Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5413-2024
© 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-17-5413-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Jul 2024
Development and technical paper |
| 16 Jul 2024
| 16 Jul 2024
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Dongyu Zheng
CORRESPONDING AUTHOR
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China
Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications, MNR & Institute of Sedimentary Geology, Chengdu University of Technology, Chengdu 610059, China
School of Earth and Environment, University of Leeds, Leeds, UK
Andrew S. Merdith
School of Earth and Environment, University of Leeds, Leeds, UK
School of Physics, Chemistry and Earth Sciences, University of Adelaide, Adelaide, Australia
Yves Goddéris
Géosciences Environnement Toulouse, CNRS, Toulouse, France
Yannick Donnadieu
Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement, CNRS–Aix-Marseille Université, Aix-en-Provence, France
Khushboo Gurung
School of Earth and Environment, University of Leeds, Leeds, UK
Benjamin J. W. Mills
School of Earth and Environment, University of Leeds, Leeds, UK
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Loïc Sablon, Pierre Maffre, Yves Goddéris, Paul J. Valdes, Justin Gérard, Jarno J. C. Huygh, Anne-Christine Da Silva, and Michel Crucifix
EGUsphere, https://doi.org/10.5194/egusphere-2025-1696, https://doi.org/10.5194/egusphere-2025-1696, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We propose an innovative climate modelling framework that combines statistical methods with climate simulations to study Earth's environmental systems. The model captures how orbital changes and carbon dioxide levels influence climate atmospheric dynamics, offering a detailed and efficient way to explore long-term processes. This tool provides new opportunities to investigate Earth's climate history and its implications for future changes.
Pierre Maffre, Yves Goddéris, Guillaume Le Hir, Élise Nardin, Anta-Clarisse Sarr, and Yannick Donnadieu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-220, https://doi.org/10.5194/gmd-2024-220, 2024
Revised manuscript accepted for GMD
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A new version (v7) of the numerical model GEOCLIM is presented here. GEOCLIM models the evolution of ocean and atmosphere chemical composition on multi-million years timescale, including carbon and oxygen cycles, CO2 and climate. GEOCLIM is associated to a climate model, and a new procedure to link the climate model to GEOCLIM is presented here. GEOCLIM is applied here to investigate the evolution of ocean oxygenation following Earth's orbital parameter variations, around 94 million years ago.
Nick R. Hayes, Daniel J. Lunt, Yves Goddéris, Richard D. Pancost, and Heather L. Buss
EGUsphere, https://doi.org/10.5194/egusphere-2024-2811, https://doi.org/10.5194/egusphere-2024-2811, 2024
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The breakdown of volcanic rocks by water helps balance the climate of the earth by sequestering atmospheric CO2 . The rate of CO2 sequestration is referred to as "weatherability". Our modelling study finds that continental position strongly impacts CO2 concentrations, that runoff strongly controls weatherability, that changes in weatherability may explain long term trends in atmospheric CO2 concentrations, and that even relatively localised changes in weatherability may have global impacts.
R. Dietmar Müller, Nicolas Flament, John Cannon, Michael G. Tetley, Simon E. Williams, Xianzhi Cao, Ömer F. Bodur, Sabin Zahirovic, and Andrew Merdith
Solid Earth, 13, 1127–1159, https://doi.org/10.5194/se-13-1127-2022, https://doi.org/10.5194/se-13-1127-2022, 2022
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We have built a community model for the evolution of the Earth's plate–mantle system. Created with open-source software and an open-access plate model, it covers the last billion years, including the formation, breakup, and dispersal of two supercontinents, as well as the creation and destruction of numerous ocean basins. The model allows us to
seeinto the Earth in 4D and helps us unravel the connections between surface tectonics and the
beating heartof the Earth, its convecting mantle.
