Articles | Volume 20, issue 3
https://doi.org/10.5194/os-20-689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-20-689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 May 2024
Research article |
| 27 May 2024
| 27 May 2024
Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, 34100 Trieste, Italy
Anna Teruzzi
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, 34100 Trieste, Italy
Gloria Pietropolli
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, 34100 Trieste, Italy
Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, H2bis Building, Via Alfonso Valerio 12/1, 34127 Trieste, Italy
Luca Manzoni
Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, H2bis Building, Via Alfonso Valerio 12/1, 34127 Trieste, Italy
Gianluca Coidessa
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, 34100 Trieste, Italy
Gianpiero Cossarini
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, 34100 Trieste, Italy
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A noticeable cold spell occurred in Eastern Europe at the beginning of 2022 and was the main driver of intense deep-water formation and the associated transport of nutrients to the surface. Southeast of Crete, the availability of both light and nutrients in the surface layer stimulated an anomalous phytoplankton bloom. In the area, chlorophyll concentration (a proxy for bloom intensity) and primary production were considerably higher than usual, suggesting possible impacts on fishery catches.
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Simone Spada, Anna Teruzzi, Stefano Maset, Stefano Salon, Cosimo Solidoro, and Gianpiero Cossarini
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Valeria Di Biagio, Stefano Salon, Laura Feudale, and Gianpiero Cossarini
Biogeosciences, 19, 5553–5574, https://doi.org/10.5194/bg-19-5553-2022, https://doi.org/10.5194/bg-19-5553-2022, 2022
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Marco Reale, Gianpiero Cossarini, Paolo Lazzari, Tomas Lovato, Giorgio Bolzon, Simona Masina, Cosimo Solidoro, and Stefano Salon
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Anna Teruzzi, Giorgio Bolzon, Laura Feudale, and Gianpiero Cossarini
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During summer, maxima of phytoplankton chlorophyll concentration (DCM) occur in the subsurface of the Mediterranean Sea and can play a relevant role in carbon sequestration into the ocean interior. A numerical model based on in situ and satellite observations provides insights into the range of DCM conditions across the relatively small Mediterranean Sea and shows a western DCM that is 25 % shallower and with a higher phytoplankton chlorophyll concentration than in the eastern Mediterranean.
Valeria Di Biagio, Gianpiero Cossarini, Stefano Salon, and Cosimo Solidoro
Biogeosciences, 17, 5967–5988, https://doi.org/10.5194/bg-17-5967-2020, https://doi.org/10.5194/bg-17-5967-2020, 2020
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Events that influence the functioning of the Earth’s ecosystems are of interest in relation to a changing climate. We propose a method to identify and characterise
wavesof extreme events affecting marine ecosystems for multi-week periods over wide areas. Our method can be applied to suitable ecosystem variables and has been used to describe different kinds of extreme event waves of phytoplankton chlorophyll in the Mediterranean Sea, by analysing the output from a high-resolution model.
Stefano Salon, Gianpiero Cossarini, Giorgio Bolzon, Laura Feudale, Paolo Lazzari, Anna Teruzzi, Cosimo Solidoro, and Alessandro Crise
Ocean Sci., 15, 997–1022, https://doi.org/10.5194/os-15-997-2019, https://doi.org/10.5194/os-15-997-2019, 2019
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After 10 years of research and development, validated analysis and forecasts of the main parameters of the Mediterranean Sea biogeochemistry (e.g. phytoplankton, nutrients, oxygen, pH, carbon fluxes) at high spatial and temporal resolution are provided in the frame of the EU Copernicus Marine Environment Monitoring Service. Along with a traditional skill performance assessment, novel metrics exploiting the Biogeochemical Argo floats data are designed to estimate the forecasts uncertainty.
