Articles | Volume 16, issue 5
https://doi.org/10.5194/os-16-1297-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-1297-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Oct 2020
Research article |
| 29 Oct 2020
| 29 Oct 2020
Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean
Yeray Santana-Falcón
CORRESPONDING AUTHOR
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Pierre Brasseur
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Jean Michel Brankart
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Florent Garnier
LEGOS, University of Toulouse, CNRS, IRD, CNES, UPS, Toulouse, France
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Related subject area
Approach: Data Assimilation | Depth range: All Depths | Geographical range: Deep Seas: North Atlantic | Phenomena: Biological Processes
Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998–2006 based on the assimilation of SeaWiFS chlorophyll data
C. Fontana, P. Brasseur, and J.-M. Brankart
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Cited articles
Anderson, J. L.: A method for producing and evaluating probabilistic forecasts
from ensemble model integrations, J. Climate, 9, 1518–1530,
https://doi.org/10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2, 1996. a
Auger, P. A., Machu, E., Gorgues, T., Grima, N., and Waeles, M.: Comparative
study of potential transfer of natural and anthropogenic cadmium to plankton
communities in the North-West African upwelling, Sci. Total
Environ., 505, 870–888, https://doi.org/10.1016/j.scitotenv.2014.10.045, 2015. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a, b
Barnier, B., Madec, G., Penduff, T., Molines, J. M., Treguier, A. M.,
Le Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval,
C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud,
M., McClean, J., and De Cuevas, B.: Impact of partial steps and momentum
advection schemes in a global ocean circulation model at eddy-permitting
resolution, Ocean Dynam., 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1,
2006. a, b
Béal, D., Brasseur, P., Brankart, J.-M., Ourmières, Y., and Verron, J.: Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis, Ocean Sci., 6, 247–262, https://doi.org/10.5194/os-6-247-2010, 2010. a
Berner, J., Achatz, U., Batté, L., Bengtsson, L., Cámara, A., Christensen,
H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I.,
Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S.,
Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., Palmer, T. N., Penland, C.,
Sakradzija, M., von Storch, J. S., Weisheimer, A., Weniger, M., Williams,
P. D., and Yano, J. I.: Stochastic parameterization: Toward a new view of
weather and climate models, B. Am. Meteorol. Soc.,
98, 565–588, https://doi.org/10.1175/BAMS-D-15-00268.1, 2017. a
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential data assimilation
techniques in oceanography, Int. Stat. Rev., 71, 223–241,
https://doi.org/10.1111/j.1751-5823.2003.tb00194.x, 2003. a
Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G.: Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, https://doi.org/10.5194/gmd-10-1091-2017, 2017. a
Bopp, L., Lévy, M., Resplandy, L., and Sallée, J. B.: Pathways of
anthropogenic carbon subduction in the global ocean, Geophys. Res.
Lett., 42, 6416–6423, https://doi.org/10.1002/2015GL065073, 2015. a
Boyer, T. P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E.,
Grodsky, A., Johnson, D. R., Locarnini, R. A., Mishonov, A. V., O'Brien,
T. D., Paver, C. R., Reagan, J. R., Seidov, D., Smolyar, I. V., and Zweng,
M. M.: World Ocean Database 2013, edited by: Levitus, S. and Mishonov, A. T., NOAA Atlas NESDIS 72, 209 pp., https://doi.org/10.7289/V5NZ85MT, 2013. a
Brankart, J.-M., Testut, C.-E., Béal, D., Doron, M., Fontana, C., Meinvielle, M., Brasseur, P., and Verron, J.: Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations, Ocean Sci., 8, 121–142, https://doi.org/10.5194/os-8-121-2012, 2012. a, b
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015. a, b
Brasseur, P. and Verron, J.: The SEEK filter method for data assimilation in
oceanography: a synthesis, Ocean Dynam., 56, 650–661,
https://doi.org/10.1007/s10236-006-0080-3, 2006. a, b
Brasseur, P., Gruber, N., Barciela, R., Brander, K., Doron, M., Elmoussaoui,
A., Hobday, A. J., Huret, M., Kremeur, A. S., Lehodey, P., Matear, R., Moulin, C., Murtugudde,
R., Senina, I., and Svendsen, E.:
Integrating biogeochemistry and ecology into ocean data assimilation systems,
Oceanography, 22, 206–215, https://doi.org/10.2307/24861004, 2009. a, b
Buizza, R., Milleer, M., and Palmer, T. N.: Stochastic representation of model
uncertainties in the ECMWF ensemble prediction system, Q. J.