Agathe Toumoulin, Delphine Tardif, Yannick Donnadieu, Alexis Licht, Jean-Baptiste Ladant, Lutz Kunzmann, and Guillaume Dupont-Nivet
Clim. Past, 18, 341–362, https://doi.org/10.5194/cp-18-341-2022, https://doi.org/10.5194/cp-18-341-2022, 2022
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Temperature seasonality is an important climate parameter for biodiversity. Fossil plants describe its middle Eocene to early Oligocene increase in the Northern Hemisphere, but underlying mechanisms have not been studied in detail yet. Using climate simulations, we map global seasonality changes and show that major contemporary forcing – atmospheric CO2 lowering, Antarctic ice-sheet expansion and particularly related sea level drop – participated in this phenomenon and its spatial distribution.
Daniel J. Lunt, Fran Bragg, Wing-Le Chan, David K. Hutchinson, Jean-Baptiste Ladant, Polina Morozova, Igor Niezgodzki, Sebastian Steinig, Zhongshi Zhang, Jiang Zhu, Ayako Abe-Ouchi, Eleni Anagnostou, Agatha M. de Boer, Helen K. Coxall, Yannick Donnadieu, Gavin Foster, Gordon N. Inglis, Gregor Knorr, Petra M. Langebroek, Caroline H. Lear, Gerrit Lohmann, Christopher J. Poulsen, Pierre Sepulchre, Jessica E. Tierney, Paul J. Valdes, Evgeny M. Volodin, Tom Dunkley Jones, Christopher J. Hollis, Matthew Huber, and Bette L. Otto-Bliesner
Clim. Past, 17, 203–227, https://doi.org/10.5194/cp-17-203-2021, https://doi.org/10.5194/cp-17-203-2021, 2021
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This paper presents the first modelling results from the Deep-Time Model Intercomparison Project (DeepMIP), in which we focus on the early Eocene climatic optimum (EECO, 50 million years ago). We show that, in contrast to previous work, at least three models (CESM, GFDL, and NorESM) produce climate states that are consistent with proxy indicators of global mean temperature and polar amplification, and they achieve this at a CO2 concentration that is consistent with the CO2 proxy record.
Jon D. Richey, Isabel P. Montañez, Yves Goddéris, Cindy V. Looy, Neil P. Griffis, and William A. DiMichele
Clim. Past, 16, 1759–1775, https://doi.org/10.5194/cp-16-1759-2020, https://doi.org/10.5194/cp-16-1759-2020, 2020
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Our 40 Myr CO2 reconstruction substantially refines existing late Paleozoic CO2 estimates, provides the best resolved pre-Cenozoic CO2 record, and indicates a close temporal relationship to changes in marine and terrestrial ecosystems. The GEOCLIM model used in our study allows for insight into the relative influences of uplift of the Central Pangean Mountains, intensifying aridity, and increasing mafic-to-granite ratio of outcropping rocks on changes in pCO2 through the late Paleozoic.
Yurui Zhang, Thierry Huck, Camille Lique, Yannick Donnadieu, Jean-Baptiste Ladant, Marina Rabineau, and Daniel Aslanian
Clim. Past, 16, 1263–1283, https://doi.org/10.5194/cp-16-1263-2020, https://doi.org/10.5194/cp-16-1263-2020, 2020
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The early Eocene (~ 55 Ma) was an extreme warm period accompanied by a high atmospheric CO2 level. We explore the relationships between ocean dynamics and this warm climate with the aid of the IPSL climate model. Our results show that the Eocene was characterized by a strong overturning circulation associated with deepwater formation in the Southern Ocean, which is analogous to the present-day North Atlantic. Consequently, poleward ocean heat transport was strongly enhanced.
