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

Skákala, Jozef; Ford, David; Brewin, Robert J. W.; McEwan, Robert; Kay, Susan; Taylor, Benjamin H.; Mora, L. De; Ciavatta, Stefano

doi:10.1029/2018jc014153

Cited by 43 publications

(90 citation statements)

References 92 publications

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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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“…The observational operator

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

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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“…Currently, the main biogeochemical data stream used in assimilation is satellite ocean color, but this measurement is limited to the surface ocean and provides an imperfect proxy of phytoplankton biomass that, by itself, is insufficient for constraining the multiple biogeochemically active pools in the euphotic zone. Efforts are being made to maximize the benefits of ocean color observations, e.g., by assimilating spectral bands (Baird et al, 2016(Baird et al, , 2018Jones et al, 2016) or satellite-derived PFTs (Xiao and Friedrichs, 2014a,b;Ciavatta et al, 2018;Skákala et al, 2018). The limited availability of observations is especially serious in coastal applications where altimetry and ocean color measurements are compromised by bathymetry and a variety of optical constituents.…”

Section: Potential Benefits Current Status and Challengesmentioning

confidence: 99%

“…Examples of analysis and prediction tools for ocean biogeochemistry and ecosystems. Mode refers to research-driven (RD), pre-operational (PO), operational (O); 3 Product class refers to reanalysis (R), prediction (P), scenarios (S); aAumont et al, 2015;Lellouche et al, 2018; b Edwards et al, 2012;O'Dea et al, 2017;Skákala et al, 2018; c Ciavatta et al, 2014 c Ciavatta et al, , 2016 c Ciavatta et al, , 2018 d Song et al, 2016a,b,c;Mattern et al, 2017; Baird et al, 2016Baird et al, , 2018Jones et al, 2016; f Feng et al, 2015;Da et al, 2018;Irby et al, 2018;Irby and Friedrichs, 2019; g Hetland and DiMarco, 2008;Yu et al, 2015; h Fennel et al, 2011;Laurent et al, 2012; i Lazzari et al, 2016;Teruzzi et al, 2018;Cossarini et al, 2019; j Grégoire et al, 2008;Capet et al, 2016; k Cossarini et al, 2019. …”

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confidence: 99%

Advancing Marine Biogeochemical and Ecosystem Reanalyses and Forecasts as Tools for Monitoring and Managing Ecosystem Health

Fennel¹,

Gehlen²,

Brasseur³

et al. 2019

Front. Mar. Sci.

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Ocean ecosystems are subject to a multitude of stressors, including changes in ocean physics and biogeochemistry, and direct anthropogenic influences. Implementation of protective and adaptive measures for ocean ecosystems requires a combination of ocean observations with analysis and prediction tools. These can guide assessments of the current state of ocean ecosystems, elucidate ongoing trends and shifts, and anticipate impacts of climate change and management policies. Analysis and prediction tools are defined here as ocean circulation models that are coupled to biogeochemical or ecological models. The range of potential applications for these systems is broad, ranging from reanalyses for the assessment of past and current states, and short-term and seasonal forecasts, to scenario simulations including climate change projections. The objectives of this article are to illustrate current capabilities with regard to the three types of applications, and to discuss the challenges and opportunities. Representative examples of global and regional systems are described with particular emphasis on those in operational or pre-operational use. With regard to the benefits and challenges, similar considerations apply to biogeochemical and ecological prediction systems as do to physical systems. However, at present there are at least two major differences: (1) biogeochemical observation streams are much sparser than physical streams presenting a significant hinderance, and (2) biogeochemical and ecological models are largely unconstrained because of insufficient observations. Expansion of biogeochemical and ecological observation systems will allow for significant advances in the development and application of analysis and prediction tools for ocean biogeochemistry and ecosystems, with multiple societal benefits.

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“…Basically, the above studies demonstrate successful assimilation on global and regional scales improving at least the observed fields. Recently, Skákala et al () assimilated PFTs, univariately on the northwest European Shelf improving the PFTs and total chlorophyll and discussed the impact on unassimilated variables. When total chlorophyll data are assimilated into a model simulating multiple phytoplankton groups, the correction of the total chlorophyll needs to be distributed over the different groups.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

et al. 2019

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The coupled ocean circulation-ecosystem model MITgcm-REcoM2 is used to simulate biogeochemical variables in a global configuration. The ecosystem model REcoM2 simulates two phytoplankton groups, diatoms and small phytoplankton, using a quota formulation with variable carbon, nitrogen, and chlorophyll contents of the cells. To improve the simulation of the phytoplankton variables, chlorophyll-a data from the European Space Agency Ocean-Color Climate Change Initiative (OC-CCI) for 2008 and 2009 are assimilated with an ensemble Kalman filter. Utilizing the multivariate cross covariances estimated by the model ensemble, the assimilation constrains all model variables describing the two phytoplankton groups. Evaluating the assimilation results against the satellite data product SynSenPFT shows an improvement of total chlorophyll and more importantly of individual phytoplankton groups. The assimilation improves both phytoplankton groups in the tropical and midlatitude regions, whereas the assimilation has a mixed response in the high-latitude regions. Diatoms are most improved in the major ocean basins, whereas small phytoplankton show small deteriorations in the Southern Ocean. The improvement of diatoms is larger when the multivariate assimilation is computed using the ensemble-estimated cross covariances between total chlorophyll and the phytoplankton groups than when the groups are updated so that their ratio to total chlorophyll is preserved. The comparison with in situ observations shows that the correlation of the simulated chlorophyll of both phytoplankton groups with these data is increased whereas the bias and error are decreased. Overall, the multivariate assimilation of total chlorophyll modifies the two phytoplankton groups separately, even though the sum of their individual chlorophyll concentrations represents the total chlorophyll.Plain Language Summary Different types of plankton are simulated globally with ocean ecosystem models. To further increase their prediction quality, we combine the model with satellite observations of chlorophyll using modern methods called data assimilation. This method allows us not only to improve the modeled total chlorophyll but also the simulation of the different plankton types. Further, we can fill gaps in the satellite data that results, for example, from clouds. Thus, we are able to better predict the ocean ecosystem, which in turn helps to understand climate change patterns and carbon cycle processes.

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

Cited by 43 publications

References 92 publications

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

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

Advancing Marine Biogeochemical and Ecosystem Reanalyses and Forecasts as Tools for Monitoring and Managing Ecosystem Health

Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

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