Gianpiero Cossarini, Stefano Querin, Cosimo Solidoro, Gianmaria Sannino, Paolo Lazzari, Valeria Di Biagio, and Giorgio Bolzon
Geosci. Model Dev., 10, 1423–1445, https://doi.org/10.5194/gmd-10-1423-2017, https://doi.org/10.5194/gmd-10-1423-2017, 2017
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The BFMCOUPLER (v1.0) is a coupling scheme that links the MITgcm and BFM models for ocean biogeochemistry simulations. The online coupling is based on an open-source code characterizd by a modular structure. Modularity preserves the potentials of the two models, allowing for a sustainable programming effort to handle future evolutions in the two codes. The BFMCOUPLER code is released along with an idealized problem (a cyclonic gyre in a mid-latitude closed basin).
G. Cossarini, P. Lazzari, and C. Solidoro
Biogeosciences, 12, 1647–1658, https://doi.org/10.5194/bg-12-1647-2015, https://doi.org/10.5194/bg-12-1647-2015, 2015
Related subject area
Approach: Data Assimilation | Properties and processes: Biogeochemistry and nutrient cycles
Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model
Frauke Bunsen, Judith Hauck, Lars Nerger, and Sinhué Torres-Valdés
EGUsphere, https://doi.org/10.5194/egusphere-2024-1750, https://doi.org/10.5194/egusphere-2024-1750, 2024
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Computer models are used to derive estimates of the ocean CO2 uptake. Because such idealized models don't always correspond precisely to the real-world, we combine real-world observations of ocean temperature and salinity with a model, and study the effect on the modeled air-sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean during winter, but a comparatively small effect on the global ocean CO2 uptake.
Cited articles
Amadio, C., TERUZZI, A., Feudale, L., BOLZON, G., DI BIAGIO, V., Lazzari, P., Álvarez, E., Coidessa, G., Salon, S., and COSSARINI, G.:. Mediterranean Quality checked BGC-Argo 2013–2022 dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.10391759, 2023. a, b, c
Barbieux, M., Uitz, J., Gentili, B., Pasqueron de Fommervault, O., Mignot, A., Poteau, A., Schmechtig, C., Taillandier, V., Leymarie, E., Penkerc'h, C., D'Ortenzio, F., Claustre, H., and Bricaud, A.: Bio-optical characterization of subsurface chlorophyll maxima in the Mediterranean Sea from a Biogeochemical-Argo float database, Biogeosciences, 16, 1321–1342, https://doi.org/10.5194/bg-16-1321-2019, 2019. a
Barbieux, M., Uitz, J., Mignot, A., Roesler, C., Claustre, H., Gentili, B., Taillandier, V., D'Ortenzio, F., Loisel, H., Poteau, A., Leymarie, E., Penkerc'h, C., Schmechtig, C., and Bricaud, A.: Biological production in two contrasted regions of the Mediterranean Sea during the oligotrophic period: an estimate based on the diel cycle of optical properties measured by BioGeoChemical-Argo profiling floats, Biogeosciences, 19, 1165–1194, https://doi.org/10.5194/bg-19-1165-2022, 2022. a
Bethoux, J., Gentili, B., Morin, P., Nicolas, E., Pierre, C., and Ruiz-Pino, D.: The Mediterranean Sea: a miniature ocean for climatic and environmental studies and a key for the climatic functioning of the North Atlantic, Prog. Oceanogr., 44, 131–146, 1999. a
Bittig, H. C., Körtzinger, A., Neill, C., Van Ooijen, E., Plant, J. N., Hahn, J., Johnson, K. S., Yang, B., and Emerson, S. R.: Oxygen optode sensors: principle, characterization, calibration, and application in the ocean, Front. Mar. Sci., 4, 429, https://doi.org/10.3389/fmars.2017.00429, 2018a. a, b, c, d
Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzède, R., Körtzinger, A., and Gattuso, J.-P.: An alternative to static climatologies: Robust estimation of open ocean CO2 variables and nutrient concentrations from T, S, and O2 data using Bayesian neural networks, Front. Mar. Sci., 5, 328, https://doi.org/10.3389/fmars.2018.00328, 2018b. a, b, c
Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Coidessa, G., and Cossarini, G.: ogstm (4.1), Zenodo [code], https://doi.org/10.5281/zenodo.8283447, 2023a. a, b
Bolzon, G., Teruzzi, A., Salon, S., Di Biagio, V., Feudale, L., Amadio, C., Coidessa, G., and Cossarini, G.: bit.sea (1.7), Zenodo [code], https://doi.org/10.5281/zenodo.8283692, 2023b.