Roy. Meteor. Soc., 125, 2887–2908,
https://doi.org/10.1002/qj.49712556006, 1999. a
Campbell, J. W.: The lognormal distribution as a model for bio-optical
variability in the sea, J. Geophys. Res.-Oceans, 100,
13237–13254, https://doi.org/10.1029/95JC00458, 1995. a
Candille, G., Brankart, J.-M., and Brasseur, P.: Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation, Ocean Sci., 11, 425–438, https://doi.org/10.5194/os-11-425-2015, 2015. a, b
Ciavatta, S., Brewin, R. J. W., Skakala, J., Polimene, L., de Mora, L.,
Artioli, Y., and Allen, J. I.: Assimilation of ocean-color plankton
functional types to improve marine ecosystem simulations, J.
Geophys. Res.-Oceans, 123, 834–854, https://doi.org/10.1002/2017JC013490,
2018. a, b
Claustre, H.: Bio-optical profiling floats as new observational tools for
biogeochemical and ecosystem studies: Potential synergies with ocean color
remote sensing, Proceedings of OceanObs'09: Sustained Ocean Observations
and Information for Society, Vol. 2, https://doi.org/10.5270/OceanObs09.cwp.17, 2009. a
Cossarini, G., Mariotti, L., Feudale, L., Mignot, A., Salon, S., Taillandier,
V., Teruzzi, A., and D'Ortenzio, F.: Towards operational 3D-Var assimilation
of chlorophyll Biogeochemical-Argo float data into a biogeochemical model of
the Mediterranean Sea, Ocean Model., 133, 112–128,
https://doi.org/10.1016/j.ocemod.2018.11.005, 2019. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van
de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J.,
Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. -J., Park, B. -K.,
Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. -N., and Vitart, F.: The
ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137,
553–597, https://doi.org/10.1002/qj.828, 2011. a
DeYoung, B., Heath, M., Werner, F., Chai, F., Megrey, B., and Monfray, P.:
Challenges of modeling ocean basin ecosystems, Science, 304, 1463–1466,
https://doi.org/10.1126/science.1094858, 2004. a
Doron, M., Brasseur, P., and Brankart, J. M.: Stochastic estimation of
biogeochemical parameters of a 3D ocean coupled physical–biogeochemical
model: Twin experiments, J. Marine Syst., 87, 194–207,
https://doi.org/10.1016/j.jmarsys.2011.04.001, 2011. a
Doron, M., Brasseur, P., Brankart, J. M., Losa, S. N., and Melet, A.:
Stochastic estimation of biogeochemical parameters from Globcolour ocean
colour satellite data in a North Atlantic 3D ocean coupled
physical–biogeochemical model, J. Marine Syst., 117, 81–95,
https://doi.org/10.1016/j.jmarsys.2013.02.007, 2013. a
Dutkiewicz, S., Follows, M., Marshall, J., and Gregg, W. W.: Interannual
variability of phytoplankton abundances in the North Atlantic, Deep-Sea
Res. Pt. II, 48, 2323–2344,
https://doi.org/10.1016/S0967-0645(00)00178-8, 2001. a
Elmoussaoui, A., Perruche, C., Greiner, E., Ethé, C., and Gehlen, M.:
Integration of biogeochemistry into Mercator Ocean systems, Mercator Ocean
newsletter, 40, 3–14, 2011. a
Fennel, K., Gehlen, M., Brasseur, P., Brown, C. W., Ciavatta, S., Cossarini,
G., Crise, A., Edwards, C. A., Ford, D., Friedrichs, M. A. M., Gregoire, M.,
Jones, E., Kim, H. C., Lamouroux, J., Murtugudde, R., and Perruche, C.:
Advancing Marine Biogeochemical and Ecosystem Reanalyses and Forecasts as
tools for monitoring and managing Ecosystem Health, Frontiers in Marine
Science, 6, 1–9, https://doi.org/10.3389/fmars.2019.00089, 2019. a
Follows, M. and Dutkiewicz, S.: Meteorological modulation of the North
Atlantic spring bloom, Deep-Sea Res. Pt. I, 49, 321–344, https://doi.org/10.1016/S0967-0645(01)00105-9, 2001. a
Fontana, C., Brasseur, P., and Brankart, J.-M.: Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998–2006 based on the assimilation of SeaWiFS chlorophyll data, Ocean Sci., 9, 37–56, https://doi.org/10.5194/os-9-37-2013, 2013. a, b
Ford, D. A. and Barciela, R.: Global marine biogeochemical reanalyses
assimilating two different sets of merged ocean colour products, Remote
