Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-2021-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2021-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Luke Oberhagemann
CORRESPONDING AUTHOR
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht Str. 24/25, Potsdam, Germany
Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473 Potsdam, Germany
Maik Billing
Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473 Potsdam, Germany
Werner von Bloh
Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473 Potsdam, Germany
Markus Drüke
Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473 Potsdam, Germany
Deutscher Wetterdienst, Hydrometeorologie, Frankfurter Str. 135, 63067 Offenbach, Germany
Matthew Forrest
Senckenberg Biodiversity and Climate Research Centre, Senckenberganlage 25, 60325 Frankfurt, Germany
Simon P. K. Bowring
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL-CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Jessica Hetzer
Senckenberg Biodiversity and Climate Research Centre, Senckenberganlage 25, 60325 Frankfurt, Germany
Jaime Ribalaygua Batalla
Meteogrid, Almansa 88, Madrid, Spain
Kirsten Thonicke
Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473 Potsdam, Germany
Related authors
Jéssica Schüler, Sarah Bereswill, Werner von Bloh, Maik Billing, Boris Sakschewski, Luke Oberhagemann, Kirsten Thonicke, and Mercedes M. C. Bustamante
EGUsphere, https://doi.org/10.5194/egusphere-2025-2225, https://doi.org/10.5194/egusphere-2025-2225, 2025
Short summary
Short summary
We introduced a new plant type into a global vegetation model to better represent the ecology of the Cerrado, South America's second largest biome. This improved the model’s ability to simulate vegetation structure, root systems, and fire dynamics, aligning more closely with observations. Our results enhance understanding of tropical savannas and provide a stronger basis for studying their responses to fire and climate change at regional and global scales.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
Short summary
Short summary
Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Zhixuan Guo, Wei Li, Philippe Ciais, Stephen Sitch, Guido R. van der Werf, Simon P. K. Bowring, Ana Bastos, Florent Mouillot, Jiaying He, Minxuan Sun, Lei Zhu, Xiaomeng Du, Nan Wang, and Xiaomeng Huang
Earth Syst. Sci. Data, 17, 3599–3618, https://doi.org/10.5194/essd-17-3599-2025, https://doi.org/10.5194/essd-17-3599-2025, 2025
Short summary
Short summary
To address the limitations of short time spans in satellite data and spatiotemporal discontinuity in site records, we reconstructed global monthly burned area maps at a 0.5° resolution for 1901–2020 using machine learning models. The global burned area is predicted at 3.46 × 106–4.58 × 106 km² per year, showing a decline from 1901 to 1978, an increase from 1978 to 2008 and a sharper decrease from 2008 to 2020. This dataset provides a benchmark for studies on fire ecology and the carbon cycle.
Chuanlong Zhou, Biqing Zhu, Antoine Halff, Steven J. Davis, Zhu Liu, Simon Bowring, Simon Ben Arous, and Philippe Ciais
Earth Syst. Sci. Data, 17, 3431–3446, https://doi.org/10.5194/essd-17-3431-2025, https://doi.org/10.5194/essd-17-3431-2025, 2025
Short summary
Short summary
After Russia's 2022 invasion of Ukraine, Europe's energy dynamics shifted significantly. Our study introduces updated datasets tracking changes in natural gas supply, usage, and transmission within the EU27&UK. We discovered that Europe adapted to losing Russian gas by increasing LNG (liquefied natural gas) imports and shifting to renewables. Our insights could shape future energy policies and climate research.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Jéssica Schüler, Sarah Bereswill, Werner von Bloh, Maik Billing, Boris Sakschewski, Luke Oberhagemann, Kirsten Thonicke, and Mercedes M. C. Bustamante
EGUsphere, https://doi.org/10.5194/egusphere-2025-2225, https://doi.org/10.5194/egusphere-2025-2225, 2025
Short summary
Short summary
We introduced a new plant type into a global vegetation model to better represent the ecology of the Cerrado, South America's second largest biome. This improved the model’s ability to simulate vegetation structure, root systems, and fire dynamics, aligning more closely with observations. Our results enhance understanding of tropical savannas and provide a stronger basis for studying their responses to fire and climate change at regional and global scales.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
Short summary
Short summary
An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
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.
