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

We assimilated plankton functional types (PFTs) derived from ocean colour 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 to 2003. The skill of the reference and reanalysis simulations in estimating ocean colour 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-colour 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.

[1]  J. Kindle,et al.  Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment , 2009 .

[2]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[3]  Richard A. Feely,et al.  A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP) , 2004 .

[4]  S. Masina,et al.  Ensemble spread‐based assessment of observation impact: application to a global ocean analysis system , 2013 .

[5]  Robert J. W. Brewin,et al.  Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups , 2017, Front. Mar. Sci..

[6]  G. Evensen,et al.  Assimilation of ocean colour data into a biochemical model of the North Atlantic: Part 1. Data assimilation experiments , 2003 .

[7]  Thomas M. Smith,et al.  Daily High-Resolution-Blended Analyses for Sea Surface Temperature , 2007 .

[8]  Rosalia Santoleri,et al.  Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data , 2017, Front. Mar. Sci..

[9]  Marco Zavatarelli,et al.  Model of interactions between dissolved organic carbon and bacteria in marine systems , 2006 .

[10]  Robert J. W. Brewin,et al.  Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development , 2017, Front. Mar. Sci..

[11]  J. Christensen,et al.  The HIRHAM Regional Climate Model. Version 5 (beta) , 2007 .

[12]  M. Friedrichs,et al.  The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model , 2014 .

[13]  Timothy S. Moore,et al.  A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product , 2009 .

[14]  Paul J. Somerfield,et al.  A multivariate approach to model skill assessment , 2009 .

[15]  Alberto Borges,et al.  Present day carbon dioxide fluxes in the coastal ocean and possible feedbacks under global change , 2011 .

[16]  Watson W. Gregg,et al.  Recent decadal trends in global phytoplankton composition , 2015 .

[17]  Jacqueline Boutin,et al.  An update to the Surface Ocean CO2 Atlas (SOCAT version 2) , 2013 .

[18]  George B. Arhonditsis,et al.  Phytoplankton functional type modelling: Running before we can walk? A critical evaluation of the current state of knowledge , 2016 .

[19]  Momme Butenschön,et al.  Building the capacity for forecasting marine biogeochemistry and ecosystems: recent advances and future developments , 2015 .

[20]  Sydney Levitus,et al.  World ocean atlas 2005. Vol. 4, Nutrients (phosphate, nitrate, silicate) , 2006 .

[21]  Stefano Ciavatta,et al.  Can ocean color assimilation improve biogeochemical hindcasts in shelf seas , 2011 .

[22]  Rosa Barciela,et al.  Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products , 2017 .

[23]  Momme Butenschön,et al.  Modeling the carbon fluxes of the northwest European continental shelf: Validation and budgets , 2012 .

[24]  T. Kana,et al.  Dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and the chlorophyll a:carbon ratio to light, nutrient-limitation and temperature , 1997 .

[25]  Anna Teruzzi,et al.  A 3-D variational assimilation scheme in coupled transport-biogeochemical models: Forecast of Mediterranean biogeochemical properties , 2014, Journal of geophysical research. Oceans.

[26]  Lars Nerger,et al.  Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK filter , 2007 .

[27]  Dennis A. Hansell.  Recalcitrant dissolved organic carbon fractions. , 2013, Annual review of marine science.

[28]  J. Burkholder,et al.  Misuse of the phytoplankton-zooplankton dichotomy : the need to assign organisms as mixotrophs within plankton functional types , 2013 .

[29]  S. Sathyendranath,et al.  A three-component model of phytoplankton size class for the Atlantic Ocean , 2010 .

[30]  Jerry Blackford,et al.  Ecosystem dynamics at six contrasting sites: a generic modelling study , 2004 .

[31]  M. Grant,et al.  Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data , 2017, Remote sensing of environment.

[32]  J. Allen,et al.  Modelling a light-driven phytoplankton succession , 2014 .

[33]  Robert J. W. Brewin,et al.  Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class , 2015 .

[34]  Martin Verlaan,et al.  Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms , 2014 .

[35]  Jason T. Holt,et al.  Oceanic controls on the primary production of the northwest European continental shelf: model experiments under recent past conditions and a potential future scenario , 2012 .

[36]  M. Friedrichs,et al.  Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models: effects of increasing the complexity of the planktonic food web , 2014 .

[37]  Thomas R. Anderson,et al.  MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies , 2013 .

[38]  Julian Icarus Allen,et al.  ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels , 2015 .

[39]  W. Lahoz,et al.  Data assimilation: making sense of Earth Observation , 2014, Front. Environ. Sci..

