Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5713-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-5713-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Jul 2022
Development and technical paper |
| 22 Jul 2022
| 22 Jul 2022
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
Marcus Falls
CORRESPONDING AUTHOR
Department of Earth Sciences, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain
Raffaele Bernardello
Department of Earth Sciences, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain
Miguel Castrillo
Department of Earth Sciences, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain
Mario Acosta
Department of Earth Sciences, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain
Joan Llort
Department of Earth Sciences, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain
Department of Earth Sciences, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain
Institut de Ciències del Mar, ICM-CSIC, Barcelona, Catalonia, Spain
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Jenny Hieronymus, Magnus Hieronymus, Matthias Gröger, Jörg Schwinger, Raffaele Bernadello, Etienne Tourigny, Valentina Sicardi, Itzel Ruvalcaba Baroni, and Klaus Wyser
Biogeosciences, 21, 2189–2206, https://doi.org/10.5194/bg-21-2189-2024, https://doi.org/10.5194/bg-21-2189-2024, 2024
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The timing of the net primary production annual maxima in the North Atlantic in the period 1750–2100 is investigated using two Earth system models and the high-emissions scenario SSP5-8.5. It is found that, for most of the region, the annual maxima occur progressively earlier, with the most change occurring after the year 2000. Shifts in the seasonality of the primary production may impact the entire ecosystem, which highlights the need for long-term monitoring campaigns in this area.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Yona Silvy, Thomas L. Frölicher, Jens Terhaar, Fortunat Joos, Friedrich A. Burger, Fabrice Lacroix, Myles Allen, Raffaele Bernadello, Laurent Bopp, Victor Brovkin, Jonathan R. Buzan, Patricia Cadule, Martin Dix, John Dunne, Pierre Friedlingstein, Goran Georgievski, Tomohiro Hajima, Stuart Jenkins, Michio Kawamiya, Nancy Y. Kiang, Vladimir Lapin, Donghyun Lee, Paul Lerner, Nadine Mengis, Estela A. Monteiro, David Paynter, Glen P. Peters, Anastasia Romanou, Jörg Schwinger, Sarah Sparrow, Eric Stofferahn, Jerry Tjiputra, Etienne Tourigny, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-488, https://doi.org/10.5194/egusphere-2024-488, 2024
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We apply the Adaptive Emission Reduction Approach with Earth System Models to provide simulations in which all ESMs converge at 1.5 °C and 2 °C warming levels. These simulations provide compatible emission pathways for a given warming level, uncovering uncertainty ranges previously missing in the CMIP scenarios. This new type of target-based emission-driven simulations offers a more coherent assessment across ESMs for studying both the carbon cycle and impacts under climate stabilization.
Sankirna D. Joge, Anoop Sharad Mahajan, Shrivardhan Hulswar, Christa Marandino, Martí Galí, Thomas Bell, and Rafel Simo
EGUsphere, https://doi.org/10.5194/egusphere-2024-173, https://doi.org/10.5194/egusphere-2024-173, 2024
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Dimethyl sulfide (DMS) is the largest natural source of sulfur into the atmosphere and leads to the formation of CCN. DMS emissions, and hence the quantification of its impacts, have large uncertainties, but a detailed study on the range of emissions and drivers of their uncertainty is missing to date. The emissions are usually calculated from the seawater DMS concentrations and a flux parameterization. Here we quantify the differences in DMS seawater products, which can affect the DMS fluxes.
Sankirna D. Joge, Anoop Sharad Mahajan, Shrivardhan Hulswar, Christa Marandino, Marti Gali, Thomas Bell, Mingxi Yang, and Rafel Simo
EGUsphere, https://doi.org/10.5194/egusphere-2024-175, https://doi.org/10.5194/egusphere-2024-175, 2024
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Dimethyl sulfide (DMS) is the largest natural source of sulfur into the atmosphere and leads to the formation of CCN. DMS emissions, and hence the quantification of its impacts, have large uncertainties, but a detailed study on the range of emissions and drivers of their uncertainty is missing to date. The emissions are usually calculated from the seawater DMS concentrations and a flux parameterization. Here we quantify the differences in the effect of flux parameterisations used in models.