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
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.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Ryan Vella, Matthew Forrest, Andrea Pozzer, Alexandra P. Tsimpidi, Thomas Hickler, Jos Lelieveld, and Holger Tost
Atmos. Chem. Phys., 25, 243–262, https://doi.org/10.5194/acp-25-243-2025, https://doi.org/10.5194/acp-25-243-2025, 2025
Short summary
Short summary
This study examines how land cover changes influence biogenic volatile organic compound (BVOC) emissions and atmospheric states. Using a coupled chemistry–climate–vegetation model, we compare present-day land cover (deforested for crops and grazing) with natural vegetation and an extreme reforestation scenario. We find that vegetation changes significantly impact global BVOC emissions and organic aerosols but have a relatively small effect on total aerosols, clouds, and radiative effects.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
Short summary
Short summary
Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Blessing Kavhu, Matthew Forrest, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3595, https://doi.org/10.5194/egusphere-2024-3595, 2024
Short summary
Short summary
We developed a model to predict global wildfire patterns by examining weather, vegetation, and human activities. This tool helps forecast seasonal fire risks across diverse regions and focuses on seasonal changes, unlike existing models. Its simplicity makes it valuable for climate and fire management planning, as well as for use in global climate studies, helping communities better prepare for and adapt to rising wildfire threats.
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.
Jamir Priesner, Boris Sakschewski, Maik Billing, Werner von Bloh, Sebastian Fiedler, Sarah Bereswill, Kirsten Thonicke, and Britta Tietjen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3066, https://doi.org/10.5194/egusphere-2024-3066, 2024
Short summary
Short summary
Our simulations suggest that increased drought frequencies lead to a drastic reduction in biomass in pine monoculture and mixed forest. Mixed forest eventually recovered, as long as drought frequencies was not too high. The higher resilience of mixed forests was due to higher adaptive capacity. After adaptation mixed forests were mainly composed of smaller, broad-leaved trees with higher wood density and slower growth.This would have strong implications for forestry and other ecosystem services.
Markus Drüke, Wolfgang Lucht, Werner von Bloh, Stefan Petri, Boris Sakschewski, Arne Tobian, Sina Loriani, Sibyll Schaphoff, Georg Feulner, and Kirsten Thonicke
Earth Syst. Dynam., 15, 467–483, https://doi.org/10.5194/esd-15-467-2024, https://doi.org/10.5194/esd-15-467-2024, 2024
Short summary
Short summary
The planetary boundary framework characterizes major risks of destabilization of the Earth system. We use the comprehensive Earth system model POEM to study the impact of the interacting boundaries for climate change and land system change. Our study shows the importance of long-term effects on carbon dynamics and climate, as well as the need to investigate both boundaries simultaneously and to generally keep both boundaries within acceptable ranges to avoid a catastrophic scenario for humanity.
Dana A. Lapides, W. Jesse Hahm, Matthew Forrest, Daniella M. Rempe, Thomas Hickler, and David N. Dralle
Biogeosciences, 21, 1801–1826, https://doi.org/10.5194/bg-21-1801-2024, https://doi.org/10.5194/bg-21-1801-2024, 2024
Short summary
Short summary
Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models despite increasing recognition of its importance. Here, we add a weathered bedrock component to a widely used vegetation model. Using a case study of two sites in California and model runs across the United States, we show that more accurately representing subsurface water storage and hydrology increases summer plant water use so that it better matches patterns in distributed data products.
