Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1299-2019
© Author(s) 2019. 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-12-1299-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Apr 2019
Model description paper |
| 03 Apr 2019
| 03 Apr 2019
MAgPIE 4 – a modular open-source framework for modeling global land systems
Jan Philipp Dietrich
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Benjamin Leon Bodirsky
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Florian Humpenöder
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Isabelle Weindl
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Miodrag Stevanović
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Kristine Karstens
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Ulrich Kreidenweis
Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Member of the Leibniz Association, Max-Eyth-Allee 100, 14469 Potsdam, Germany
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Xiaoxi Wang
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Abhijeet Mishra
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
David Klein
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Geanderson Ambrósio
Universidade Federal de Viçosa, Departamento de Economia Rural – DER, Av. Purdue s/no, Campus Universitário, CEP 36570-900 Viçosa, Brazil
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Ewerton Araujo
Universidade Federal de Pernambuco, Programa de Pós-Graduação em Economia – PIMES, Av. dos Economistas s/no, Centro de Ciências Sociais Aplicadas,
Cidade Universitária, CEP 50670-901 Recife, Brazil
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Amsalu Woldie Yalew
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Lavinia Baumstark
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Stephen Wirth
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Anastasis Giannousakis
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Felicitas Beier
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
David Meng-Chuen Chen
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Humboldt-Universität zu Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin, Germany
Hermann Lotze-Campen
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Humboldt-Universität zu Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin, Germany
Alexander Popp
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Related authors
Edna Johanna Molina Bacca, Miodrag Stevanović, Benjamin Leon Bodirsky, Jonathan C. Doelman, Louise Parsons Chini, Jan Volkholz, Katja Frieler, Christopher Reyer, George Hurtt, Florian Humpenöder, Kristine Karstens, Jens Heinke, Christoph Müller, Jan Philipp Dietrich, Hermann Lotze-Campen, Elke Stehfest, and Alexander Popp
EGUsphere, https://doi.org/10.5194/egusphere-2024-2441, https://doi.org/10.5194/egusphere-2024-2441, 2024
Short summary
Short summary
Land-use change projections are vital for impact studies. This study compares updated land-use model projections, including CO2 fertilization among other upgrades, from the MAgPIE and IMAGE models under three scenarios, highlighting differences, uncertainty hotspots, and harmonization effects. Key findings include reduced bioenergy crop demand projections and differences in grassland area allocation and sizes, with socioeconomic-climate scenarios' largest effect on variance starting in 2030.
Kristine Karstens, Benjamin Leon Bodirsky, Jan Philipp Dietrich, Marta Dondini, Jens Heinke, Matthias Kuhnert, Christoph Müller, Susanne Rolinski, Pete Smith, Isabelle Weindl, Hermann Lotze-Campen, and Alexander Popp
Biogeosciences, 19, 5125–5149, https://doi.org/10.5194/bg-19-5125-2022, https://doi.org/10.5194/bg-19-5125-2022, 2022
Short summary
Short summary
Soil organic carbon (SOC) has been depleted by anthropogenic land cover change and agricultural management. While SOC models often simulate detailed biochemical processes, the management decisions are still little investigated at the global scale. We estimate that soils have lost around 26 GtC relative to a counterfactual natural state in 1975. Yet, since 1975, SOC has been increasing again by 4 GtC due to a higher productivity, recycling of crop residues and manure, and no-tillage practices.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
Short summary
Short summary
This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
Short summary
Short summary
Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Marie Brunel, Stephen Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
EGUsphere, https://doi.org/10.5194/egusphere-2025-922, https://doi.org/10.5194/egusphere-2025-922, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Farmers often use fire to clear dead pasture biomass, impacting vegetation and soil nutrients. This study integrates fire management into a DGVM to assess its effects, focusing on Brazil. The results show that combining grazing and fire management reduces vegetation carbon and soil nitrogen over time. The research highlights the need to include these practices in models to improve pasture management assessments and calls for better data on fire usage and its long-term effects.
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
Short summary
Short summary
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.
Edna Johanna Molina Bacca, Miodrag Stevanović, Benjamin Leon Bodirsky, Jonathan C. Doelman, Louise Parsons Chini, Jan Volkholz, Katja Frieler, Christopher Reyer, George Hurtt, Florian Humpenöder, Kristine Karstens, Jens Heinke, Christoph Müller, Jan Philipp Dietrich, Hermann Lotze-Campen, Elke Stehfest, and Alexander Popp
EGUsphere, https://doi.org/10.5194/egusphere-2024-2441, https://doi.org/10.5194/egusphere-2024-2441, 2024
Short summary
Short summary
Land-use change projections are vital for impact studies. This study compares updated land-use model projections, including CO2 fertilization among other upgrades, from the MAgPIE and IMAGE models under three scenarios, highlighting differences, uncertainty hotspots, and harmonization effects. Key findings include reduced bioenergy crop demand projections and differences in grassland area allocation and sizes, with socioeconomic-climate scenarios' largest effect on variance starting in 2030.