Colin Gareth Jones, Fanny Adloff, Ben Booth, Peter Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene Hewitt, Hazel Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm Roberts, Benjamin Sanderson, Roland Séférian, Samuel Somot, Pier-Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco J. Doblas-Reyes, Markus G. Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Froelicher, Neven Fuckar, Matthew Gidden, Helge Goessling, Rune Grand Graversen, Silvio Gualdi, Jose Manuel Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason A. Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Melia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard Wood, Shuting Yang, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2024-453, https://doi.org/10.5194/egusphere-2024-453, 2024
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We propose a number of priority areas for the international climate research community to address over the coming ~6 years (i.e. to 2030). Advances in these areas will increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, as well as deliver significantly improved scientific support to international climate policy, such as the 7th IPCC Assessment Report and the UNFCCC Global Stocktake under the Paris Climate Agreement.
Raffaele Bernardello, Valentina Sicardi, Vladimir Lapin, Pablo Ortega, Yohan Ruprich-Robert, Etienne Tourigny, and Eric Ferrer
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2023-40, https://doi.org/10.5194/esd-2023-40, 2023
Revised manuscript has not been submitted
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The ocean mitigates climate change by absorbing about 25 % of the carbon that is emitted to the atmosphere. However, ocean CO2 uptake is not constant in time and improving our understanding of the mechanisms regulating this variability can potentially lead to a better predictive capacity of its future behavior. In this study we compare two ocean modeling practices that are used to reconstruct the historical ocean carbon uptake, demonstrating the abilities of one over the other.
George Manville, Thomas G. Bell, Jane P. Mulcahy, Rafel Simó, Martí Galí, Anoop S. Mahajan, Shrivardhan Hulswar, and Paul R. Halloran
Biogeosciences, 20, 1813–1828, https://doi.org/10.5194/bg-20-1813-2023, https://doi.org/10.5194/bg-20-1813-2023, 2023
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We present the first global investigation of controls on seawater dimethylsulfide (DMS) spatial variability over scales of up to 100 km. Sea surface height anomalies, density, and chlorophyll a help explain almost 80 % of DMS variability. The results suggest that physical and biogeochemical processes play an equally important role in controlling DMS variability. These data provide independent confirmation that existing parameterisations of seawater DMS concentration use appropriate variables.
Flavienne Bruyant, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Lise Artigue, Lucas Barbedo de Freitas, Guislain Bécu, Simon Bélanger, Pascaline Bourgain, Annick Bricaud, Etienne Brouard, Camille Brunet, Tonya Burgers, Danielle Caleb, Katrine Chalut, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Marine Cusa, Fanny Cusset, Laeticia Dadaglio, Marty Davelaar, Gabrièle Deslongchamps, Céline Dimier, Julie Dinasquet, Dany Dumont, Brent Else, Igor Eulaers, Joannie Ferland, Gabrielle Filteau, Marie-Hélène Forget, Jérome Fort, Louis Fortier, Martí Galí, Morgane Gallinari, Svend-Erik Garbus, Nicole Garcia, Catherine Gérikas Ribeiro, Colline Gombault, Priscilla Gourvil, Clémence Goyens, Cindy Grant, Pierre-Luc Grondin, Pascal Guillot, Sandrine Hillion, Rachel Hussherr, Fabien Joux, Hannah Joy-Warren, Gabriel Joyal, David Kieber, Augustin Lafond, José Lagunas, Patrick Lajeunesse, Catherine Lalande, Jade Larivière, Florence Le Gall, Karine Leblanc, Mathieu Leblanc, Justine Legras, Keith Lévesque, Kate-M. Lewis, Edouard Leymarie, Aude Leynaert, Thomas Linkowski, Martine Lizotte, Adriana Lopes dos Santos, Claudie Marec, Dominique Marie, Guillaume Massé, Philippe Massicotte, Atsushi