CDOM Spatiotemporal Variability in the Mediterranean Sea: A Modelling Study

Abstract

This study investigates the spatial and temporal variability of chromophoric-dissolved organic matter (CDOM) in the Mediterranean Sea. The analysis is carried out using a state-of-the-art 3D biogeochemical model. The model describes the plankton dynamics, the cycles of the most important limiting nutrients, and the particulate and dissolved pools of carbon. The source of CDOM is directly correlated to the dynamics of dissolved organic carbon (DOC) by a fixed production quota. Then CDOM degrades by photobleaching and remineralization. The main innovation of the system is the inclusion of a bio-optical radiative transfer model that computes surface upwelling irradiance, and therefore simulates remotely sensed reflectance (Rrs). Simulation results of three model configurations are evaluated using satellite Rrs, particularly at 412 nm, 443 nm, and 490 nm. All simulations show a winter minimum in Rrs for the considered bands. However, different parameterizations of DOC-release induce a different accumulation of CDOM, especially in the eastern Mediterranean, and a different Rrs signature: a more active microbial loop during summer implies a decrease of Rrs at 412 nm. We demonstrate how the usage of a bio-optical model allows us to corroborate hypotheses on CDOM-cycling based on blue–violet Rrs data, supporting the importance of this complementary data stream with respect to satellite-derived chlorophyll.

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

“…The BFM was recently developed with a carbonate system (Cossarini et al, 2015) and revised phytoplankton nutrient uptake processes (Lazzari et al, 2016). Moreover, the BFM has been implemented in the Mediterranean Sea in a wide range of applications, including 1D and 3D configurations aimed at studying the interaction between optics and biogeochemistry (Terzić et al, 2021(Terzić et al, , 2019, conducting climate scenario simulations (Lazzari et al, 2014), estimating carbon sequestration services (Melaku Canu et al, 2015), simulating high-resolution coastal dynamics in marginal seas (Cossarini et al, 2017), analysing temporal scales of multi-decadal variability (Di Biagio et al, 2019), and studying CDOM (chromophoric-dissolved organic matter) spatiotemporal variability (Lazzari et al, 2021). 3DVar-Bio is the data assimilation scheme for the correction of phytoplankton biomass and nutrient concentrations (i.e.…”

“…In 3DVarBio, assimilation is performed through the minimization of a cost function that is defined on the basis of Bayes' theorem (Lorenc, 1986) as the weighted sum of the square mismatches between the model background state x b (the model state before the assimilation) and the analysis x a (the assimilation result) and the observations y. Each square mismatch is weighted according to its accuracy estimations, meaning that x a − x b is weighted by the background error covariance matrix B, while (y − H (x b )) is weighted by the observation error covariance matrix R:…”

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

“…MedBFM is the biogeochemical component of the Mediterranean CMEMS model system aimed at providing short-term forecasts and multi-annual reanalysis (Salon et al, 2019). Moreover, the BFM has been implemented in the Mediterranean Sea in a wide range of applications, including 1D and 3D configurations aimed at studying the interaction between optics and biogeochemistry (Terzić et al, 2021(Terzić et al, , 2019, conducting climate scenario simulations (Lazzari et al, 2014), estimating carbon sequestration services (Melaku Canu et al, 2015), simulating high-resolution coastal dynamics in marginal seas (Cossarini et al, 2017), analysing temporal scales of multi-decadal variability (Di Biagio et al, 2019), and studying CDOM (chromophoricdissolved organic matter) spatiotemporal variability (Lazzari et al, 2021).…”