Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products

Ford, David; Barciela, Rosa

doi:10.1016/j.rse.2017.03.040

Cited by 44 publications

(65 citation statements)

References 73 publications

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“…We note that the deterioration of not‐assimilated nutrients can occur when assimilating ocean color (see e.g., the discussions in Ford and Barciela, ; Teruzzi et al, ; Tsiaras et al, ), in particular when model issues lead the simulation to overestimate systematically both the phytoplankton biomass and the nutrient concentrations (Gregg et al, ), as it happened in our application for nitrate (Figure ). The overestimation of nitrate in the North East Atlantic is a recurrent feature of the POLCOMS‐ERSEM model applied here, in particular in summer and in the coastal region (e.g., Butenschön et al, 2016; Ciavatta et al, ; Wakelin et al, ).…”

Section: Discussionmentioning

confidence: 61%

“…In our application, this outcome might be also related to the use of the PFT parameterization by Butenschön et al (2016), which enhanced the nonlinear relationships between some PFTs and total chlorophyll in ERSEM. However, a systematic comparison of the assimilation of the two ocean color products was outside the scope of this work and we recognize the utility of assimilating total chlorophyll with different parameterizations of ERSEM (e.g., Ciavatta et al, ) or different models (e.g., Ford & Barciela, ) or using assimilation methods that do not rely on linear assumptions, e.g., particle filters (e.g., Mattern et al, ).…”

Section: Discussionmentioning

confidence: 99%

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Assimilation of Ocean‐Color Plankton Functional Types to Improve Marine Ecosystem Simulations

et al. 2018

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We assimilated phytoplankton functional types (PFTs) derived from ocean color into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error‐characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton, and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical‐biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998–2003. The skill of the reference and reanalysis simulations in estimating ocean color and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the assimilation of total chlorophyll in estimating the ocean‐color PFTs (except nanoplankton), as well as the not‐assimilated total chlorophyll, leading the model to simulate better the plankton community structure. Crucially, the reanalysis improved the estimates of not‐assimilated in situ data of PFTs, as well as of phosphate and pCO2, impacting the simulation of the air‐sea carbon flux. However, the reanalysis increased further the model overestimation of nitrate, in spite of increases in plankton nitrate uptake. The method proposed here is easily adaptable for use with other ecosystem models that simulate PFTs, for, e.g., reanalysis of carbon fluxes in the global ocean and for operational forecasts of biogeochemical indicators in shelf‐sea ecosystems.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Assimilation of Ocean‐Color Plankton Functional Types to Improve Marine Ecosystem Simulations

et al. 2018

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“…The diagonal elements of the background error covariance matrix were a monthly climatology of log‐transformed error variances obtained from the 100‐member EnKF POLCOMS‐ERSEM reanalysis of Ciavatta et al (). These variances were regularized and smoothed using the moving averages algorithm and rescaled to the range 0.02–1.5

\log_{10}

(mg/m 3 ), so that the average ratio of background error to obervation error was similar to that calculated in the region when assimilating OC‐CCI data into NEMO‐HadOCC (Ford & Barciela, ). Experiments using different ratios demonstrated the results to be relatively insensitive to the average ratio.…”

Section: Methodsmentioning

confidence: 99%

“…The off‐diagonal elements of the background error covariance matrix were parametrised using correlation length scales set equal to the Rossby radius, as in Waters et al (). The diagonal elements of the observation error covariance matrix were set equal to the per‐pixel observation uncertainties from the OC‐CCI products (Ciavatta et al, ), plus a constant of 0.01

\log_{10}

(mg/m 3 ; Ford & Barciela, ), to take account of the remaining representation error (Janjić et al, 2017) not included in the OC‐CCI uncertainties, whilst maintaining the average ratios suggested by Ford and Barciela (2017). The off‐diagonal elements of the observation error covariance matrix were set to 0.…”

