Articles | Volume 25, issue 23
https://doi.org/10.5194/acp-25-17429-2025
© 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/acp-25-17429-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Radiative forcing due to shifting southern African fire regimes
Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom
Nick Schutgens
Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Eleftherios Ioannidis
Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
now at: Research and Development Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Ivar R. van der Velde
Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
SRON Space Research Organisation Netherlands, Leiden, the Netherlands
Max J. van Gerrevink
Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Roland Vernooij
Meteorology and Air Quality Group, Wageningen University and Research, Wageningen, the Netherlands
Guido R. van der Werf
Meteorology and Air Quality Group, Wageningen University and Research, Wageningen, the Netherlands
Related authors
Elizabeth Quaye, Ben T. Johnson, James M. Haywood, Guido R. van der Werf, Roland Vernooij, Stephen A. Sitch, and Tom Eames
EGUsphere, https://doi.org/10.5194/egusphere-2025-3936, https://doi.org/10.5194/egusphere-2025-3936, 2025
Short summary
Short summary
We find aerosol optical depths in a global climate model are overestimated during extreme wildfire events if emissions are scaled up by a factor of two, typically applied to improve simulated aerosol on seasonal–annual timescales. We propose a technique where a variable scaling factor is determined by fuel consumption, improving correlation in five fire-affected areas. We explore the impact of this change on aerosol radiative effects, during extreme events and on broader space and time scales.
Roland Vernooij, Tom Eames, Jeremy Russell-Smith, Cameron Yates, Robin Beatty, Jay Evans, Andrew Edwards, Natasha Ribeiro, Martin Wooster, Tercia Strydom, Marcos Vinicius Giongo, Marco Assis Borges, Máximo Menezes Costa, Ana Carolina Sena Barradas, Dave van Wees, and Guido R. Van der Werf
Earth Syst. Dynam., 14, 1039–1064, https://doi.org/10.5194/esd-14-1039-2023, https://doi.org/10.5194/esd-14-1039-2023, 2023
Short summary
Short summary
Savannas account for over half of global landscape fire emissions. Although environmental and fuel conditions affect the ratio of species the fire emits, these dynamics have not been implemented in global models. We measured CO2, CO, CH4, and N2O emission factors (EFs), fuel parameters, and fire severity proxies during 129 individual fires. We identified EF patterns and trained models to estimate EFs of these species based on satellite observations, reducing the estimation error by 60–85 %.
Roland Vernooij, Ulrike Dusek, Maria Elena Popa, Peng Yao, Anupam Shaikat, Chenxi Qiu, Patrik Winiger, Carina van der Veen, Thomas Callum Eames, Natasha Ribeiro, and Guido R. van der Werf
Atmos. Chem. Phys., 22, 2871–2890, https://doi.org/10.5194/acp-22-2871-2022, https://doi.org/10.5194/acp-22-2871-2022, 2022
Short summary
Short summary
Landscape fires are a major source of greenhouse gases and aerosols, particularly in sub-tropical savannas. Stable carbon isotopes in emissions can be used to trace the contribution of C3 plants (e.g. trees or shrubs) and C4 plants (e.g. savanna grasses) to greenhouse gases and aerosols if the process is well understood. This helps us to link individual vegetation types to emissions, identify biomass burning emissions in the atmosphere, and improve the reconstruction of historic fire regimes.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida B. M. Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre A. G. Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido van der Werf, Sebastiaan J. van de Velde, Anthony Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-659, https://doi.org/10.5194/essd-2025-659, 2025
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Douglas I. Kelley, Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Maria L. F. Barbosa, Esther Brambleby, Joe R. McNorton, Zhongwei Liu, Anna S. I. Bradley, Katie Blackford, Eleanor Burke, Andrew Ciavarella, Enza Di Tomaso, Jonathan Eden, Igor José M. Ferreira, Lukas Fiedler, Andrew J. Hartley, Theodore R. Keeping, Seppe Lampe, Anna Lombardi, Guilherme Mataveli, Yuquan Qu, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Miguel Ángel Torres-Vázquez, Renata Veiga, Dave van Wees, Jakob B. Wessel, Emily Wright, Bibiana Bilbao, Mathieu Bourbonnais, Cong Gao, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Aya Brigitte N'Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John T. Abatzoglou, Niels Andela, Rachel Carmenta, Emilio Chuvieco, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Meier, Mark Parrington, Mojtaba Sadegh, Jesus San-Miguel-Ayanz, Fernando Sedano, Marco Turco, Guido R. van der Werf, Sander Veraverbeke, Liana O. Anderson, Hamish Clarke, Paulo M. Fernandes, and Crystal A. Kolden