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.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
Short summary
Short summary
We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
Short summary
Short summary
Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
Short summary
Short summary
The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
Boris Sakschewski, Werner von Bloh, Markus Drüke, Anna Amelia Sörensson, Romina Ruscica, Fanny Langerwisch, Maik Billing, Sarah Bereswill, Marina Hirota, Rafael Silva Oliveira, Jens Heinke, and Kirsten Thonicke
Biogeosciences, 18, 4091–4116, https://doi.org/10.5194/bg-18-4091-2021, https://doi.org/10.5194/bg-18-4091-2021, 2021
Short summary
Short summary
This study shows how local adaptations of tree roots across tropical and sub-tropical South America explain patterns of biome distribution, productivity and evapotranspiration on this continent. By allowing for high diversity of tree rooting strategies in a dynamic global vegetation model (DGVM), we are able to mechanistically explain patterns of mean rooting depth and the effects on ecosystem functions. The approach can advance DGVMs and Earth system models.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Yvonne Jans, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, https://doi.org/10.5194/hess-25-2027-2021, 2021
Short summary
Short summary
Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
Cited articles
Albini, F. A.: Computer-based models of wildland fire behavior: a user's manual, Tech. rep., USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, https://www.frames.gov/documents/behaveplus/publications/Albini_1976_FIREMOD_ocr.pdf (last access: 21 March 2025), 1976. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Andela, N., Morton, D. C., Giglio, L., Chen, Y., Van Der Werf, G. R., Kasibhatla, P. S., DeFries, R. S., Collatz, G. J., Hantson, S., Kloster, S., Bachelet, D., Forrest, M., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Yue, C., and Randerson, J. T.: A human-driven decline in global burned area, Science, 356, 1356–1362, https://doi.org/10.1126/science.aal4108, 2017. a, b
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, https://doi.org/10.5194/essd-11-529-2019, 2019. a, b, c
Andrews, P. L.: The Rothermel surface fire spread model and associated developments: A comprehensive explanation, Gen. Tech. Rep. RMRS-GTR-371, Fort Collins, CO, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, 121p., 371, https://doi.org/10.2737/RMRS-GTR-371, 2018. a, b, c, d, e, f, g
Andrews, P. L., Cruz, M. G., and Rothermel, R. C.: Examination of the wind speed limit function in the Rothermel surface fire spread model, Int. J. Wildland Fire, 22, 959–969, https://doi.org/10.1071/WF12122, 2013. a, b
Aragoneses, E., García, M., Salis, M., Ribeiro, L. M., and Chuvieco, E.: Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system, Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, 2023. a
Archibald, S., Roy, D. P., Van Wilgen, B. W., and Scholes, R. J.: What limits fire? An examination of drivers of burnt area in Southern Africa, Glob. Change Biol., 15, 613–630, https://doi.org/10.1111/j.1365-2486.2008.01754.x, 2009. a, b
Archibald, S., Lehmann, C. E. R., Belcher, C. M., Bond, W. J., Bradstock, R. A., Daniau, A.-L., Dexter, K. G., Forrestel, E. J., Greve, M., He, T., Higgins, S. I., Hoffmann, W. A., Lamont, B. B., McGlinn, D. J., Moncrieff, G. R., Osborne, C. P., Pausas, J. G., Price, O., Ripley, B. S., Rogers, B. M., Schwilk, D. W., Simon, M. F., Turetsky, M. R., van der Werf, G. R., and Zanne, A. E.: Biological and geophysical feedbacks with fire in the Earth system, Environ. Res. Lett., 13, 033003, https://doi.org/10.1088/1748-9326/aa9ead, 2018. a
Balch, J. K., Abatzoglou, J. T., Joseph, M. B., Koontz, M. J., Mahood, A. L., McGlinchy, J., Cattau, M. E., and Williams, A. P.: Warming weakens the night-time barrier to global fire, Nature, 602, 442–448, https://doi.org/10.1038/s41586-021-04325-1, 2022. a