Felix Jäger, Jonas Schwaab, Yann Quilcaille, Michael Windisch, Jonathan Doelman, Stefan Frank, Mykola Gusti, Petr Havlik, Florian Humpenöder, Andrey Lessa Derci Augustynczik, Christoph Müller, Kanishka Balu Narayan, Ryan Sebastian Padrón, Alexander Popp, Detlef van Vuuren, Michael Wögerer, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 15, 1055–1071, https://doi.org/10.5194/esd-15-1055-2024, https://doi.org/10.5194/esd-15-1055-2024, 2024
Short summary
Short summary
Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Kristine Karstens, Benjamin Leon Bodirsky, Jan Philipp Dietrich, Marta Dondini, Jens Heinke, Matthias Kuhnert, Christoph Müller, Susanne Rolinski, Pete Smith, Isabelle Weindl, Hermann Lotze-Campen, and Alexander Popp
Biogeosciences, 19, 5125–5149, https://doi.org/10.5194/bg-19-5125-2022, https://doi.org/10.5194/bg-19-5125-2022, 2022
Short summary
Short summary
Soil organic carbon (SOC) has been depleted by anthropogenic land cover change and agricultural management. While SOC models often simulate detailed biochemical processes, the management decisions are still little investigated at the global scale. We estimate that soils have lost around 26 GtC relative to a counterfactual natural state in 1975. Yet, since 1975, SOC has been increasing again by 4 GtC due to a higher productivity, recycling of crop residues and manure, and no-tillage practices.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
Short summary
Short summary
This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
Short summary
Short summary
Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
Short summary
Short summary
To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Wagner de Oliveira Garcia, Thorben Amann, Jens Hartmann, Kristine Karstens, Alexander Popp, Lena R. Boysen, Pete Smith, and Daniel Goll
Biogeosciences, 17, 2107–2133, https://doi.org/10.5194/bg-17-2107-2020, https://doi.org/10.5194/bg-17-2107-2020, 2020
Short summary
Short summary
Biomass-based terrestrial negative emission technologies (tNETS) have high potential to sequester CO2. Many CO2 uptake estimates do not include the effect of nutrient deficiencies in soils on biomass production. We show that nutrients can be partly resupplied by enhanced weathering (EW) rock powder application, increasing the effectiveness of tNETs. Depending on the deployed amounts of rock powder, EW could also improve soil hydrology, adding a new dimension to the coupling of tNETs with EW.
Wei Li, Philippe Ciais, Elke Stehfest, Detlef van Vuuren, Alexander Popp, Almut Arneth, Fulvio Di Fulvio, Jonathan Doelman, Florian Humpenöder, Anna B. Harper, Taejin Park, David Makowski, Petr Havlik, Michael Obersteiner, Jingmeng Wang, Andreas Krause, and Wenfeng Liu
Earth Syst. Sci. Data, 12, 789–804, https://doi.org/10.5194/essd-12-789-2020, https://doi.org/10.5194/essd-12-789-2020, 2020
Short summary
Short summary
We generated spatially explicit bioenergy crop yields based on field measurements with climate, soil condition and remote-sensing variables as explanatory variables and the machine-learning method. We further compared our yield maps with the maps from three integrated assessment models (IAMs; IMAGE, MAgPIE and GLOBIOM) and found that the median yields in our maps are > 50 % higher than those in the IAM maps.
Matthew J. Gidden, Keywan Riahi, Steven J. Smith, Shinichiro Fujimori, Gunnar Luderer, Elmar Kriegler, Detlef P. van Vuuren, Maarten van den Berg, Leyang Feng, David Klein, Katherine Calvin, Jonathan C. Doelman, Stefan Frank, Oliver Fricko, Mathijs Harmsen, Tomoko Hasegawa, Petr Havlik, Jérôme Hilaire, Rachel Hoesly, Jill Horing, Alexander Popp, Elke Stehfest, and Kiyoshi Takahashi
Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, https://doi.org/10.5194/gmd-12-1443-2019, 2019
Short summary
Short summary
We present a suite of nine scenarios of future emissions trajectories of anthropogenic sources for use in CMIP6. Integrated assessment model results are provided for each scenario with consistent transitions from the historical data to future trajectories. We find that the set of scenarios enables the exploration of a variety of warming pathways. A wide range of scenario data products are provided for the CMIP6 scientific community including global, regional, and gridded emissions datasets.
HyeJin Kim, Isabel M. D. Rosa, Rob Alkemade, Paul Leadley, George Hurtt, Alexander Popp, Detlef P. van Vuuren, Peter Anthoni, Almut Arneth, Daniele Baisero, Emma Caton, Rebecca Chaplin-Kramer, Louise Chini, Adriana De Palma, Fulvio Di Fulvio, Moreno Di Marco, Felipe Espinoza, Simon Ferrier, Shinichiro Fujimori, Ricardo E. Gonzalez, Maya Gueguen, Carlos Guerra, Mike Harfoot, Thomas D. Harwood, Tomoko Hasegawa, Vanessa Haverd, Petr Havlík, Stefanie Hellweg, Samantha L. L. Hill, Akiko Hirata, Andrew J. Hoskins, Jan H. Janse, Walter Jetz, Justin A. Johnson, Andreas Krause, David Leclère, Ines S. Martins, Tetsuya Matsui, Cory Merow, Michael Obersteiner, Haruka Ohashi, Benjamin Poulter, Andy Purvis, Benjamin Quesada, Carlo Rondinini, Aafke M. Schipper, Richard Sharp, Kiyoshi Takahashi, Wilfried Thuiller, Nicolas Titeux, Piero Visconti, Christopher Ware, Florian Wolf, and Henrique M. Pereira
Geosci. Model Dev., 11, 4537–4562, https://doi.org/10.5194/gmd-11-4537-2018, https://doi.org/10.5194/gmd-11-4537-2018, 2018
Short summary
Short summary
This paper lays out the protocol for the Biodiversity and Ecosystem Services Scenario-based Intercomparison of Models (BES-SIM) that projects the global impacts of land use and climate change on biodiversity and ecosystem services over the coming decades, compared to the 20th century. BES-SIM uses harmonized scenarios and input data and a set of common output metrics at multiple scales, and identifies model uncertainties and research gaps.
