Articles | Volume 12, issue 12
https://doi.org/10.5194/gmd-12-5029-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-12-5029-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data
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, Unter den Linden 6, 10099 Berlin, Germany
Matthias Forkel
Technische Universität Wien, Department of Geodesy and Geoinformation, Gusshausstr. 27–29, 1040 Vienna, Austria
Werner von Bloh
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03,
14412 Potsdam, Germany
Boris Sakschewski
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03,
14412 Potsdam, Germany
Manoel Cardoso
Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas, 1.758 – Jardim da Granja, 12227-010, São José dos Campos, São Paulo, Brazil
Mercedes Bustamante
Instituto de Ciências Biologicas, Universidade de Brasília, Campus Universitário Darcy Ribeiro – Asa Norte, 70910-900, Brasília, Brazil
Jürgen Kurths
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, Unter den Linden 6, 10099 Berlin, Germany
Kirsten Thonicke
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03,
14412 Potsdam, Germany
Related authors
Marie Brunel, Stephen B. Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
Biogeosciences, 23, 939–965, https://doi.org/10.5194/bg-23-939-2026, https://doi.org/10.5194/bg-23-939-2026, 2026
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 Dynamic Global Vegetation Model (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.
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
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.
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.
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.
Sibyll Schaphoff, David Hötten, Christoph Müller, Dieter Gerten, Sebastian Ostberg, and Werner von Bloh
Geosci. Model Dev., 19, 7615–7651, https://doi.org/10.5194/gmd-19-7615-2026, https://doi.org/10.5194/gmd-19-7615-2026, 2026
Short summary
Short summary
Methane is a powerful driver of climate change, yet emissions from wetlands and farming remain uncertain. We improved a global vegetation model that links water, plants and soils to methane production and release. The model now captures waterlogged areas, methane creation and escape, and flood-tolerant plants. It reproduces global wetland patterns and emissions more realistically, helping assess how climate and land-use change may affect future methane release and improve climate projections.
Siyuan Wang, Hui Yang, Sujan Koirala, Maurizio Santoro, Anna Candotti, Ulrich Weber, Ranit De, Claire Robin, Felix Cremer, Matthias Forkel, Markus Reichstein, and Nuno Carvalhais
Earth Syst. Sci. Data, 18, 5895–5913, https://doi.org/10.5194/essd-18-5895-2026, https://doi.org/10.5194/essd-18-5895-2026, 2026
Short summary
Short summary
Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as
regimeleave a unique footprint in a forest's spatial biomass patterns. We trained a model on millions of computer simulations to learn this link. By applying this model to detailed satellite biomass, we could read these patterns to infer the disturbance regime globally, helping make climate projections more accurate.
Jannes Breier, Luana Schwarz, Hannah Prawitz, Werner von Bloh, Christoph Müller, Stephen Björn Wirth, Max Bechthold, Dieter Gerten, and Jonathan F. Donges
Geosci. Model Dev., 19, 6829–6855, https://doi.org/10.5194/gmd-19-6829-2026, https://doi.org/10.5194/gmd-19-6829-2026, 2026
Short summary
Short summary
We present a new modelling framework that combines simulations of environmental change and human decision-making in an integrated approach. It allows researchers to explore how farming decisions affect land, water, and climate, and how these environmental changes influence future decisions. By connecting people and nature, it helps investigate food security, climate adaptation, and sustainability challenges, and supports the development of more realistic scenarios of future environmental change.
Tobias Braun, Sara M. Vallejo-Bernal, Norbert Marwan, Juergen Kurths, Johannes Quaas, Albert Díaz-Guilera, Luis Gimeno, and Miguel D. Mahecha
Earth Syst. Dynam., 17, 695–716, https://doi.org/10.5194/esd-17-695-2026, https://doi.org/10.5194/esd-17-695-2026, 2026
Short summary
Short summary
Atmospheric rivers (ARs) move vast amounts of water through the atmosphere and often cause weather extremes, yet they are usually studied as regional events. Using 84 years of mapped AR trajectories, we reveal the global "roadmap" of ARs, a transport network of high-activity hubs, sparse atmospheric highways & hierarchical basins. Our approach shows how water vapor is systematically channelled through an atmospheric transport network, offering new ways to study changes in the global water cycle.
