Articles | Volume 15, issue 1
https://doi.org/10.5194/bg-15-73-2018
© Author(s) 2018. 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-15-73-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jan 2018
Research article |
| 04 Jan 2018
| 04 Jan 2018
Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea
Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA, USA
Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA, USA
John C. P. Hemmings
Wessex Environmental Associates, Salisbury, UK
now at: Met Office, Exeter, UK
Walker O. Smith Jr.
Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA, USA
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Cited
18 citations as recorded by crossref.
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation V. Vervatis et al. 10.1016/j.ocemod.2021.101781
- Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model C. Amadio et al. 10.5194/os-20-689-2024
- Quantifying Seasonal Particulate Organic Carbon Concentrations and Export Potential in the Southwestern Ross Sea Using Autonomous Gliders M. Meyer et al. 10.1029/2022JC018798
- Chromophoric dissolved organic matter dynamics revealed through the optimization of an optical–biogeochemical model in the northwestern Mediterranean Sea E. Álvarez et al. 10.5194/bg-20-4591-2023
- The seasonal patterns of hydrographic and biogeochemical variables in the Ross Sea: A BGC-Argo analysis R. Cao et al. 10.1016/j.dsr2.2024.105436
- Climatological temporal and spatial distributions of nutrients and particulate matter in the Ross Sea W. Smith & D. Kaufman 10.1016/j.pocean.2018.10.003
- Quantifying biological carbon pump pathways with a data-constrained mechanistic model ensemble approach M. Stukel et al. 10.5194/bg-19-3595-2022
- Drivers of the spatial phytoplankton gradient in estuarine–coastal systems: generic implications of a case study in a Dutch tidal bay L. Jiang et al. 10.5194/bg-17-4135-2020
- Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
- Southern Ocean phytoplankton dynamics and carbon export: insights from a seasonal cycle approach S. Thomalla et al. 10.1098/rsta.2022.0068
- Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0) V. Sauerland et al. 10.5194/gmd-11-1181-2018
- Spatial and temporal scales of chlorophyll variability using high-resolution glider data H. Little et al. 10.1016/j.jmarsys.2018.06.011
- Ocean Circulation Causes Strong Variability in the Mid‐Atlantic Bight Nitrogen Budget M. Friedrichs et al. 10.1029/2018JC014424
- Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition P. Anugerahanti et al. 10.3389/fmars.2021.675428
- A CMA‐ES Algorithm Allowing for Random Parameters in Model Calibration V. Sauerland et al. 10.1029/2022MS003390
- Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea C. Shu et al. 10.3390/rs14051297
- How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? J. Valdes-Abellan et al. 10.2136/vzj2018.07.0142
- Early warning of cyanobacteria blooms outbreak based on stoichiometric analysis and catastrophe theory model L. Wang et al. 10.1007/s10910-019-01052-x
18 citations as recorded by crossref.
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation V. Vervatis et al. 10.1016/j.ocemod.2021.101781
- Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model C. Amadio et al. 10.5194/os-20-689-2024
- Quantifying Seasonal Particulate Organic Carbon Concentrations and Export Potential in the Southwestern Ross Sea Using Autonomous Gliders M. Meyer et al. 10.1029/2022JC018798
- Chromophoric dissolved organic matter dynamics revealed through the optimization of an optical–biogeochemical model in the northwestern Mediterranean Sea E. Álvarez et al. 10.5194/bg-20-4591-2023
- The seasonal patterns of hydrographic and biogeochemical variables in the Ross Sea: A BGC-Argo analysis R. Cao et al. 10.1016/j.dsr2.2024.105436
- Climatological temporal and spatial distributions of nutrients and particulate matter in the Ross Sea W. Smith & D. Kaufman 10.1016/j.pocean.2018.10.003
- Quantifying biological carbon pump pathways with a data-constrained mechanistic model ensemble approach M. Stukel et al. 10.5194/bg-19-3595-2022
- Drivers of the spatial phytoplankton gradient in estuarine–coastal systems: generic implications of a case study in a Dutch tidal bay L. Jiang et al. 10.5194/bg-17-4135-2020
- Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
- Southern Ocean phytoplankton dynamics and carbon export: insights from a seasonal cycle approach S. Thomalla et al. 10.1098/rsta.2022.0068
- Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0) V. Sauerland et al. 10.5194/gmd-11-1181-2018
- Spatial and temporal scales of chlorophyll variability using high-resolution glider data H. Little et al. 10.1016/j.jmarsys.2018.06.011
- Ocean Circulation Causes Strong Variability in the Mid‐Atlantic Bight Nitrogen Budget M. Friedrichs et al. 10.1029/2018JC014424
- Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition P. Anugerahanti et al. 10.3389/fmars.2021.675428
- A CMA‐ES Algorithm Allowing for Random Parameters in Model Calibration V. Sauerland et al. 10.1029/2022MS003390
- Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea C. Shu et al. 10.3390/rs14051297
- How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? J. Valdes-Abellan et al. 10.2136/vzj2018.07.0142
- Early warning of cyanobacteria blooms outbreak based on stoichiometric analysis and catastrophe theory model L. Wang et al. 10.1007/s10910-019-01052-x
Latest update: 14 Dec 2024
Short summary
Computer simulations of the highly variable phytoplankton in the Ross Sea demonstrated how incorporating data from different sources (satellite, ship, or glider) results in different system interpretations. For example, simulations assimilating satellite-based data produced lower carbon export estimates. Combining observations with models in this remote, harsh, and biologically variable environment should include consideration of the potential impacts of data frequency, duration, and coverage.
Computer simulations of the highly variable phytoplankton in the Ross Sea demonstrated how...
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