DOI: 10.5281/zenodo.18481145
title: Reconstruction of δ13CDIC in the Atlantic Ocean: A Probabilistic Machine Learning Approach for Filling Historical Data Gaps

description: The data description paper is submitted to the journal Earth System Science Data. 

Gao, H., Wu, Z., Sun, Z., Cai, D., Jin, M., and Cai, W.-J.: Reconstruction of δ13CDIC in the Atlantic Ocean: A Probabilistic Machine Learning Approach for Filling Historical Data Gaps, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-517, in review, 2025.

 

OverviewThis Zenodo archive contains three complementary datasets related to oceanic dissolved inorganic carbon isotope (δ¹³C_DIC) measurements and reconstructions in the Atlantic Ocean. Developed to address the extreme sparsity of historical δ¹³C_DIC observations, the datasets align with community standards (e.g., GLODAPv2) to ensure interoperability, transparency, and utility for diverse ocean carbon cycle studies. Each dataset serves a distinct purpose, from direct GLODAPv2 supplementation to raw observational support and spatially continuous gridded analysis, enabling researchers to select the most suitable product for their specific applications.

Below is a detailed description of each dataset, their structures, variables, and intended applications.

Dataset 1: GPR-Reconstructed δ¹³C_DIC DatasetName:GLODAPv2.2023_Atlantic_Ocean_with_Reconstructed_d13C

Origin and Purpose: This dataset is derived from the GLODAPv2.2023 Atlantic Ocean subset, with the primary goal of supplementing the original dataset with machine learning (GPR)-reconstructed δ¹³C_DIC values for sparse or missing observations. It is designed to complement GLODAPv2’s existing basin-specific products and facilitate direct comparison with the original δ¹³C_DIC measurements.

Structure and Compatibility: The dataset retains all original variable names, units, and data structures of GLODAPv2.2023’s Atlantic subset, ensuring full interoperability with GLODAPv2-standard tools and workflows. No modifications are made to the original GLODAPv2 variables.

Key Added Variables:ReC13: Reconstructed δ¹³C_DIC value (unit: ‰, relative to VPDB).ReC13f: Quality flag for the reconstructed δ¹³C_DIC (consistent with GLODAPv2 flag conventions: 2 = good data, 3 = questionable data, 9 = missing data).

Dataset 2: Atlantic Observational Compilation DatasetName: Atlantic_cruises_with_c13

Origin and Purpose: This dataset is a comprehensive compilation of raw and reprocessed δ¹³C_DIC observational data from 51 Atlantic cruises (see Table 1 in the associated manuscript). It includes data from both GLODAPv2-included cruises and non-GLODAPv2 cruises, serving as a supporting dataset for the GPR reconstruction (not a final reconstructed product). It provides transparent access to raw observations and inter-cruise adjustments for method development and independent validation.

Structure and Variables: Unlike Dataset 1, this dataset is not strictly constrained to GLODAPv2 conventions, as it integrates non-GLODAPv2 cruises with limited ancillary data. For non-GLODAPv2 cruises, key biogeochemical variables (e.g., apparent oxygen utilization [AOU], pH) were calculated using the seawater, GSW, and CO2SYS toolboxes. As a result, some variable names and units may differ slightly from GLODAPv2 standards.

Key Unique Variable:adjusted_C13: Reprocessed δ¹³C_DIC value (unit: ‰, relative to VPDB) after applying inter-cruise adjustments and secondary quality control. 

 

Dataset 3: Climatological Gridded δ¹³C_DIC Dataset Name: GLODAPv2.2016b.c13_recon.nc

Origin and Purpose: This dataset generates a spatially continuous δ¹³C_DIC field using the same GPR algorithm (Gao et al., 2025) and gridded environmental parameters from GLODAPv2.2016 (Lauvset et al., 2016), a global 1°×1° biogeochemical climatology. Its core goal is to address the limitations of site-level data for basin-scale analyses, providing a spatially complete product that aligns with GLODAP’s gridded framework.

Structure and Compatibility: Spatial resolution: 1°×1° Only Atlantic Ocean has valid data, other regions are set to NaN. Vertical coverage: 33 standard depth layers. Temporal scale: Climatological monthly mean (integrating 1972–2013 observations). Format: NetCDF 4 (CF-1.8 compliant), with exact alignment of lon/lat/depth coordinates with GLODAPv2.2016b for seamless multi-parameter integration.

Key Variables:

c13_recon: Reconstructed δ¹³C_DIC value (unit: ‰, relative to VPDB) based on GLODAPv2.2016 mapped climatology.

 

Citation GuidelinesTo ensure proper attribution, users must cite the appropriate sources based on the data used:Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., Álvarez, M., Azetsu-Scott, K., Brown, P. J., Carter, B. R., Cotrim Da Cunha, L., Hoppema, M., Humphreys, M. P., Ishii, M., Jeansson, E., Murata, A., Müller, J. D., Pérez, F. F., Schirnick, C., Steinfeldt, R., Suzuki, T., Ulfsbo, A., Velo, A., Woosley, R. J., and Key, R. M.: The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product, Earth Syst. Sci. Data, 16, 2047–2072, https://doi.org/10.5194/essd-16-2047-2024, 2024.

Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1° ×  1° GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, https://doi.org/10.5194/essd-8-325-2016, 2016.

Gao, H., Wu, Z., Sun, Z., Cai, D., Jin, M., & Cai, W.-J.: Reconstruction of δ¹³CDIC in the Atlantic Ocean [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18481145.

Contact InformationFor questions regarding dataset structure, variables, or usage, please contact:huigao@udel.edu