Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling
The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans, and is a critical region for the ocean uptake of anthropogenic CO 2 . However, estimates of the Southern Ocean air-sea CO 2 flux are highly uncertain due to limited data coverage. Increased sampli...
| Main Authors: | , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/bg-2023-160 https://bg.copernicus.org/preprints/bg-2023-160/ |
| Summary: | The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans, and is a critical region for the ocean uptake of anthropogenic CO 2 . However, estimates of the Southern Ocean air-sea CO 2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO 2 . Here, we use a Large Ensemble Testbed (LET) of Earth System Models and the pCO 2 -Residual reconstruction method to assess improvements in pCO 2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO 2 Atlas (SOCAT) observations. The LET allows us to robustly evaluate the skill of pCO 2 reconstructions in space and time through comparison to ‘model truth’. With only SOCAT sampling, Southern Ocean and global pCO 2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating Uncrewed Surface Vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO 2 . A modest number of additional observations in southern hemisphere winter and across meridional gradients in the Southern Ocean leads to improvement in reconstruction bias and root-mean squared error (RMSE) can be improved by as much as 65 % and 19 %, respectively, as compared to using SOCAT sampling alone. Lastly, the large decadal variability of air-sea CO 2 fluxes shown by SOCAT-only sampling, may be partially attributable to undersampling of the Southern Ocean. |
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