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to infer unresolved scale parametrization, Philos. T. R. Soc. A, 379, 20200086, https://doi.org/10.1098/rsta.2020.0086, 2021. a
Buga, L., Sarbu, G., Fryberg, L., Magnus, W., Wesslander, K., Gatti, J., Leroy, D., Iona, S., Larsen, M., Koefoed Rømer, J., Østrem, A. K., Lipizer, M., and Giorgietti, A.: EMODnet Chemistry Eutrophication and Acidity aggregated datasets v2018, EMODnet Thematic Lot no. 4/SI2.749773, https://doi.org/10.6092/EC8207EF-ED81-4EE5-BF48-E26FF16BF02E, 2018. a, b
Buizza, C., Casas, C. Q., Nadler, P., Mack, J., Marrone, S., Titus, Z., Le Cornec, C., Heylen, E., Dur, T., Ruiz, L. B., Heaney, C., Díaz Lopez, J. A., Kumar, K. S. S., and Arcucci, R.: Data learning: integrating data assimilation and machine learning, J. Comput. Sci., 58, 101525, https://doi.org/10.1016/j.jocs.2021.101525, 2022. a, b, c, d, e
Canu, D. M., Ghermandi, A., Nunes, P. A., Lazzari, P., Cossarini, G., and Solidoro, C.: Estimating the value of carbon sequestration ecosystem services in the Mediterranean Sea: An ecological economics approach, Glob. Environ. Change, 32, 87–95, 2015. a
Capet, A., Stanev, E. V., Beckers, J.-M., Murray, J. W., and Grégoire, M.: Decline of the Black Sea oxygen inventory, Biogeosciences, 13, 1287–1297, https://doi.org/10.5194/bg-13-1287-2016, 2016. a
Clementi, E., Oddo, P., Drudi, M., Pinardi, N., Korres, G., and Grandi, A.: Coupling hydrodynamic and wave models: first step and sensitivity experiments in the Mediterranean Sea, Ocean Dynam., 67, 1293–1312, 2017. a
Coppini, G., Clementi, E., Cossarini, G., Salon, S., Korres, G., Ravdas, M., Lecci, R., Pistoia, J., Goglio, A. C., Drudi, M., Grandi, A., Aydogdu, A., Escudier, R., Cipollone, A., Lyubartsev, V., Mariani, A., Cretì, S., Palermo, F., Scuro, M., Masina, S., Pinardi, N., Navarra, A., Delrosso, D., Teruzzi, A., Di Biagio, V., Bolzon, G., Feudale, L., Coidessa, G., Amadio, C., Brosich, A., Miró, A., Alvarez, E., Lazzari, P., Solidoro, C., Oikonomou, C., and Zacharioudaki, A.: The Mediterranean forecasting system. Part I: evolution and performance, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1337, 2023. a
Coppola, L., Legendre, L., Lefevre, D., Prieur, L., Taillandier, V., and Riquier, E. D.: Seasonal and inter-annual variations of dissolved oxygen in the northwestern Mediterranean Sea (DYFAMED site), Prog. Oceanogr., 162, 187–201, 2018. a
Cossarini, G., Lazzari, P., and Solidoro, C.: Spatiotemporal variability of alkalinity in the Mediterranean Sea, Biogeosciences, 12, 1647–1658, https://doi.org/10.5194/bg-12-1647-2015, 2015a. 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, 2019. a, b, c, d, e, f, g
Cossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro, C., Di Biagio, V., Amadio, C., Lazzari, P., Brosich, A., Cossarini, G., Di Biagio, V., Lazzari, P., and Coidessa, G.: High-Resolution Reanalysis of the Mediterranean Sea Biogeochemistry (1999–2019), Front. Mar. Sci., 8, 741486, https://doi.org/10.3389/fmars.2021.741486, 2021. a
Dall'Olmo, G. and Mork, K. A.: Carbon export by small particles in the Norwegian Sea, Geophys. Res. Lett., 41, 2921–2927, 2014. a
Dang, X., Peng, H., Wang, X., and Zhang, H.: The Theil-Sen Estimators in a Multiple Linear Regression Model, https://home.olemiss.edu/~xdang/papers/MTSE.pdf (last access: 17 July 2023), 2008. a
