CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system
Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards e...
| Published in: | Earth System Science Data |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/essd-16-121-2024 https://noa.gwlb.de/receive/cop_mods_00070934 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00069258/essd-16-121-2024.pdf https://essd.copernicus.org/articles/16/121/2024/essd-16-121-2024.pdf |
| Summary: | Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim to develop a sustainable production chain of observation-derived global ocean carbonate system datasets at high space–time resolutions. As the start of the long-term objective, this study introduces a new global 0.25∘ monthly reconstruction, namely CMEMS-LSCE (Laboratoire des Sciences du Climat et de l'Environnement) for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables, including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (CT), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT; 1σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of pCO2 estimates or the total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25∘ resolution pCO2 product outperforms a coarser spatial resolution (1∘) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the ... |
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