Andreia F. S. Ribeiro, Maik Biling, Kirsten Thonicke, Werner von Bloh, Jakob Wessel, Sabine Undorf, Matthias Forkel, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2026-2952, https://doi.org/10.5194/egusphere-2026-2952, 2026
Short summary
Short summary
Wildfires are becoming more extreme, yet our state-of-the-art tools fail to capture the full risk. We simulate a large ensemble of wildfire simulations capturing a broader range of physically plausible extreme wildfire events beyond what observations alone can reveal. Extreme fire danger alone does not explain the worst impacts: ignitions, fuel and vegetation-fire feedback need to be incorporated. This modelling framework is transferable to other climate-impact sectors beyond wildfires.
Marie Hemmen, Heidi Webber, Werner von Bloh, Jens Heinke, and Christoph Müller
EGUsphere, https://doi.org/10.5194/egusphere-2026-1898, https://doi.org/10.5194/egusphere-2026-1898, 2026
Short summary
Short summary
In this study we present a lightweight approach to compute crop canopy temperatures in computationally expensive models, which we apply in the global Lund-Potsdam-Jena managed Land model. The evaluation reveals that the developed approach reproduces cooling and heating effects of the canopy for daily maximum temperatures and suggests that replacement of standard 2 m air temperature input by simulated canopy temperatures improves the skill to model high temperature impacts on crop growth.
Olivia Hau, Matthias Forkel, Wolfgang Buermann, Johanna Kranz, Mirco Migliavacca, Ulrich Weber, and Alexander Josef Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2026-910, https://doi.org/10.5194/egusphere-2026-910, 2026
Short summary
Short summary
Shifts in spring and autumn growth due to climate warming change how plants reflect sunlight and release heat and moisture into the air, modulating surface warming. The strength of these effects and their regional variability remain poorly understood. Using satellite and climate data, we show that earlier spring growth increases moisture release, especially in forests, while autumn changes are smaller and less consistent. Impacts on land-atmosphere interactions vary by ecosystem and data source.
Marie Brunel, Stephen B. Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
Biogeosciences, 23, 939–965, https://doi.org/10.5194/bg-23-939-2026, https://doi.org/10.5194/bg-23-939-2026, 2026
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 Dynamic Global Vegetation Model (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.
Jéssica Schüler, Sarah Bereswill, Werner von Bloh, Maik Billing, Boris Sakschewski, Luke Oberhagemann, Kirsten Thonicke, and Mercedes M. C. Bustamante
Biogeosciences, 23, 95–113, https://doi.org/10.5194/bg-23-95-2026, https://doi.org/10.5194/bg-23-95-2026, 2026
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.
Evripidis Avouris, Christopher Marrs, Kristina Beetz, Lucie Kudláčková, Markéta Poděbradská, Miroslav Trnka, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4859, https://doi.org/10.5194/egusphere-2025-4859, 2025
Short summary
Short summary
Wildfires are increasing in Central Europe. We studied how they could threaten settlements in the Saxon–Czech border region. Using satellite information, local data, and computer simulations, we mapped where fires are most likely and how intense they could be. We tested the model against a destructive fire that occurred in 2022. The results are shared in an interactive web map with the aim of helping residents and agencies improve preparedness and coordinate cross-border disaster response.