Susanne Rolinski, Christoph Müller, Jens Heinke, Isabelle Weindl, Anne Biewald, Benjamin Leon Bodirsky, Alberte Bondeau, Eltje R. Boons-Prins, Alexander F. Bouwman, Peter A. Leffelaar, Johnny A. te Roller, Sibyll Schaphoff, and Kirsten Thonicke
Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, https://doi.org/10.5194/gmd-11-429-2018, 2018
Short summary
Short summary
One-third of the global land area is covered with grasslands which are grazed by or mowed for livestock feed. These areas contribute significantly to the carbon capture from the atmosphere when managed sensibly. To assess the effect of this management, we included different options of grazing and mowing into the global model LPJmL 3.6. We found in polar regions even low grazing pressure leads to soil carbon loss whereas in temperate regions up to 1.4 livestock units per hectare can be sustained.
Katja Frieler, Stefan Lange, Franziska Piontek, Christopher P. O. Reyer, Jacob Schewe, Lila Warszawski, Fang Zhao, Louise Chini, Sebastien Denvil, Kerry Emanuel, Tobias Geiger, Kate Halladay, George Hurtt, Matthias Mengel, Daisuke Murakami, Sebastian Ostberg, Alexander Popp, Riccardo Riva, Miodrag Stevanovic, Tatsuo Suzuki, Jan Volkholz, Eleanor Burke, Philippe Ciais, Kristie Ebi, Tyler D. Eddy, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Christian Hof, Veronika Huber, Jonas Jägermeyr, Valentina Krysanova, Rafael Marcé, Hannes Müller Schmied, Ioanna Mouratiadou, Don Pierson, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Matthias F. Biber, Richard A. Betts, Benjamin Leon Bodirsky, Delphine Deryng, Steve Frolking, Chris D. Jones, Heike K. Lotze, Hermann Lotze-Campen, Ritvik Sahajpal, Kirsten Thonicke, Hanqin Tian, and Yoshiki Yamagata
Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, https://doi.org/10.5194/gmd-10-4321-2017, 2017
Short summary
Short summary
This paper describes the simulation scenario design for the next phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is designed to facilitate a contribution to the scientific basis for the IPCC Special Report on the impacts of 1.5 °C global warming. ISIMIP brings together over 80 climate-impact models, covering impacts on hydrology, biomes, forests, heat-related mortality, permafrost, tropical cyclones, fisheries, agiculture, energy, and coastal infrastructure.
Andreas Krause, Thomas A. M. Pugh, Anita D. Bayer, Jonathan C. Doelman, Florian Humpenöder, Peter Anthoni, Stefan Olin, Benjamin L. Bodirsky, Alexander Popp, Elke Stehfest, and Almut Arneth
Biogeosciences, 14, 4829–4850, https://doi.org/10.5194/bg-14-4829-2017, https://doi.org/10.5194/bg-14-4829-2017, 2017
Short summary
Short summary
Many climate change mitigation scenarios require negative emissions from land management. However, environmental side effects are often not considered. Here, we use projections of future land use from two land-use models as input to a vegetation model. We show that carbon removal via bioenergy production or forest maintenance and expansion affect a range of ecosystem functions. Largest impacts are found for crop production, nitrogen losses, and emissions of biogenic volatile organic compounds.
K. Frieler, A. Levermann, J. Elliott, J. Heinke, A. Arneth, M. F. P. Bierkens, P. Ciais, D. B. Clark, D. Deryng, P. Döll, P. Falloon, B. Fekete, C. Folberth, A. D. Friend, C. Gellhorn, S. N. Gosling, I. Haddeland, N. Khabarov, M. Lomas, Y. Masaki, K. Nishina, K. Neumann, T. Oki, R. Pavlick, A. C. Ruane, E. Schmid, C. Schmitz, T. Stacke, E. Stehfest, Q. Tang, D. Wisser, V. Huber, F. Piontek, L. Warszawski, J. Schewe, H. Lotze-Campen, and H. J. Schellnhuber
Earth Syst. Dynam., 6, 447–460, https://doi.org/10.5194/esd-6-447-2015, https://doi.org/10.5194/esd-6-447-2015, 2015
V. Huber, H. J. Schellnhuber, N. W. Arnell, K. Frieler, A. D. Friend, D. Gerten, I. Haddeland, P. Kabat, H. Lotze-Campen, W. Lucht, M. Parry, F. Piontek, C. Rosenzweig, J. Schewe, and L. Warszawski
Earth Syst. Dynam., 5, 399–408, https://doi.org/10.5194/esd-5-399-2014, https://doi.org/10.5194/esd-5-399-2014, 2014
Related subject area
Biogeosciences
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
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
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
H2MV (v1.0): Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
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
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
Short summary
Short summary
Parameterisation 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, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
Short summary
Short summary
We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Alexandratos, N. and Bruinsma, J.: World agriculture towards 2030/2050: the
2012 revision, Tech. rep., ESA Working paper,
available at:
http://environmentportal.in/files/file/World agriculture towards 2030.pdf
(last access: 15 March 2019), 2012.
Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J.,
Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., de Boer, H. S., van den
Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat, D.,
Havlik, P., Johnson, N., Klein, D., Kyle, P., Marangoni, G., Masui, T.,
Pietzcker, R. C., Strubegger, M., Wise, M., Riahi, K., and van Vuuren, D. P.:
Shared Socio-Economic Pathways of the Energy Sector – Quantifying the
Narratives, Global Environ. Chang., 42, 316–330,
https://doi.org/10.1016/j.gloenvcha.2016.07.006, 2017.
Biewald, A., Rolinski, S., Lotze-Campen, H., Schmitz, C., and Dietrich, J.
P.: Valuing the impact of trade on local blue water, Ecol. Econ., 101,
43–53, https://doi.org/10.1016/j.ecolecon.2014.02.003, 2014.
Bodirsky, B. L., Popp, A., Weindl, I., Dietrich, J. P., Rolinski, S.,
Scheiffele, L., Schmitz, C., and Lotze-Campen, H.: N2O emissions from
the global agricultural nitrogen cycle – current state and future scenarios,
Biogeosciences, 9, 4169–4197, https://doi.org/10.5194/bg-9-4169-2012, 2012
Bodirsky, B. L., Popp, A., Lotze-Campen, H., Dietrich, J. P., Rolinski, S.,
Weindl, I., Schmitz, C., Müller, C., Bonsch, M., Humpenöder, F.,
Biewald, A., and Stevanovic, M.: Reactive nitrogen requirements to feed the
world in 2050 and potential to mitigate nitrogen pollution, Nat. Commun., 5,
https://doi.org/10.1038/ncomms4858, 2014.
Bodirsky, B. L., Rolinski, S., Biewald, A., Weindl, I., Popp, A., and
Lotze-Campen, H.: Global food demand scenarios for the 21st century, PLoS
ONE, 10, e0139201, https://doi.org/10.1371/journal.pone.0139201, 2015.
Bodirsky, B. L., Humpenoeder, F., Dietrich, J. P., Stevanovic, M., Weindl,
I., Karstens, K., Wang, X., Mishra, A., Breier, J., Yalew, A. W., Chen, D.,
Biewald, A., and Wirth, S.: magpie4: MAgPIE outputs R package for MAgPIE
version 4.x, r package version 1.29.0, https://doi.org/10.5281/zenodo.1158582, 2018a.
Bodirsky, B. L., Humpenoeder, F., Mishra, A., and Karstens, K.: magpiesets:
MAgPIE sets for R, r package version 0.34.0, https://doi.org/10.5281/zenodo.1158588,
2018b.
Bondeau, A., Smith, P., Zaehle, S. O. N., Schaphoff, S., Lucht, W., Cramer,
W., Gerten, D., Lotze-Campen, H., Müller, C., and Reichstein, M.:
Modelling the role of agriculture for the 20th century global terrestrial
carbon balance, Glob. Change Biol., 13, 679–706, 2007.
Bonsch, M., Humpenöder, F., Popp, A., Bodirsky, B., Dietrich, J. P.,
Rolinski, S., Biewald, A., Lotze-Campen, H., Weindl, I., Gerten, D., and
Stevanovic, M.: Trade-offs between land and water requirements for
large-scale bioenergy production, GCB Bioenergy, 8, 11–24,
https://doi.org/10.1111/gcbb.12226, 2014.
Bonsch, M., Popp, A., Biewald, A., Rolinski, S., Schmitz, C., Weindl, I.,
Stevanovic, M., Högner, K., Heinke, J., Ostberg, S., Dietrich, J. P.,
Bodirsky, B., Lotze-Campen, H., and Humpenöder, F.: Environmental flow
provision: Implications for agricultural water and land-use at the global
scale, Global Environ. Chang., 30, 113–132,
https://doi.org/10.1016/j.gloenvcha.2014.10.015, 2015.
Bonsch, M., Dietrich, J. P., Klein, D., and Humpenoeder, F.: lusweave:
Sweave/Knitr Utilities, r package version 1.46.0,
https://doi.org/10.5281/zenodo.1158594, 2018.
Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B.,
Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R. A.,
and Marland, G.: Contributions to accelerating atmospheric CO2 growth from
economic activity, carbon intensity, and efficiency of natural sinks,
P. Natl. Acad. Sci. USA, 104, 18866–18870, https://doi.org/10.1073/pnas.0702737104,
2007.
Dietrich, J. P.: MAgPIE 4.0 paper – model runs,
https://doi.org/10.5281/zenodo.2572620, 2019a.
Dietrich, J. P.: MAgPIE 4.0 paper – evaluation documents,
https://doi.org/10.5281/zenodo.2572581, 2019b.
Dietrich, J. P. and Humpenoeder, F.: shinyresults: A collection of shiny apps
and modules to visualize/handle model results, r package version 0.17.0,
https://doi.org/10.5281/zenodo.1478922, 2018.
Dietrich, J. P. and Karstens, K.: goxygen: Documentation package for modular
GAMS code, r package version 0.21.3, https://doi.org/10.5281/zenodo.1411404, 2018.
Dietrich, J. P., Schmitz, C., Müller, C., Fader, M., Lotze-Campen, H.,
and Popp, A.: Measuring agricultural land-use intensity – A global analysis
using a model-assisted approach, Ecol. Model., 232, 109–118,
https://doi.org/10.1016/j.ecolmodel.2012.03.002, 2012.
Dietrich, J. P., Popp, A., and Lotze-Campen, H.: Reducing the loss of
information and gaining accuracy with clustering methods in a global land-use
model, Ecol. Model., 263, 233–243, https://doi.org/10.1016/j.ecolmodel.2013.05.009,
2013.
