Articles | Volume 10, issue 6
https://doi.org/10.5194/bg-10-4319-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-10-4319-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Jun 2013
Research article |
| 27 Jun 2013
A novel method for diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle data using neural networks
T. P. Sasse
Climate Change Research Centre, Faculty of Science, University of New South Wales, Sydney, Australia
B. I. McNeil
Climate Change Research Centre, Faculty of Science, University of New South Wales, Sydney, Australia
G. Abramowitz
Climate Change Research Centre, Faculty of Science, University of New South Wales, Sydney, Australia
ARC Centre of Excellence for Climate Systems Science and Climate Change Research Centre, UNSW, Sydney, Australia
Viewed
Total article views: 4,063 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 01 Nov 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,493 | 1,376 | 194 | 4,063 | 147 | 108 |
- HTML: 2,493
- PDF: 1,376
- XML: 194
- Total: 4,063
- BibTeX: 147
- EndNote: 108
Total article views: 3,290 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Jun 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,151 | 977 | 162 | 3,290 | 135 | 108 |
- HTML: 2,151
- PDF: 977
- XML: 162
- Total: 3,290
- BibTeX: 135
- EndNote: 108
Total article views: 773 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 01 Nov 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 342 | 399 | 32 | 773 | 12 | 0 |
- HTML: 342
- PDF: 399
- XML: 32
- Total: 773
- BibTeX: 12
- EndNote: 0
Cited
33 citations as recorded by crossref.
- Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data I. Shaik et al. 10.1109/TGRS.2024.3390179
- Variability of total alkalinity in coastal surface waters determined using an in-situ analyzer in conjunction with the application of a neural network-based prediction model L. Qiu et al. 10.1016/j.scitotenv.2023.168271
- Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis A. Roobaert et al. 10.5194/bg-15-1701-2018
- OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification L. Gregor & N. Gruber 10.5194/essd-13-777-2021
- Recent increase in oceanic carbon uptake driven by weaker upper-ocean overturning T. DeVries et al. 10.1038/nature21068
- The potential of using remote sensing data to estimate air–sea CO<sub>2</sub> exchange in the Baltic Sea G. Parard et al. 10.5194/esd-8-1093-2017
- Empirical methods for the estimation of Southern Ocean CO<sub>2</sub>: support vector and random forest regression L. Gregor et al. 10.5194/bg-14-5551-2017
- Climatological variations of total alkalinity and total dissolved inorganic carbon in the Mediterranean Sea surface waters E. Gemayel et al. 10.5194/esd-6-789-2015
- Optimum satellite remote sensing of the marine carbonate system using empirical algorithms in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal P. Land et al. 10.1016/j.rse.2019.111469
- Recent Trends and Variability in the Oceanic Storage of Dissolved Inorganic Carbon L. Keppler et al. 10.1029/2022GB007677
- Estimating carbonate parameters from hydrographic data for the intermediate and deep waters of the Southern Hemisphere oceans H. Bostock et al. 10.5194/bg-10-6199-2013
- Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach P. Renosh et al. 10.3390/rs9121320
- Future ocean hypercapnia driven by anthropogenic amplification of the natural CO2 cycle B. McNeil & T. Sasse 10.1038/nature16156
- Retrieving monthly and interannual total-scale pH (pH<sub>T</sub>) on the East China Sea shelf using an artificial neural network: ANN-pH<sub>T</sub>-v1 X. Li et al. 10.5194/gmd-13-5103-2020
- Predicting Carbonate Chemistry on the Northwest Atlantic Shelf Using Neural Networks I. Lima et al. 10.1029/2023JG007536
- Analysis of longitudinal variations in North Pacific alkalinity to improve predictive algorithms C. Fry et al. 10.1002/2016GB005398
- A synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2022: the SNAPO-CO2-v1 dataset N. Metzl et al. 10.5194/essd-16-89-2024
- Improved estimation of global surface ocean total alkalinity fields using in-situ and satellite observations I. Shaik et al. 10.1080/2150704X.2024.2391091
- OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model–data intercomparisons P. Land et al. 10.5194/essd-15-921-2023
- What drives the latitudinal gradient in open-ocean surface dissolved inorganic carbon concentration? Y. Wu et al. 10.5194/bg-16-2661-2019
- Quantifying the influence of CO<sub>2</sub> seasonality on future aragonite undersaturation onset T. Sasse et al. 10.5194/bg-12-6017-2015
- Reconstruction of Surface Seawater pH in the North Pacific J. Wang et al. 10.3390/su15075796
- Robust Estimates of the Total Alkalinity From Satellite Oceanographic Data in the Global Ocean K. Krishna & P. Shanmugam 10.1109/ACCESS.2023.3271516
- Remote sensing the sea surface CO<sub>2</sub> of the Baltic Sea using the SOMLO methodology G. Parard et al. 10.5194/bg-12-3369-2015
- A comparative assessment of the uncertainties of global surface ocean CO<sub>2</sub> estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
- Ocean carbon from space: Current status and priorities for the next decade R. Brewin et al. 10.1016/j.earscirev.2023.104386
- Interannual drivers of the seasonal cycle of CO<sub>2</sub> in the Southern Ocean L. Gregor et al. 10.5194/bg-15-2361-2018
- Air–sea CO2exchange in the Baltic Sea—A sensitivity analysis of the gas transfer velocity L. Gutiérrez-Loza et al. 10.1016/j.jmarsys.2021.103603
- Seasonal variability in carbonate chemistry and air–sea CO2 fluxes in the southern Great Barrier Reef E. Shaw & B. McNeil 10.1016/j.marchem.2013.11.007
- A Neural Network-Based Analysis of the Seasonal Variability of Surface Total Alkalinity on the East China Sea Shelf X. Li et al. 10.3389/fmars.2020.00219
- Using satellite data to estimate partial pressure of CO2 in the Baltic Sea G. Parard et al. 10.1002/2015JG003064
- Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data L. Gloege et al. 10.1029/2021MS002620
- Surface ocean CO2 concentration and air-sea flux estimate by machine learning with modelled variable trends J. Zeng et al. 10.3389/fmars.2022.989233
32 citations as recorded by crossref.
- Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data I. Shaik et al. 10.1109/TGRS.2024.3390179
- Variability of total alkalinity in coastal surface waters determined using an in-situ analyzer in conjunction with the application of a neural network-based prediction model L. Qiu et al. 10.1016/j.scitotenv.2023.168271
- Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis A. Roobaert et al. 10.5194/bg-15-1701-2018
- OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification L. Gregor & N. Gruber 10.5194/essd-13-777-2021
- Recent increase in oceanic carbon uptake driven by weaker upper-ocean overturning T. DeVries et al. 10.1038/nature21068
- The potential of using remote sensing data to estimate air–sea CO<sub>2</sub> exchange in the Baltic Sea G. Parard et al. 10.5194/esd-8-1093-2017
- Empirical methods for the estimation of Southern Ocean CO<sub>2</sub>: support vector and random forest regression L. Gregor et al. 10.5194/bg-14-5551-2017
- Climatological variations of total alkalinity and total dissolved inorganic carbon in the Mediterranean Sea surface waters E. Gemayel et al. 10.5194/esd-6-789-2015
- Optimum satellite remote sensing of the marine carbonate system using empirical algorithms in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal P. Land et al. 10.1016/j.rse.2019.111469
- Recent Trends and Variability in the Oceanic Storage of Dissolved Inorganic Carbon L. Keppler et al. 10.1029/2022GB007677
- Estimating carbonate parameters from hydrographic data for the intermediate and deep waters of the Southern Hemisphere oceans H. Bostock et al. 10.5194/bg-10-6199-2013
- Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach P. Renosh et al. 10.3390/rs9121320
- Future ocean hypercapnia driven by anthropogenic amplification of the natural CO2 cycle B. McNeil & T. Sasse 10.1038/nature16156
- Retrieving monthly and interannual total-scale pH (pH<sub>T</sub>) on the East China Sea shelf using an artificial neural network: ANN-pH<sub>T</sub>-v1 X. Li et al. 10.5194/gmd-13-5103-2020
- Predicting Carbonate Chemistry on the Northwest Atlantic Shelf Using Neural Networks I. Lima et al. 10.1029/2023JG007536
- Analysis of longitudinal variations in North Pacific alkalinity to improve predictive algorithms C. Fry et al. 10.1002/2016GB005398
- A synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2022: the SNAPO-CO2-v1 dataset N. Metzl et al. 10.5194/essd-16-89-2024
- Improved estimation of global surface ocean total alkalinity fields using in-situ and satellite observations I. Shaik et al. 10.1080/2150704X.2024.2391091
- OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model–data intercomparisons P. Land et al. 10.5194/essd-15-921-2023
- What drives the latitudinal gradient in open-ocean surface dissolved inorganic carbon concentration? Y. Wu et al. 10.5194/bg-16-2661-2019
- Quantifying the influence of CO<sub>2</sub> seasonality on future aragonite undersaturation onset T. Sasse et al. 10.5194/bg-12-6017-2015
- Reconstruction of Surface Seawater pH in the North Pacific J. Wang et al. 10.3390/su15075796
- Robust Estimates of the Total Alkalinity From Satellite Oceanographic Data in the Global Ocean K. Krishna & P. Shanmugam 10.1109/ACCESS.2023.3271516
- Remote sensing the sea surface CO<sub>2</sub> of the Baltic Sea using the SOMLO methodology G. Parard et al. 10.5194/bg-12-3369-2015
- A comparative assessment of the uncertainties of global surface ocean CO<sub>2</sub> estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
- Ocean carbon from space: Current status and priorities for the next decade R. Brewin et al. 10.1016/j.earscirev.2023.104386
- Interannual drivers of the seasonal cycle of CO<sub>2</sub> in the Southern Ocean L. Gregor et al. 10.5194/bg-15-2361-2018
- Air–sea CO2exchange in the Baltic Sea—A sensitivity analysis of the gas transfer velocity L. Gutiérrez-Loza et al. 10.1016/j.jmarsys.2021.103603
- Seasonal variability in carbonate chemistry and air–sea CO2 fluxes in the southern Great Barrier Reef E. Shaw & B. McNeil 10.1016/j.marchem.2013.11.007
- A Neural Network-Based Analysis of the Seasonal Variability of Surface Total Alkalinity on the East China Sea Shelf X. Li et al. 10.3389/fmars.2020.00219
- Using satellite data to estimate partial pressure of CO2 in the Baltic Sea G. Parard et al. 10.1002/2015JG003064
- Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data L. Gloege et al. 10.1029/2021MS002620
1 citations as recorded by crossref.
Saved (final revised paper)
Saved (preprint)
Latest update: 21 Nov 2024
Altmetrics
Final-revised paper
Preprint