Pierre Sepulchre, Arnaud Caubel, Jean-Baptiste Ladant, Laurent Bopp, Olivier Boucher, Pascale Braconnot, Patrick Brockmann, Anne Cozic, Yannick Donnadieu, Jean-Louis Dufresne, Victor Estella-Perez, Christian Ethé, Frédéric Fluteau, Marie-Alice Foujols, Guillaume Gastineau, Josefine Ghattas, Didier Hauglustaine, Frédéric Hourdin, Masa Kageyama, Myriam Khodri, Olivier Marti, Yann Meurdesoif, Juliette Mignot, Anta-Clarisse Sarr, Jérôme Servonnat, Didier Swingedouw, Sophie Szopa, and Delphine Tardif
Geosci. Model Dev., 13, 3011–3053, https://doi.org/10.5194/gmd-13-3011-2020, https://doi.org/10.5194/gmd-13-3011-2020, 2020
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Our paper describes IPSL-CM5A2, an Earth system model that can be integrated for long (several thousands of years) climate simulations. We describe the technical aspects, assess the model computing performance and evaluate the strengths and weaknesses of the model, by comparing pre-industrial and historical runs to the previous-generation model simulations and to observations. We also present a Cretaceous simulation as a case study to show how the model simulates deep-time paleoclimates.
Marie Laugié, Yannick Donnadieu, Jean-Baptiste Ladant, J. A. Mattias Green, Laurent Bopp, and François Raisson
Clim. Past, 16, 953–971, https://doi.org/10.5194/cp-16-953-2020, https://doi.org/10.5194/cp-16-953-2020, 2020
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To quantify the impact of major climate forcings on the Cretaceous climate, we use Earth system modelling to progressively reconstruct the Cretaceous state by changing boundary conditions one by one. Between the preindustrial and the Cretaceous simulations, the model simulates a global warming of more than 11°C. The study confirms the primary control exerted by atmospheric CO2 on atmospheric temperatures. Palaeogeographic changes represent the second major contributor to the warming.
Delphine Tardif, Frédéric Fluteau, Yannick Donnadieu, Guillaume Le Hir, Jean-Baptiste Ladant, Pierre Sepulchre, Alexis Licht, Fernando Poblete, and Guillaume Dupont-Nivet
Clim. Past, 16, 847–865, https://doi.org/10.5194/cp-16-847-2020, https://doi.org/10.5194/cp-16-847-2020, 2020
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The Asian monsoons onset has been suggested to be as early as 40 Ma, in a palaeogeographic and climatic context very different from modern conditions. We test the likeliness of an early monsoon onset through climatic modelling. Our results reveal a very arid central Asia and several regions in India, Myanmar and eastern China experiencing highly seasonal precipitations. This suggests that monsoon circulation is not paramount in triggering the highly seasonal patterns recorded in the fossils.
Sébastien Carretier, Yves Goddéris, Javier Martinez, Martin Reich, and Pierre Martinod
Earth Surf. Dynam., 6, 217–237, https://doi.org/10.5194/esurf-6-217-2018, https://doi.org/10.5194/esurf-6-217-2018, 2018
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The role of mountain uplift and associated silicate weathering in the global climate over geological times is controversial. Previous soil column models suggest that weathering falls at a high denudation rate. We present the results of a 3-D model that couples erosion and weathering, a CO2 consumer during mountain uplift. Our model suggests that the weathering of temporarily stocked colluvium may contribute significantly to the mountain weathering outflux at high denudation rates.
Daniel J. Lunt, Matthew Huber, Eleni Anagnostou, Michiel L. J. Baatsen, Rodrigo Caballero, Rob DeConto, Henk A. Dijkstra, Yannick Donnadieu, David Evans, Ran Feng, Gavin L. Foster, Ed Gasson, Anna S. von der Heydt, Chris J. Hollis, Gordon N. Inglis, Stephen M. Jones, Jeff Kiehl, Sandy Kirtland Turner, Robert L. Korty, Reinhardt Kozdon, Srinath Krishnan, Jean-Baptiste Ladant, Petra Langebroek, Caroline H. Lear, Allegra N. LeGrande, Kate Littler, Paul Markwick, Bette Otto-Bliesner, Paul Pearson, Christopher J. Poulsen, Ulrich Salzmann, Christine Shields, Kathryn Snell, Michael Stärz, James Super, Clay Tabor, Jessica E. Tierney, Gregory J. L. Tourte, Aradhna Tripati, Garland R. Upchurch, Bridget S. Wade, Scott L. Wing, Arne M. E. Winguth, Nicky M. Wright, James C. Zachos, and Richard E. Zeebe
Geosci. Model Dev., 10, 889–901, https://doi.org/10.5194/gmd-10-889-2017, https://doi.org/10.5194/gmd-10-889-2017, 2017
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In this paper we describe the experimental design for a set of simulations which will be carried out by a range of climate models, all investigating the climate of the Eocene, about 50 million years ago. The intercomparison of model results is called 'DeepMIP', and we anticipate that we will contribute to the next IPCC report through an analysis of these simulations and the geological data to which we will compare them.