Matsuoka, Lisa A. Miller, Sharif Mirshak, Nathalie Morata, Brivaela Moriceau, Philippe-Israël Morin, Simon Morisset, Anders Mosbech, Alfonso Mucci, Gabrielle Nadaï, Christian Nozais, Ingrid Obernosterer, Thimoté Paire, Christos Panagiotopoulos, Marie Parenteau, Noémie Pelletier, Marc Picheral, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Llúcia Ribot Lacosta, Jean-François Rontani, Blanche Saint-Béat, Julie Sansoulet, Noé Sardet, Catherine Schmechtig, Antoine Sciandra, Richard Sempéré, Caroline Sévigny, Jordan Toullec, Margot Tragin, Jean-Éric Tremblay, Annie-Pier Trottier, Daniel Vaulot, Anda Vladoiu, Lei Xue, Gustavo Yunda-Guarin, and Marcel Babin
Earth Syst. Sci. Data, 14, 4607–4642, https://doi.org/10.5194/essd-14-4607-2022, https://doi.org/10.5194/essd-14-4607-2022, 2022
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This paper presents a dataset acquired during a research cruise held in Baffin Bay in 2016. We observed that the disappearance of sea ice in the Arctic Ocean increases both the length and spatial extent of the phytoplankton growth season. In the future, this will impact the food webs on which the local populations depend for their food supply and fisheries. This dataset will provide insight into quantifying these impacts and help the decision-making process for policymakers.
Shrivardhan Hulswar, Rafel Simó, Martí Galí, Thomas G. Bell, Arancha Lana, Swaleha Inamdar, Paul R. Halloran, George Manville, and Anoop Sharad Mahajan
Earth Syst. Sci. Data, 14, 2963–2987, https://doi.org/10.5194/essd-14-2963-2022, https://doi.org/10.5194/essd-14-2963-2022, 2022
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The third climatological estimation of sea surface dimethyl sulfide (DMS) concentrations based on in situ measurements was created (DMS-Rev3). The update includes a much larger input dataset and includes improvements in the data unification, filtering, and smoothing algorithm. The DMS-Rev3 climatology provides more realistic monthly estimates of DMS, and shows significant regional differences compared to past climatologies.
Enza Di Tomaso, Jerónimo Escribano, Sara Basart, Paul Ginoux, Francesca Macchia, Francesca Barnaba, Francesco Benincasa, Pierre-Antoine Bretonnière, Arnau Buñuel, Miguel Castrillo, Emilio Cuevas, Paola Formenti, María Gonçalves, Oriol Jorba, Martina Klose, Lucia Mona, Gilbert Montané Pinto, Michail Mytilinaios, Vincenzo Obiso, Miriam Olid, Nick Schutgens, Athanasios Votsis, Ernest Werner, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 14, 2785–2816, https://doi.org/10.5194/essd-14-2785-2022, https://doi.org/10.5194/essd-14-2785-2022, 2022
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MONARCH reanalysis of desert dust aerosols extends the existing observation-based information for mineral dust monitoring by providing 3-hourly upper-air, surface and total column key geophysical variables of the dust cycle over Northern Africa, the Middle East and Europe, at a 0.1° horizontal resolution in a rotated grid, from 2007 to 2016. This work provides evidence of the high accuracy of this data set and its suitability for air quality and health and climate service applications.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
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Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Martí Galí, Marcus Falls, Hervé Claustre, Olivier Aumont, and Raffaele Bernardello
Biogeosciences, 19, 1245–1275, https://doi.org/10.5194/bg-19-1245-2022, https://doi.org/10.5194/bg-19-1245-2022, 2022
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Part of the organic matter produced by plankton in the upper ocean is exported to the deep ocean. This process, known as the biological carbon pump, is key for the regulation of atmospheric carbon dioxide and global climate. However, the dynamics of organic particles below the upper ocean layer are not well understood. Here we compared the measurements acquired by autonomous robots in the top 1000 m of the ocean to a numerical model, which can help improve future climate projections.