Baudena, M., Santana, V. M., Baeza, M. J., Bautista, S., Eppinga, M. B., Hemerik, L., Garcia Mayor, A., Rodriguez, F., Valdecantos, A., Vallejo, V. R., Vasques, A., and Rietkerk, M.: Increased aridity drives post fire recovery of Mediterranean forests towards open shrublands, New Phytol., 225, 1500–1515, https://doi.org/10.1111/nph.16252, 2020. a, b
Boulanger, Y., Pascual, J., Bouchard, M., D'Orangeville, L., Périé, C., and Girardin, M. P.: Multi model projections of tree species performance in Quebec, Canada under future climate change, Glob. Change Biol., 28, 1884–1902, https://doi.org/10.1111/gcb.16014, 2022. a
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D'Antonio, C. M., DeFries, R. S., Doyle, J. C., Harrison, S. P., Johnston, F. H., Keeley, J. E., Krawchuk, M. A., Kull, C. A., Marston, J. B., Moritz, M. A., Prentice, I. C., Roos, C. I., Scott, A. C., Swetnam, T. W., Van Der Werf, G. R., and Pyne, S. J.: Fire in the Earth system, Science, 324, 481–484, https://doi.org/10.1126/science.1163886, 2009. a
Bristiel, P., Gillespie, L., Østrem, L., Balachowski, J., Violle, C., and Volaire, F.: Experimental evaluation of the robustness of the growth–stress tolerance trade off within the perennial grass Dactylis glomerata, Funct. Ecol., 32, 1944–1958, https://doi.org/10.1111/1365-2435.13112, 2018. a, b, c, d
Brown, T. P., Hoylman, Z. H., Conrad, E., Holden, Z., Jencso, K., and Jolly, W. M.: Decoupling between soil moisture and biomass drives seasonal variations in live fuel moisture across co-occurring plant functional types, Fire Ecol., 18, 14, https://doi.org/10.1186/s42408-022-00136-5, 2022. a, b, c
Burgan, R. E.: Estimating live fuel moisture for the 1978 National Fire Danger Rating System, Gen. Tech. Rep. INT-226, US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, https://www.frames.gov/catalog/5573 (last access: 21 March 2025), 1979. a, b
Carlson, J. D., Bradshaw, L. S., Nelson Jr., R. M., Bensch, R. R., and Jabrzemski, R.: Application of the Nelson model to four timelag fuel classes using Oklahoma field observations: model evaluation and comparison with national fire danger rating system algorithms, Int. J. Wildland Fire, 16, 204–216, https://doi.org/10.1071/wf06073, 2007. a
Chaste, E., Girardin, M. P., Kaplan, J. O., Portier, J., Bergeron, Y., and Hély, C.: The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model, Biogeosciences, 15, 1273–1292, https://doi.org/10.5194/bg-15-1273-2018, 2018. a
Christian, H. J.: Global frequency and distribution of lightning as observed from space by the optical transient detector, J. Geophys. Res., 108, 4005, https://doi.org/10.1029/2002JD002347, 2003. a
Chuvieco, E., González, I., Verdú, F., Aguado, I., and Yebra, M.: Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem, Int. J. Wildland Fire, 18, 430–441, https://doi.org/10.1071/WF08020, 2009. a, b
Chuvieco, E., Yebra, M., Martino, S., Thonicke, K., Gómez-Giménez, M., San-Miguel, J., Oom, D., Velea, R., Mouillot, F., Molina, J. R., Miranda, A. I., Lopes, D., Salis, M., Bugaric, M., Sofiev, M., Kadantsev, E., Gitas, I. Z., Stavrakoudis, D., Eftychidis, G., Bar-Massada, A., Neidermeier, A., Pampanoni, V., Pettinari, M. L., Arrogante-Funes, F., Ochoa, C., Moreira, B., and Viegas, D.: Towards an integrated approach to wildfire risk assessment: when, where, what and how may the landscapes burn, Fire, 6, 215, https://doi.org/10.3390/fire6050215, 2023. a
Collin, A., Bernardin, D., and Séro-Guillaume, O.: A physical-based cellular automaton model for forest-fire propagation, Combust. Sci. Technol., 183, 347–369, https://doi.org/10.1080/00102202.2010.508476, 2011. a
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020. a
Drüke, M., Forkel, M., von Bloh, W., Sakschewski, B., Cardoso, M., Bustamante, M., Kurths, J., and Thonicke, K.: Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data, Geosci. Model Dev., 12, 5029–5054, https://doi.org/10.5194/gmd-12-5029-2019, 2019. a, b, c, d
Drüke, M., Sakschewski, B., von Bloh, W., Billing, M., Lucht, W., and Thonicke, K.: Fire may prevent future Amazon forest recovery after large-scale deforestation, Communications Earth and Environment, 4, 1–10, https://doi.org/10.1038/s43247-023-00911-5, 2023. a
