Articles | Volume 17, issue 24
https://doi.org/10.5194/gmd-17-9023-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-9023-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
20 Dec 2024
Methods for assessment of models |
| 20 Dec 2024
| 20 Dec 2024
Reconciling surface deflections from simulations of global mantle convection
Conor P. B. O'Malley
Department of Earth Science and Engineering, Imperial College London, London, SW7 2BP, UK
now at: Cathie Group, 2–4 Hanover Square, Newcastle upon Tyne, NE1 3NP, UK
Gareth G. Roberts
CORRESPONDING AUTHOR
Department of Earth Science and Engineering, Imperial College London, London, SW7 2BP, UK
James Panton
School of Earth and Environmental Sciences, Cardiff University, Park Place, Cardiff, CF10 3AT, UK
Fred D. Richards
Department of Earth Science and Engineering, Imperial College London, London, SW7 2BP, UK
J. Huw Davies
School of Earth and Environmental Sciences, Cardiff University, Park Place, Cardiff, CF10 3AT, UK
Victoria M. Fernandes
Department of Earth Science and Engineering, Imperial College London, London, SW7 2BP, UK
now at: Section 4.6 Geomorphology, GFZ Potsdam, Telegrafenberg, 14473 Potsdam, Germany
Sia Ghelichkhan
Research School of Earth Sciences, Australian National University, 142 Mills Road, Acton, ACT 0200, Australia
Related authors
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Gareth G. Roberts
Earth Surf. Dynam., 13, 563–570, https://doi.org/10.5194/esurf-13-563-2025, https://doi.org/10.5194/esurf-13-563-2025, 2025
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The use of new artificial intelligence (AI) techniques to learn how landscapes evolve is demonstrated. A few “snapshots” of an eroding landscape at different stages of its history provide enough information for AI to ascertain rules governing its evolution. Once the rules are known, predicting landscape evolution is extremely rapid and efficient, providing new tools to understand landscape change.
Matthew J. Morris and Gareth G. Roberts
EGUsphere, https://doi.org/10.5194/egusphere-2025-1953, https://doi.org/10.5194/egusphere-2025-1953, 2025
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We run many computer models that describe how landscapes evolve through time. We change how randomness (noise) is added to the models to explore how it affects the shapes and properties of the final landscape. We add different types of noise at the start, during, and at the end of models, aiming to mimic reality. The range of shapes and properties produced from different noises can be as large as ranges possibly caused by climate, but running many models can help with measuring this uncertainty.
Gwynfor T. Morgan, J. Huw Davies, Robert Myhill, and James Panton
Solid Earth, 16, 297–314, https://doi.org/10.5194/se-16-297-2025, https://doi.org/10.5194/se-16-297-2025, 2025
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Phase transitions can influence mantle convection, inhibiting or promoting vertical flow. We are motivated by two examples: the post-spinel reaction proceeding via akimotoite at cool temperatures and a curving post-garnet boundary. Some have suggested these could change mantle dynamics. We find this is unlikely for both reactions: the first due to the uniqueness of thermodynamic state and the second due to the low magnitude of the boundary’s slope in pressure–temperature space and density change.
Duo Zhang and J. Huw Davies
Solid Earth, 15, 1113–1132, https://doi.org/10.5194/se-15-1113-2024, https://doi.org/10.5194/se-15-1113-2024, 2024
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We numerically model the influence of an arc on back-arc extension. The arc is simulated by placing a hot region on the overriding plate. We investigate how plate ages and properties of the hot region affect back-arc extension and present regime diagrams illustrating the nature of back-arc extension for these models. We find that back-arc extension occurs not only in the hot region but also, surprisingly, away from it, and a hot region facilitates extension on the overriding plate.
Andrew Hollyday, Maureen E. Raymo, Jacqueline Austermann, Fred Richards, Mark Hoggard, and Alessio Rovere
Earth Surf. Dynam., 12, 883–905, https://doi.org/10.5194/esurf-12-883-2024, https://doi.org/10.5194/esurf-12-883-2024, 2024
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Sea level was significantly higher during the Pliocene epoch, around 3 million years ago. The present-day elevations of shorelines that formed in the past provide a data constraint on the extent of ice sheet melt and the global sea level response under warm Pliocene conditions. In this study, we identify 10 escarpments that formed from wave-cut erosion during Pliocene times and compare their elevations with model predictions of solid Earth deformation processes to estimate past sea level.
Sia Ghelichkhan, Angus Gibson, D. Rhodri Davies, Stephan C. Kramer, and David A. Ham
Geosci. Model Dev., 17, 5057–5086, https://doi.org/10.5194/gmd-17-5057-2024, https://doi.org/10.5194/gmd-17-5057-2024, 2024
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We introduce the Geoscientific ADjoint Optimisation PlaTform (G-ADOPT), designed for inverse modelling of Earth system processes, with an initial focus on mantle dynamics. G-ADOPT is built upon Firedrake, Dolfin-Adjoint and the Rapid Optimisation Library, which work together to optimise models using an adjoint method, aligning them with seismic and geologic datasets. We demonstrate G-ADOPT's ability to reconstruct mantle evolution and thus be a powerful tool in geosciences.
D. Rhodri Davies, Stephan C. Kramer, Sia Ghelichkhan, and Angus Gibson
Geosci. Model Dev., 15, 5127–5166, https://doi.org/10.5194/gmd-15-5127-2022, https://doi.org/10.5194/gmd-15-5127-2022, 2022
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Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth's mantle over geological timescales, which is ultimately the engine driving our dynamic Earth.
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Short summary
We wish to understand how the history of flowing rock within Earth's interior impacts deflection of its surface. Observations exist to address this problem, and mathematics and different computing tools can be used to predict histories of flow. We explore how modeling choices impact calculated vertical deflections. The sensitivity of vertical motions at Earth's surface to deep flow is assessed, demonstrating how surface observations can enlighten flow histories.
We wish to understand how the history of flowing rock within Earth's interior impacts deflection...