Xavier Yepes-Arbós, Gijs van den Oord, Mario C. Acosta, and Glenn D. Carver
Geosci. Model Dev., 15, 379–394, https://doi.org/10.5194/gmd-15-379-2022, https://doi.org/10.5194/gmd-15-379-2022, 2022
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Climate prediction models produce a large volume of simulated data that sometimes might not be efficiently managed. In this paper we present an approach to address this issue by reducing the computing time and storage space. As a case study, we analyse the output writing process of the ECMWF atmospheric model called IFS, and we integrate into it a data writing tool called XIOS. The results suggest that the integration between the two components achieves an adequate computational performance.
Roberto Bilbao, Simon Wild, Pablo Ortega, Juan Acosta-Navarro, Thomas Arsouze, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Rubén Cruz-García, Ivana Cvijanovic, Francisco Javier Doblas-Reyes, Markus Donat, Emanuel Dutra, Pablo Echevarría, An-Chi Ho, Saskia Loosveldt-Tomas, Eduardo Moreno-Chamarro, Núria Pérez-Zanon, Arthur Ramos, Yohan Ruprich-Robert, Valentina Sicardi, Etienne Tourigny, and Javier Vegas-Regidor
Earth Syst. Dynam., 12, 173–196, https://doi.org/10.5194/esd-12-173-2021, https://doi.org/10.5194/esd-12-173-2021, 2021
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This paper presents and evaluates a set of retrospective decadal predictions with the EC-Earth3 climate model. These experiments successfully predict past changes in surface air temperature but show poor predictive capacity in the subpolar North Atlantic, a well-known source region of decadal climate variability. The poor predictive capacity is linked to an initial shock affecting the Atlantic Ocean circulation, ultimately due to a suboptimal representation of the Labrador Sea density.
Betty Croft, Randall V. Martin, Richard H. Moore, Luke D. Ziemba, Ewan C. Crosbie, Hongyu Liu, Lynn M. Russell, Georges Saliba, Armin Wisthaler, Markus Müller, Arne Schiller, Martí Galí, Rachel Y.-W. Chang, Erin E. McDuffie, Kelsey R. Bilsback, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 1889–1916, https://doi.org/10.5194/acp-21-1889-2021, https://doi.org/10.5194/acp-21-1889-2021, 2021
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North Atlantic Aerosols and Marine Ecosystems Study measurements combined with GEOS-Chem-TOMAS modeling suggest that several not-well-understood key factors control northwest Atlantic aerosol number and size. These synergetic and climate-relevant factors include particle formation near and above the marine boundary layer top, particle growth by marine secondary organic aerosol on descent, particle formation/growth related to dimethyl sulfide, sea spray aerosol, and ship emissions.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
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The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Rein Haarsma, Mario Acosta, Rena Bakhshi, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Susanna Corti, Paolo Davini, Eleftheria Exarchou, Federico Fabiano, Uwe Fladrich, Ramon Fuentes Franco, Javier García-Serrano, Jost von Hardenberg, Torben Koenigk, Xavier Levine, Virna Loana Meccia, Twan van Noije, Gijs van den Oord, Froila M. Palmeiro, Mario Rodrigo, Yohan Ruprich-Robert, Philippe Le Sager, Etienne Tourigny, Shiyu Wang, Michiel van Weele, and Klaus Wyser
Geosci. Model Dev., 13, 3507–3527, https://doi.org/10.5194/gmd-13-3507-2020, https://doi.org/10.5194/gmd-13-3507-2020, 2020
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HighResMIP is an international coordinated CMIP6 effort to investigate the improvement in climate modeling caused by an increase in horizontal resolution. This paper describes EC-Earth3P-(HR), which has been developed for HighResMIP. First analyses reveal that increasing resolution does improve certain aspects of the simulated climate but that many other biases still continue, possibly related to phenomena that are still not yet resolved and need to be parameterized.
