Articles | Volume 18, issue 3
https://doi.org/10.5194/essd-18-2443-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-2443-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reconstruction of δ13CDIC in the Atlantic Ocean: a probabilistic machine learning approach for filling historical data gaps
Hui Gao
School of Marine Science and Policy, University of Delaware, Newark, Delaware, USA
College of Chemistry and Environmental Science, Guangdong Ocean University, Zhanjiang, China
School of Marine Science and Policy, University of Delaware, Newark, Delaware, USA
Zhentao Sun
School of Marine Science and Policy, University of Delaware, Newark, Delaware, USA
Diana Cai
Center for Computational Mathematics, Flatiron Institute, New York, New York, USA
Meibing Jin
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, USA
School of Marine Science and Policy, University of Delaware, Newark, Delaware, USA
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Li-Qing Jiang, Amanda Fay, Jens Daniel Müller, Luke Gregor, Alizée Roobaert, Lydia Keppler, Dustin Carroll, Siv K. Lauvset, Tim DeVries, Judith Hauck, Christian Rödenbeck, Nicolas Metzl, Andrea J. Fassbender, Jean-Pierre Gattuso, Peter Landschützer, Rik Wanninkhof, Christopher Sabine, Simone R. Alin, Mario Hoppema, Are Olsen, Matthew P. Humphreys, Kunal Chakraborty, Ana C. Franco, Kumiko Azetsu-Scott, Dorothee C. E. Bakker, Leticia Barbero, Nicholas R. Bates, Nicole Besemer, Henry C. Bittig, Albert E. Boyd, Daniel Broullón, Wei-Jun Cai, Brendan R. Carter, Thi-Tuyet-Trang Chau, Chen-Tung Arthur Chen, Frédéric Cyr, John E. Dore, Ian Enochs, Richard A. Feely, Hernan E. Garcia, Marion Gehlen, Prasanna Kanti Ghoshal, Lucas Gloege, Melchor González-Dávila, Nicolas Gruber, Debby Ianson, Yosuke Iida, Masao Ishii, Apurva Padamnabh Joshi, Esther Kennedy, Alex Kozyr, Nico Lange, Claire Lo Monaco, Derek P. Manzello, Galen A. McKinley, Natalie M. Monacci, Xose A. Padin, Ana M. Palacio-Castro, Fiz F. Pérez, J. Magdalena Santana-Casiano, Jonathan Sharp, Adrienne Sutton, Jim Swift, Toste Tanhua, Maciej Telszewski, Jens Terhaar, Ruben van Hooidonk, Anton Velo, Andrew J. Watson, Angelicque E. White, Zelun Wu, Liang Xue, Hyelim Yoo, Jiye Zeng, and Guorong Zhong
Earth Syst. Sci. Data, 18, 1405–1462, https://doi.org/10.5194/essd-18-1405-2026, https://doi.org/10.5194/essd-18-1405-2026, 2026
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This review article provides an overview of 68 existing ocean carbonate chemistry data products and data product sets, encompassing a broad range of types, including compilations of cruise datasets, gap-filled observational products, model simulations, and more. It is designed to help researchers identify and access the data products that best support their scientific objectives, thereby facilitating progress in understanding the ocean's changing carbonate chemistry.
Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, Eric Mortenson, Benjamin Tobey Saenz, Nadja Steiner, and Martin Vancoppenolle
The Cryosphere, 20, 723–736, https://doi.org/10.5194/tc-20-723-2026, https://doi.org/10.5194/tc-20-723-2026, 2026
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Sea ice hosts tiny algae that support polar marine life, yet their growth remains challenging to simulate. We tested six computer models using data from a 2015 Arctic drifting ice expedition to see how well they reproduced spring algae blooms and nutrient changes. While tuning helped models better match algae growth, nutrients remained difficult to capture. Our results highlight key challenges in representing fragile sea‑ice habitats that are expected to become more common as the Arctic warms.
Xueying Zhang, Enhui Liao, Wenfang Lu, Zelun Wu, Guansuo Wang, Xueming Zhu, and Shiyu Liang
Earth Syst. Sci. Data, 17, 6071–6095, https://doi.org/10.5194/essd-17-6071-2025, https://doi.org/10.5194/essd-17-6071-2025, 2025
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We created a new global dataset that reveals how ocean surface carbon dioxide has changed each month over the past four decades. By applying a deep learning model trained on both observational data and model simulations, we improved the representation of interannual variability and more accurately captured ocean responses to climate events like El Niño. This work supports global efforts to understand the ocean’s role in the carbon cycle and its response to climate change.
Zelun Wu, Wenfang Lu, Alizée Roobaert, Luping Song, Xiao-Hai Yan, and Wei-Jun Cai
Earth Syst. Sci. Data, 17, 43–63, https://doi.org/10.5194/essd-17-43-2025, https://doi.org/10.5194/essd-17-43-2025, 2025
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This study addresses the lack of comprehensive sea surface partial pressure of CO2 (pCO2) data in the North American Atlantic Coastal Ocean Margin (NAACOM) by developing the Reconstructed Coastal Acidification Database (ReCAD-NAACOM-pCO2). The product reconstructed sea surface pCO2 from 1993 to 2021 using machine-learning and environmental data, capturing seasonal cycles, regional variations, and long-term trends of pCO2 for coastal carbon research.
Aubin Thibault de Chanvalon, George W. Luther, Emily R. Estes, Jennifer Necker, Bradley M. Tebo, Jianzhong Su, and Wei-Jun Cai
Biogeosciences, 20, 3053–3071, https://doi.org/10.5194/bg-20-3053-2023, https://doi.org/10.5194/bg-20-3053-2023, 2023
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The intensity of the oceanic trap of CO2 released by anthropogenic activities depends on the alkalinity brought by continental weathering. Between ocean and continent, coastal water and estuaries can limit or favour the alkalinity transfer. This study investigate new interactions between dissolved metals and alkalinity in the oxygen-depleted zone of estuaries.
Li-Qing Jiang, Richard A. Feely, Rik Wanninkhof, Dana Greeley, Leticia Barbero, Simone Alin, Brendan R. Carter, Denis Pierrot, Charles Featherstone, James Hooper, Chris Melrose, Natalie Monacci, Jonathan D. Sharp, Shawn Shellito, Yuan-Yuan Xu, Alex Kozyr, Robert H. Byrne, Wei-Jun Cai, Jessica Cross, Gregory C. Johnson, Burke Hales, Chris Langdon, Jeremy Mathis, Joe Salisbury, and David W. Townsend
Earth Syst. Sci. Data, 13, 2777–2799, https://doi.org/10.5194/essd-13-2777-2021, https://doi.org/10.5194/essd-13-2777-2021, 2021
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Coastal ecosystems account for most of the economic activities related to commercial and recreational fisheries and aquaculture industries, supporting about 90 % of the global fisheries yield and 80 % of known species of marine fish. Despite the large potential risks from ocean acidification (OA), internally consistent water column OA data products in the coastal ocean still do not exist. This paper is the first time we report a high quality OA data product in North America's coastal waters.
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Short summary
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.
Observations of stable carbon isotopes in dissolved inorganic carbon are sparse, limiting their...
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