Luana Schwarz, Jannes Breier, Hannah Prawitz, Max Bechthold, Werner von Bloh, Sara M. Constantino, Dieter Gerten, Jobst Heitzig, Ronja Hotz, Leander John, Christoph Müller, Johan Rockström, and Jonathan F. Donges
EGUsphere, https://doi.org/10.5194/egusphere-2025-4079, https://doi.org/10.5194/egusphere-2025-4079, 2025
Short summary
Short summary
We present a novel global model that links farmer decisions with ecological processes to explore how agricultural systems co-evolve. Unlike previous tools, it captures feedbacks between society and nature at up-to planetary scale. We find that conservation practices can restore soil health and support stable harvests. Adoption spreads through learning and norms, showing how regeneration at the farm scale can ripple outward, contributing to global sustainability and Earth system resilience.
Renata Moura da Veiga, Celso von Randow, Chantelle Burton, Douglas I. Kelley, Manoel Cardoso, and Fabiano Morelli
Nat. Hazards Earth Syst. Sci., 25, 3581–3601, https://doi.org/10.5194/nhess-25-3581-2025, https://doi.org/10.5194/nhess-25-3581-2025, 2025
Short summary
Short summary
We systematically reviewed 77 papers to understand the Cerrado’s fire emissions within the global carbon budget by evaluating how fire parameters can inform emission estimates and mitigation strategies. Estimating fire emissions in the Cerrado requires a holistic approach, combining fire carbon emission estimates, fire dynamic parameters, and fire management and policy. We highlight key research gaps that could provide more comprehensive insights into accounting for fire emissions in the Cerrado.
Jamir Priesner, Boris Sakschewski, Maik Billing, Werner von Bloh, Sebastian Fiedler, Sarah Bereswill, Kirsten Thonicke, and Britta Tietjen
Nat. Hazards Earth Syst. Sci., 25, 3309–3331, https://doi.org/10.5194/nhess-25-3309-2025, https://doi.org/10.5194/nhess-25-3309-2025, 2025
Short summary
Short summary
In our simulations increased drought frequencies lead to a drastic reduction in biomass in temperate pine monoculture and mixed forests. Mixed forests eventually recovered as long as drought frequency 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, broadleaved trees with higher wood density and slower growth. This would have strong implications for forestry and other ecosystem services.
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
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.
Adarsh Jojo Thomas, Jürgen Kurths, and Daniel Schertzer
Nonlin. Processes Geophys., 32, 131–138, https://doi.org/10.5194/npg-32-131-2025, https://doi.org/10.5194/npg-32-131-2025, 2025
Short summary
Short summary
We have developed a systematic approach to study the climate system at multiple scales using climate networks, which have been previously used to study correlations between time series in space at only a single scale. This new approach is used to upscale precipitation climate networks to study the Indian summer monsoon and to analyze strong dependencies between spatial regions, which change with changing scales.
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.
Marcos B. Sanches, Manoel Cardoso, Celso von Randow, Chris Jones, and Mathew Williams
EGUsphere, https://doi.org/10.5194/egusphere-2025-942, https://doi.org/10.5194/egusphere-2025-942, 2025
Preprint archived
Short summary
Short summary
This study examines South America's role in the global carbon cycle using flux and stock analyses from CMIP6 Earth System Models. We discuss the continent’s relevance, model-observation agreement, and the impacts of dry and wet years on major biomes. Additionally, we assess model results indicating that parts of South America could shift from carbon sinks to emitters, significantly affecting the global carbon balance.
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.
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.
Adrianus de Laat, Vincent Huijnen, Niels Andela, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2024-732, https://doi.org/10.5194/egusphere-2024-732, 2024
Preprint archived
Short summary
Short summary
This study assesses state-of-the art and more advanced and innovative satellite-observation-based (bottom-up) wildfire emission estimates. They are evaluated by comparison with satellite observation of single fire emission plumes. Results indicate that more advanced fire emission estimates – more information – are more realistic but that especially for a limited number of very large fires certain differences remain – for unknown reasons.
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.
Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023, https://doi.org/10.5194/hess-27-2645-2023, 2023
Short summary
Short summary
Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary
Short summary
Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
Short summary
Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
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.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
Short summary
Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
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.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
Short summary
Short summary
Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Vera Porwollik, Susanne Rolinski, Jens Heinke, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Biogeosciences, 19, 957–977, https://doi.org/10.5194/bg-19-957-2022, https://doi.org/10.5194/bg-19-957-2022, 2022
Short summary
Short summary
The study assesses impacts of grass cover crop cultivation on cropland during main-crop off-season periods applying the global vegetation model LPJmL (V.5.0-tillage-cc). Compared to simulated bare-soil fallowing practices, cover crops led to increased soil carbon content and reduced nitrogen leaching rates on the majority of global cropland. Yield responses of main crops following cover crops vary with location, duration of altered management, crop type, water regime, and tillage practice.
Cited articles
Alvares, C. A., Stape, J. L., Sentelhas, P. C.,
de Moraes Gonçalves, J. L., and Sparovek,
G.: Köppen's climate classification map for
Brazil, Meteorol. Z., 22, 711–728, https://doi.org/10.1127/0941-2948/2013/0507, 2013. a
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
Arpaci, A., Eastaugh, C. S., and Vacik, H.: Selecting the best performing fire
weather indices for Austrian ecoregions, Theor. Appl. Climatol., 114,
393–406, https://doi.org/10.1007/s00704-013-0839-7, 2013. a, b
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L.,
Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J.,
Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E.,
Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A.,
Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry,
S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R.,
Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical
biomass map using multiple reference datasets, Global Change Biol., 22,
1406–1420, https://doi.org/10.1111/gcb.13139, 2016. a, b
Beuchle, R., Grecchi, R. C., Shimabukuro, Y. E., Seliger, R., Eva, H. D., Sano,
E., and Achard, F.: Land cover changes in the Brazilian Cerrado and Caatinga
biomes from 1990 to 2010 based on a systematic remote sensing sampling
approach, Appl. Geogr., 58, 116–127, https://doi.org/10.1016/j.apgeog.2015.01.017,
2015. a
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,
Gerten, D., Lotze-Campen, H., Müller, C.,
Reichstein, M., and Smith, B.: Modelling the role of agriculture for the
20th century global terrestrial carbon balance, Global Change Biol., 13,
679–706, https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007. a
Broyden, C. G.: The Convergence of a Class of Double-rank Minimization
Algorithms 1. General Considerations, IMA J. Appl. Math., 6, 76–90,
https://doi.org/10.1093/imamat/6.1.76, 1970. a
Chambers, J. Q. and Artaxo, P.: Biosphere–atmosphere interactions:
Deforestation size influences rainfall, Nat. Clim. Change, 7, 175–176,
https://doi.org/10.1038/nclimate3238, 2017. a
Christian, H. J., Blakeslee, R. J., Boccippio, D. J., Boeck, W. L., Buechler,
D. E., Driscoll, K. T., Goodman, S. J., Hall, J. M., Koshak, W. J., Mach,
D. M., and Stewart, M. F.: Global frequency and distribution of lightning as
observed from space by the Optical Transient Detector, J. Geophys. Res.
