Articles | Volume 11, issue 4
https://doi.org/10.5194/os-11-519-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-11-519-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
08 Jul 2015
Research article |
| 08 Jul 2015
The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO2 fugacity in support of air–sea gas flux studies
L. M. Goddijn-Murphy
CORRESPONDING AUTHOR
ERI, University of the Highlands and Islands, Ormlie Road, Thurso, UK
D. K. Woolf
ICIT, Heriot-Watt University, Stromness, UK
P. E. Land
Plymouth Marine Laboratory, Prospect Place, Plymouth, UK
J. D. Shutler
University of Exeter, Centre for Geography, Environment and Society, Penryn, Cornwall, UK
C. Donlon
European Space Agency/ESTEC, Noordwijk, the Netherlands
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Ocean Sci., 20, 1657–1676, https://doi.org/10.5194/os-20-1657-2024, https://doi.org/10.5194/os-20-1657-2024, 2024
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Earth Syst. Sci. Data, 15, 921–947, https://doi.org/10.5194/essd-15-921-2023, https://doi.org/10.5194/essd-15-921-2023, 2023
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The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
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Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
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The Cryosphere, 15, 3129–3134, https://doi.org/10.5194/tc-15-3129-2021, https://doi.org/10.5194/tc-15-3129-2021, 2021
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With present-day techniques, ocean surface currents are poorly known near the Equator and globally for spatial scales under 200 km and timescales under 30 d. Wide-swath radar Doppler measurements are an alternative technique. Such direct surface current measurements are, however, affected by platform motions and waves. These contributions are analyzed in data collected during the DRIFT4SKIM airborne and in situ experiment, demonstrating the possibility of measuring currents from space globally.
Guillaume Dodet, Jean-François Piolle, Yves Quilfen, Saleh Abdalla, Mickaël Accensi, Fabrice Ardhuin, Ellis Ash, Jean-Raymond Bidlot, Christine Gommenginger, Gwendal Marechal, Marcello Passaro, Graham Quartly, Justin Stopa, Ben Timmermans, Ian Young, Paolo Cipollini, and Craig Donlon
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Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
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For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
P. E. Land, J. D. Shutler, R. D. Cowling, D. K. Woolf, P. Walker, H. S. Findlay, R. C. Upstill-Goddard, and C. J. Donlon
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Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
Optimal adjustment of the atmospheric forcing parameters of ocean models using sea surface temperature data assimilation
Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme
Jiye Zeng, Tsuneo Matsunaga, Nobuko Saigusa, Tomoko Shirai, Shin-ichiro Nakaoka, and Zheng-Hong Tan
Ocean Sci., 13, 303–313, https://doi.org/10.5194/os-13-303-2017, https://doi.org/10.5194/os-13-303-2017, 2017
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Three machine learning models were investigated for the reconstruction of global surface ocean CO2 concentration. They include self-organizing maps (SOMs), feedforward neural networks (FNNs), and support vector machines (SVMs). Our results show that the SVM performs the best, the FNN the second, and the SOM the worst. While the SOM does not have over-fitting problems, it is sensitive to data scaling and its discrete interpolation may not be good for some applications.
M. Meinvielle, J.-M. Brankart, P. Brasseur, B. Barnier, R. Dussin, and J. Verron
Ocean Sci., 9, 867–883, https://doi.org/10.5194/os-9-867-2013, https://doi.org/10.5194/os-9-867-2013, 2013
C. Rödenbeck, R. F. Keeling, D. C. E. Bakker, N. Metzl, A. Olsen, C. Sabine, and M. Heimann
Ocean Sci., 9, 193–216, https://doi.org/10.5194/os-9-193-2013, https://doi.org/10.5194/os-9-193-2013, 2013
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Short summary
We describe the OceanFlux Greenhouse Gases methodology for creating an ocean surface CO2 climatology. In situ measurements valid for instantaneous sea surface temperature (SST) were recomputed using a more consistent and averaged SST. The results were normalised to year 2010, averaged by month, and interpolated onto a global 1°×1° grid. The 12 monthly distributions of ocean surface CO2 (see supplement) can be used in air-sea gas flux calculations together with climatologies of other variables.
We describe the OceanFlux Greenhouse Gases methodology for creating an ocean surface CO2...
