Articles | Volume 22, issue 15
https://doi.org/10.5194/bg-22-3747-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-3747-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Characterisation of uncertainties in an ocean radiative transfer model for the Black Sea through ensemble simulations
FOCUS-MAST research group, Department of Astrophysics, Geophysics and Oceanography, University of Liège, Liège, Belgium
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, Grenoble, France
Luc Vandenbulcke
FOCUS-MAST research group, Department of Astrophysics, Geophysics and Oceanography, University of Liège, Liège, Belgium
Jean-Michel Brankart
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, Grenoble, France
Pierre Brasseur
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, Grenoble, France
Marilaure Grégoire
FOCUS-MAST research group, Department of Astrophysics, Geophysics and Oceanography, University of Liège, Liège, Belgium
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Cited articles
Aas, E.: Two-stream irradiance model for deep waters, Appl. Optics, 26, 2095–2101, https://doi.org/10.1364/ao.26.002095, 1987. a
Ackleson, S., Balch, W., and Holligan, P.: Response of water-leaving radiance to particulate calcite and chlorophyll a concentrations: A model for Gulf of Maine coccolithophore blooms, J. Geophys. Res., 99, 7483–7499, https://doi.org/10.1029/93JC02150, 1994. a
Álvarez, E., Cossarini, G., Teruzzi, A., Bruggeman, J., Bolding, K., Ciavatta, S., Vellucci, V., D'Ortenzio, F., Antoine, D., and Lazzari, P.: Chromophoric dissolved organic matter dynamics revealed through the optimization of an optical–biogeochemical model in the northwestern Mediterranean Sea, Biogeosciences, 20, 4591–4624, https://doi.org/10.5194/bg-20-4591-2023, 2023. a
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000. 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
Baird, M. E., Wild-Allen, K. A., Parslow, J., Mongin, M., Robson, B., Skerratt, J., Rizwi, F., Soja-Woźniak, M., Jones, E., Herzfeld, M., Margvelashvili, N., Andrewartha, J., Langlais, C., Adams, M. P., Cherukuru, N., Gustafsson, M., Hadley, S., Ralph, P. J., Rosebrock, U., Schroeder, T., Laiolo, L., Harrison, D., and Steven, A. D. L.: CSIRO Environmental Modelling Suite (EMS): scientific description of the optical and biogeochemical models (vB3p0), Geosci. Model Dev., 13, 4503–4553, https://doi.org/10.5194/gmd-13-4503-2020, 2020. a
Bissett, W., Carder, K., Walsh, J., and Dieterle, D.: Carbon cycling in the upper waters of the Sargasso Sea: II. Numerical simulation of apparent and inherent optical properties, Deep-Sea Res. Pt. I, 46, 271–317, https://doi.org/10.1016/S0967-0637(98)00063-6, 1999. a
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
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley, S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels, Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, 2016. a
Cahill, B., Schofield, O., Chant, R., Wilkin, J., Hunter, E., Glenn, S., and Bissett, P.: Dynamics of turbid buoyant plumes and the feedbacks on near-shore biogeochemistry and physics, Geophys. Res. Lett., 35, L10605, https://doi.org/10.1029/2008GL033595, 2008. a
Cahill, B. E., Kowalczuk, P., Kritten, L., Gräwe, U., Wilkin, J., and Fischer, J.: Estimating the seasonal impact of optically significant water constituents on surface heating rates in the western Baltic Sea, Biogeosciences, 20, 2743–2768, https://doi.org/10.5194/bg-20-2743-2023, 2023. a
Candille, G. and Talagrand, O.: Evaluation of probabilistic prediction systems for a scalar variable, Q. J. Roy. Meteor. Soc., 131, 2131–2150, https://doi.org/10.1256/qj.04.71, 2004. a
Capet, A.: Study of the multi-decadal evolution of the Black Sea hydrodynamics and biogeochemistry using mathematical modelling, PhD thesis, ULiège – Université de Liège, https://hdl.handle.net/2268/163502 (last access: 13 May 2025), 2014. a