Di Biagio, V., Salon, S., Feudale, L., and Cossarini, G.: Subsurface oxygen maximum in oligotrophic marine ecosystems: mapping the interaction between physical and biogeochemical processes, Biogeosciences, 19, 5553–5574, https://doi.org/10.5194/bg-19-5553-2022, 2022. a, b
Dobricic, S., Pinardi, N., Adani, M., Tonani, M., Fratianni, C., Bonazzi, A., and Fernandez, V.: Daily oceanographic analyses by Mediterranean Forecasting System at the basin scale, Ocean Sci., 3, 149–157, https://doi.org/10.5194/os-3-149-2007, 2007. a
D’Ortenzio, F., Taillandier, V., Claustre, H., Prieur, L. M., Leymarie, E., Mignot, A., Poteau, A., Penkerc’h, C., and Schmechtig, C. M.: Biogeochemical Argo: The test case of the NAOS Mediterranean array, Front. Mar. Sci., 7, 120, https://doi.org/10.3389/fmars.2020.00120, 2020. a
D'Ortenzio, F., Taillandier, V., Claustre, H., Coppola, L., Conan, P., Dumas, F., Durrieu du Madron, X., Fourrier, M., Gogou, A., Karageorgis, A., Lefevre, D., Leymarie, E., Oviedo, A, Pavlidou, A., Poteau, A., Poulain, P. M., Prieur, L., Psarra, S., Puyo-Pay, M., Ribera d'Alcalà, M., Schmechtig, C., Terrats, L., Velaoras, D., Wagener, T., and Wimart-Rousseau, C.: BGC-Argo floats observe nitrate injection and spring phytoplankton increase in the surface layer of Levantine Sea (Eastern Mediterranean), Geophys. Res. Lett., 48, e2020GL091649, https://doi.org/10.1029/2020GL091649, 2021. a, b
Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., and Pinardi, N.: A High Resolution Reanalysis for the Mediterranean Sea, Front. Earth Sci., 9, 702285, https://doi.org/10.3389/feart.2021.702285, 2021. a
Fennel, K., Gehlen, M., Brasseur, P., Brown, C. M., Ciavatta, S., Cossarini, G., Crise, A., Edwards, C. A., Ford, D., Friedrichs, M. A. M., Gregoire, M., Jones, E., Kim, H., Lamouroux, J., Murtugudde, R., Perrucheet, C., and the GODAE OceanView Marine Ecosystem Analysis and Prediction Task Team: Advancing marine biogeochemical and ecosystem reanalyses and forecasts as tools for monitoring and managing ecosystem health, Front. Mar. Sci., 6, 89, https://doi.org/10.3389/fmars.2019.00089, 2019. a
Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Di Biagio, V., Coidessa, G., Alvarez Suarez, E., Amadio, C., and Cossarini, G.: Mediterranean Sea Biogeochemical Analysis and Forecast, Copernicus Marine Service MED-Biogeochemistry, MedBFM4 system [data set], https://hdl.handle.net/20.500.14083/18527 (last access: 17 July 2023), 2022. a
Fischler, M. A. and Bolles, R. C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM, 24, 381–395, 1981. a
Ford, D.: Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design, Biogeosciences, 18, 509–534, https://doi.org/10.5194/bg-18-509-2021, 2021. a, b, c
Fourrier, M., Coppola, L., Claustre, H., D’Ortenzio, F., Sauzède, R., and Gattuso, J.-P.: A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED, Front. Mar. Sci., 7, 620, https://doi.org/10.3389/fmars.2020.00620, 2020. a, b, c
Fourrier, M., Coppola, L., Claustre, H., D'Ortenzio, F., Sauzède, R., and Gattuso, J.-P.: Corrigendum: A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED, Front. Mar. Sci., 8, 650509, https://doi.org/10.3389/fmars.2020.00620, 2021. a
Garcia, H. E., Boyer, T. P., Baranova, O. K., Locarnini, R. A., Mishonov, A. V., Grodsky, A., Paver, C. R., Weathers, K. W., Smolyar, I. V., Reagan, J. R., Seidov, D., and Zweng, M. M.: World Ocean Atlas 2018: Product Documentation, NOAA, Technical Editor, Mishonov, A., retrieved from: https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18documentation.pdf (last access: 17 July 2023), 2019. a, b