Svetlana Botsyun, Pierre Sepulchre, Camille Risi, and Yannick Donnadieu
Clim. Past, 12, 1401–1420, https://doi.org/10.5194/cp-12-1401-2016, https://doi.org/10.5194/cp-12-1401-2016, 2016
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We use an isotope-equipped GCM and develop original theoretical expression for the precipitation composition to assess δ18O of paleo-precipitation changes with the Tibetan Plateau uplift. We show that δ18O of precipitation is very sensitive to climate changes related to the growth of mountains, notably changes in relative humidity and precipitation amount. Topography is shown to be not an exclusive controlling factor δ18O in precipitation that have crucial consequences for paleoelevation studies
Sébastien Carretier, Pierre Martinod, Martin Reich, and Yves Godderis
Earth Surf. Dynam., 4, 237–251, https://doi.org/10.5194/esurf-4-237-2016, https://doi.org/10.5194/esurf-4-237-2016, 2016
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We introduce moving clasts (grains, minerals, cobbles) in a landscape evolution model.
This coupling has many potential applications, such as sediment provenance or the tracing of weathered material. It fills a gap between long-term landscape dynamics, which are difficult to tackle, and sediment clast populations studied in the field.
G. Hoareau, B. Bomou, D. J. J. van Hinsbergen, N. Carry, D. Marquer, Y. Donnadieu, G. Le Hir, B. Vrielynck, and A.-V. Walter-Simonnet
Clim. Past, 11, 1751–1767, https://doi.org/10.5194/cp-11-1751-2015, https://doi.org/10.5194/cp-11-1751-2015, 2015
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The impact of Neo-Tethys closure on early Cenozoic warming has been tested. First, the volume of subducted sediments and the amount of CO2 emitted along the northern Tethys margin has been calculated. Second, corresponding pCO2 have been tested using the GEOCLIM model. Despite high CO2 production, maximum pCO2 values (750ppm) do not reach values inferred from proxies. Other cited sources of excess CO2 such as the NAIP are also below fluxes required by GEOCLIM to fit with proxy data.
N. Vigier and Y. Goddéris
Clim. Past, 11, 635–645, https://doi.org/10.5194/cp-11-635-2015, https://doi.org/10.5194/cp-11-635-2015, 2015
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We develop here a new approach that couples the carbon and lithium cycles for reconstructing the Cenozoic Li isotope record. We show that this record does not provide persuasive, unique evidence for Cenozoic change in erosional forcing as it could, alternatively, be consistent with climatic control on soil production rates. The Li storage in continental secondary phases plays a key role, in particular, during the Early Cenozoic.