Earth Syst. Sci. Data, 17, 5377–5488, https://doi.org/10.5194/essd-17-5377-2025, https://doi.org/10.5194/essd-17-5377-2025, 2025
Short summary
Short summary
The second State of Wildfires report examines extreme wildfire events from 2024 to early 2025. It analyses key regional events in Southern California, Northeast Amazonia, Pantanal–Chiquitano, and the Congo Basin, assessing their drivers and predictability and attributing them to climate change and land use. Seasonal outlooks and decadal projections are provided. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Elizabeth Quaye, Ben T. Johnson, James M. Haywood, Guido R. van der Werf, Roland Vernooij, Stephen A. Sitch, and Tom Eames
EGUsphere, https://doi.org/10.5194/egusphere-2025-3936, https://doi.org/10.5194/egusphere-2025-3936, 2025
Short summary
Short summary
We find aerosol optical depths in a global climate model are overestimated during extreme wildfire events if emissions are scaled up by a factor of two, typically applied to improve simulated aerosol on seasonal–annual timescales. We propose a technique where a variable scaling factor is determined by fuel consumption, improving correlation in five fire-affected areas. We explore the impact of this change on aerosol radiative effects, during extreme events and on broader space and time scales.
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.
August Thomasson, Pontus Roldin, Nick Schutgens, Babitha George, Hugo Denier van der Gon, Guillaume Monteil, and Marko Scholze
EGUsphere, https://doi.org/10.5194/egusphere-2025-1568, https://doi.org/10.5194/egusphere-2025-1568, 2025
Short summary
Short summary
We present top-down black carbon emissions estimates in Europe based on surface observations of concentrations at 24 rural sites from 2021. The annual emissions are 411 ± 10 Gg, overall 18 % higher compared to a traditional bottom-up estimate. Emissions are higher in for instance eastern Europe and the Iberian peninsula but lower in Poland and Italy. Validation with independent observations show overall better match and the uncertainties are reduced.
Yusuf Bhatti, Duncan Watson-Parris, Leighton Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Irfan Muhammed, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-2848, https://doi.org/10.5194/egusphere-2025-2848, 2025
Short summary
Short summary
Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and emulating millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
Short summary
Short summary
In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Gijs Leguijt, Joannes D. Maasakkers, Hugo A. C. Denier van der Gon, Arjo J. Segers, Tobias Borsdorff, Ivar R. van der Velde, and Ilse Aben
Atmos. Chem. Phys., 25, 555–574, https://doi.org/10.5194/acp-25-555-2025, https://doi.org/10.5194/acp-25-555-2025, 2025
Short summary
Short summary
The production of steel coincides with large emissions of greenhouse gases and air pollutants including carbon monoxide. European facilities are required to report their emissions, which are estimated using a variety of methods. We evaluate these estimates using carbon monoxide concentrations measured via satellite. We find generally good agreement between our values and those reported but also identify some uncertainties, showing that satellites can provide insights into these emissions.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
Short summary
Short summary
This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
Short summary
Short summary
This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yang Chen, Joanne Hall, Dave van Wees, Niels Andela, Stijn Hantson, Louis Giglio, Guido R. van der Werf, Douglas C. Morton, and James T. Randerson
Earth Syst. Sci. Data, 15, 5227–5259, https://doi.org/10.5194/essd-15-5227-2023, https://doi.org/10.5194/essd-15-5227-2023, 2023
Short summary
Short summary
Using multiple sets of remotely sensed data, we created a dataset of monthly global burned area from 1997 to 2020. The estimated annual global burned area is 774 million hectares, significantly higher than previous estimates. Burned area declined by 1.21% per year due to extensive fire loss in savanna, grassland, and cropland ecosystems. This study enhances our understanding of the impact of fire on the carbon cycle and climate system, and may improve the predictions of future fire changes.