Emmett, K. D., Renwick, K. M., and Poulter, B.: Adapting a dynamic vegetation model for regional biomass, plant biogeography, and fire modeling in the greater Yellowstone ecosystem: evaluating LPJ-GUESS-LMfireCF, Ecol. Model., 440, 109417, https://doi.org/10.1016/j.ecolmodel.2020.109417, 2021. a
Ephrath, J. E., Goudriaan, J., and Marani, A.: Modelling diurnal patterns of air temperature, radiation wind speed and relative humidity by equations from daily characteristics, Agr. Syst., 51, 377–393, https://doi.org/10.1016/0308-521X(95)00068-G, 1996. a
Felsberg, A., Kloster, S., Wilkenskjeld, S., Krause, A., and Lasslop, G.: Lightning forcing in global fire models: the importance of temporal resolution, J. Geophys. Res.-Biogeo., 123, 168–177, https://doi.org/10.1002/2017JG004080, 2018. a, b
Finney, M. A.: FARSITE: Fire Area Simulator-model development and evaluation, Res. Pap. RMRS-RP-4, Revised 2004, Ogden, UT, US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 47p., 4, https://doi.org/10.2737/RMRS-RP-4, 1998. a
Finney, M. A.: An overview of FlamMap fire modeling capabilities, in: Fuels Management-How to Measure Success: Conference Proceedings, USDA Forest Service, Rocky Mountain Research Station, Portland, OR, 213–220, https://research.fs.usda.gov/treesearch/25948 (last access: 21 March 2025), 2006. a
Fischer, R., Rödig, E., and Huth, A.: Consequences of a reduced number of plant functional types for the simulation of forest productivity, Forests, 9, 460, https://doi.org/10.3390/f9080460, 2018. a, b
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014. a, b
Forkel, M., Andela, N., Harrison, S. P., Lasslop, G., van Marle, M., Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F., Melton, J., Sitch, S., Yue, C., and Arneth, A.: Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models, Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, 2019a. a, b
Forkel, M., Drüke, M., Thurner, M., Dorigo, W., Schaphoff, S., Thonicke, K., von Bloh, W., and Carvalhais, N.: Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations, Sci. Rep.-UK, 9, 18757, https://doi.org/10.1038/s41598-019-55187-7, 2019b. a
Forkel, M., Schmidt, L., Zotta, R.-M., Dorigo, W., and Yebra, M.: Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth, Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, 2023. a, b
Giglio, L., Randerson, J. T., and Van Der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth generation global fire emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013. a
Hantson, S., Lasslop, G., Kloster, S., and Chuvieco, E.: Anthropogenic effects on global mean fire size, Int. J. Wildland Fire, 24, 589–596, https://doi.org/10.1071/WF14208, 2015. a
Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin, S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo, P., Bachelet, D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler, T., Kaplan, J. O., Kloster, S., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Meyn, A., Sitch, S., Spessa, A., van der Werf, G. R., Voulgarakis, A., and Yue, C.: The status and challenge of global fire modelling, Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, 2016. a
Hantson, S., Kelley, D. I., Arneth, A., Harrison, S. P., Archibald, S., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Nieradzik, L., Rabin, S. S., Prentice, I. C., Sheehan, T., Sitch, S., Teckentrup, L., Voulgarakis, A., and Yue, C.: Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, 2020. a, b
Harrison, S. P., Prentice, I. C., Bloomfield, K. J., Dong, N., Forkel, M., Forrest, M., Ningthoujam, R. K., Pellegrini, A., Shen, Y., Baudena, M., Cardoso, A. W., Huss, J. C., Joshi, J., Oliveras, I., Pausas, J. G., and Simpson, K. J.: Understanding and modelling wildfire regimes: an ecological perspective, Environ. Res. Lett., 16, 125008, https://doi.org/10.1088/1748-9326/ac39be, 2021. a
He, Y., Monahan, A. H., and McFarlane, N. A.: Diurnal variations of land surface wind speed probability distributions under clear sky and low cloud conditions, Geophys. Res. Lett., 40, 3308–3314, https://doi.org/10.1002/grl.50575, 2013. a