A. Pohl, Y. Donnadieu, G. Le Hir, J.-F. Buoncristiani, and E. Vennin
Clim. Past, 10, 2053–2066, https://doi.org/10.5194/cp-10-2053-2014, https://doi.org/10.5194/cp-10-2053-2014, 2014
J.-B. Ladant, Y. Donnadieu, and C. Dumas
Clim. Past, 10, 1957–1966, https://doi.org/10.5194/cp-10-1957-2014, https://doi.org/10.5194/cp-10-1957-2014, 2014
G. Le Hir, Y. Teitler, F. Fluteau, Y. Donnadieu, and P. Philippot
Clim. Past, 10, 697–713, https://doi.org/10.5194/cp-10-697-2014, https://doi.org/10.5194/cp-10-697-2014, 2014
Y. Goddéris, S. L. Brantley, L. M. François, J. Schott, D. Pollard, M. Déqué, and M. Dury
Biogeosciences, 10, 135–148, https://doi.org/10.5194/bg-10-135-2013, https://doi.org/10.5194/bg-10-135-2013, 2013
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The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
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Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
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Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
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We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
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This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
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The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
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Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
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Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
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Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Cited articles
Argaw, D. M. and Kweon, I. S.: Long-term video frame interpolation via feature propagation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3543–3552, 2022.
Berner, R. A.: Examination of hypotheses for the Permo-Triassic boundary extinction by carbon cycle modeling, P. Natl. Acad. Sci. USA, 99, 4172–4177, https://doi.org/10.1073/pnas.032095199, 2002.
Chen, G., Cheng, Q., Lyons, T. W., Shen, J., Agterberg, F., Huang, N., and Zhao, M.: Reconstructing Earth's atmospheric oxygenation history using machine learning, Nat. Commun., 13, 5862, https://doi.org/10.1038/s41467-022-33388-5, 2022.
Cui, C., and Cao, C.: Increased aridity across the Permian–Triassic transition in the mid‐latitude NE Pangea, Geol. J., 56, 6162–6175, 2021.
Dong, J., Ota, K., and Dong, M.: Video frame interpolation: A comprehensive survey, ACM T. Multim. Comput., 19, 1–31, https://doi.org/10.1145/3556544, 2023.
Donnadieu, Y., Pierrehumbert, R., Jacob, R., and Fluteau, F.: Modelling the primary control of paleogeography on Cretaceous climate, Earth Planet. Sc. Lett., 248, 426–437, 2006.
Fielding, C. R., Frank, T. D., McLoughlin, S., Vajda, V., Mays, C., Tevyaw, A. P., Winguth, A., Winguth, C., Nicoll, R. S., Bocking, M., and Crowley, J. L.: Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis, Nat. Commun., 10, 385, https://doi.org/10.1038/s41467-018-07934-z, 2019.
Foster, G. L., Royer, D. L., and Lunt, D. J.: Future climate forcing potentially without precedent in the last 420 million years, Nat. Commun., 8, 14845, https://doi.org/10.1038/ncomms14845, 2017.
Glasspool, I. J. and Scott, A. C.: Phanerozoic concentrations of atmospheric oxygen reconstructed from sedimentary charcoal, Nat. Geosci., 3, 627–630, 2010.
Goddéris, Y., Donnadieu, Y., Le Hir, G., Lefebvre, V., and Nardin, E.: The role of palaeogeography in the Phanerozoic history of atmospheric CO2 and climate, Earth-Sci. Rev., 128, 122–138, https://doi.org/10.1016/j.earscirev.2013.11.004, 2014.
Goddéris, Y., Donnadieu, Y., and Mills, B. J. W.: What models tell us about the evolution of carbon sources and sinks over the Phanerozoic, Annu. Rev. Earth Planet. Sci., 51, 471–492, 2023.
Hinton, G. E., Osindero, S., and Teh, Y.-W.: A Fast Learning Algorithm for Deep Belief Nets, Neural Comput., 18, 1527–1554, 2006.
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT Press, ISBN 9780262035613, 2016.
Lenton, T. M. and Watson, A. J.: Biotic enhancement of weathering, atmospheric oxygen and carbon dioxide in the Neoproterozoic, Geophys. Res. Lett., 31, L05202, https://doi.org/10.1029/2003GL018802, 2004.
Lenton, T. M., Crouch, M., Johnson, M., Pires, N., and Dolan, L.: First plants cooled the Ordovician, Nat. Geosci., 5, 86–89, 2012.