François Massonnet, Martin Ménégoz, Mario Acosta, Xavier Yepes-Arbós, Eleftheria Exarchou, and Francisco J. Doblas-Reyes
Geosci. Model Dev., 13, 1165–1178, https://doi.org/10.5194/gmd-13-1165-2020, https://doi.org/10.5194/gmd-13-1165-2020, 2020
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Earth system models (ESMs) are one of the cornerstones of modern climate science. They are usually run on high-performance computers (HPCs). Whether the choice of HPC can affect the model results is a question of importance for optimizing the design of scientific studies. Here, we introduce a protocol for testing the replicability of the EC-Earth3 ESM across different HPCs. We find the simulation results to be replicable only if specific precautions are taken at the compilation stage.
Philippe Massicotte, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Mathieu Ardyna, Laurent Arnaud, Lise Artigue, Cyril Aubry, Pierre Ayotte, Guislain Bécu, Simon Bélanger, Ronald Benner, Henry C. Bittig, Annick Bricaud, Éric Brossier, Flavienne Bruyant, Laurent Chauvaud, Debra Christiansen-Stowe, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Christine Cox, Aurelie Delaforge, Thibaud Dezutter, Céline Dimier, Florent Domine, Francis Dufour, Christiane Dufresne, Dany Dumont, Jens Ehn, Brent Else, Joannie Ferland, Marie-Hélène Forget, Louis Fortier, Martí Galí, Virginie Galindo, Morgane Gallinari, Nicole Garcia, Catherine Gérikas Ribeiro, Margaux Gourdal, Priscilla Gourvil, Clemence Goyens, Pierre-Luc Grondin, Pascal Guillot, Caroline Guilmette, Marie-Noëlle Houssais, Fabien Joux, Léo Lacour, Thomas Lacour, Augustin Lafond, José Lagunas, Catherine Lalande, Julien Laliberté, Simon Lambert-Girard, Jade Larivière, Johann Lavaud, Anita LeBaron, Karine Leblanc, Florence Le Gall, Justine Legras, Mélanie Lemire, Maurice Levasseur, Edouard Leymarie, Aude Leynaert, Adriana Lopes dos Santos, Antonio Lourenço, David Mah, Claudie Marec, Dominique Marie, Nicolas Martin, Constance Marty, Sabine Marty, Guillaume Massé, Atsushi Matsuoka, Lisa Matthes, Brivaela Moriceau, Pierre-Emmanuel Muller, Christopher-John Mundy, Griet Neukermans, Laurent Oziel, Christos Panagiotopoulos, Jean-Jacques Pangrazi, Ghislain Picard, Marc Picheral, France Pinczon du Sel, Nicole Pogorzelec, Ian Probert, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Erin Reimer, Jean-François Rontani, Søren Rysgaard, Blanche Saint-Béat, Makoto Sampei, Julie Sansoulet, Catherine Schmechtig, Sabine Schmidt, Richard Sempéré, Caroline Sévigny, Yuan Shen, Margot Tragin, Jean-Éric Tremblay, Daniel Vaulot, Gauthier Verin, Frédéric Vivier, Anda Vladoiu, Jeremy Whitehead, and Marcel Babin
Earth Syst. Sci. Data, 12, 151–176, https://doi.org/10.5194/essd-12-151-2020, https://doi.org/10.5194/essd-12-151-2020, 2020
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The Green Edge initiative was developed to understand the processes controlling the primary productivity and the fate of organic matter produced during the Arctic spring bloom (PSB). In this article, we present an overview of an extensive and comprehensive dataset acquired during two expeditions conducted in 2015 and 2016 on landfast ice southeast of Qikiqtarjuaq Island in Baffin Bay.
