Articles | Volume 14, issue 4
https://doi.org/10.5194/essd-14-2081-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2081-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A monthly surface pCO2 product for the California Current Large Marine Ecosystem
Jonathan D. Sharp
CORRESPONDING AUTHOR
Cooperative Institute for Climate, Ocean, and Ecosystem Studies
(CICOES), University of Washington, Seattle, WA 98195, USA
NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115,
USA
Andrea J. Fassbender
NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115,
USA
Brendan R. Carter
Cooperative Institute for Climate, Ocean, and Ecosystem Studies
(CICOES), University of Washington, Seattle, WA 98195, USA
NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115,
USA
Paige D. Lavin
Cooperative Institute for Satellite Earth System Studies/Earth System
Science Interdisciplinary Center (CISESS/ESSIC), University of Maryland,
College Park, MD 20740, USA
NOAA/NESDIS Center for Satellite Applications and Research, College
Park, MD 20740, USA
Adrienne J. Sutton
NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115,
USA
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Cited
14 citations as recorded by crossref.
- A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020 A. Roobaert et al. https://doi.org/10.5194/essd-16-421-2024
- Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. https://doi.org/10.5194/bg-20-3919-2023
- Regression-based characterization of the marine carbonate system across shelf and nearshore waters of Queen Charlotte Sound A. Hare et al. https://doi.org/10.1016/j.marchem.2025.104511
- Analysis of surface deoxygenation trends in the Benguela upwelling system: An interpretable machine learning approach T. Mashifane et al. https://doi.org/10.1002/lol2.70090
- Reconstruction of δ13CDIC in the Atlantic Ocean: a probabilistic machine learning approach for filling historical data gaps H. Gao et al. https://doi.org/10.5194/essd-18-2443-2026
- Improving coastal ocean pH estimates through assimilation of glider observations and hybrid statistical methods J. Mattern et al. https://doi.org/10.5194/bg-23-2621-2026
- Magnitude, Trends, and Variability of the Global Ocean Carbon Sink From 1985 to 2018 T. DeVries et al. https://doi.org/10.1029/2023GB007780
- A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 Z. Wu et al. https://doi.org/10.5194/essd-17-43-2025
- A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States J. Sharp et al. https://doi.org/10.1038/s41597-024-03530-7
- A high-resolution synthesis dataset for multistressor analyses along the US West Coast E. Kennedy et al. https://doi.org/10.5194/essd-16-219-2024
- A new gridded offshore wind profile product for US coasts using machine learning and satellite observations J. Frech et al. https://doi.org/10.5194/wes-10-1077-2025
- Sea Ice Loss leads to regime shifts in the arctic biological pump M. Wu et al. https://doi.org/10.1038/s41467-025-65285-y
- Monitoring, reporting, and verification for ocean alkalinity enhancement D. Ho et al. https://doi.org/10.5194/sp-2-oae2023-12-2023
- Long-term trends of pH, alkalinity, and hydrogen ion concentration in an upwelling-dominated coastal ecosystem: Ría de Vigo, NW Spain S. Cameselle et al. https://doi.org/10.1038/s41598-024-68694-z
14 citations as recorded by crossref.
- A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020 A. Roobaert et al. https://doi.org/10.5194/essd-16-421-2024
- Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. https://doi.org/10.5194/bg-20-3919-2023
- Regression-based characterization of the marine carbonate system across shelf and nearshore waters of Queen Charlotte Sound A. Hare et al. https://doi.org/10.1016/j.marchem.2025.104511
- Analysis of surface deoxygenation trends in the Benguela upwelling system: An interpretable machine learning approach T. Mashifane et al. https://doi.org/10.1002/lol2.70090
- Reconstruction of δ13CDIC in the Atlantic Ocean: a probabilistic machine learning approach for filling historical data gaps H. Gao et al. https://doi.org/10.5194/essd-18-2443-2026
- Improving coastal ocean pH estimates through assimilation of glider observations and hybrid statistical methods J. Mattern et al. https://doi.org/10.5194/bg-23-2621-2026
- Magnitude, Trends, and Variability of the Global Ocean Carbon Sink From 1985 to 2018 T. DeVries et al. https://doi.org/10.1029/2023GB007780
- A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 Z. Wu et al. https://doi.org/10.5194/essd-17-43-2025
- A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States J. Sharp et al. https://doi.org/10.1038/s41597-024-03530-7
- A high-resolution synthesis dataset for multistressor analyses along the US West Coast E. Kennedy et al. https://doi.org/10.5194/essd-16-219-2024
- A new gridded offshore wind profile product for US coasts using machine learning and satellite observations J. Frech et al. https://doi.org/10.5194/wes-10-1077-2025
- Sea Ice Loss leads to regime shifts in the arctic biological pump M. Wu et al. https://doi.org/10.1038/s41467-025-65285-y
- Monitoring, reporting, and verification for ocean alkalinity enhancement D. Ho et al. https://doi.org/10.5194/sp-2-oae2023-12-2023
- Long-term trends of pH, alkalinity, and hydrogen ion concentration in an upwelling-dominated coastal ecosystem: Ría de Vigo, NW Spain S. Cameselle et al. https://doi.org/10.1038/s41598-024-68694-z
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
Oceanographers calculate the exchange of carbon between the ocean and atmosphere by comparing partial pressures of carbon dioxide (pCO2). Because seawater pCO2 is not measured everywhere at all times, interpolation schemes are required to fill observational gaps. We describe a monthly gap-filled dataset of pCO2 in the northeast Pacific Ocean off the west coast of North America created by machine-learning interpolation. This dataset is unique in its robust representation of coastal seasonality.
Oceanographers calculate the exchange of carbon between the ocean and atmosphere by comparing...
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