Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3679-2025
© Author(s) 2025. 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-17-3679-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning
Arianna Olivelli
CORRESPONDING AUTHOR
Grantham Institute for Climate Change and the Environment, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
now at: the Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400, Ostend, Belgium
Rossella Arcucci
Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
Mark Rehkämper
Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
Tina van de Flierdt
Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
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Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn, Rossella Arcucci, Marc Bocquet, and Sibo Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-2836, https://doi.org/10.5194/egusphere-2025-2836, 2025
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We introduce the first denoising diffusion model for wildfire spread prediction, a new kind of generative AI model that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. This allows us to capture the inherent uncertainty of wildfire dynamics. Our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Caili Zhong, Sibo Cheng, Matthew Kasoar, and Rossella Arcucci
Nat. Hazards Earth Syst. Sci., 23, 1755–1768, https://doi.org/10.5194/nhess-23-1755-2023, https://doi.org/10.5194/nhess-23-1755-2023, 2023
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This paper introduces a digital twin fire model using machine learning techniques to improve the efficiency of global wildfire predictions. The proposed model also manages to efficiently adjust the prediction results thanks to data assimilation techniques. The proposed digital twin runs 500 times faster than the current state-of-the-art physics-based model.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-8, https://doi.org/10.5194/gmd-2023-8, 2023
Revised manuscript not accepted
Short summary
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Ice sheet models can help predict how Antarctica’s ice sheets respond to environmental change; such models benefit from comparison to geological data. Here, we use ice sheet model results, plus other data, to predict the erosion of Antarctic debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264, https://doi.org/10.5194/gmd-16-1231-2023, https://doi.org/10.5194/gmd-16-1231-2023, 2023
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We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (Nd v1.0). Nd fluxes from seafloor sediment and incorporation of Nd onto sinking particles represent the major global sources and sinks, respectively. However, model–data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Suzanne Robinson, Ruza Ivanovic, Lauren Gregoire, Lachlan Astfalck, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, and Kazuyo Tachikawa
EGUsphere, https://doi.org/10.5194/egusphere-2022-937, https://doi.org/10.5194/egusphere-2022-937, 2022
Preprint archived
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The neodymium (Nd) isotope (εNd) scheme in the ocean model of FAMOUS is used to explore a benthic Nd flux to seawater. Our results demonstrate that sluggish modern Pacific waters are sensitive to benthic flux alterations, whereas the well-ventilated North Atlantic displays a much weaker response. In closing, there are distinct regional differences in how seawater acquires its εNd signal, in part relating to the complex interactions of Nd addition and water advection.
Molly O. Patterson, Richard H. Levy, Denise K. Kulhanek, Tina van de Flierdt, Huw Horgan, Gavin B. Dunbar, Timothy R. Naish, Jeanine Ash, Alex Pyne, Darcy Mandeno, Paul Winberry, David M. Harwood, Fabio Florindo, Francisco J. Jimenez-Espejo, Andreas Läufer, Kyu-Cheul Yoo, Osamu Seki, Paolo Stocchi, Johann P. Klages, Jae Il Lee, Florence Colleoni, Yusuke Suganuma, Edward Gasson, Christian Ohneiser, José-Abel Flores, David Try, Rachel Kirkman, Daleen Koch, and the SWAIS 2C Science Team
Sci. Dril., 30, 101–112, https://doi.org/10.5194/sd-30-101-2022, https://doi.org/10.5194/sd-30-101-2022, 2022
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How much of the West Antarctic Ice Sheet will melt and how quickly it will happen when average global temperatures exceed 2 °C is currently unknown. Given the far-reaching and international consequences of Antarctica’s future contribution to global sea level rise, the SWAIS 2C Project was developed in order to better forecast the size and timing of future changes.
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Short summary
In this study, we use machine learning models to produce the first global maps of Pb concentrations and isotope compositions in the ocean. In line with observations, we find that (i) the surface Indian Ocean has the highest levels of pollution, (ii) pollution from previous decades is sinking in the North Atlantic and Pacific oceans, and (iii) waters carrying Pb pollution are spreading from the Southern Ocean throughout the Southern Hemisphere at intermediate depths.
In this study, we use machine learning models to produce the first global maps of Pb...
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