Dietrich, J. P., Schmitz, C., Lotze-Campen, H., Popp, A., and Müller, C.:
Forecasting technological change in agriculture–An endogenous implementation
in a global land use model, Technol. Forecast. Soc., 81, 236–249,
https://doi.org/10.1016/j.techfore.2013.02.003, 2014.
Dietrich, J. P., Baumstark, L., Wirth, S., Giannousakis, A., Rodrigues, R.,
Bodirsky, B. L., and Kreidenweis, U.: madrat: May All Data be Reproducible
and Transparent (MADRaT), r package version 1.54.0,
https://doi.org/10.5281/zenodo.1115490, 2018a.
Dietrich, J. P., Bodirsky, B. L., Bonsch, M., Humpenoeder, F., Bi, S., and
Karstens, K.: magclass: Data Class and Tools for Handling Spatial-Temporal
Data, r package version 4.89.0, https://doi.org/10.5281/zenodo.1158580, 2018b.
Dietrich, J. P., Bodirsky, B. L., Bonsch, M., Kreidenweiss, U., Hennig,
R. J., and Humpenoeder, F.: luscale: PIK Landuse Group Data Scaling Tools, r
package version 2.14.0, https://doi.org/10.5281/zenodo.1158584, 2018c.
Dietrich, J. P., Bodirsky, B. L., Humpenöder, F., Weindl, I., Stevanovic,
M., Karstens, K., Wang, X., Mishra, A., Klein, D., Ambrosio, G., Araujo, E.,
Yalew, A. W., Beier, F., Chen, D., and Popp, A.: MAgPIE 4.0 Model
Documentation, https://doi.org/10.5281/zenodo.1471526, 2018d.
Dietrich, J. P., Bodirsky, B. L., Weindl, I., Humpenöder, F., Stevanovic,
M., Kreidenweis, U., Wang, X., Karstens, K., Mishra, A., Klein, D.,
Ambrósio, G., Araujo, E., Biewald, A., Lotze-Campen, H., and Popp, A.:
MAgPIE – An Open Source land-use modeling framework – Version 4.0,
https://doi.org/10.5281/zenodo.1445533, 2018e.
Dietrich, J. P., Giannousakis, A., and Bonsch, M. B.: gdx: Interface package
for GDX files in R, r package version 1.50.0, https://doi.org/10.5281/zenodo.1158598,
2018f.
Dietrich, J. P., Giannousakis, A., Klein, D., Bonsch, M., and Baumstark,
L. B.: lucode: PIK Landuse Group Code Manipulation and Analysis Tools, r
package version 2.137.0, https://doi.org/10.5281/zenodo.1158596, 2018g.
Dirkse, S., Ferris, M., and Jain, R.: gdxrrw: An Interface Between “GAMS'
and R,r package version 1.0.2, available at:
https://support.gams.com/gdxrrw:interfacing_gams_and_r
(last access: 15 March 2019), 2016.
EDGAR: Emission Database for Global Atmospheric Research (EDGAR), release
version 4.2., available at: http://edgar.jrc.ec.europa.eu (last access:
15 March 2019), 2010.
FAOSTAT: Food & Agriculture Organization of the United Nations Statistics
Division, available at: http://www.fao.org/faostat (last access:
15 March 2019), 2016.
Foden, W. B., Butchart, S. H. M., Stuart, S. N., Vié, J.-C.,
Akçakaya, H. R., Angulo, A., DeVantier, L. M., Gutsche, A., Turak, E.,
Cao, L., Donner, S. D., Katariya, V., Bernard, R., Holland, R. A., Hughes,
A. F., O'Hanlon, S. E., Garnett, S. T., Şekercioğlu, C. H., and Mace,
G. M.: Identifying the World's Most Climate Change Vulnerable Species: A
Systematic Trait-Based Assessment of all Birds, Amphibians and Corals, PLOS
ONE, 8, e65427, https://doi.org/10.1371/journal.pone.0065427, 2013.
Fujimori, S., Hasegawa, T., Masui, T., Takahashi, K., Herran, D. S., Dai, H.,
Hijioka, Y., and Kainuma, M.: SSP3: AIM implementation of Shared
Socioeconomic Pathways, Global Environ. Chang., 42, 268–283,
https://doi.org/10.1016/j.gloenvcha.2016.06.009, 2017.
GAMS Development Corporation: General Algebraic Modeling System (GAMS)
Release 24.8.1, GAMS Development Corporation, Washington, DC, USA, available
at: https://www.gams.com (last access: 15 March 2019), 2016.
Gütschow, J., Jeffery, L., Gieseke, R., and Gebel, R.: The PRIMAP-hist
national historical emissions time series v1.1 (1850–2014),
https://doi.org/10.5880/PIK.2017.001, 2017.
Harris, N. L., Brown, S., Hagen, S. C., Saatchi, S. S., Petrova, S., Salas,
W., Hansen, M. C., Potapov, P. V., and Lotsch, A.: Baseline Map of Carbon
Emissions from Deforestation in Tropical Regions, Science, 336, 1573–1576,
https://doi.org/10.1126/science.1217962, 2012.
Havlík, P., Valin, H., Herrero, M., Obersteiner, M., Schmid, E., Rufino,
M. C., Mosnier, A., Thornton, P. K., Böttcher, H., Conant, R. T., Frank,
S., Fritz, S., Fuss, S., Kraxner, F., and Notenbaert, An.: Climate change
mitigation through livestock system transitions, P. Natl. Acad. Sci. USA,
111, 3709–3714, 2014.