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.: The art and science of climate model tuning, B. Am. Meteorol. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a, b, c
Jolly, W. M., Nemani, R., and Running, S. W.: A generalized, bioclimatic index to predict foliar phenology in response to climate, Glob. Change Biol., 11, 619–632, https://doi.org/10.1111/j.1365-2486.2005.00930.x, 2005. a, b, c
Jung, C. and Schindler, D.: Integration of small-scale surface properties in a new high resolution global wind speed model, Energ. Convers. Manage., 210, 112733, https://doi.org/10.1016/j.enconman.2020.112733, 2020. a
Kaplan, J. O. and Lau, K. H.-K.: The WGLC global gridded lightning climatology and time series, Earth Syst. Sci. Data, 13, 3219–3237, https://doi.org/10.5194/essd-13-3219-2021, 2021. a
Kaplan, J. O., Pfeiffer, M., Kolen, J. C. A., and Davis, B. A. S.: Large scale anthropogenic reduction of forest cover in last glacial maximum Europe, PLoS One, 11, e0166726, https://doi.org/10.1371/journal.pone.0166726, 2016. a
Keep, T., Sampoux, J., Barre, P., Blanco Pastor, J., Dehmer, K. J., Durand, J., Hegarty, M., Ledauphin, T., Muylle, H., Roldán Ruiz, I., Ruttink, T., Surault, F., Willner, E., and Volaire, F.: To grow or survive: Which are the strategies of a perennial grass to face severe seasonal stress?, Funct. Ecol., 35, 1145–1158, https://doi.org/10.1111/1365-2435.13770, 2021. a, b, c, d
Kelley, D. I., Harrison, S. P., and Prentice, I. C.: Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1), Geosci. Model Dev., 7, 2411–2433, https://doi.org/10.5194/gmd-7-2411-2014, 2014. a
Klein Goldewijk, K., Beusen, A., and Janssen, P.: Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1, Holocene, 20, 565–573, https://doi.org/10.1177/0959683609356587, 2010. a
Kovesi, P.: Good Colour Maps: How to Design Them, Tech. Rep., arXiv [preprint], https://doi.org/10.48550/arXiv.1509.03700, 2015. a
Krueger, E. S., Levi, M. R., Achieng, K. O., Bolten, J. D., Carlson, J. D., Coops, N. C., Holden, Z. A., Magi, B. I., Rigden, A. J., Ochsner, T. E., Krueger, E. S., Levi, M. R., Achieng, K. O., Bolten, J. D., Carlson, J. D., Coops, N. C., Holden, Z. A., Magi, B. I., Rigden, A. J., and Ochsner, T. E.: Using soil moisture information to better understand and predict wildfire danger: a review of recent developments and outstanding questions, Int. J. Wildland Fire, 32, 111–132, https://doi.org/10.1071/WF22056, 2022. a, b, c
Lacand, M., Asselin, H., Magne, G., Aakala, T., Remy, C. C., Seppä, H., and Ali, A. A.: Multimillennial fire history of northern Finland along a latitude/elevation gradient, Quaternary Sci. Rev., 312, 108171, https://doi.org/10.1016/j.quascirev.2023.108171, 2023. a
Lasslop, G., Thonicke, K., and Kloster, S.: SPITFIRE within the MPI Earth system model: model development and evaluation, J. Adv. Model. Earth Sy., 6, 740–755, https://doi.org/10.1002/2013MS000284, 2014. a, b, c
Lasslop, G., Hantson, S., Harrison, S. P., Bachelet, D., Burton, C., Forkel, M., Forrest, M., Li, F., Melton, J. R., Yue, C., Archibald, S., Scheiter, S., Arneth, A., Hickler, T., and Sitch, S.: Global ecosystems and fire: multi model assessment of fire induced tree cover and carbon storage reduction, Glob. Change Biol., 26, 5027–5041, https://doi.org/10.1111/gcb.15160, 2020. a
Lehsten, V., Tansey, K., Balzter, H., Thonicke, K., Spessa, A., Weber, U., Smith, B., and Arneth, A.: Estimating carbon emissions from African wildfires, Biogeosciences, 6, 349–360, https://doi.org/10.5194/bg-6-349-2009, 2009. a, b
Lehsten, V., Arneth, A., Spessa, A., Thonicke, K., Moustakas, A., Lehsten, V., Arneth, A., Spessa, A., Thonicke, K., and Moustakas, A.: The effect of fire on tree–grass coexistence in savannas: a simulation study, Int. J. Wildland Fire, 25, 137–146, https://doi.org/10.1071/WF14205, 2015. a, b
Li, F., Val Martin, M., Andreae, M. O., Arneth, A., Hantson, S., Kaiser, J. W., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kluzek, E., Liu, X., Mangeon, S., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S. S.: Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP), Atmos. Chem. Phys., 19, 12545–12567, https://doi.org/10.5194/acp-19-12545-2019, 2019. a
