthesis parameters from data on in situ production profiles, which have been collected globally for more than half a century, thereby augmenting the world archive of these parameters for application in ecosystem modelling and estimation of primary production from remotely sensed data.

We develop and analyse a simple, two-compartment (chlorophyll and nitrate) model of the surface mixed layer of the ocean. The mixed-layer depth is modulated intermittently to simulate the effects of storms. The optical properties of the water column are linked to changes in the chlorophyll biomass. The model can be treated analytically. Mathematical bounds are found for the autotrophic biomass and the residual nitrate in terms of the intensity and frequency of storms and the bio-optical properties of the phytoplankton. The results are discussed in the context of the high-nutrient, low-chlorophyll regimes, where unconsumed nitrate is a persistent occurrence.

Variations in the shapes of the action and absorption spectra, and the physiological parameters of phytoplankton (a", the biomass (B)-specific initial slope of the photosynthesis-irradi- ance curve; PS,, the plateau of the curve; m, the maximum quantum yield of photosynthesis), were studied. Data were collected in the North Atlantic in five biogeochemical provinces for two seasons: in fall of 1992 and spring of 1993. Further, some nine independent variables were tested for their ability to predict the physiological parameters, using linear regression analyses. It was found that the parameters were more variable between seasons than between provinces, although the differences in the spectral shapes between provinces were significant. It was also shown that several independent variables (alone or in combination) were able to predict a significant fraction of the total variance in the parameters. However, the correlation between variables and parameters differed unsystemati- cally both between provinces and seasons. The results suggest that predicting a parameter from an independent variable would have to be carried out at a province level: the relationship changed or disappeared when data from two or more provinces were pooled.

Ecosystem processes are important determinants of the biogeochemistry of the ocean, and they can be profoundly affected by changes in climate. Ocean models currently express ecosystem processes through empirically derived parameterizations that tightly link key geochemical tracers to ocean physics. The explicit inclusion of ecosystem processes in models will permit ecological changes to be taken into account, and will allow us to address several important questions, including the causes of observed glacial-interglacial changes in atmospheric trace gases and aerosols, and how the oceanic uptake of CO2 is likely to change in the future. There is an urgent need to assess our mechanistic understanding of the environmental factors that exert control over marine ecosystems, and to represent their natural complexity based on theoretical understanding. We present a prototype design for a Dynamic Green Ocean Model (DGOM) based on the identification of (a) key plankton functional types that need to be simulated explicitly to capture important biogeochemical processes in the ocean; (b) key processes controlling the growth and mortality of these functional types and hence their interactions; and (c) sources of information necessary to parameterize each of these processes within a modeling framework. We also develop a strategy for model evaluation, based on simulation of both past and present mean state and variability, and identify potential sources of validation data for each. Finally, we present a DGOM-based strategy for addressing key questions in ocean biogeochemistry. This paper thus presents ongoing work in ocean biogeochemical modeling, which, it is hoped will motivate international collaborations to improve our understanding of the role of the ocean in the climate system.

Absorption spectra of laboratory cultures of eight species of phytoplankton were studied. These spectra, normalized per unit of chlorophyll a concentration (specific absorption) show variability in magnitude and spectral form. Specific absorption at 440 nm varied over a factor of three. Spectra of phytoplankton belonging to the same taxonomic group tended to have similar forms. Over 95% of the variability in specific absorption could be explained in terms of changes in cell size, intracellular pigment concentration, and the relative importance of auxiliary pigments. Shape was not an important factor for this sample set. The spectral characteristics of absorption by intact cells of phytoplankton have been the subject of various studies because of their importance in primary production, radiative transfer in seawater, and in passive remote-sensing of phytoplankton. At sufficiently low concentrations, the total absorption coefficient a of a given substance at a given wavelength X can be expressed as a product of a specific absorption coefficient a*c and the concentration of the substance C. That is