Roland Vernooij, Tom Eames, Jeremy Russell-Smith, Cameron Yates, Robin Beatty, Jay Evans, Andrew Edwards, Natasha Ribeiro, Martin Wooster, Tercia Strydom, Marcos Vinicius Giongo, Marco Assis Borges, Máximo Menezes Costa, Ana Carolina Sena Barradas, Dave van Wees, and Guido R. Van der Werf
Earth Syst. Dynam., 14, 1039–1064, https://doi.org/10.5194/esd-14-1039-2023, https://doi.org/10.5194/esd-14-1039-2023, 2023
Short summary
Short summary
Savannas account for over half of global landscape fire emissions. Although environmental and fuel conditions affect the ratio of species the fire emits, these dynamics have not been implemented in global models. We measured CO2, CO, CH4, and N2O emission factors (EFs), fuel parameters, and fire severity proxies during 129 individual fires. We identified EF patterns and trained models to estimate EFs of these species based on satellite observations, reducing the estimation error by 60–85 %.
Athanasios Tsikerdekis, Otto P. Hasekamp, Nick A. J. Schutgens, and Qirui Zhong
Atmos. Chem. Phys., 23, 9495–9524, https://doi.org/10.5194/acp-23-9495-2023, https://doi.org/10.5194/acp-23-9495-2023, 2023
Short summary
Short summary
Aerosols are tiny particles of different substances (species) that can be emitted into the atmosphere by natural processes or by anthropogenic activities. However, the actual aerosol emission amount per species is highly uncertain. Thus in this work we correct the aerosol emissions used to drive a global aerosol–climate model using satellite observations through a process called data assimilation. These more accurate aerosol emissions can lead to a more accurate weather and climate prediction.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
Short summary
Short summary
Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
Short summary
Short summary
We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Qirui Zhong, Nick Schutgens, Guido van der Werf, Twan van Noije, Kostas Tsigaridis, Susanne E. Bauer, Tero Mielonen, Alf Kirkevåg, Øyvind Seland, Harri Kokkola, Ramiro Checa-Garcia, David Neubauer, Zak Kipling, Hitoshi Matsui, Paul Ginoux, Toshihiko Takemura, Philippe Le Sager, Samuel Rémy, Huisheng Bian, Mian Chin, Kai Zhang, Jialei Zhu, Svetlana G. Tsyro, Gabriele Curci, Anna Protonotariou, Ben Johnson, Joyce E. Penner, Nicolas Bellouin, Ragnhild B. Skeie, and Gunnar Myhre
Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, https://doi.org/10.5194/acp-22-11009-2022, 2022
Short summary
Short summary
Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Bernd Heinold, Holger Baars, Boris Barja, Matthew Christensen, Anne Kubin, Kevin Ohneiser, Kerstin Schepanski, Nick Schutgens, Fabian Senf, Roland Schrödner, Diego Villanueva, and Ina Tegen
Atmos. Chem. Phys., 22, 9969–9985, https://doi.org/10.5194/acp-22-9969-2022, https://doi.org/10.5194/acp-22-9969-2022, 2022
Short summary
Short summary
The extreme 2019–2020 Australian wildfires produced massive smoke plumes lofted into the lower stratosphere by pyrocumulonimbus convection. Most climate models do not adequately simulate the injection height of such intense fires. By combining aerosol-climate modeling with prescribed pyroconvective smoke injection and lidar observations, this study shows the importance of the representation of the most extreme wildfire events for estimating the atmospheric energy budget.