Ciavatta, S., Torres, R., Martinez-Vicente, V., Smyth, T., Dall’Olmo, G., Polimene, L., and Allen, J. I.: Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling, Prog. Oceanogr., 127, 74–95, https://doi.org/10.1016/j.pocean.2014.06.002, 2014. a
Copernicus Marine in Situ tac Data Management Team: Copernicus Marine In Situ TAC NetCDF format manual, Copernicus Marine in situ TAC, https://doi.org/10.13155/59938, 2023. a
Dechenne, A.: Black Sea and diazotrophs, toward an improvement of modeling the nitrogen cycle, Master's thesis, Université de Liège, https://matheo.uliege.be/handle/2268.2/18590 (last access: 13 May 2025), 2023. a
Dutkiewicz, S., Hickman, A. E., and Jahn, O.: Modelling ocean-colour-derived chlorophyll a, Biogeosciences, 15, 613–630, https://doi.org/10.5194/bg-15-613-2018, 2018. a, b, c
E.U. Copernicus Marine Service Information (CMEMS): Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997–ongoing), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00303, 2025. a
Fujii, M., Boss, E., and Chai, F.: The value of adding optics to ecosystem models: a case study, Biogeosciences, 4, 817–835, https://doi.org/10.5194/bg-4-817-2007, 2007. a
Gallée, H., Trouvilliez, A., Agosta, C., Genthon, C., Favier, V., and Naaim-Bouvet, F.: Transport of Snow by the Wind: A Comparison Between Observations in Adélie Land, Antarctica, and Simulations Made with the Regional Climate Model MAR, Bound.-Lay. Meteorol., 146, 133–147, https://doi.org/10.1007/s10546-012-9764-z, 2013. a
Gallegos, C., Werdell, P., and McClain, C.: Long‐term changes in light scattering in Chesapeake Bay inferred from Secchi depth, light attenuation, and remote sensing measurements, J. Geophys. Res., 116, C00H08, https://doi.org/10.1029/2011JC007160, 2011. a, b, c
Garnier, F.: Paramétrisations stochastiques de processus biogéochimiques non résolus dans un modèle couplé NEMO/PISCES de l’Atlantique Nord, PhD Thesis, https://theses.hal.science/tel-01661414 (last access: 13 May 2025), 2016. a
Garnier, F., Brankart, J.-M., Brasseur, P., and Cosme, E.: Stochastic parameterizations of biogeochemical uncertainties in a ° 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
Grailet, J.-F., Hogan, R. J., Ghilain, N., Bolsée, D., Fettweis, X., and Grégoire, M.: Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle, Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, 2025. a
Gregg, W. and Casey, N.: Skill assessment of a spectral ocean–atmosphere radiative model, J. Marine Syst., 76, 49–63, https://doi.org/10.1016/j.jmarsys.2008.05.007, 2009. a
Gregg, W. and Rousseaux, C.: Directional and Spectral Irradiance in Ocean Models: Effects on Simulated Global Phytoplankton, Nutrients, and Primary Production, Front. Mar. Sci., 3, 240, https://doi.org/10.3389/fmars.2016.00240, 2016. a
Grégoire, M. and Soetart, K.: Carbon, nitrogen, oxygen and sulfide budgets in the Black Sea: A biogeochemical model of the whole water column coupling the oxic and anoxic parts, Ecol. Model., 221, 2287–2301, https://doi.org/10.1016/j.ecolmodel.2010.06.007, 2010. a
Grégoire, M., Raick, C., and Soetart, K.: Numerical modeling of the central Black Sea ecosystem functioning during the eutrophication phase, Prog. Oceanogr., 76, 286–333, https://doi.org/10.1016/j.pocean.2008.01.002, 2008. a
Grégoire, M., Alvera-Azcarate, A., Buga, L., Capet, A., Constantin, S., D'Ortenzio, F., Doxaran, D., Faugeras, Y., Garcia-Espriu, A., Golumbeanu, M., Gonzalez-Haro, C., González-Gambau, V., Kasprzyk, J.-P., Ivanov, E., Mason, E., Mateescu, R., Meulders, C., Olmedo, E., Pons, L., Pujol, M.-I., Sarbu, G., Turiel, A., Vandenbulcke, L., and Rio, M.-H.: Monitoring Black Sea environmental changes from space: New products for altimetry, ocean colour and salinity. Potentialities and requirements for a dedicated in-situ observing system, Front. Mar. Sci., 9, 998970, https://doi.org/10.3389/fmars.2022.998970, 2023. a
Hogan, R. and Bozzo, A.: A Flexible and Efficient Radiation Scheme for the ECMWF Model, J. Adv. Model. Earth Sy., 10, 1990–2008, https://doi.org/10.1029/2018MS001364, 2018. a
