Articles | Volume 18, issue 3
https://doi.org/10.5194/essd-18-2443-2026
https://doi.org/10.5194/essd-18-2443-2026
Data description article
 | 
02 Apr 2026
Data description article |  | 02 Apr 2026

Reconstruction of δ13CDIC in the Atlantic Ocean: a probabilistic machine learning approach for filling historical data gaps

Hui Gao, Zelun Wu, Zhentao Sun, Diana Cai, Meibing Jin, and Wei-Jun Cai

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Cited articles

Alling, V., Porcelli, D., Mörth, C.-M., Anderson, L. G., Sanchez-Garcia, L., Gustafsson, Ö., Andersson, P. S., and Humborg, C.: Degradation of terrestrial organic carbon, primary production and out-gassing of CO2 in the Laptev and East Siberian Seas as inferred from δ13C values of DIC, Geochim. Cosmochim. Ac., 95, 143–159, https://doi.org/10.1016/j.gca.2012.07.028, 2012. 
Becker, M., Andersen, N., Erlenkeuser, H., Humphreys, M. P., Tanhua, T., and Körtzinger, A.: An internally consistent dataset of δ13C-DIC in the North Atlantic Ocean – NAC13v1, Earth Syst. Sci. Data, 8, 559–570, https://doi.org/10.5194/essd-8-559-2016, 2016. 
Bennington, V., Galjanic, T., and McKinley, G. A.: Explicit Physical Knowledge in Machine Learning for Ocean Carbon Flux Reconstruction: The pCO2 – Residual Method, J. Adv. Model Earth Sy., 14, e2021MS002960, https://doi.org/10.1029/2021MS002960, 2022. 
Claret, M., Sonnerup, R. E., and Quay, P. D.: A Next Generation Ocean Carbon Isotope Model for Climate Studies I: Steady State Controls on Ocean 13C, Global Biogeochem. Cy., 35, e2020GB006757, https://doi.org/10.1029/2020GB006757, 2021. 
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Observations of stable carbon isotopes in dissolved inorganic carbon are sparse, limiting their potential in carbon cycle studies. We compiled 51 cruises and used a machine learning method trained on 37 cruises that passed secondary quality control to reconstruct isotope values in the Atlantic. The reconstruction expands usable samples from 8,941 to 68,435, reducing noise, filling gaps, preserving decadal trend, and strengthening studies of carbon variability and model validation.
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