Roland Vernooij, Patrik Winiger, Martin Wooster, Tercia Strydom, Laurent Poulain, Ulrike Dusek, Mark Grosvenor, Gareth J. Roberts, Nick Schutgens, and Guido R. van der Werf
Atmos. Meas. Tech., 15, 4271–4294, https://doi.org/10.5194/amt-15-4271-2022, https://doi.org/10.5194/amt-15-4271-2022, 2022
Short summary
Short summary
Landscape fires are a substantial emitter of greenhouse gases and aerosols. Previous studies have indicated savanna emission factors to be highly variable. Improving fire emission estimates, and understanding future climate- and human-induced changes in fire regimes, requires in situ measurements. We present a drone-based method that enables the collection of a large amount of high-quality emission factor measurements that do not have the biases of aircraft or surface measurements.
Enza Di Tomaso, Jerónimo Escribano, Sara Basart, Paul Ginoux, Francesca Macchia, Francesca Barnaba, Francesco Benincasa, Pierre-Antoine Bretonnière, Arnau Buñuel, Miguel Castrillo, Emilio Cuevas, Paola Formenti, María Gonçalves, Oriol Jorba, Martina Klose, Lucia Mona, Gilbert Montané Pinto, Michail Mytilinaios, Vincenzo Obiso, Miriam Olid, Nick Schutgens, Athanasios Votsis, Ernest Werner, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 14, 2785–2816, https://doi.org/10.5194/essd-14-2785-2022, https://doi.org/10.5194/essd-14-2785-2022, 2022
Short summary
Short summary
MONARCH reanalysis of desert dust aerosols extends the existing observation-based information for mineral dust monitoring by providing 3-hourly upper-air, surface and total column key geophysical variables of the dust cycle over Northern Africa, the Middle East and Europe, at a 0.1° horizontal resolution in a rotated grid, from 2007 to 2016. This work provides evidence of the high accuracy of this data set and its suitability for air quality and health and climate service applications.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Athanasios Tsikerdekis, Nick A. J. Schutgens, Guangliang Fu, and Otto P. Hasekamp
Geosci. Model Dev., 15, 3253–3279, https://doi.org/10.5194/gmd-15-3253-2022, https://doi.org/10.5194/gmd-15-3253-2022, 2022
Short summary
Short summary
In our study we quantify the ability of the future satellite sensor SPEXone, part of the NASA PACE mission, to estimate aerosol emissions. The sensor will be able to retrieve accurate information of aerosol light extinction and most importantly light absorption. We simulate SPEXone spatial coverage and combine it with an aerosol model. We found that SPEXone will be able to estimate species-specific (e.g. dust, sea salt, organic or black carbon, sulfates) aerosol emissions very accurately.
Roland Vernooij, Ulrike Dusek, Maria Elena Popa, Peng Yao, Anupam Shaikat, Chenxi Qiu, Patrik Winiger, Carina van der Veen, Thomas Callum Eames, Natasha Ribeiro, and Guido R. van der Werf
Atmos. Chem. Phys., 22, 2871–2890, https://doi.org/10.5194/acp-22-2871-2022, https://doi.org/10.5194/acp-22-2871-2022, 2022
Short summary
Short summary
Landscape fires are a major source of greenhouse gases and aerosols, particularly in sub-tropical savannas. Stable carbon isotopes in emissions can be used to trace the contribution of C3 plants (e.g. trees or shrubs) and C4 plants (e.g. savanna grasses) to greenhouse gases and aerosols if the process is well understood. This helps us to link individual vegetation types to emissions, identify biomass burning emissions in the atmosphere, and improve the reconstruction of historic fire regimes.