Lenton, T. M., Dahl, T. W., Daines, S. J., Mills, B. J. W., Ozaki, K., Saltzman, M. R., and Porada, P.: Earliest land plants created modern levels of atmospheric oxygen, P. Natl. Acad. Sci. USA, 113, 9704–9709, 2016.
Longman, J., Mills, B. J. W., Donnadieu, Y., and Goddéris, Y.: Assessing Volcanic Controls on Miocene Climate Change, Geophys. Res. Lett., 49, 1–11, https://doi.org/10.1029/2021GL096519, 2022.
Macdonald, F. A., Swanson-Hysell, N. L., Park, Y., Lisiecki, L., and Jagoutz, O.: Arc-continent collisions in the tropics set Earth’s climate state, Science, 364, 181–184, 2019.
Maffre, P., Ladant, J. B., Moquet, J. S., Carretier, S., Labat, D., and Goddéris, Y.: Mountain ranges, climate and weathering. Do orogens strengthen or weaken the silicate weathering carbon sink?, Earth Planet. Sc. Lett., 493, 174–185, https://doi.org/10.1016/j.epsl.2018.04.034, 2018.
Maher, K. and Chamberlain, C. P.: Hydrologic regulation of chemical weathering and the geologic carbon cycle, Science, 343, 1502–1504, 2014.
Marcilly, C. M., Torsvik, T. H., Domeier, M., and Royer, D. L.: New paleogeographic and degassing parameters for long-term carbon cycle models, Gondwana Res., 97, 176–203, 2021.
Mills, B. J. W.: bjwmills/SCION: v1.1.6 (v1.1.6), Zenodo [code], https://doi.org/10.5281/zenodo.7790169, 2023.
Mills, B. J. W., Donnadieu, Y., and Goddéris, Y.: Spatial continuous integration of Phanerozoic global biogeochemistry and climate, Gondwana Res., 100, 73–86, https://doi.org/10.1016/j.gr.2021.02.011, 2021.
Niklaus, S., Mai, L., and Liu, F.: Video frame interpolation via adaptive separable convolution, in: Proceedings of the IEEE international conference on computer vision, 261–270, 2017.
Park, Y., Maffre, P., Goddéris, Y., MacDonald, F. A., Anttila, E. S. C., and Swanson-Hysell, N. L.: Emergence of the Southeast Asian islands as a driver for Neogene cooling, P. Natl. Acad. Sci. USA, 117, 25319–25326, https://doi.org/10.1073/pnas.2011033117, 2020.
Reda, F., Kontkanen, J., Tabellion, E., Sun, D., Pantofaru, C., and Curless, B.: FILM: Frame Interpolation for Large Motion, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 13667 LNCS, 250–266, https://doi.org/10.1007/978-3-031-20071-7_15, 2022.
Reda, F., Kontkanen, J., Tabellion, E., Sun, D., Pantofaru, C., and Curless, B.: FILM: Frame Interpolation for Large Motion, European Conference on Computer Vision (ECCV) (ECCV), Zenodo [code], https://doi.org/10.5281/zenodo.10602810, 2024.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Royer, D. L., Berner, R. A., Montañez, I. P., Tabor, N. J., and Beerling, D. J.: CO2 as a primary driver of phanerozoic climate, GSA today, 14, 4–10, 2004.
Scotese, C. R.: An atlas of Phanerozoic paleogeographic maps: the seas come in and the seas go out, Annu. Rev. Earth Planet. Sci., 49, 679–728, 2021.
Scotese, C. R. and Wright, N.: PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic PaleoDEMs, PALEOMAP Project, https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018 (last access: 10 April 2023), 2018.
Scotese, C. R., Song, H., Mills, B. J. W., and van der Meer, D. G.: Phanerozoic paleotemperatures: The earth's changing climate during the last 540 million years, Earth-Sci. Rev., 215, 103503, https://doi.org/10.1016/j.earscirev.2021.103503, 2021.
Shi, Z., Xu, X., Liu, X., Chen, J., and Yang, M.-H.: Video frame interpolation transformer, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 17482–17491, 2022.