Gasparin, F., Guinehut, S., Mao, C., Mirouze, I., Rémy, E., King, R. R., Hamon, M., Reid, R., Storto, A., Le Traon, P.-Y., Martin, M. J., and Masina, S.: Requirements for an Integrated in situ Atlantic Ocean Observing System From Coordinated Observing System Simulation Experiments, Front. Mar. Sci., 6, 83, https://doi.org/10.3389/fmars.2019.00083, 2019. a
Geer, A.: Learning earth system models from observations: machine learning or data assimilation?, Philos. T. R. Soc. A, 379, 20200089, https://doi.org/10.1098/rsta.2020.0089, 2021. a, b
Hollingsworth, A., Shaw, D., Lönnberg, P., Illari, L., Arpe, K., and Simmons, A.: Monitoring of observation and analysis quality by a data assimilation system, Mon. Weather Rev., 114, 861–879, 1986. a
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359–366, 1989. a
Johnson, K. and Claustre, H.: Bringing biogeochemistry into the Argo age, Eos, Trans. Am. Geophys. Union, https://doi.org/10.13155/84370, 2016. a, b
Johnson, K., Maurer, T., Plant, J., Bittig, H., Schallenberg, C., and Schmechtig, C.: BGC-Argo quality control manual for nitrate concentration, https://doi.org/10.13155/84370, 2021. a
Johnson, K. S., Coletti, L. J., Jannasch, H. W., Sakamoto, C. M., Swift, D. D., and Riser, S. C.: Long-term nitrate measurements in the ocean using the In Situ Ultraviolet Spectrophotometer: sensor integration into the Apex profiling float, J. Atmos. Ocean. Technol., 30, 1854–1866, 2013. a
Kaufman, D. E., Friedrichs, M. A. M., Hemmings, J. C. P., and Smith Jr., W. O.: Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea, Biogeosciences, 15, 73–90, https://doi.org/10.5194/bg-15-73-2018, 2018. a
Kumar, S. V., Peters-Lidard, C. D., Santanello, J. A., Reichle, R. H., Draper, C. S., Koster, R. D., Nearing, G., and Jasinski, M. F.: Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes, Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, 2015. a
Lary, D. J., Zewdie, G. K., Liu, X., et al.: Machine learning applications for earth observation, Earth observation open science and innovation, Springer, Cham, 165–218, https://doi.org/10.1007/978-3-319-65633-5, 2018. a, b
Lazzari, P., Solidoro, C., Ibello, V., Salon, S., Teruzzi, A., Béranger, K., Colella, S., and Crise, A.: Seasonal and inter-annual variability of plankton chlorophyll and primary production in the Mediterranean Sea: a modelling approach, Biogeosciences, 9, 217–233, https://doi.org/10.5194/bg-9-217-2012, 2012. a, b
Lazzari, P., Bolzon, G., Salon, S., Teruzzi, A., Di Biagio, V., Amadio, C., Alvarez, E., and Cossarini, G.: BFM (5.0), Zenodo [code], https://doi.org/10.5281/zenodo.8283629, 2023. a
Le Traon, P. Y.: From satellite altimetry to Argo and operational oceanography: three revolutions in oceanography, Ocean Sci., 9, 901–915, https://doi.org/10.5194/os-9-901-2013, 2013. a
Le Traon, P. Y., Reppucci, A., Alvarez Fanjul, E., et al.: From observation to information and users: The Copernicus Marine Service perspective, Front. Mar. Sci., 6, 234, https://doi.org/10.3389/fmars.2019.00234, 2019. a
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects, IEEE transactions on neural networks and learning systems, Vol. 33, 6999–7019, https://doi.org/10.1109/TNNLS.2021.3084827, 2021. a