Sens. Environ., 203, 40–54, https://doi.org/10.1016/j.rse.2017.03.040, 2017. a
Ford, D. A., Edwards, K. P., Lea, D., Barciela, R. M., Martin, M. J., and Demaria, J.: Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model, Ocean Sci., 8, 751–771, https://doi.org/10.5194/os-8-751-2012, 2012. a
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: NOAA Atlas NESDIS 84 WORLD OCEAN ATLAS 2018 Volume 4:
Dissolved Inorganic Nutrients (phosphate, nitrate and nitrate+nitrite,
silicate), World Ocean Atlas, 2019. a
Garnier, F., Brankart, J. M., Brasseur, P., and Cosme, E.: Stochastic
parameterizations of biogeochemical uncertainties in a 1/4∘
NEMO/PISCES model for probabilistic comparisons with ocean color data,
J. Marine Syst., 155, 59–72, https://doi.org/10.1016/j.jmarsys.2015.10.012,
2016. a, b, c, d, e, f, g, h, i, j, k
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: 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, Mar. Ecol. Prog. Ser., 148, 187–200,
https://doi.org/10.3354/meps148187, 1997. a
Germineaud, C., Brankart, J. M., and Brasseur, P.: An ensemble-based
probabilistic score approach to compare observation scenarios: an application
to biogeochemical-Argo deployments, J. Atmos. Ocean.
Tech., 36, 2307–2326, https://doi.org/10.1175/JTECH-D-19-0002.1, 2019. a
Gregg, W.: Assimilation of SeaWiFS ocean chlorophyll data into a
three-dimensional global ocean model, J. Marine Syst., 69,
205–225, https://doi.org/10.1016/j.jmarsys.2006.02.015, 2008. a, b
Gregg, W. W. and Casey, N. W.: Global and regional evaluation of the SeaWiFS
chlorophyll data set, Remote Sens. Environ., 93, 463–479,
https://doi.org/10.1016/j.rse.2003.12.012, 2004. a, b
Gregg, W. W., Friedrichs, M. A. M., Robinson, A. R., Rose, K. A., Schlitzer,
R., Thompson, K. R., and Doney, S. C.: Skill assessment in ocean biological
data assimilation, J. Marine Syst., 76, 16–33,
https://doi.org/10.1016/j.jmarsys.2008.05.006, 2009. a, b, c
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J.,
Dai, X., Maskell, K., and Johnson, C. A.: Climate change 2001: the scientific
basis, The Press Syndicate of the University of Cambridge, 2001. a
Jose, Y. S., Aumont, O., Machu, E., Penven, P., Moloney, C. L., and Maury, O.:
Influence of mesoscale eddies on biological production in the Mozambique
Channel: Several contrasted examples from a coupled ocean-biogeochemistry
model, Deep-Sea Res. Pt. II, 100,
79–93, https://doi.org/10.1016/j.dsr2.2013.10.018, 2014. a
Juricke, S., Palmer, T. N., and Zanna, L.: Stochastic subgrid-scale ocean
mixing: impacts on low-frequency variability, J. Climate, 30,
4997–5019, https://doi.org/10.1175/JCLI-D-16-0539.1, 2017. a
Lahoz, W., Khattatov, B., and Ménard, R.: Data assimilation: making sense
of observations, Part I: Theory, in: Data Assimilation, Springer, 2010. a
Le Fouest, V., Zakardjian, B., Saucier, F. J., and Cizmeli, S. A.: Application
of SeaWIFS- and AVHRR-derived data for mesoscale and regional validation of a
3-D high-resolution physical–biological model of the Gulf of St. Lawrence
(Canada), J. Marine Syst., 60, 30–50,
https://doi.org/10.1016/j.jmarsys.2005.11.008, 2006. a
Lefort, S., Aumont, O., Bopp, L., Arsouze, T., Gehlen, M., and Maury, O.:
Spatial and body-size dependent response of marine pelagic communities to
projected global climate change, Glob. Change Biol., 21, 154–164,
https://doi.org/10.1111/gcb.12679, 2015. a
Leutbecher, M., Lock, S. J., Ollinaho, P., Lang, S. T. K., Balsamo, G.,
Bechtold, P., Bonavita, M., Christensen, H. M., Diamantakis, M., Dutra, E.,
English, S., Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan,
R. J., Juricke, S., Lawrence, H., MacLeod, D., Magnusson, L., Malardel, S.,
Massart, S., Sandu, I., Smolarkiewicz, P. K., Subramanian, A., Vitart, F.,
Wedi, N., and Weisheimer, A.: Stochastic representations of model
uncertainties at ECMWF: State of the art and future vision, Q.