Heesch, D. V.: Doxygen: Source code documentation generator tool, available
at: http://www.doxygen.org (last access: 15 March 2019), 2008.
Humpenöder, F., Popp, A., Dietrich, J. P., Klein, D., Lotze-Campen, H.,
Bonsch, M., Bodirsky, B. L., Weindl, I., Stevanovic, M., and Müller, C.:
Investigating afforestation and bioenergy CCS as climate change mitigation
strategies, Environ. Res. Lett., 9, 064029,
https://doi.org/10.1088/1748-9326/9/6/064029, 2014.
Humpenöder, F., Popp, A., Stevanovic, M., Müller, C., Bodirsky,
B. L., Bonsch, M., Dietrich, J. P., Lotze-Campen, H., Weindl, I., Biewald,
A., and Rolinski, S.: Land-Use and Carbon Cycle Responses to
Moderate Climate Change: Implications for Land-Based
Mitigation?, Environ. Sci. Technol., 49, 6731–6739,
https://doi.org/10.1021/es506201r, 2015.
Humpenöder, F., Popp, A., Bodirsky, B. L., Weindl, I., Biewald, A.,
Lotze-Campen, H., Dietrich, J. P., Klein, D., Kreidenweis, U., Müller,
C., Rolinski, S., and Stevanovic, M.: Large-scale bioenergy production: How
to resolve sustainability trade-offs?, Environ. Res. Lett., 13, 024011,
https://doi.org/10.1088/1748-9326/aa9e3b, 2018.
Hurtt, G., Chini, L., Sahajpal, R., Frolking, S., Fisk, J.,
Bodirsky, B., Calvin, K., Fujimori, S., Goldewijk, K., Hasegawa,
T., Havlik, P., Heinimann, A., Humpenöder, F., Kaplan, J.,
Krisztin, T., Lawrence, D., Lawrence, P., Mertz, O., Popp, A.,
Riahi, K., Stehfest, E., van Vuuren, D., de Waal, L., and Zhang,
X.: Harmonization of global land-use scenarios for the period 850–2100,
Geosci. Model Dev., in preparation, available at:
http://gsweb1vh2.umd.edu/data.shtml (last access: 15 March 2019), 2019.
IAMC: SSP Database, available at: https://tntcat.iiasa.ac.at/SspDb
(last access: 15 March 2019), 2016.
ISO 3166-1:2013: Codes for the representation of names of countries and their
subdivisions – Country codes, Standard, International Organization for
Standardization, Geneva, CH, 2013.
Kindermann, G. E., Rametsteiner, E., Obersteiner, M., and McCallcum, I.:
Predicting the Deforestation – Trend Under Different Carbon –
Prices, SSRN Electronic Journal, FEEM Working Paper No. 29, 28 pp.,
https://doi.org/10.2139/ssrn.976406, 2006.
Klein, D., Humpenöder, F., Bauer, N., Dietrich, J. P., Popp, A.,
Bodirsky, B. L., Bonsch, M., and Lotze-Campen, H.: The global economic
long-term potential of modern biomass in a climate-constrained world,
Environ. Res. Lett., 9, 074017, https://doi.org/10.1088/1748-9326/9/7/074017, 2014.
Klein, D., Dietrich, J. P., Baumstark, L., Humpenoeder, F., Stevanovic, M.,
and Wirth, S.: mip: Comparison of multi-model runs, r package version
0.116.0, https://doi.org/10.5281/zenodo.1158586, 2018.
Krause, M., Lotze-Campen, H., and Popp, A.: Spatially-explicit scenarios on
global cropland expansion and available forest land in an integrated
modelling framework, in: 27th International Association of Agricultural
Economists Conference, Beijing, China, 16–22 August 2009, available at:
https://www.gtap.agecon.purdue.edu/resources/download/4526.pdf (last
access: 15 March 2019), 2009.
Krause, M., Lotze-Campen, H., Popp, A., Dietrich, J. P., and Bonsch, M.:
Conservation of undisturbed natural forests and economic impacts on
agriculture, Land Use Policy, 30, 344–354,
https://doi.org/10.1016/j.landusepol.2012.03.020, 2013.
Kreidenweis, U., Humpenöder, F., Stevanović, M., Bodirsky, B. L.,
Kriegler, E., Lotze-Campen, H., and Popp, A.: Afforestation to mitigate
climate change: impacts on food prices under consideration of albedo effects,
Environ. Res. Lett., 11, 085001, https://doi.org/10.1088/1748-9326/11/8/085001, 2016.
Kriegler, E. and Lucht, W.: Overview of the PIK REMIND-MAgPIE-LPJml
integrated assessment framework, available at:
http://www.iiasa.ac.at/web/home/about/events/5_PIK_(Kriegler).pdf (last
access: 15 March 2019), 2015.
Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M.,
Strefler, J., Baumstark, L., Bodirsky, B. L., Hilaire, J., Klein, D.,
Mouratiadou, I., Weindl, I., Bertram, C., Dietrich, J.-P., Luderer, G., Pehl,
M., Pietzcker, R., Piontek, F., Lotze-Campen, H., Biewald, A., Bonsch, M.,
Giannousakis, A., Kreidenweis, U., Müller, C., Rolinski, S., Schultes,
A., Schwanitz, J., Stevanovic, M., Calvin, K., Emmerling, J., Fujimori, S.,
and Edenhofer, O.: Fossil-fueled development (SSP5): An energy and resource
intensive scenario for the 21st century, Global Environ. Chang., 42,
297–315, 2017.