Section: Methodsmentioning

confidence: 99%

The Assimilation of Phytoplankton Functional Types for Operational Forecasting in the Northwest European Shelf

et al. 2018

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This paper proposes the use of assimilation of phytoplankton functional types (PFTs) surface chlorophyll for operational forecasting of biogeochemistry on the North‐West European (NWE) Shelf. We explicitly compare the 5‐day forecasting skill of three runs of a physical‐biogeochemical model: (a) a free reference run, (b) a run with daily data assimilation (DA) of total surface chlorophyll (ChlTot), and (c) a run with daily PFTs DA. We show that small total chlorophyll model bias hides comparatively large biases in PFTs chlorophyll, which ChlTot DA fails to correct. This is because the ChlTot DA splits the assimilated total chlorophyll into PFTs by preserving their simulated ratios, rather than taking account of the observed PFT concentrations. Unlike ChlTot DA, PFTs DA substantially improves model representation of PFTs chlorophyll. During forecasting the DA reanalysis skill in representing PFTs chlorophyll degrades toward the free run skill; however, PFTs DA outperforms free run within the whole 5‐day forecasting period. We validated our results with in situ data, and we demonstrated that (in both DA cases) the DA substantially improves the model representation of CO2 fugacity (PFTs DA more than ChlTot DA). ChlTot DA has a positive impact on the representation of silicate, while the PFTs DA seems to have a negative impact. The impact of DA on nitrate and phosphate is not significant. The implications of using a univariate assimilation method, which preserves the phytoplankton stochiometry, and the impact of model biases on the nonassimilated variables are discussed.

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“…The observational operator

H ([], x_{i}^{f (l)})

links the four state variables representing the ERSEM phytoplankton types to the respective ocean color products (Ciavatta et al, ). Observations and model states are log‐transformed prior to the analysis, to guarantee positivity of the solutions (Janjić et al, ), as in the applications by Nerger and Gregg (), Ciavatta et al (, ), Ford and Barciela (), and Skákala et al ().…”

Section: Methodsmentioning

confidence: 99%

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

et al. 2019

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In this work we produced a long‐term reanalysis of the phytoplankton community structure in the Mediterranean Sea and used it to define ecoregions. These were based on the spatial variability of the phytoplankton type fractions and their influence on selected carbon fluxes. A regional ocean color product of four phytoplankton functional types (PFTs; diatoms, dinoflagellates, nanophytoplankton, and picophytoplankton) was assimilated into a coupled physical‐biogeochemical model of the Mediterranean Sea (Proudman Oceanographic Laboratory Coastal Ocean Modelling System‐European Regional Seas Ecosystem Model, POLCOMS–ERSEM) by using a 100‐member ensemble Kalman filter, in a reanalysis simulation for years 1998–2014. The reanalysis outperformed the reference simulation in representing the assimilated ocean color PFT fractions to total chlorophyll, although the skill for the ocean color PFT concentrations was not improved significantly. The reanalysis did not impact noticeably the reference simulation of not assimilated in situ observations, with the exception of a slight bias reduction for the situ PFT concentrations, and a deterioration of the phosphate simulation. We found that the Mediterranean Sea can be subdivided in three PFT‐based ecoregions, derived from the spatial variability of the PFT fraction dominance or relevance. Picophytoplankton dominates the largest part of open ocean waters; microphytoplankton dominates in a few, highly productive coastal spots near large‐river mouths; nanophytoplankton is relevant in intermediate‐productive coastal and Atlantic‐influenced waters. The trophic and carbon sedimentation efficiencies are highest in the microphytoplankton ecoregion and lowest in the picophytoplankton and nanophytoplankton ecoregions. The reanalysis and regionalization offer new perspectives on the variability of the structure and functioning of the phytoplankton community and related biogeochemical fluxes, with foreseeable applications in Blue Growth of the Mediterranean Sea.

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Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products

Cited by 44 publications

References 73 publications

Assimilation of Ocean‐Color Plankton Functional Types to Improve Marine Ecosystem Simulations

Assimilation of Ocean‐Color Plankton Functional Types to Improve Marine Ecosystem Simulations

The Assimilation of Phytoplankton Functional Types for Operational Forecasting in the Northwest European Shelf

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

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