Li, Z. Q., Liu, Z. H., and Lu, S. L.: Global Argo data fast receiving and post-quality-control system. InIOP Conference Series: Earth and Environmental Science 2020 May 1, Vol. 502, p. 012012, IOP Publishing, https://doi.org/10.1088/1755-1315/502/1/012012, 2020. a
Marañón, E., Van Wambeke, F., Uitz, J., Boss, E. S., Dimier, C., Dinasquet, J., Engel, A., Haëntjens, N., Pérez-Lorenzo, M., Taillandier, V., and Zäncker, B.: Deep maxima of phytoplankton biomass, primary production and bacterial production in the Mediterranean Sea, Biogeosciences, 18, 1749–1767, https://doi.org/10.5194/bg-18-1749-2021, 2021. a
Martinez, E., Brini, A., Gorgues, T., Drumetz, L., Roussillon, J., Tandeo, P., Maze, G., and Fablet, R.: Neural network approaches to reconstruct phytoplankton time-series in the global ocean, Remote Sens., 12, 4156, 2020a. a
Martinez, E., Gorgues, T., Lengaigne, M., Fontana, C., Sauzède, R., Menkes, C., Uitz, J., Di Lorenzo, E., and Fablet, R.: Reconstructing global chlorophyll-a variations using a non-linear statistical approach, Front. Mar. Sci., 7, 464, https://doi.org/10.3389/fmars.2020.00464, 2020b. a
Maurer, T. L., Plant, J. N., and Johnson, K. S.: Delayed-mode quality control of oxygen, nitrate, and pH data on SOCCOM biogeochemical profiling floats, Front. Mar. Sci., 8, 683207, https://doi.org/10.3389/fmars.2021.683207, 2021. a, b, c, d
Mavropoulou, A.-M., Vervatis, V., and Sofianos, S.: Dissolved oxygen variability in the Mediterranean Sea, J. Mar. Syst., 208, 103348, https://doi.org/10.1016/j.jmarsys.2020.103348, 2020. a, b, c
Mignot, A., Claustre, H., Uitz, J., Poteau, A., d'Ortenzio, F., and Xing, X.: Understanding the seasonal dynamics of phytoplankton biomass and the deep chlorophyll maximum in oligotrophic environments: A Bio-Argo float investigation, Global Biogeochem. Cy., 28, 856–876, 2014. a
Mignot, A., D'Ortenzio, F., Taillandier, V., Cossarini, G., and Salon, S.: Quantifying observational errors in Biogeochemical-Argo oxygen, nitrate, and chlorophyll a concentrations, Geophys. Res. Lett., 46, 4330–4337, 2019. a
Miloslavich, P., Seeyave, S., Muller-Karger, F., Bax, N., Ali, E., Delgado, C., Evers-King, H., Loveday, B., Lutz, V., Newton, J., Nolan G., Peralta Brichtova AC., Traeger-Chatterjee C., and Urban, E.: Challenges for global ocean observation: the need for increased human capacity, J. Operat. Oceanogr., 12, S137–S156, https://doi.org/10.1080/1755876X.2018.1526463, 2019. a
Oddo, P., Adani, M., Pinardi, N., Fratianni, C., Tonani, M., and Pettenuzzo, D.: A nested Atlantic-Mediterranean Sea general circulation model for operational forecasting, Ocean Sci., 5, 461–473, https://doi.org/10.5194/os-5-461-2009, 2009. a
Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Simoncelli, S., Tonani, M., Lyubartsev, V., Dobricic, S., and Bonaduce A.: Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis, Prog. Oceanogr., 132, 318–332, 2015. a, b
Raicich, F. and Rampazzo, A.: Observing System Simulation Experiments for the assessment of temperature sampling strategies in the Mediterranean Sea, Ann. Geophys., 21, 151–165, https://doi.org/10.5194/angeo-21-151-2003, 2003. a
Ricour, F., Capet, A., D'Ortenzio, F., Delille, B., and Grégoire, M.: Dynamics of the deep chlorophyll maximum in the Black Sea as depicted by BGC-Argo floats, Biogeosciences, 18, 755–774, https://doi.org/10.5194/bg-18-755-2021, 2021. a