Atmos., 108, 4005, https://doi.org/10.1029/2002JD002347, 2003. a, b
Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J.,
Martín, M. P., Vilar, L.,
Martínez, J.,
Martín, S., Ibarra, P., de la Riva, J.,
Baeza, J., Rodríguez, F., Molina, J. R.,
Herrera, M. A., and Zamora, R.: Development of a framework for fire risk
assessment using remote sensing and geographic information system
technologies, Ecol. Modell., 221, 46–58,
https://doi.org/10.1016/j.ecolmodel.2008.11.017, 2010. a
Civil, O. and Environmental Engineering/Princeton University, D.: Global
Meteorological Forcing Dataset for Land Surface Modeling, UCAR/NCAR –
Research Data Archive, available at: https://rda.ucar.edu/datasets/ds314.0 (last access: 20 March 2019), 2006. a
Cochrane, M. A. and Laurance, W. F.: Synergisms among Fire, Land Use, and
Climate Change in the Amazon, Ambio, 37, 522–527, https://doi.org/10.2307/25547943,
2008. a
Drüke, M., Forkel, M., von Bloh, W., Sakschewski, B., Cardoso, M., Bustamante, M., Kurths, J., and Thonicke, K.: LPJmL4 Model Code, V 4.0.003, Gitlab, https://doi.org/10.5281/zenodo.3497213, 2019. a
Fader, M., Rost, S., Müller, C., Bondeau, A., and
Gerten, D.: Virtual water content of temperate cereals and maize: Present
and potential future patterns, J. Hydrol., 384, 218–231,
https://doi.org/10.1016/j.jhydrol.2009.12.011, 2010. a
Fletcher, R.: A new approach to variable metric algorithms, comjnl., 13,
317–322, https://doi.org/10.1093/comjnl/13.3.317, 1970. a
Forkel, M. and Drüke, M.: LPJmLmdi Model Code, V 1.31, Gitlab, https://doi.org/10.5281/zenodo.3497201, 2019. a
Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., and Thonicke, K.: A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1), Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, 2017. 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, c
Forkel, M., Dorigo, W., Lasslop, G., Chuvieco, E., Hantson, S., Heil, A.,
Teubner, I., Thonicke, K., and Harrison, S. P.: Recent global and regional
trends in burned area and their compensating environmental controls,
Environ. Res. Commun., 1, 051005, https://doi.org/10.1088/2515-7620/ab25d2,
2019b. a
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.:
Terrestrial vegetation and water
balance–hydrological evaluation of a dynamic
global vegetation model, J. Hydrol., 286, 249–270,
https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004. a
Goff, J. and Gratch, S.: List 1947, Smithsonian meteorological tables,
Transactions of the American Society of Ventilation Engineering, vol. 52, p. 95,
1946. a
Goldewijk, K. K., Beusen, A., van Drecht, G., and de Vos, M.: The HYDE 3.1
spatially explicit database of human-induced global land-use change over the
past 12,000 years, Global Ecol. Biogeogr., 20, 73–86,
https://doi.org/10.1111/j.1466-8238.2010.00587.x, 2011. a
Goldfarb, D.: A family of variable-metric methods derived by variational
means, Math. Comput., 24, 23–26, https://doi.org/10.1090/S0025-5718-1970-0258249-6,
1970. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. 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, b
Harvard: Harvard WorldMap, available at: http://worldmap.harvard.edu,
last access: 27 March, 2019. a
Hoffmann, W. A., Jackson, R. B., Hoffmann, W. A., and Jackson, R. B.:
Vegetation–Climate Feedbacks in the Conversion of Tropical
Savanna to Grassland, J. Climate, 13, 1593–1602, https://doi.org/10.1175/1520-0442(2000)013<1593:VCFITC>2.0.CO;2, 2000. a
IBGE: Mapa de Biomas e de Vegetação,
available at: https://ww2.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm,
last access: 14 February, 2019. a
Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J.,
Williamson, G. J., and Bowman, D. M. J. S.: Climate-induced variations in
