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: | Text |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/essd-16-121-2024 https://essd.copernicus.org/articles/16/121/2024/ |
| 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 CO 2 ( p CO 2 ), total alkalinity ( A T ), total dissolved inorganic carbon ( C T ), surface ocean pH, and saturation states with respect to aragonite ( Ω ar ) and calcite ( Ω ca ). Reconstructing p CO 2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO 2 ATlas (SOCAT). Surface ocean A T is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO 2 system speciation given the reconstructed p CO 2 and A T 1 σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of p CO 2 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 p CO 2 product outperforms a coarser spatial resolution (1 ∘ ) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in p CO 2 across diverse ... |
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