Valdes, P. J., Scotese, C. R., and Lunt, D. J.: Deep ocean temperatures through time, Clim. Past, 17, 1483–1506, https://doi.org/10.5194/cp-17-1483-2021, 2021.
Walker, J. C. G., Hays, P. B., and Kasting, J. F.: A negative feedback mechanism for the long-term stabilization of Earth's surface temperature, J. Geophys. Res.-Ocean., 86, 9776–9782, 1981.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.: Image quality assessment: from error visibility to structural similarity, IEEE T. Image Process., 13, 600–612, 2004.
West, A. J.: Thickness of the chemical weathering zone and implications for erosional and climatic drivers of weathering and for carbon-cycle feedbacks, Geology, 40, 811–814, 2012.
Witkowski, C. R., Weijers, J. W. H., Blais, B., Schouten, S., and Sinninghe Damsté, J. S.: Molecular fossils from phytoplankton reveal secular PCO2 trend over the Phanerozoic, Sci. Adv., 4, eaat4556, https://doi.org/10.1126/sciadv.aat4556, 2018.
Wong Hearing, T. W., Pohl, A., Williams, M., Donnadieu, Y., Harvey, T. H. P., Scotese, C. R., Sepulchre, P., Franc, A., and Vandenbroucke, T. R. A.: Quantitative comparison of geological data and model simulations constrains early Cambrian geography and climate, Nat. Commun., 12, 1–11, https://doi.org/10.1038/s41467-021-24141-5, 2021.
Wu, H., Zhang, X., Xie, W., Zhang, Y., and Wang, Y.: Boost Video Frame Interpolation via Motion Adaptation, arXiv [preprint], arXiv2306.13933, 2023.
Wu, Q., Zhang, H., Ramezani, J., Zhang, F. F., Erwin, D. H., Feng, Z., Shao, L. Y., Cai, Y. F., Zhang, S. H., Xu, Y. G., and Shen, S. Z.: The terrestrial end-Permian mass extinction in the paleotropics postdates the marine extinction, Sci. Adv., 10, eadi7284, https://doi.org/10.1126/sciadv.adi7284, 2024.
Xu, G., Shen, J., Algeo, T. J., Yu, J., Feng, Q., Frank, T. D., Fielding, C. R., Yan, J., Deconink, J. F., and Lei, Y.: Limited change in silicate chemical weathering intensity during the Permian–Triassic transition indicates ineffective climate regulation by weathering feedbacks, Earth Planet. Sc. Lett., 616, 118235, https://doi.org/10.1016/j.epsl.2023.118235, 2023.
Yang, W., Wan, M., Crowley, J. L., Wang, J., Luo, X., Tabor, N., Angielczyk, K. D., Gastaldo, R., Geissman, J., and Liu, F.: Paleoenvironmental and paleoclimatic evolution and cyclo-and chrono-stratigraphy of upper Permian–Lower Triassic fluvial-lacustrine deposits in Bogda Mountains, NW China – Implications for diachronous plant evolution across the Permian–Triassic boundary, Earth-Sci. Rev., 222, 103741, https://doi.org/10.1016/j.earscirev.2021.103741, 2021.
Zheng, D.: DeepFOAM – creating Figures, Zenodo [code], https://doi.org/10.5281/zenodo.10578608, 2024.
Zheng, D., Wu, S., Ma, C., Xiang, L., Hou, L., Chen, A., and Hou, M.: Zircon classification from cathodoluminescence images using deep learning, Geosci. Front., 13, 101436, https://doi.org/10.1016/j.gsf.2022.101436, 2022.
Zheng, D., Zhong, H., Camps-Valls, G., Cao, Z., Ma, X., Mills, B., Hu, X., Hou, M., and Ma, C.: Explainable deep learning for automatic rock classification, Comput. Geosci., 184, 105511, https://doi.org/10.1016/j.cageo.2023.105511, 2024.
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
This study uses a deep learning method to upscale the time resolution of paleoclimate...