global wildfire danger from 1979 to 2013, Nat. Commun., 6, 7537,
https://doi.org/10.1038/ncomms8537, 2015. a, b
Keeley, J. E., Pausas, J. G., Rundel, P. W., Bond, W. J., and Bradstock, R. A.:
Fire as an evolutionary pressure shaping plant traits, Trends Plant Sci.,
16, 406–411, https://doi.org/10.1016/j.tplants.2011.04.002, 2011. a
Keenan, T. F., Carbone, M. S., Reichstein, M., and Richardson, A. D.: The
model–data fusion pitfall: assuming certainty in an uncertain
world, Oecologia, 167, 587, https://doi.org/10.1007/s00442-011-2106-x, 2011. a
Keetch, J. J. and Byram, G. M.: A Drought Index for Forest Fire Control, Southeastern Forest Experiment Station, U.S. Department of Agriculture, Asheville, NC, USA, Forest Service, Res. Pap. SE-38, 35 pp.,
available at: https://www.fs.usda.gov/treesearch/pubs/40 (last access: 20 March 2019), 1968. a
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013. a
Knorr, W., Arneth, A., and Jiang, L.: Demographic controls of future global
fire risk, Nat. Clim. Change, 6, 781–758, https://doi.org/10.1038/nclimate2999, 2016. a
Krawchuk, M. A. and Moritz, M. A.: Constraints on global fire activity vary
across a resource gradient, Ecology, 92, 121–132, https://doi.org/10.1890/09-1843.1,
2011. a
Lahsen, M., Bustamante, M. M. C., and Dalla-Nora, E. L.: Undervaluing and
Overexploiting the Brazilian Cerrado at Our Peril, Environment, 58, 4–15,
https://doi.org/10.1080/00139157.2016.1229537, 2016. a, b
Langmann, B., Duncan, B., Textor, C., Trentmann, J., and van der Werf, G. R.:
Vegetation fire emissions and their impact on air pollution and climate,
Atmos. Environ., 43, 107–116, https://doi.org/10.1016/j.atmosenv.2008.09.047, 2009. a
Lasslop, G., Thonicke, K., and Kloster, S.: SPITFIRE within the MPI Earth
system model: Model development and evaluation, J. Adv. Model. Earth Syst.,
6, 740–755, https://doi.org/10.1002/2013MS000284, 2014. a
Lasslop, G., Hantson, S., and Kloster, S.: Influence of wind speed on the
global variability of burned fraction: a global fire model's perspective,
Int. J. Wildland Fire, 24, 989–1000, https://doi.org/10.1071/WF15052, 2015. a
Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S., and Kloster, S.:
Multiple stable states of tree cover in a global land surface model due to a
fire-vegetation feedback, Geophys. Res. Lett., 43, 6324–6331,
https://doi.org/10.1002/2016GL069365, 2016. a
Le Quéré, C., Moriarty, R., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Friedlingstein, P., Peters, G. P., Andres, R. J., Boden, T. A., Houghton, R. A., House, J. I., Keeling, R. F., Tans, P., Arneth, A., Bakker, D. C. E., Barbero, L., Bopp, L., Chang, J., Chevallier, F., Chini, L. P., Ciais, P., Fader, M., Feely, R. A., Gkritzalis, T., Harris, I., Hauck, J., Ilyina, T., Jain, A. K., Kato, E., Kitidis, V., Klein Goldewijk, K., Koven, C., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lima, I. D., Metzl, N., Millero, F., Munro, D. R., Murata, A., Nabel, J. E. M. S., Nakaoka, S., Nojiri, Y., O'Brien, K., Olsen, A., Ono, T., Pérez, F. F., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Rödenbeck, C., Saito, S., Schuster, U., Schwinger, J., Séférian, R., Steinhoff, T., Stocker, B. D., Sutton, A. J., Takahashi, T., Tilbrook, B., van der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Vandemark, D., Viovy, N., Wiltshire, A., Zaehle, S., and Zeng, N.: Global Carbon Budget 2015, Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, 2015. a
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., and Peng, S.: Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015), Earth Syst. Sci. Data, 10, 219–234, https://doi.org/10.5194/essd-10-219-2018, 2018. a
Mebane, Jr., W. R. and Sekhon, J. S.: Genetic Optimization Using Derivatives:
