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We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical–ecosystem component in a dynamic three-dimensional physical framework. The radiative transfer component resolves the penetration of spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (different phytoplankton functional types; detrital particles; and coloured dissolved organic matter, CDOM). The model is evaluated against in situ-observed and satellite-derived products. In particular we compare to concurrently measured biogeochemical, ecosystem, and optical data along a meridional transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We find that incorporating the different optically important constituents explicitly and including spectral irradiance was crucial to capture the variability in the depth of the subsurface chlorophyll a (Chl a) maximum. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, as well as the crucial feedbacks between the light field, the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at attenuating light at short wavelengths and in more productive waters, phytoplankton absorption is relatively more important at the subsurface Chl a maximum, and water molecules have the greatest contribution when concentrations of other constituents are low, such as in the oligotrophic gyres. Scattering had less effect on attenuation, but since it is important for the amount and type of upwelling irradiance, it is crucial for setting sea surface reflectance. Strikingly, sensitivity experiments in which absorption by any of the o, United States. National Aeronautics and Space Administration (Grant NNX13AC34G), National Science Foundation (U.S.) (Grant OCE1434007), National Science Foundation (U.S.) (Grant OCE1155295)

We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical–ecosystem component in a dynamic three-dimensional physical framework. The radiative transfer component resolves the penetration of spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (different phytoplankton functional types; detrital particles; and coloured dissolved organic matter, CDOM). The model is evaluated against in situ-observed and satellite-derived products. In particular we compare to concurrently measured biogeochemical, ecosystem, and optical data along a meridional transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We find that incorporating the different optically important constituents explicitly and including spectral irradiance was crucial to capture the variability in the depth of the subsurface chlorophyll a (Chl a) maximum. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, as well as the crucial feedbacks between the light field, the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at attenuating light at short wavelengths and in more productive waters, phytoplankton absorption is relatively more important at the subsurface Chl a maximum, and water molecules have the greatest contribution when concentrations of other constituents are low, such as in the oligotrophic gyres. Scattering had less effect on attenuation, but since it is important for the amount and type of upwelling irradiance, it is crucial for setting sea surface reflectance. Strikingly, sensitivity experiments in which absorption by any of the o, United States. National Aeronautics and Space Administration (NNX13AC34G), Natural Environment Research Council (Great Britain) (NE/H015930/2), National Science Foundation (U.S.) (OCE1434007), National Science Foundation (U.S.) (OCE1155295), Gordon and Betty Moore Foundation

Modeling and monitoring plankton functional types (PFTs) is challenged by the insufficient amount of field measurements of ground truths in both plankton models and bio-optical algorithms. In this study, we combine remote sensing data and a dynamic plankton model to simulate an ecologically sound spatial and temporal distribution of phyto-PFTs. We apply an innovative ecological indicator approach to modeling PFTs and focus on resolving the question of diatom-coccolithophore coexistence in the subpolar high-nitrate and low-chlorophyll regions. We choose an artificial neural network as our modeling framework because it has the potential to interpret complex nonlinear interactions governing complex adaptive systems, of which marine ecosystems are a prime example. Using ecological indicators that fulfill the criteria of measurability, sensitivity and specificity, we demonstrate that our diagnostic model correctly interprets some basic ecological rules similar to ones emerging from dynamic models. Our time series highlight a dynamic phyto-PFT community composition in all high-latitude areas and indicate seasonal coexistence of diatoms and coccolithophores. This observation, though consistent with in situ and remote sensing measurements, has so far not been captured by state-of-the-art dynamic models, which struggle to resolve this "paradox of the plankton". We conclude that an ecological indicator approach is useful for ecological modeling of phytoplankton and potentially higher trophic levels. Finally, we speculate that it could serve as a powerful tool in advancing ecosystem-based management of marine resources.

hlorophyll concentration estimates by ocean-biogeochemical models showtypically significant errors. Data assimilation algorithms based onthe Kalman filter can be applied to improve the model state. However,these algorithms do usually not account for possible biases in themodel prediction. Taking model bias explicitly into account canimprove the assimilation estimates.Here, the effect of bias estimation is studied with the assimilationof chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) into the NASA Ocean Biogeochemical Model (NOBM). Theensemble-based SEIK filter has been combined with an online biascorrection scheme. A static error covariance matrix is used for simplicity. The performance of the filter algorithm is assessed by comparisonwith independent in situ data over the 7-year period 1998--2004.Compared to the assimilation without bias estimation, the biascorrection results in significant improvements of the surfacechlorophyll. With bias estimation, the daily surface chlorophyllestimates from the assimilation show about 3.3\% lower error thanSeaWiFS data. In contrast, the error in the global surfacechlorophyll estimate without bias estimation is 10.9\%.

Chlorophyll concentration estimates by ocean-biogeochemical models showtypically significant errors. Data assimilation algorithms based onthe Kalman filter can be applied to improve the model state. However,these algorithms do usually not account for possible biases in themodel prediction. Taking model bias explicitly into account canimprove the assimilation estimates.Here, the effect of bias estimation is studied with the assimilationof chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) into the NASA Ocean Biogeochemical Model (NOBM). Theensemble-based SEIK filter has been combined with an online biascorrection scheme. A static error covariance matrix is used for simplicity. The performance of the filter algorithm is assessed by comparisonwith independent in situ data over the 7-year period 1998--2004.Compared to the assimilation without bias estimation, the biascorrection results in significant improvements of the surfacechlorophyll. With bias estimation, the daily surface chlorophyllestimates from the assimilation show about 3.3\% lower error thanSeaWiFS data. In contrast, the error in the global surfacechlorophyll estimate without bias estimation is 10.9\%. Next to theimprovement of the estimated chlorophyll concentrations, the estimatedbiases indicate regions with systemic errors in the model-represenedchlorophyll.

Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) is assimilated into the three-dimensional global NASA OceanBiogeochemical Model (NOBM) for the period 1998-2004. Theensemble-based SEIK filter is applied in a multivariateconfiguration. It is used here with a localized analysis andsimplified by the use of a constant covariance matrix. In addition, anonline bias estimation algorithm is applied. The multivariateassimilation updates the four phytoplankton groups of the model aswell as nutrient fields. With assimilation, the chlorophyll estimatesbecome superior to both the free-run model and SeaWiFS data. However,the results are less clear for the nutrients. We discuss the behaviorand issues involved by the multivariate assimilation process.

Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) is assimilated into the three-dimensional global NASA OceanBiogeochemical Model (NOBM) for the period 1998-2004. The assimilationis performed by a multivariate configuration of the SEIK filter whichis an ensemble-based Kalman filter scheme. The filter is simplified bythe use of a static error covariance matrix. It operates with alocalized analysis and is amended by an online bias correction scheme.The multivariate assimilation is applied to update the fourphytoplankton groups of the model as well as the simulated nutrientfields. The chlorophyll estimates of the model can be improved by theassimilation such that they outperform the assimilated SeaWiFS data.However, the results are less clear for the nutrients where the biasestimation is required for stability but reduces the assimilationimprovements.