Roya Ghahreman, Wanmin Gong, Martí Galí, Ann-Lise Norman, Stephen R. Beagley, Ayodeji Akingunola, Qiong Zheng, Alexandru Lupu, Martine Lizotte, Maurice Levasseur, and W. Richard Leaitch
Atmos. Chem. Phys., 19, 14455–14476, https://doi.org/10.5194/acp-19-14455-2019, https://doi.org/10.5194/acp-19-14455-2019, 2019
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Atmospheric DMS(g) is a climatically important compound and the main source of biogenic sulfate in the Arctic. Its abundance in the Arctic increases during summer due to greater ice-free sea surface and higher biological activity. In this study, we implemented DMS(g) in a regional air quality forecast model configured for the Arctic. The study showed a significant impact from DMS(g) on sulfate aerosols, particularly in the 50–100 nm size range, in the Arctic marine boundary layer during summer.
Oriol Tintó Prims, Mario C. Acosta, Andrew M. Moore, Miguel Castrillo, Kim Serradell, Ana Cortés, and Francisco J. Doblas-Reyes
Geosci. Model Dev., 12, 3135–3148, https://doi.org/10.5194/gmd-12-3135-2019, https://doi.org/10.5194/gmd-12-3135-2019, 2019
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Mixed-precision approaches can provide substantial speed-ups for both computing- and memory-bound codes, requiring little effort. A novel method to enable modern and legacy codes to benefit from a reduction of precision without sacrificing accuracy is presented. Using a precision emulator and a divide-and-conquer algorithm it identifies the parts that cannot handle reduced precision and the ones that can. The method has been proved using two ocean models, NEMO and ROMS, with promising results.
Rashed Mahmood, Knut von Salzen, Ann-Lise Norman, Martí Galí, and Maurice Levasseur
Atmos. Chem. Phys., 19, 6419–6435, https://doi.org/10.5194/acp-19-6419-2019, https://doi.org/10.5194/acp-19-6419-2019, 2019
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This study evaluates impacts of surface seawater dimethylsulfide on Arctic sulfate aerosol budget, changes in cloud droplet number concentration (CDNC), and cloud radiative forcing under current and future sea ice conditions using an atmospheric global climate model. In the future, sulfate wet removal efficiency is increased by enhanced precipitation; however, simulated aerosol nucleation rates are higher, which result in an overall increase in CDNC and negative cloud radiative forcing.
Jonathan P. D. Abbatt, W. Richard Leaitch, Amir A. Aliabadi, Allan K. Bertram, Jean-Pierre Blanchet, Aude Boivin-Rioux, Heiko Bozem, Julia Burkart, Rachel Y. W. Chang, Joannie Charette, Jai P. Chaubey, Robert J. Christensen, Ana Cirisan, Douglas B. Collins, Betty Croft, Joelle Dionne, Greg J. Evans, Christopher G. Fletcher, Martí Galí, Roya Ghahreman, Eric Girard, Wanmin Gong, Michel Gosselin, Margaux Gourdal, Sarah J. Hanna, Hakase Hayashida, Andreas B. Herber, Sareh Hesaraki, Peter Hoor, Lin Huang, Rachel Hussherr, Victoria E. Irish, Setigui A. Keita, John K. Kodros, Franziska Köllner, Felicia Kolonjari, Daniel Kunkel, Luis A. Ladino, Kathy Law, Maurice Levasseur, Quentin Libois, John Liggio, Martine Lizotte, Katrina M. Macdonald, Rashed Mahmood, Randall V. Martin, Ryan H. Mason, Lisa A. Miller, Alexander Moravek, Eric Mortenson, Emma L. Mungall, Jennifer G. Murphy, Maryam Namazi, Ann-Lise Norman, Norman T. O'Neill, Jeffrey R. Pierce, Lynn M. Russell, Johannes Schneider, Hannes Schulz, Sangeeta Sharma, Meng Si, Ralf M. Staebler, Nadja S. Steiner, Jennie L. Thomas, Knut von Salzen, Jeremy J. B. Wentzell, Megan D. Willis, Gregory R. Wentworth, Jun-Wei Xu, and Jacqueline D. Yakobi-Hancock
Atmos. Chem. Phys., 19, 2527–2560, https://doi.org/10.5194/acp-19-2527-2019, https://doi.org/10.5194/acp-19-2527-2019, 2019
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The Arctic is experiencing considerable environmental change with climate warming, illustrated by the dramatic decrease in sea-ice extent. It is important to understand both the natural and perturbed Arctic systems to gain a better understanding of how they will change in the future. This paper summarizes new insights into the relationships between Arctic aerosol particles and climate, as learned over the past five or so years by a large Canadian research consortium, NETCARE.
