the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
What reflectance reveals about particle scattering in bio-optical models across contrasting Mediterranean waters
Abstract. Spectral ocean-colour reflectance is increasingly used to constrain marine ecosystem models, yet its potential to provide direct observational constraints on process-based biogeochemical models remains underexplored. Here we calibrate multi-wavelength remote sensing reflectance (RRS(λ)) using a genetic algorithm within a one-dimensional configuration of the Biogeochemical Flux Model. The approach is applied to two contrasting Mediterranean environments: an open-ocean ecosystem in the Ligurian Sea (BOUSSOLE observatory) and an optically complex coastal site in the Northern Adriatic Sea (Acqua Alta oceanographic tower – AAOT). At both sites, the calibration successfully reproduces observed RRS(λ) at six visible wavelengths, demonstrating that the model captures the main features of the RRS(λ) spectra across contrasting trophic regimes. However, the mechanisms used by the model to match RRS(λ) differ markedly and reveal distinct controls on particle scattering. At BOUSSOLE, RRS(λ) calibration primarily adjusts phytoplankton and small-particle optical properties, improving phytoplankton biomass estimates and increasing particulate backscattering. This highlights the dominant role of small and algal particles in shaping backscattering in oligotrophic waters. In contrast, at AAOT, RRS(λ) calibration mainly modifies suspended particulate matter (SPM) optical parameters, with limited changes in the biogeochemical state. Overall, RRS(λ) calibration reproduces observed optical signals while revealing site-dependent controls on particulate backscattering. Sensitivity and calibration exercises improved predictive capability within the calibrated parameter space, but residual error patterns revealed potential structural limitations or simplifications in the model's optical representation. This work demonstrates the potential of RRS(λ) observables to guide improvements in the representation of particle optical properties and biogeochemical processes in marine ecosystem models.
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Data sets
OCEANCOLOUR_MED_BGC_L3_MY E.U. Copernicus Marine Service https://doi.org/10.48670/moi-00299
Suspended Particle Matter (SPM) product EUR-L4-SPIM-ATL-v01 Ifremer ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/ocean-color/atlantic/EUR-L4-SPIM-ATL-v01/
Carbon from the Carbon-based Productivity Model (CbPM) MODIS T. Westberry et al. https://orca.science.oregonstate.edu/2160.by.4320.8day.hdf.carbon2.m.php
Carbon from the Carbon-based Productivity Model (CbPM) VIIRS T. Westberry et al. https://orca.science.oregonstate.edu/2160.by.4320.8day.hdf.carbon2.v.php
CoASTS-BiOMaP dataset G. Zibordi and J.-F. Berthon https://doi.org/10.1594/PANGAEA.971945
DYFAMED observatory data L. Coppola et al. https://doi.org/10.17882/43749
BOUSSOLE pigment data M. Golbol et al. https://doi.org/10.5067/SeaBASS/BOUSSOLE/DATA001
Model code and software
General Ocean Turbulence Model (GOTM) code H. Burchard et al. https://www.github.com/gotm-model
Framework for Aquatic Biogeochemical Models (FABM) code J. Bruggeman and K. Bolding https://github.com/fabm-model/fabm.git
Paralell Sensitivity Analysis and Calibration (ParSAC) tool J. Bruggeman and K. Bolding https://doi.org/10.5281/zenodo.4276111
Biogechemical Flux Model (BFM) code for FABM E. Álvarez et al. https://github.com/inogs/bfmforfabm.git
Forward adjoint model code P. Lazzari et al. https://github.com/BIOPTIMOD/Forward_Adjoint.git
BOUSSOLE GOTM-FABM 1D setup code E. Álvarez et al. https://github.com/ealvarez-s/setup_BFM1D_BOUSSOLE.git
AAOT GOTM-FABM 1D setup code E. Álvarez et al. https://github.com/ealvarez-s/setup_BFM1D_AcquaAlta.git
Interactive computing environment
SEAMLESS notebooks J. Bruggeman et al. https://github.com/BoldingBruggeman/seamless-notebooks
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