J. Roy. Meteor. Soc., 143, 2315–2339,
https://doi.org/10.1002/qj.3094, 2017. a
Levitus, S., Boyer, T. P., Conkright, M. E., O’brien, T., Antonov, J.,
Stephens, C., Stathoplos, L., Johnson, D., and Gelfeld, R.: NOAA Atlas
NESDIS 18, World Ocean Database 1998: vol. 1: Introduction, US Government
Printing Office, Washington DC, 346, 1998. a
Lévy, M., Iovino, D., Resplandy, L., Klein, P., Madec, G., Tréguier,
A. M., Masson, S., and Takahashi, K.: Large-scale impacts of submesoscale
dynamics on phytoplankton: Local and remote effects, Ocean Model., 43,
77–93, https://doi.org/10.1016/j.ocemod.2011.12.003, 2012. a
Longhurst, A., Sathyendranath, S., Platt, T., and Caverhill, C.: An estimate of
global primary production in the ocean from satellite radiometer data,
J. Plankton Res., 17, 1245–1271, https://doi.org/10.1093/plankt/17.6.1245,
1995. a, b
Madec, G., Bourdallé-Badie, R., Bouttier, P. A., Bricaud, C., Bruciarferri, D., Calvert,
D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Ethé, C., Flavoni, S., Graham, T.,
Harle, J., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S.,
Paul, J., Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO ocean engine, Zenodo, https://doi.org/10.5281/zenodo.3248739, 2015. a
Mattern, J. P., Song, H., Edwards, C. A., Moore, A. M., and Fiechter, J.: Data
assimilation of physical and chlorophyll a observations in the California
Current System using two biogeochemical models, Ocean Model., 109,
55–71, https://doi.org/10.1016/j.ocemod.2016.12.002, 2017. a
Mélin, F., Sclep, G., Jackson, T., and Sathyendranath, S.: Uncertainty
estimates of remote sensing reflectance derived from comparison of ocean
color satellite data sets, Remote Sens. Environ., 177, 107–124,
https://doi.org/10.1016/j.rse.2016.02.014, 2016. a
NEMO Consortium: https://www.nemo-ocean.eu/, last access: 27 October
2020.
Oschlies, A. and Garçon, V.: Eddy-induced enhancement of primary production in
a model of the North Atlantic Ocean, Nature, 394, 266–269,
https://doi.org/10.1038/28373, 1998. a
Ourmières, Y., Brasseur, P., Lévy, M., Brankart, J. M., and Verron, J.:
On the key role of nutrient data to constrain a coupled
physical–biogeochemical assimilative model of the North Atlantic Ocean,
J. Marine Syst., 75, 100–115, https://doi.org/10.1016/j.jmarsys.2008.08.003,
2009. a, b, c, d
Palmer, T. N.: Towards the probabilistic Earth-system simulator: a vision for
the future of climate and weather prediction, Q. J. Roy.
Meteor. Soc., 138, 841–861, https://doi.org/10.1002/qj.1923, 2012. a
Pérez, V., Fernández, E., Marañón, E., Morán, X.
A. G., and Zubkov, M. V.: Vertical distribution of phytoplankton biomass,
production and growth in the Atlantic subtropical gyres, Deep-Sea Res.
Pt. I, 53, 1616–1634,
https://doi.org/10.1016/j.dsr.2006.07.008, 2006. a
Pham, D. T., Verron, J., and Roubaud, M. C.: A singular evolutive extended
Kalman filter for data assimilation in oceanography, J. Marine
Syst., 16, 323–340, https://doi.org/10.1016/S0924-7963(97)00109-7, 1998. a, b
Rousseaux, C. S. and Gregg, W. W.: Climate variability and phytoplankton
composition in the Pacific Ocean, J. Geophys. Res.-Oceans,
117, C10006, https://doi.org/10.1029/2012JC008083, 2012. a
SESAM: http://pp.ige-grenoble.fr/pageperso/brankarj/SESAM/, last access: 27 October 2020.