[40]  Marjorie A. M. Friedrichs,et al.  Ecosystem model complexity versus physical forcing: Quantification of their relative impact with assimilated Arabian Sea data , 2006 .

[41]  J. G. Baretta-Bekker,et al.  Microbial dynamics in the marine ecosystem model ERSEM II with decoupled carbon assimilation and nutrient uptake , 1997 .

[42]  Julian Icarus Allen,et al.  How should sparse marine in situ measurements be compared to a continuous model: an example , 2013 .

[43]  Timothy J. Smyth,et al.  Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling , 2014 .

[44]  P. Ruardij,et al.  The European regional seas ecosystem model, a complex marine ecosystem model , 1995 .

[45]  JC Blackford,et al.  An analysis of benthic biological dynamics in a North Sea ecosystem model , 1997 .

[46]  Wenche Aas,et al.  Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972–2009 , 2012 .

[47]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[48]  Timothy P. Boyer,et al.  World Ocean Atlas 2005, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation [+DVD] , 2006 .

[49]  I. D. James,et al.  Advection schemes for shelf sea models , 1996 .

[50]  Momme Butenschön,et al.  The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM , 2016 .

[51]  L. Prieur,et al.  A three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters , 1989 .

[52]  J. C. Blackford,et al.  pH variability and CO2 induced acidification in the North Sea , 2007 .

[53]  G. Evensen,et al.  An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea , 2003 .

[54]  Y. Heyden,et al.  Robust statistics in data analysis — A review: Basic concepts , 2007 .

[55]  K. Fennel,et al.  Particle filter‐based data assimilation for a three‐dimensional biological ocean model and satellite observations , 2013 .

[56]  Watson W. Gregg,et al.  Skill Assessment in Ocean Biological Data Assimilation , 2009 .

[57]  R. Jahnke.  Global Synthesis 1 , 2010 .

[58]  Annick Bricaud,et al.  Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water‐leaving radiances at SeaWiFS channels in a continental shelf region off Brazil , 2006 .

[59]  Momme Butenschön,et al.  Decadal reanalysis of biogeochemical indicators and fluxes in the North West European shelf‐sea ecosystem , 2016 .

[60]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[61]  Ricardo Torres,et al.  Sequential data assimilation in an upwelling influenced estuary , 2006 .

[62]  J. I. Allen,et al.  What can ecosystem models tell us about the risk of eutrophication in the North Sea? , 2014, Climatic Change.

[63]  Marjorie A. M. Friedrichs,et al.  Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web , 2014 .

[64]  J. Holt,et al.  An s coordinate density evolving model of the northwest European continental shelf: 1. Model description and density structure , 2001 .

[65]  Marcello Vichi,et al.  A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory , 2007 .

[66]  P. Duarte,et al.  Oceans and the atmospheric carbon content , 2011 .

[67]  Watson W. Gregg,et al.  Phytoplankton and iron: validation of a global three-dimensional ocean biogeochemical model , 2003 .

[68]  Richard W. Gould,et al.  Impact of bio-optical data assimilation on short-term coupled physical, bio-optical model predictions , 2013 .

[69]  Stéphane Blain,et al.  An ecosystem model of the global ocean including Fe, Si, P colimitations , 2003 .

[70]  J. Lovell,et al.  Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef , 2016 .

[71]  Mike Ashworth,et al.  Advective controls on primary production in the stratified western Irish Sea: An eddy-resolving model study , 2004 .

[72]  Andrew J. Watson,et al.  Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models , 2005 .

[73]  Jason T. Holt,et al.  The carbonate system in the North Sea: sensitivity and model validation , 2012 .

[74]  Sallie W. Chisholm,et al.  Emergent Biogeography of Microbial Communities in a Model Ocean , 2007, Science.

[75]  Shubha Sathyendranath,et al.  An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications , 2017 .

[76]  W. Gregg.  Reports of the International Ocean-Colour Coordinating Group , 2007 .

[77]  T. Pitcher,et al.  Towards sustainability in world fisheries , 2002, Nature.

[78]  D. Ford,et al.  Observing and modelling phytoplankton community structure in the North Sea , 2017 .

[79]  Roger Proctor,et al.  Predicting the consequences of nutrient reduction on the eutrophication status of the North Sea , 2010 .

[80]  Katja Fennel,et al.  Data assimilation with a local Ensemble Kalman Filter applied to a three-dimensional biological model of the Middle Atlantic Bight , 2012 .

[81]  Momme Butenschön,et al.  Wavelet-based spatial comparison technique for analysing and evaluating two-dimensional geophysical model fields , 2011 .

[82]  I. Hoteit,et al.  A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean , 2017, Ocean Dynamics.