Roussillon, J., Fablet, R., Gorgues, T., Drumetz, L., Littaye, J., and Martinez, E.: A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers, Front. Mar. Sci., 10, 1077623, https://doi.org/10.3389/fmars.2023.1077623, 2023. a
Sakov, P. and Sandery, P.: An adaptive quality control procedure for data assimilation, Tellus A, 69, 1318031, https://doi.org/10.1080/16000870.2017.1318031, 2017. a
Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C., and Crise, A.: Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts, Ocean Sci., 15, 997–1022, https://doi.org/10.5194/os-15-997-2019, 2019. a, b, c, d, e, f, g
Sauzède, R., Claustre, H., Uitz, J., Jamet, C., Dall'Olmo, G., d'Ortenzio, F., Gentili, B., Poteau, A., and Schmechtig, C.: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res.-Ocean., 121, 2552–2571, 2016. a
Sauzède, R., Johnson, J. E., Claustre, H., Camps-Valls, G., and Ruescas, A. B.: Estimation of oceanic particulate organic carbon with machine learning. ISPRS Annals of the Photogrammetry, Remote Sens. Spat. Inf. Sci., 2, 949–956, 2020. a
Sauzède, R., Bittig, H. C., Claustre, H., Pasqueron de Fommervault, O., Gattuso, J.-P., Legendre, L., and Johnson, K. S.: Estimates of water-column nutrient concentrations and carbonate system parameters in the global ocean: a novel approach based on neural networks, Front. Mar. Sci., 4, 128, https://doi.org/10.3389/fmars.2017.00128, 2017. a, b, c
Siokou-Frangou, I., Christaki, U., Mazzocchi, M. G., Montresor, M., Ribera d'Alcalá, M., Vaqué, D., and Zingone, A.: Plankton in the open Mediterranean Sea: a review, Biogeosciences, 7, 1543–1586, https://doi.org/10.5194/bg-7-1543-2010, 2010. a, b
Sisma-Ventura, G., Kress, N., Silverman, J., Gertner, Y., Ozer, T., Biton, E., Lazar, A., Gertman, I., Rahav, E., and Herut, B.: Post-eastern mediterranean transient oxygen decline in the deep waters of the southeast mediterranean sea supports weakening of ventilation rates, Front. Mar. Sci., 7, 598686, https://doi.org/10.3389/fmars.2020.598686, 2021. a
Skakala, J., Ford, D., Bruggeman, J., Hull, T., Kaiser, J., King, R. R., Loveday, B., Palmer, M. R., Smyth, T., Williams, C. A., and Ciavatta, S.: Towards a multi-platform assimilative system for North Sea biogeochemistry, J. Geophys. Res.-Ocean., 126, e2020JC016649, https://doi.org/10.1029/2020JC016649, 2021. a, b
Stanev, E. V., Wahle, K., and Staneva, J.: The synergy of data from profiling floats, machine learning and numerical modeling: Case of the Black Sea euphotic zone, J. Geophys. Res.-Ocean., 127, e2021JC018012, https://doi.org/10.1029/2021JC018012, 2022. a, b
Storto, A., Dobricic, S., Masina, S., and Di Pietro, P.: Assimilating along-track altimetric observations through local hydrostatic adjustment in a global ocean variational assimilation system, Mon. Weather Rev., 139, 738–754, 2011. a
Storto, A., Masina, S., and Dobricic, S.: Estimation and impact of nonuniform horizontal correlation length scales for global ocean physical analyses, J. Atmos. Ocean. Technol., 31, 2330–2349, 2014. a
Storto, A., De Magistris, G., Falchetti, S., and Oddo, P.: A neural network–based observation operator for coupled ocean–acoustic variational data assimilation, Mon. Weather Rev., 149, 1967–1985, 2021. a
Taillandier, V., Prieur, L., D'Ortenzio, F., Ribera d'Alcalà, M., and Pulido-Villena, E.: Profiling float observation of thermohaline staircases in the western Mediterranean Sea and impact on nutrient fluxes, Biogeosciences, 17, 3343–3366, https://doi.org/10.5194/bg-17-3343-2020, 2020. a