Kriegler, E., Bertram, C., Kuramochi, T., Jakob, M., Pehl, M.,
Stevanović, M., Höhne, N., Luderer, G., Minx, J. C., Fekete, H.,
Hilaire, J., Luna, L., Popp, A., Steckel, J. C., Sterl, S., Yalew, A. W.,
Dietrich, J. P., and Edenhofer, O.: Short term policies to keep the door open
for Paris climate goals, Environ. Res. Lett., 13, 074022,
https://doi.org/10.1088/1748-9326/aac4f1, 2018.
Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., and Garnier, J.: 50
year trends in nitrogen use efficiency of world cropping systems: the
relationship between yield and nitrogen input to cropland, Environ. Res.
Lett., 9, 105011, https://doi.org/10.1088/1748-9326/9/10/105011, 2014.
Lobell, D. B., Schlenker, W., and Costa-Roberts, J.: Climate Trends and
Global Crop Production Since 1980, Science, 333, 616–620,
https://doi.org/10.1126/science.1204531, 2011.
Lotze-Campen, H., Müller, C., Bondeau, A., Rost, S., Popp, A., and Lucht,
W.: Global food demand, productivity growth, and the scarcity of land and
water resources: a spatially explicit mathematical programming approach, Agr.
Econ., 39, 325–338, https://doi.org/10.1111/j.1574-0862.2008.00336.x, 2008.
Lotze-Campen, H., Popp, A., Beringer, T., Müller, C., Bondeau, A., Rost,
S., and Lucht, W.: Scenarios of global bioenergy production: The trade-offs
between agricultural expansion, intensification and trade, Ecol. Model., 221,
2188–2196, https://doi.org/10.1016/j.ecolmodel.2009.10.002, 2010.
Lucas, P. L., van Vuuren, D. P., Olivier, J. G. J., and den Elzen, M. G. J.:
Long-term reduction potential of non-CO2 greenhouse gases, Environ.
Sci. Policy, 10, 85–103, 2007.
Nakicenovic, N., Alcamo, J., Grubler, A., Riahi, K., Roehrl, R. A., Rogner,
H.-H., and Victor, N.: Special Report on Emissions Scenarios (SRES), A
Special Report of Working Group III of the Intergovernmental Panel on Climate
Change, Cambridge University Press, available at:
http://pure.iiasa.ac.at/id/eprint/6101 (last access: 15 March 2019),
2000.
Ooms, J.: curl: A Modern and Flexible Web Client for R, r package version
3.1, available at: https://CRAN.R-project.org/package=curl (last
access: 15 March 2019), 2017.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K.,
Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K.,
Levy, M., and Solecki, W.: The roads ahead: Narratives for shared
socioeconomic pathways describing world futures in the 21st century, Global
Environ. Chang., 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Popp, A., Lotze-Campen, H., and Bodirsky, B.: Food consumption, diet shifts
and associated non-CO2 greenhouse gases from agricultural production,
Global Environ. Chang., 20, 451–462, https://doi.org/10.1016/j.gloenvcha.2010.02.001,
2010.
Popp, A., Dietrich, J. P., Lotze-Campen, H., Klein, D., Bauer, N., Krause,
M., Beringer, T., Gerten, D., and Edenhofer, O.: The economic potential of
bioenergy for climate change mitigation with special attention given to
implications for the land system, Environ. Res. Lett., 6, 034017,
https://doi.org/10.1088/1748-9326/6/3/034017, 2011a.
Popp, A., Lotze-Campen, H., Leimbach, M., Knopf, B., Beringer, T., Bauer, N.,
and Bodirsky, B.: On sustainability of bioenergy production: Integrating
co-emissions from agricultural intensification, Biomass Bioenerg., 35,
4770–4780, https://doi.org/10.1016/j.biombioe.2010.06.014, 2011b.
Popp, A., Krause, M., Dietrich, J. P., Lotze-Campen, H., Leimbach, M.,
Beringer, T., and Bauer, N.: Additional CO2 emissions from land use
change – forest conservation as a precondition for sustainable production of
second generation bioenergy, Ecol. Econ., 74, 64–70, 2012.
Popp, A., Humpenöder, F., Weindl, I., Bodirsky, B. L., Bonsch, M.,
Lotze-Campen, H., Müller, C., Biewald, A., Rolinski, S., Stevanovic, M.,
and Dietrich, J. P.: Land-use protection for climate change mitigation, Nat.
Clim. Change, 4, 1095–1098, https://doi.org/10.1038/nclimate2444, 2014.
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest,
E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., Hasegawa,
T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H.,
Waldhoff, S., Weindl, I., Wise, M., Kriegler, E., Lotze-Campen, H., Fricko,
O., Riahi, K., and Vuuren, D. P. v.: Land-use futures in the shared
socio-economic pathways, Global Environ. Chang., 42, 331–345, 2017.
Pradhan, P., Costa, L., Rybski, D., Lucht, W., and Kropp, J. P.: A Systematic
Study of Sustainable Development Goal (SDG) Interactions, Earth's Future, 5,
1169–1179, 2017.
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.R-project.org (last access: 15 March 2019), 2017.
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C.,
Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann,
K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., and
Jones, J. W.: Assessing agricultural risks of climate change in the 21st
century in a global gridded crop model intercomparison, P. Natl. Acad. Sci.
USA, 111, 3268–3273, https://doi.org/10.1073/pnas.1222463110, 2014.
