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Atlantic Ocean δ13CDIC reconstructed from sparse historical data

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Research area:ocean-marinemarine-ecosystems

What the study found

The study reconstructed stable carbon isotope composition of marine dissolved inorganic carbon, or δ13C DIC, across the Atlantic Ocean. The authors report that the resulting dataset expands coverage, improves spatial and temporal continuity, and includes a gridded three-dimensional product for the Atlantic.

Why the authors say this matters

The authors say these reconstructed datasets provide new opportunities to resolve regional carbon cycle dynamics, validate Earth system models, refine estimates of ocean carbon uptake on decadal timescales, and extend climate reanalysis records. They also suggest the data can support assessment of decadal trends.

What the researchers tested

The researchers used a probabilistic machine learning method called Gaussian Process Regression, or GPR, to reconstruct δ13C DIC in the Atlantic Ocean. They compiled data from 51 historical cruises, applied secondary quality control using crossover analysis, retained 37 cruises for model training, validation, and testing, and used GLODAPv2.2023 Atlantic data as predictors. They also applied the validated framework to a global interior ocean mapped climatology to produce a gridded Atlantic dataset.

What worked and what didn't

The trained GPR model achieved an average bias of −0.007 ± 0.082 ‰ and an overall uncertainty of 0.11 ‰, with uncertainty attributed to measurement, mapping, and input-variable errors. The reconstruction increased acceptable δ13C DIC samples from 8,941 to 68,435, a factor of 7.65, and improved resolution in longitude, latitude, depth, and time. The authors also report that numerical model-based validation confirmed the model's robustness.

What to keep in mind

Validation was limited by sparse observations, which is why the authors supplemented it with numerical model-based validation. The abstract does not describe other major limitations beyond the available observational coverage and the fact that the work is focused on the Atlantic Ocean.

Key points

  • δ13C DIC was reconstructed for the Atlantic Ocean using Gaussian Process Regression.
  • The study used data from 51 historical cruises, with 37 retained after quality control.
  • The reconstruction increased acceptable samples from 8,941 to 68,435.
  • Reported model performance included an average bias of −0.007 ± 0.082 ‰ and overall uncertainty of 0.11 ‰.
  • The authors produced both an expanded observational reconstruction and a gridded 3D Atlantic dataset.

Disclosure

Research title:
Atlantic Ocean δ13CDIC reconstructed from sparse historical data
Authors:
Hui Gao, Zelun Wu, Zhentao Sun, Diana Cai, Meibing Jin, Wei-Jun Cai
Institutions:
Flatiron Health (United States), Guangdong Ocean University, Nanjing University of Information Science and Technology, University of Alaska Fairbanks, University of Delaware, University of Delaware, University of Delaware, University of Delaware
Publication date:
2026-04-02
DOI:
10.5194/essd-18-2443-2026
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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