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019. a, b, c, d
McCarty, J. L., Aalto, J., Paunu, V.-V., Arnold, S. R., Eckhardt, S., Klimont, Z., Fain, J. J., Evangeliou, N., Venäläinen, A., Tchebakova, N. M., Parfenova, E. I., Kupiainen, K., Soja, A. J., Huang, L., and Wilson, S.: Reviews and syntheses: Arctic fire regimes and emissions in the 21st century, Biogeosciences, 18, 5053–5083, https://doi.org/10.5194/bg-18-5053-2021, 2021. a
Mendiguren, G., Pilar Martín, M., Nieto, H., Pacheco-Labrador, J., and Jurdao, S.: Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site, Biogeosciences, 12, 5523–5535, https://doi.org/10.5194/bg-12-5523-2015, 2015. a, b, c
Morin, K. and Davis, J. L.: Cross-validation: What is it and how is it used in regression?, Commun. Stat. Theory, 46, 5238–5251, https://doi.org/10.1080/03610926.2015.1099672, 2017. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Narayanaraj, G. and Wimberly, M. C.: Influences of forest roads on the spatial pattern of wildfire boundaries, Int. J. Wildland Fire, 20, 792–803, https://doi.org/10.1071/WF10032, 2011. a
Nelson, R. M.: Prediction of diurnal change in 10-h fuel stick moisture content, Can. J. Forest Res., 30, 1071–1087, https://doi.org/10.1139/x00-032, 2000. a
Oberhagemann, L., Billing, M., von Bloh, W., Drueke, M., Forrest, M., Bowring, S. P. K., Hetzer, J., Ribalaygua Batalla, J., and Thonicke, K.: Model Code and Data for “Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements” (Version Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.11473450, 2024. a
Pechony, O. and Shindell, D. T.: Fire parameterization on a global scale, J. Geophys. Res.-Atmos., 114, D16115, https://doi.org/10.1029/2009JD011927, 2009. a
Perkins, O., Matej, S., Erb, K., and Millington, J.: Towards a global behavioural model of anthropogenic fire: the spatiotemporal distribution of land-fire systems, Socio-Environmental Systems Modelling, 4, 18130–18130, https://doi.org/10.18174/sesmo.18130, 2022. a
Pfeiffer, M., Spessa, A., and Kaplan, J. O.: A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0), Geosci. Model Dev., 6, 643–685, https://doi.org/10.5194/gmd-6-643-2013, 2013. a, b, c, d
Potter, B. E.: Atmospheric interactions with wildland fire behaviour – I. Basic surface interactions, vertical profiles and synoptic structures, Int. J. Wildland Fire, 21, 779–801, https://doi.org/10.1071/WF11128, 2012. a
Prentice, I. C., Kelley, D. I., Foster, P. N., Friedlingstein, P., Harrison, S. P., and Bartlein, P. J.: Modeling fire and the terrestrial carbon balance, Global Biogeochem. Cy., 25, GB3005, https://doi.org/10.1029/2010GB003906, 2011. a
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., Voulgarakis, A., Kelley, D. I., Prentice, I. C., Sitch, S., Harrison, S., and Arneth, A.: The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, 2017. a, b, c, d
Randerson, J. T., Van Der Werf, G. R., Giglio, L., Collatz, G. J., and Kasibhatla, P. S.: Global Fire Emissions Database, Version 4.1 (GFEDv4), ORNL DAAC, https://doi.org/10.3334/ORNLDAAC/1293, 2015. a, b
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018a. a, b, c, d, e
Schaphoff, S., von Bloh, W., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Müller, C., Rolinski, S., Waha, K., Stehfest, E., de Waal, L., Heyder, U., Gumpenberger, M., and Beringer, T.: LPJmL4 Model Code, Climate Institute for Climate Impact Research, https://doi.org/10.5880/PIK.2018.002, 2018b. a, b
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Langerwisch, F., Lucht, W., Rolinski, S., Waha, K., Ostberg, S., Wirth, S. B., Fader, M., Drüke, M., Jans, Y., Lutz, F., Herzfeld, T., Minoli, S., Porwollik, V., Stehfest, E., de Waal, L., Beringer (Erbrecht), T., Rost (Jachner), S., Gumpenberger, M., Heyder, U., Werner, C., Braun, J., Breier, J., Stenzel, F., Mathesius, S., Hemmen, M., Billing, M., Oberhagemann, L., Sakschewski, B., and Müller, C.: LPJmL: central open-source github repository of LPJmL at PIK, Zenodo [code], https://doi.org/10.5281/zenodo.11105506, 2024. a