Martí Galí, Maurice Levasseur, Emmanuel Devred, Rafel Simó, and Marcel Babin
Biogeosciences, 15, 3497–3519, https://doi.org/10.5194/bg-15-3497-2018, https://doi.org/10.5194/bg-15-3497-2018, 2018
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We developed a new algorithm to estimate the sea-surface concentration of dimethylsulfide (DMS) using satellite data. DMS is a gas produced by marine plankton that, once emitted to the atmosphere, plays a key climatic role by seeding cloud formation. We used the algorithm to produce global DMS maps and also regional DMS time series. The latter suggest that DMS can vary largely from one year to another, which should be taken into account in atmospheric studies.
Jost Heintzenberg, Peter Tunved, Martí Galí, and Caroline Leck
Atmos. Chem. Phys., 17, 6153–6175, https://doi.org/10.5194/acp-17-6153-2017, https://doi.org/10.5194/acp-17-6153-2017, 2017
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Events of new particle formation (NPF) were analyzed objectively in a 10-year data set of hourly particle size distributions recorded on Mt. Zeppelin, Spitsbergen, Svalbard. Three different types of NPF events were identified that were hypothesized to be different expressions of related source processes. Back trajectories and ancillary atmospheric and marine data strongly point to marine biogenic sources causing new particle formation in the summer Arctic.
A. Cabré, I. Marinov, R. Bernardello, and D. Bianchi
Biogeosciences, 12, 5429–5454, https://doi.org/10.5194/bg-12-5429-2015, https://doi.org/10.5194/bg-12-5429-2015, 2015
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We analyze simulations of the Pacific Ocean oxygen minimum zones (OMZs) from 11 Earth system model contributions to the Coupled Model Intercomparison Project Phase 5, focusing on the mean state, climate change projections, and interannual variability. We analyse biases with respect to observations and consistency of results across simulations.
C. Prados-Roman, C. A. Cuevas, T. Hay, R. P. Fernandez, A. S. Mahajan, S.-J. Royer, M. Galí, R. Simó, J. Dachs, K. Großmann, D. E. Kinnison, J.-F. Lamarque, and A. Saiz-Lopez
Atmos. Chem. Phys., 15, 583–593, https://doi.org/10.5194/acp-15-583-2015, https://doi.org/10.5194/acp-15-583-2015, 2015
C. Lo Monaco, N. Metzl, F. D'Ovidio, J. Llort, and C. Ridame
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-17543-2014, https://doi.org/10.5194/bgd-11-17543-2014, 2014
Revised manuscript has not been submitted
Related subject area
Biogeosciences
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Exploring the Potential of History Matching for Land Surface Model Calibration
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Assimilation of Carbonyl Sulfide (COS) fluxes within the adjoint-based data assimilation system–Nanjing University Carbon Assimilation System (NUCAS v1.0)
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
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.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-6, https://doi.org/10.5194/gmd-2024-6, 2024
Revised manuscript accepted for GMD
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This study introduces a new scheme to simulate 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 response 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.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996, https://doi.org/10.5194/egusphere-2023-2996, 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. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
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This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-242, https://doi.org/10.5194/gmd-2023-242, 2024
Revised manuscript accepted for GMD
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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 as 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.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
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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. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1955, https://doi.org/10.5194/egusphere-2023-1955, 2023
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In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
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Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
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Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
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Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
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Short summary
This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
This paper describes and tests a method which uses a genetic algorithm (GA), a type of...
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