Hogan, R. and Matricardi, M.: A Tool for Generating Fast k-Distribution Gas-Optics Models for Weather and Climate Applications, J. Adv. Model. Earth Sy., 14, e2022MS003033, https://doi.org/10.1029/2022MS003033, 2022. a
In Situ tac Partners: Product User Manual for In Situ Products INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030, INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031, INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032, INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033, INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034, INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035, INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036. Ref. CMEMS-INS-PUM-013-030-036, Copernicus Marine In Situ TAC, https://doi.org/10.13155/43494, 2024. a
Jones, E. M., Baird, M. E., Mongin, M., Parslow, J., Skerratt, J., Lovell, J., Margvelashvili, N., Matear, R. J., Wild-Allen, K., Robson, B., Rizwi, F., Oke, P., King, E., Schroeder, T., Steven, A., and Taylor, J.: Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef, Biogeosciences, 13, 6441–6469, https://doi.org/10.5194/bg-13-6441-2016, 2016. a
Kajiyama, T., D'Alimonte, D., and Zibordi, G.: Algorithms Merging for the Determination of Chlorophyll-a Concentration in the Black Sea, IEEE Geosci. Remote S., 16, 677–681, https://doi.org/10.1109/LGRS.2018.2883539, 2018. a, b
Kettle, H. and Merchant, C.: Modeling ocean primary production: sensitivity to spectral resolution of attenuation and absorption of light, Prog. Oceanogr., 78, 135–146, https://doi.org/10.1016/j.pocean.2008.04.002, 2008. a
Kitidis, V., Stubbins, A., Uher, G., Upstill Goddard, R., Law, C., and Woodward, E.: Variability of chromophoric organic matter in surface waters of the Atlantic Ocean, Deep-Sea Res. Pt. II, 53, 1666–1684, https://doi.org/10.1016/j.dsr2.2006.05.009, 2006. a
Kubryakov, A., Mikaelyan, A., and Stanichny, S.: Extremely strong coccolithophore blooms in the Black Sea: The decisive role of winter vertical entrainment of deep water, Deep-Sea Res. Pt. I, 173, 103554, https://doi.org/10.1016/j.dsr.2021.103554, 2021. a
Lee, Z., Carder, K., and Arnone, R.: Deriving inherent optical properties from water color: a multi-band quasi-analytical algorithm for optically deep waters, Appl. Optics, 41, 5755–5772, https://doi.org/10.1364/AO.41.005755, 2002. a
Lengaigne, M., Menkes, C., Aumont, O., Gorgues, T., Bopp, L., André, J.-M., and Madec, G.: Influence of the oceanic biology on the tropical Pacific climate in a coupled general circulation model, Clim. Dynam., 28, 503–516, https://doi.org/10.1007/s00382-006-0200-2, 2007. a
Manizza, M., Le Quéré, C., Watson, A., and Buitenhuis, E.: Bio-optical feedbacks among phytoplankton, upper ocean physics and sea-ice in a global model, Geophys. Res. Lett., 32, L05603, https://doi.org/10.1029/2004GL020778, 2005. a
Mason, J., Cone, M., and Fry, E.: Ultraviolet (250–550 nm) absorption spectrum of pure water, Appl. Optics, 55, 7163–7172, https://doi.org/10.1364/AO.55.007163, 2016. a
Mobley, C.: Fast light calculations for ocean ecosystem and inverse models, Opt. Express, 19, 18927–18944, https://doi.org/10.1364/oe.19.018927, 2011. a
Mobley, C., Chai, F., Xiu, P., and Sundman, L.: Impact of improved light calculations on predicted phytoplankton growth and heating in an idealized upwelling-downwelling channel geometry, J. Geophys. Res.-Oceans, 120, 875–892, https://doi.org/10.1002/2014JC010588, 2015. a, b
Morel, A.: Optical properties of pure water and pure sea water, Optical Aspects of Oceanography, 19, 1–24, 1974. a
Morel, A., Antoine, D., and Gentili, B.: Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function, Appl. Optics, 41, 6289–6306, https://doi.org/10.1364/AO.41.006289, 2002. a
Morel, A., Claustre, H., Antoine, D., and Gentili, B.: Natural variability of bio-optical properties in Case 1 waters: attenuation and reflectance within the visible and near-UV spectral domains, as observed in South Pacific and Mediterranean waters, Biogeosciences, 4, 913–925, https://doi.org/10.5194/bg-4-913-2007, 2007. a