The rgenoud Package for R, J. Stat. Softw., 42, 1–26,
https://doi.org/10.18637/jss.v042.i11, 2011. a
Moreira de Araújo, F., Ferreira, L. G., and
Arantes, A. E.: Distribution Patterns of Burned Areas in the Brazilian
Biomes: An Analysis Based on Satellite Data for the 2002–2010
Period, Remote Sens., 4, 1929–1946, https://doi.org/10.3390/rs4071929, 2012. a
Nachtergaele, F. O., van Velthuizen, H. T., and Verelst, L.: Harmonized World Soil Database, available at: http://pure.iiasa.ac.at/id/eprint/8958 (last access: 4 October 2018), 2009. a
Panisset, J. S., Libonati, R., Gouveia, C. M. P., Machado-Silva, F.,
França, D. A.,
França, J. R. A., and Peres, L. F.:
Contrasting patterns of the extreme drought episodes of 2005, 2010 and 2015
in the Amazon Basin, Int. J. Climatol., 38, 1096–1104,
https://doi.org/10.1002/joc.5224, 2017. a
Parente, L., Ferreira, L., Faria, A., Nogueira, S.,
Araújo, F., Teixeira, L., and Hagen, S.:
Monitoring the brazilian pasturelands: A new mapping approach based on the
landsat 8 spectral and temporal domains, Int. J. Appl. Earth Obs. Geoinf.,
62, 135–143, https://doi.org/10.1016/j.jag.2017.06.003, 2017. 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, b, c, d
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
Prado, D. E.: As caatingas da América do Sul, Ecologia e
conservação da Caatinga, 2, 3–74, 2003. a
Pyne, S. J., Andrews, P. L., and Laven, R. D.: Introduction to wildland fire, 2nd edn., DigitalCommons@USU,
available at: https://digitalcommons.usu.edu/barkbeetles/135 (last access: 20 March 2019), 1996. a
Rabin, S. S., Ward, D. S., Malyshev, S. L., Magi, B. I., Shevliakova, E., and Pacala, S. W.: A fire model with distinct crop, pasture, and non-agricultural burning: use of new data and a model-fitting algorithm for FINAL.1, Geosci. Model Dev., 11, 815–842, https://doi.org/10.5194/gmd-11-815-2018, 2018. a, b, c, d, e, f
Ray, D., Nepstad, D., and Moutinho, P.: Micrometeorological and canopy
controls of fire susceptibility in a forested amazon landscape, Ecol. Appl.,
15, 1664–1678, https://doi.org/10.1890/05-0404, 2005. a, b
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., Toll, D., Rodell, M., Houser, P. R.,
Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K.,
Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P.,
Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B.
Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a
Rogers, B. M., Soja, A. J., Goulden, M. L., and Randerson, J. T.: Influence of tree species on continental differences in boreal fires and climate
feedbacks, Nat. Geosci., 8, 228–234, https://doi.org/10.1038/ngeo2352, 2015. a
Roitman, I., Bustamante, M. M. C., Haidar, R. F., Shimbo, J. Z., Abdala, G. C.,
Eiten, G., Fagg, C. W., Felfili, M. C., Felfili, J. M., Jacobson, T. K. B.,
Lindoso, G. S., Keller, M., Lenza, E., Miranda, S. C., Pinto, J. R. R.,
Rodrigues, A. A., Delitti, W. B. C., Roitman, P., and Sampaio, J. M.:
Optimizing biomass estimates of savanna woodland at different spatial scales
in the Brazilian Cerrado: Re-evaluating allometric equations and
environmental influences, PLoS One, 13, e0196742,
https://doi.org/10.1371/journal.pone.0196742, 2018. a
Rothermel, R. C.: A mathematical model for predicting fire spread in wildland fuels, DigitalCommons@USU, Res. Pap. INT-115, 1,
available at: https://digitalcommons.usu.edu/barkbeetles/438 (last access: 20 March 2019), 1972. a
Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke, J., and Lucht, W.: Contribution of permafrost soils to the global carbon budget, Environ. Res. Lett., 8, 014026, https://doi.org/10.1088/1748-9326/8/1/014026, 2013. a
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