Schmitz, C., Biewald, A., Lotze-Campen, H., Popp, A., Dietrich, J. P.,
Bodirsky, B. L., Krause, M., and Weindl, I.: Trading more food:
Implications for land use, greenhouse gas emissions, and the food system,
Global Environ. Chang., 22, 189–209, https://doi.org/10.1016/j.gloenvcha.2011.09.013,
2012.
Schmitz, C., Lotze-Campen, H., Gerten, D., Dietrich, J. P., Bodirsky, B.,
Biewald, A., and Popp, A.: Blue water scarcity and the economic impacts of
future agricultural trade and demand, Water Resour. Res., 49, 3601–3617,
https://doi.org/10.1002/wrcr.20188, 2013.
Stehfest, E., van Vuuren, D., Kram, T., Bouwman, L., Alekemade, R., Bakkenes,
M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van
Minnen, J., Müller, C., and Prins, A.: Integrated Assessment of
Global Environmental Change with IMAGE 3.0 – Model description and
policy applications, Tech. Rep. 735, PBL Netherlands Environmental Assessment
Agency, The Hague, 2014.
Stevanović, M., Popp, A., Lotze-Campen, H., Dietrich, J. P., Müller,
C., Bonsch, M., Schmitz, C., Bodirsky, B. L., Humpenöder, F., and Weindl,
I.: The impact of high-end climate change on agricultural welfare, Science
Advances, 2, e1501452, https://doi.org/10.1126/sciadv.1501452, 2016.
Stevanović, M., Popp, A., Bodirsky, B. L., Humpenöder, F.,
Müller, C., Weindl, I., Dietrich, J. P., Lotze-Campen, H., Kreidenweis,
U., Rolinski, S., Biewald, A., and Wang, X.: Mitigation Strategies for
Greenhouse Gas Emissions from Agriculture and Land-Use Change:
Consequences for Food Prices, Environ. Sci. Technol., 51, 365–374, 2017.
Strzepek, K. and Boehlert, B.: Competition for water for the food system,
Philosophical Transactions of the Royal Society of London B: Biological
Sciences, 365, 2927–2940, https://doi.org/10.1098/rstb.2010.0152, 2010.
UNFCCC: Adoption of the Paris Agreement, Tech. Rep. FCCC/CP/2015/L.9/Rev.1,
http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf (last access:
15 March 2019), 2015.
United Nations: Transforming our world: the 2030 Agenda for Sustainable
Development, available at:
https://sustainabledevelopment.un.org/post2015/transformingourworld
(last access: 15 March 2019), 2015.
Wang, X., Biewald, A., Dietrich, J. P., Schmitz, C., Lotze-Campen, H.,
Humpenöder, F., Bodirsky, B. L., and Popp, A.: Taking account of
governance: Implications for land-use dynamics, food prices, and trade
patterns, Ecol. Econ., 122, 12–24, https://doi.org/10.1016/j.ecolecon.2015.11.018,
2016.
Weindl, I., Lotze-Campen, H., Popp, A., Bodirsky, B., and Rolinski, S.:
Impacts of livestock feeding technologies on greenhouse gas emissions, in:
Contributed paper at the IATRC Public Trade Policy Research and Analysis
Symposium. Climate Change in World Agriculture: Mitigation, Adaptation, Trade
and Food Security, Universität Hohenheim, Stuttgart, Germany, 2010.
Weindl, I., Lotze-Campen, H., Popp, A., Müller, C., Havlík, P.,
Herrero, M., Schmitz, C., and Rolinski, S.: Livestock in a changing climate:
production system transitions as an adaptation strategy for agriculture,
Environ. Res. Lett., 10, 094021, https://doi.org/10.1088/1748-9326/10/9/094021, 2015.
Weindl, I., Bodirsky, B. L., Rolinski, S., Biewald, A., Lotze-Campen, H.,
Müller, C., Dietrich, J. P., Humpenöder, F., Stevanović, M.,
Schaphoff, S., and Popp, A.: Livestock production and the water challenge of
future food supply: Implications of agricultural management and dietary
choices, Global Environmen. Chang., 47, 121–132,
https://doi.org/10.1016/j.gloenvcha.2017.09.010, 2017a.
Weindl, I., Popp, A., Bodirsky, B. L., Rolinski, S., Lotze-Campen, H.,
Biewald, A., Humpenöder, F., Dietrich, J. P., and Stevanović, M.:
Livestock and human use of land: Productivity trends and dietary choices as
drivers of future land and carbon dynamics, Global Planet. Change, 159,
1–10, https://doi.org/10.1016/j.gloplacha.2017.10.002, 2017b.
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New
York, https://ggplot2.tidyverse.org/ (last access:
15 March 2019), 2009.
Wise, M., Calvin, K., Kyle, P., Luckow, P., and Edmonds, J.: Economic and
physical modeling of land use in gcam 3.0 and an application to agricultural
productivity, land, and terrestrial carbon, Climate Change Economics, 05,
1450003, https://doi.org/10.1142/S2010007814500031, 2014.
World Bank: World Development Indicators,
https://data.worldbank.org/products/wdi (last access: 15 March 2019),
2018.
Short summary
We provides an overview on version 4 of the MAgPIE open-source framework for modeling global land systems. Among others, MAgPIE has been used to simulate marker scenarios of the Shared Socioeconomic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. Its recent version marks the first open-source release of the framework and introduces several new features. Via its modularity and spatial flexibility it can serve as a tool for a broad range of land-related research topics.
We provides an overview on version 4 of the MAgPIE open-source framework for modeling global...