Peneva, E. and Stips, A.: Numerical Simulations of Black Sea and Adjoined Azov Sea, Forced with Climatological and Meteorological Reanalysis Data, Tech. Rep. EUR 21504 EN, CEC JRC, Institute of Environment and Sustainability, https://doi.org/10.13140/RG.2.1.1830.4722, 2005. a
Pope, R. and Fry, E.: Absorption spectrum 380–700 nm of pure water. II. Integrating cavity measurements, Appl. Optics, 36, 8710–8723, https://doi.org/10.1364/ao.36.008710, 1997. a, b
Popov, M., Brankart, J.-M., Capet, A., Cosme, E., and Brasseur, P.: Ensemble analysis and forecast of ecosystem indicators in the North Atlantic using ocean colour observations and prior statistics from a stochastic NEMO–PISCES simulator, Ocean Sci., 20, 155–180, https://doi.org/10.5194/os-20-155-2024, 2024. a, b
Silkin, V., Mikaelyan, S., Pautova, L., and Fedorov, A.: Annual Dynamics of Phytoplankton in the Black Sea in Relation to Wind Exposure, J. Mar. Sci. Eng., 9, 1435, https://doi.org/10.3390/jmse9121435, 2021. a
Skákala, J., Bruggeman, J., Ford, D., Wakelin, S., Akpinar, A., Hull, T., Kaiser, J., Loveday, B., O'Dea, E., Williams, C., and Ciavatta, S.: The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas, Ocean Model., 172, 101976, https://doi.org/10.1016/j.ocemod.2022.101976, 2022. a
Stanev, E. and Beckers, J.-M.: Barotropic and baroclinic oscillations in strongly stratified ocean basins: Numerical study of the Black Sea, J. Marine Syst., 19, 65–112, https://doi.org/10.1016/S0924-7963(98)00024-4, 1999. a
Stramski, D., Bricaud, A., and Morel, A.: Modeling the Inherent Optical Properties of the Ocean Based on the Detailed Composition of the Planktonic Community, Appl. Optics, 40, 2929–2945, https://doi.org/10.1364/AO.40.002929, 2016. 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
Terzić, E., Miró, A., Organelli, E., Kowalczuk, P., D'Ortenzio, F., and Lazzari, P.: Radiative transfer modeling with biogeochemical Argo float data in the Mediterranean Sea, J. Geophys. Res.-Oceans, 126, e2021JC017690, https://doi.org/10.1029/2021JC017690, 2021. a, b
Thimijan, R. and Heins, R.: Photometric, Radiometric, and Quantum Light Units of Measure: A Review of Procedures for Interconversion, Hortic. Sci., 18, 818–822, https://doi.org/10.21273/HORTSCI.18.6.818, 1983. a
Twardowski, M., Boss, E., Sullivan, J., and Donaghay, P.: Ocean Color Analytical Model Explicitly Dependent on the Volume Scattering Function, Mar. Chem., 89, 69–88, https://doi.org/10.3390/app8122684, 2004. a
Uysal, Z.: Chroococcoid cyanobacteria Synechococcus spp. in the Black Sea: pigments, size, distribution, growth and diurnal variability, J. Plankton. Res., 23, 175–190, https://doi.org/10.1093/plankt/23.2.175, 2001. a
Werdell, P., McKinna, L., Boss, E., Ackleson, S., Craig, S., Gregg, W., Lee, Z., Maritorena, S., Roesler, C., Rousseaux, C., Stramski, D., Sullivan, J., Twardowski, M., Tzortziou, M., and Zhang, X.: An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing, Prog. Oceanogr., 160, 186–212, https://doi.org/10.1016/j.pocean.2018.01.001, 2018. a
Zibordi, G., Mélin, F., Berthon, J.-F., and Talone, M.: In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea, Ocean Sci., 11, 275–286, https://doi.org/10.5194/os-11-275-2015, 2015. a
Short summary
The representation of light propagation in seawater is critical for modelling marine biogeochemistry. We analyse results from a radiative transfer model that accounts for the absorption and scattering of light in the ocean with their respective uncertainties. We compare these results with in situ and remote-sensed data. Our analysis highlights the benefits of accounting for model uncertainties while using advanced representations of light in modelling frameworks.
The representation of light propagation in seawater is critical for modelling marine...
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