Teruzzi, A., Bolzon, G., Feudale, L., and Cossarini, G.: Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment, Biogeosciences, 18, 6147–6166, https://doi.org/10.5194/bg-18-6147-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Teruzzi, A., Bolzon, G., and Cossarini, G.: 3DVarBio (3.3), Zenodo [code], https://doi.org/10.5281/zenodo.8283275, 2023. 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
Thierry, V. and Bittig, H.: Argo quality control manual for dissolved oxygen concentration, Report (qualification paper (procedure, accreditation support)), FRANCE, https://doi.org/10.13155/46542, 2021. a, b
Verdy, A. and Mazloff, M. R.: A data assimilating model for estimating S outhern O cean biogeochemistry, J. Geophys. Res.-Ocean., 122, 6968–6988, 2017. a
Waller, J. A., García-Pintado, J., Mason, D. C., Dance, S. L., and Nichols, N. K.: Technical note: Assessment of observation quality for data assimilation in flood models, Hydrol. Earth Syst. Sci., 22, 3983–3992, https://doi.org/10.5194/hess-22-3983-2018, 2018. a
Wang, B. and Fennel, K.: An assessment of vertical carbon flux parameterizations using backscatter data from BGC Argo, Geophys. Res. Lett., 50, e2022GL101220, https://doi.org/10.1029/2022GL101220, 2023. a, b
Wang, B., Fennel, K., Yu, L., and Gordon, C.: Assessing the value of biogeochemical Argo profiles versus ocean color observations for biogeochemical model optimization in the Gulf of Mexico, Biogeosciences, 17, 4059–4074, https://doi.org/10.5194/bg-17-4059-2020, 2020. a, b, c
Wang, B., Fennel, K., and Yu, L.: Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico, Ocean Sci., 17, 1141–1156, https://doi.org/10.5194/os-17-1141-2021, 2021a. a
Wang, S., Flipo, N., Romary, T., and Hasanyar, M.: Particle filter for high frequency oxygen data assimilation in river systems, Environ. Model. Softw., 151, 105382, https://doi.org/10.1016/j.envsoft.2022.105382, 2022. a
Wang, T., Chai, F., Xing, X., Ning, J., Jiang, W., and Riser, S. C.: Influence of multi-scale dynamics on the vertical nitrate distribution around the Kuroshio Extension: An investigation based on BGC-Argo and satellite data, Prog. Oceanogr., 193, 102543, https://doi.org/10.1016/j.pocean.2021.102543, 2021b. 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, 2018. a
Yumruktepe, V. Ç., Mousing, E. A., Tjiputra, J., and Samuelsen, A.: An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas, Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, 2023. a
Zhou, H., Yue, X., Lei, Y., Tian, C., Ma, Y., and Cao, Y.: Large contributions of diffuse radiation to global gross primary productivity during 1981–2015, Global Biogeochem. Cy., 35, e2021GB006957, https://doi.org/10.1029/2021GB006957, 2021. a
Short summary
Forecasting of marine biogeochemistry can be improved via the assimilation of observations. Floating buoys provide multivariate information about the status of the ocean interior. Information on the ocean interior can be expanded/augmented by machine learning. In this work, we show the enhanced impact of assimilating new in situ variables (oxygen) and reconstructed variables (nitrate) in the operational forecast system (MedBFM) model of the Mediterranean Sea.
Forecasting of marine biogeochemistry can be improved via the assimilation of observations....
