Articles | Volume 18, issue 23
https://doi.org/10.5194/bg-18-6147-2021
© Author(s) 2021. 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/bg-18-6147-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Nov 2021
Research article |
| 30 Nov 2021
| 30 Nov 2021
Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, Trieste, 34100, Italy
Giorgio Bolzon
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, Trieste, 34100, Italy
Laura Feudale
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, Trieste, 34100, Italy
Gianpiero Cossarini
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, Trieste, 34100, Italy
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Cited
19 citations as recorded by crossref.
- PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al. https://doi.org/10.5194/gmd-17-7347-2024
- Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model C. Amadio et al. https://doi.org/10.5194/os-20-689-2024
- Ocean Biology Studied from Space S. Sathyendranath et al. https://doi.org/10.1007/s10712-023-09805-9
- The Mediterranean Forecasting System – Part 1: Evolution and performance G. Coppini et al. https://doi.org/10.5194/os-19-1483-2023
- A semi-automated sensitivity-based approach for simplifying marine biogeochemical models for targeted applications: A case study with the Eco3M-MED model Y. Zhang et al. https://doi.org/10.1016/j.ecolmodel.2026.111491
- Subsurface oxygen maximum in oligotrophic marine ecosystems: mapping the interaction between physical and biogeochemical processes V. Di Biagio et al. https://doi.org/10.5194/bg-19-5553-2022
- Two-phase CNN for model data fusion: Predicting 3D chlorophyll-a in the Mediterranean Sea T. Tonelli et al. https://doi.org/10.1016/j.ocemod.2026.102707
- Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study A. Baudena et al. https://doi.org/10.1016/j.rse.2025.114743
- Physics-Informed Depth-Conditional Neural Network for Chlorophyll-a Profile Reconstruction and Uncertainty Quantification Y. Yu et al. https://doi.org/10.1109/TGRS.2026.3676047
- Light‐Driven and Nutrient‐Driven Displacements of Subsurface Chlorophyll Maximum Depth in Subtropical Gyres X. Xing et al. https://doi.org/10.1029/2023GL104510
- Control of simulated ocean ecosystem indicators by biogeochemical observations S. Ciavatta et al. https://doi.org/10.1016/j.pocean.2024.103384
- A case study of impacts of an extreme weather system on the Mediterranean Sea circulation features: Medicane Apollo (2021) M. Menna et al. https://doi.org/10.1038/s41598-023-29942-w
- Profile Data Reconstruction for Deep Chl$a$ Maxima in Mediterranean Sea via Improved-MLP Networks Y. Yu et al. https://doi.org/10.1109/JSTARS.2024.3468330
- When storms stir the Mediterranean depths: chlorophyll a response to Mediterranean cyclones G. Scardino et al. https://doi.org/10.5194/os-21-2849-2025
- Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030 V. Thierry et al. https://doi.org/10.3389/fmars.2025.1593904
- Hybrid machine learning data assimilation for marine biogeochemistry I. Higgs et al. https://doi.org/10.5194/bg-23-315-2026
- Hydrography and deep chlorophyll maximum patterns of the Athos Basin and the Thracian Sea continental shelf (North Aegean Sea) combining glider and satellite observations N. Kokkos et al. https://doi.org/10.1016/j.csr.2023.105029
- Chromophoric dissolved organic matter dynamics revealed through the optimization of an optical–biogeochemical model in the northwestern Mediterranean Sea E. Álvarez et al. https://doi.org/10.5194/bg-20-4591-2023
- EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al. https://doi.org/10.5194/gmd-17-5619-2024
19 citations as recorded by crossref.
- PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al. https://doi.org/10.5194/gmd-17-7347-2024
- Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model C. Amadio et al. https://doi.org/10.5194/os-20-689-2024
- Ocean Biology Studied from Space S. Sathyendranath et al. https://doi.org/10.1007/s10712-023-09805-9
- The Mediterranean Forecasting System – Part 1: Evolution and performance G. Coppini et al. https://doi.org/10.5194/os-19-1483-2023
- A semi-automated sensitivity-based approach for simplifying marine biogeochemical models for targeted applications: A case study with the Eco3M-MED model Y. Zhang et al. https://doi.org/10.1016/j.ecolmodel.2026.111491
- Subsurface oxygen maximum in oligotrophic marine ecosystems: mapping the interaction between physical and biogeochemical processes V. Di Biagio et al. https://doi.org/10.5194/bg-19-5553-2022
- Two-phase CNN for model data fusion: Predicting 3D chlorophyll-a in the Mediterranean Sea T. Tonelli et al. https://doi.org/10.1016/j.ocemod.2026.102707
- Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study A. Baudena et al. https://doi.org/10.1016/j.rse.2025.114743
- Physics-Informed Depth-Conditional Neural Network for Chlorophyll-a Profile Reconstruction and Uncertainty Quantification Y. Yu et al. https://doi.org/10.1109/TGRS.2026.3676047
- Light‐Driven and Nutrient‐Driven Displacements of Subsurface Chlorophyll Maximum Depth in Subtropical Gyres X. Xing et al. https://doi.org/10.1029/2023GL104510
- Control of simulated ocean ecosystem indicators by biogeochemical observations S. Ciavatta et al. https://doi.org/10.1016/j.pocean.2024.103384
- A case study of impacts of an extreme weather system on the Mediterranean Sea circulation features: Medicane Apollo (2021) M. Menna et al. https://doi.org/10.1038/s41598-023-29942-w
- Profile Data Reconstruction for Deep Chl$a$ Maxima in Mediterranean Sea via Improved-MLP Networks Y. Yu et al. https://doi.org/10.1109/JSTARS.2024.3468330
- When storms stir the Mediterranean depths: chlorophyll a response to Mediterranean cyclones G. Scardino et al. https://doi.org/10.5194/os-21-2849-2025
- Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030 V. Thierry et al. https://doi.org/10.3389/fmars.2025.1593904
- Hybrid machine learning data assimilation for marine biogeochemistry I. Higgs et al. https://doi.org/10.5194/bg-23-315-2026
- Hydrography and deep chlorophyll maximum patterns of the Athos Basin and the Thracian Sea continental shelf (North Aegean Sea) combining glider and satellite observations N. Kokkos et al. https://doi.org/10.1016/j.csr.2023.105029
- Chromophoric dissolved organic matter dynamics revealed through the optimization of an optical–biogeochemical model in the northwestern Mediterranean Sea E. Álvarez et al. https://doi.org/10.5194/bg-20-4591-2023
- EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al. https://doi.org/10.5194/gmd-17-5619-2024
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
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.
During summer, maxima of phytoplankton chlorophyll concentration (DCM) occur in the subsurface...
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