Simmons, A.: ERA-Interim: New ECMWF reanalysis products from 1989 onwards,
ECMWF Newsletter, 110, 25–36, 2006. a
Teruzzi, A., Bolzon, G., Salon, S., Lazzari, P., Solidoro, C., and Cossarini,
G.: Assimilation of coastal and open sea biogeochemical data to improve
phytoplankton simulation in the Mediterranean Sea, Ocean Model., 132,
46–60, https://doi.org/10.1016/j.ocemod.2018.09.007, 2018. a
Terzić, E., Lazzari, P., Organelli, E., Solidoro, C., Salon, S., D'Ortenzio, F., and Conan, P.: Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry, Biogeosciences, 16, 2527–2542, https://doi.org/10.5194/bg-16-2527-2019, 2019. a
Toth, Z., Talagrand, O., Candille, G., and Zhu, Y.: Probability and ensemble
forecasts, Forecast Verification: A Practitioner's Guide in Atmospheric
Science, 137–163, 2003. a
von Schuckmann, K., Le Traon, P. Y., Smith, N., Pascual, A., Djavidnia, S. L.,
Gattuso, J. P., Grégoire, M., Nolan, G., Aaboe, S., Aguiar, E., Álvarez
Fanjul, E., Alvera-Azcárate, A., Aouf, L., Barciela, R., Behrens, A., Belmonte Rivas, M.,
Ben Ismail, S., Bentamy, A., Borgini, M., Brando, V. E., Bensoussan, N., Blauw, A., Bryère,
P., Buongiorno Nardelli, B., Caballero, A., Çaglar Yumruktepe, V., Cebrian, E., Chiggiato,
J., Clementi, E., Corgnati, L., de Alfonso, M., de Pascual Collar, A., Deshayes, J., Di
Lorenzo, E., Dominici, J. M., Dupouy, C., Drévillon, M., Echevin, V., Eleveld, M., Enserink,
L., García Sotillo, M., Garnesson, P., Garrabou, J., Garric, G., Gasparin, F., Gayer, G.,
Gohin, F., Grandi, A., Griffa, A., Gourrion, J., Hendricks, S., Heuzé, C., Holland, E., Iovino,
D., Juza, M., Kersting, D. K., Kipson, S., Kizilkaya, Z., Korres, G., Kõuts, M., Lagemaa, P.,
Lavergene, T., Lavigne, H., Ledoux, J. B., Legeais, J. F., Lehodey, Pl., Linares, C., Liu, Y.,
Mader, J., Maljutenko, I., Mangin, A., Manso-Narvarte, I., Mantovani, C., Markager, S.,
Mason, E., Mignot, A., Menna, M., Monier, M., Mourre, B., Müller, M., Nielsen, J. W.,
Notarstefano, G., Ocaña, O., Pascual, A., Patti, B., Payne, M. R., Periache, M., Pardo, S.,
Pérez Gómez, B., Pisano, A., Perruche, C., Peterson, K. A., Pujol, M. I., Raudsepp, U.,
Ravdas, M., Raj, R. P., Renshaw, R., Reyes, E., Ricker, R., Rubio, A., Sammartino, M.,
Santoleri, R., Sathyendranath, S., Schroeder, K., She, J., Sparnocchia, S., Staneva, J.,
Stoffelen, A., Szekely, T., Tilstone, G. H., Tinker, J., Tintoré, J., Tranchant, B., Uiboupin, R.,
Van der Zande, D., von Schuckman, K., Wood, R., Woge Nielsen, J., Zabala, M.,
Zacharioudaki, A., Zuberer, F., and Zuo, H.:
Copernicus Marine Service Ocean State Report, Issue 3, J. Oper.
Oceanogr., 12, S1–S123, https://doi.org/10.1080/1755876X.2019.1633075, 2019.
a
Xiao, Y. and Friedrichs, M. A. M.: The assimilation of satellite-derived data
into a one-dimensional lower trophic level marine ecosystem model, J.
Geophys. Res.-Oceans, 119, 2691–2712, https://doi.org/10.1002/2013JC009433,
2014. a
Xing, X. G., Zhao, D. Z., and Claustre, H.: A new autonomous observation
platform of marine biogeochemistry: Bio-Argo floats, Mar. Environ.
Sci., 5, 733–739, 2012. a
Yu, L., Fennel, K., Bertino, L., El Gharamti, M., and Thompson, K. R.: Insights
on multivariate updates of physical and biogeochemical ocean variables using
an Ensemble Kalman Filter and an idealized model of upwelling, Ocean
Model., 126, 13–28, https://doi.org/10.1016/j.ocemod.2018.04.005, 2018. a, b
Zhang, Y., Xu, H., Qiao, F., and Dong, C.: Seasonal variation of the global
mixed layer depth: comparison between Argo data and FIO-ESM, Front.
Earth Sci., 12, 24–36, https://doi.org/10.1007/s11707-017-0631-6, 2018. a
Short summary
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the...