Scheiter, S., Langan, L., and Higgins, S. I.: Next generation dynamic global vegetation models: learning from community ecology, New Phytol., 198, 957–969, https://doi.org/10.1111/nph.12210, 2013. a
Scott, J. H. and Burgan, R. E.: Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model, Gen. Tech. Rep. RMRS-GTR-153, Fort Collins, CO, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, 72p., 153, https://doi.org/10.2737/RMRS-GTR-153, 2005. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Senande-Rivera, M., Insua-Costa, D., and Miguez-Macho, G.: Towards an atmosphere more favourable to firestorm development in Europe, Environ. Res. Lett., 17, 094015, https://doi.org/10.1088/1748-9326/ac85ce, 2022. a
Sharples, J. J.: An overview of mountain meteorological effects relevant to fire behaviour and bushfire risk, Int. J. Wildland Fire, 18, 737–754, https://doi.org/10.1071/WF08041, 2009. a
Sjöström, J. and Granström, A.: A phenology-driven fire danger index for northern grasslands, Int. J. Wildland Fire, 32, 1332–1346, https://doi.org/10.1071/WF23013, 2023. a, b
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., Sitch, S., and Lasslop, G.: Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models, Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, 2019. a, b, c
The MathWorks Inc.: MATLAB version: 9.10.0 (R2021b), https://www.mathworks.com (last access: 21 March 2025), 2021. a
Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Venevsky, S., Thonicke, K., Sitch, S., and Cramer, W.: Simulating fire regimes in human-dominated ecosystems: Iberian Peninsula case study, Glob. Change Biol., 8, 984–998, https://doi.org/10.1046/j.1365-2486.2002.00528.x, 2002. a, b
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, 2018a. a, b, c
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: LPJmL5 Model Code, Climate Institute for Climate Impact Research, https://doi.org/10.5880/PIK.2018.011, 2018b. a
Ward, D. S., Shevliakova, E., Malyshev, S., and Rabin, S.: Trends and variability of global fire emissions due to historical anthropogenic activities, Global Biogeochem. Cy., 32, 122–142, https://doi.org/10.1002/2017GB005787, 2018. a, b, c
Weise, D. R., Koo, E., Zhou, X., Mahalingam, S., Morandini, F., and Balbi, J.-H.: Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels, Int. J. Wildland Fire, 25, 980–994, https://doi.org/10.1071/WF15177, 2016. a
Wirth, S. B., Braun, J., Heinke, J., Ostberg, S., Rolinski, S., Schaphoff, S., Stenzel, F., von Bloh, W., and Müller, C.: Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2946, 2024. a
Wu, M., Knorr, W., Thonicke, K., Schurgers, G., Camia, A., and Arneth, A.: Sensitivity of burned area in Europe to climate change, atmospheric CO2 levels, and demography: a comparison of two fire-vegetation models, J. Geophys. Res.-Biogeo., 120, 2256–2272, https://doi.org/10.1002/2015JG003036, 2015. a
Yebra, M., Scortechini, G., Badi, A., Beget, M. E., Boer, M. M., Bradstock, R., Chuvieco, E., Danson, F. M., Dennison, P., Resco de Dios, V., Di Bella, C. M., Forsyth, G., Frost, P., Garcia, M., Hamdi, A., He, B., Jolly, M., Kraaij, T., Martín, M. P., Mouillot, F., Newnham, G., Nolan, R. H., Pellizzaro, G., Qi, Y., Quan, X., Riaño, D., Roberts, D., Sow, M., and Ustin, S.: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications, Scientific Data, 6, 155, https://doi.org/10.1038/s41597-019-0164-9, 2019. a, b
Yue, C., Ciais, P., Cadule, P., Thonicke, K., Archibald, S., Poulter, B., Hao, W. M., Hantson, S., Mouillot, F., Friedlingstein, P., Maignan, F., and Viovy, N.: Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE – Part 1: simulating historical global burned area and fire regimes, Geosci. Model Dev., 7, 2747–2767, https://doi.org/10.5194/gmd-7-2747-2014, 2014. a
Yue, C., Ciais, P., Cadule, P., Thonicke, K., and van Leeuwen, T. T.: Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE – Part 2: Carbon emissions and the role of fires in the global carbon balance, Geosci. Model Dev., 8, 1321–1338, https://doi.org/10.5194/gmd-8-1321-2015, 2015. a
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.
Under climate change, the conditions necessary for wildfires to form are occurring more...