Schaphoff, S., Forkel, M., Müller, C., Knauer, J., von Bloh, W., Gerten, D., Jägermeyr, J., Lucht, W., Rammig, A., Thonicke, K., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation, Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, 2018b. a
Seager, R., Hooks, A., Williams, A. P., Cook, B., Nakamura, J., Henderson, N., Seager, R., Hooks, A., Williams, A. P., Cook, B., Nakamura, J., and
Henderson, N.: Climatology, Variability, and Trends in the U.S. Vapor
Pressure Deficit, an Important Fire-Related Meteorological Quantity,
available at: https://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-14-0321.1 (last access: 19 October 2018), 2015. a
Sedano, F. and Randerson, J. T.: Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems, Biogeosciences, 11, 3739–3755, https://doi.org/10.5194/bg-11-3739-2014, 2014. a
Shanno, D. F.: Conditioning of quasi-Newton methods for function
minimization, Math. Comput., 24, 647–656,
https://doi.org/10.1090/S0025-5718-1970-0274029-X, 1970. a
Silverio, D. V., Brando, P. M., Balch, J. K.,
Putz, F. E., Nepstad, D. C., Oliveira-Santos, C., and Bustamante, M. M. C.:
Testing the Amazon savannization hypothesis: fire effects on invasion of a
neotropical forest by native cerrado and exotic pasture grasses, Philos. T. R. Soc. B, 368, 1619, https://doi.org/10.1098/rstb.2012.0427, 2013. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model,
Global Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x,
2003. a
Thonicke, K., Venevsky, S., Sitch, S., and Cramer, W.: The role of fire
disturbance for global vegetation dynamics: coupling fire into a Dynamic
Global Vegetation Model, Global Ecol. Biogeogr., 10, 661–677,
https://doi.org/10.1046/j.1466-822X.2001.00175.x, 2001. 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
van der Werf, G. R., Dempewolf, J., Trigg, S. N., Randerson, J. T., Kasibhatla,
P. S., Giglio, L., Murdiyarso, D., Peters, W., Morton, D. C., Collatz, G. J.,
Dolman, A. J., and DeFries, R. S.: Climate regulation of fire emissions and
deforestation in equatorial Asia, P. Natl. Acad. Sci. USA, 105,
20350–20355, https://doi.org/10.1073/pnas.0803375105, 2008.
a
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010. a
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017. a, b, c
Wagner, C. E. V., Forest, P., Station, E., Ontario, C. R., Francais, R. U. E., and Davis, H. J.: Development and Structure of the Canadian Forest
FireWeather Index System, Can. For. Serv., Forestry Tech. Rep, 1987. a
Willmott, C. J.: Some Comments on the Evaluation of Model Performance, B.
Am. Meteorol. Soc., 63, 1309–1313, https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2,
1982. a
Wilson, R. A.: A reexamination of fire spread in free-burning porous fuel beds, U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, available at: http://www.sidalc.net/cgi-bin/wxis.exe/?IsisScript=COLPOS.xis&method\={p}ost&formato=2&cantidad=1&expresion=mfn=001388, (last access: 4 October 2018), 1982. a
Yue, X. and Unger, N.: Fire air pollution reduces global terrestrial
productivity, Nat. Commun., 9, 5413, https://doi.org/10.1038/s41467-018-07921-4, 2018. a, b
Short summary
This work shows the successful application of a systematic model–data integration setup, as well as the implementation of a new fire danger formulation, in order to optimize a process-based fire-enabled dynamic global vegetation model. We have demonstrated a major improvement in the fire representation within LPJmL4-SPITFIRE in terms of the spatial pattern and the interannual variability of burned area in South America as well as in the modelling of biomass and the distribution of plant types.
This work shows the successful application of a systematic model–data integration setup, as...
Special issue