Articles | Volume 20, issue 8
https://doi.org/10.5194/bg-20-1671-2023
© Author(s) 2023. 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-20-1671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reconstructing ocean carbon storage with CMIP6 Earth system models and synthetic Argo observations
Katherine E. Turner
CORRESPONDING AUTHOR
Department of Earth, Ocean, and Ecological Sciences, School of Environmental Sciences University of Liverpool, Liverpool, United Kingdom
Doug M. Smith
UK Met Office, Exeter, United Kingdom
Anna Katavouta
Department of Earth, Ocean, and Ecological Sciences, School of Environmental Sciences University of Liverpool, Liverpool, United Kingdom
National Oceanography Centre, Liverpool, United Kingdom
Richard G. Williams
Department of Earth, Ocean, and Ecological Sciences, School of Environmental Sciences University of Liverpool, Liverpool, United Kingdom
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Co-editor-in-chief
The paper by Turner and co-authors tackles the very timely question on how well we can reconstruct carbon inventories given the sparse observations. Using an Ensemble Optimal Interpolation approach and synthetic observations, the authors show that a large fraction of ocean carbon and its variability can be reconstructed using temperature and salinity measurements in the top 100 meter, however, reconstruction skill decreases when the top 2000 meters are considered. The authors propose a new way to use sparse observations to better understand historical carbon cycle changes, i.e., an important quantity in light of future changes driven by man-made emissions.
The paper by Turner and co-authors tackles the very timely question on how well we can...
Short summary
We present a new method for reconstructing ocean carbon using climate models and temperature and salinity observations. To test this method, we reconstruct modelled carbon using synthetic observations consistent with current sampling programmes. Sensitivity tests show skill in reconstructing carbon trends and variability within the upper 2000 m. Our results indicate that this method can be used for a new global estimate for ocean carbon content.
We present a new method for reconstructing ocean carbon using climate models and temperature and...
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