Nick Schutgens, Oleg Dubovik, Otto Hasekamp, Omar Torres, Hiren Jethva, Peter J. T. Leonard, Pavel Litvinov, Jens Redemann, Yohei Shinozuka, Gerrit de Leeuw, Stefan Kinne, Thomas Popp, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 21, 6895–6917, https://doi.org/10.5194/acp-21-6895-2021, https://doi.org/10.5194/acp-21-6895-2021, 2021
Short summary
Short summary
Absorptive aerosol has a potentially large impact on climate change. We evaluate and intercompare four global satellite datasets of absorptive aerosol optical depth (AAOD) and single-scattering albedo (SSA). We show that these datasets show reasonable correlations with the AErosol RObotic NETwork (AERONET) reference, although significant biases remain. In a follow-up paper we show that these observations nevertheless can be used for model evaluation.
Roland Vernooij, Marcos Giongo, Marco Assis Borges, Máximo Menezes Costa, Ana Carolina Sena Barradas, and Guido R. van der Werf
Biogeosciences, 18, 1375–1393, https://doi.org/10.5194/bg-18-1375-2021, https://doi.org/10.5194/bg-18-1375-2021, 2021
Short summary
Short summary
We used drones to measure greenhouse gas emission factors from fires in the Brazilian Cerrado. We compared early-dry-season management fires and late-dry-season fires to determine if fire management can be a tool for abating emissions.
Although we found some evidence of increased CO and CH4 emission factors, the seasonal effect was smaller than that found in previous studies. For N2O, the third most important greenhouse gas, we found opposite trends in grass- and shrub-dominated areas.
Athanasios Tsikerdekis, Nick A. J. Schutgens, and Otto P. Hasekamp
Atmos. Chem. Phys., 21, 2637–2674, https://doi.org/10.5194/acp-21-2637-2021, https://doi.org/10.5194/acp-21-2637-2021, 2021
Short summary
Short summary
Accurate representation of aerosols in the atmosphere is hard to achieve due to their complex microphysical and optical properties and uncertain emissions. In our work, we employ a data assimilation method which integrates model simulations with satellite observation related to the amount, size and the light absorption of aerosol. The use of these observations in an experiment improves aerosol representation and it is recommended for utilization in future data assimilation practices.
Ivar R. van der Velde, Guido R. van der Werf, Sander Houweling, Henk J. Eskes, J. Pepijn Veefkind, Tobias Borsdorff, and Ilse Aben
Atmos. Chem. Phys., 21, 597–616, https://doi.org/10.5194/acp-21-597-2021, https://doi.org/10.5194/acp-21-597-2021, 2021
Short summary
Short summary
This paper compares the relative atmospheric enhancements of CO and NO2 measured by the space-based instrument TROPOMI over different fire-prone ecosystems around the world. We find distinct spatial and temporal patterns in the ΔNO2 / ΔCO ratio that correspond to regional differences in combustion efficiency. This joint analysis provides a better understanding of regional-scale combustion characteristics and can help the fire modeling community to improve existing global emission inventories.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
Short summary
Short summary
Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Cited articles
Ackermann, I. J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F. S., and Shankar, U.: Modal aerosol dynamics model for Europe, Atmospheric Environment, 32, 2981–2999, https://doi.org/10.1016/S1352-2310(98)00006-5, 1998. a
Ahmadov, R., McKeen, S. A., Robinson, A. L., Bahreini, R., Middlebrook, A. M., de Gouw, J. A., Meagher, J., Hsie, E., Edgerton, E., Shaw, S., and Trainer, M.: A volatility basis set model for summertime secondary organic aerosols over the eastern United States in 2006, Journal of Geophysical Research: Atmospheres, 117, https://doi.org/10.1029/2011JD016831, 2012. a, b, c
Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011. a, b, c
Alexandru, A., de Elia, R., Laprise, R., Separovic, L., and Biner, S.: Sensitivity Study of Regional Climate Model Simulations to Large-Scale Nudging Parameters, Monthly Weather Review, 137, 1666–1686, https://doi.org/10.1175/2008MWR2620.1, 2009. a
Andersson, A., Kirillova, E. N., Decesari, S., DeWitt, L., Gasore, J., Potter, K. E., Prinn, R. G., Rupakheti, M., de Dieu Ndikubwimana, J., Nkusi, J., and Safari, B.: Seasonal source variability of carbonaceous aerosols at the Rwanda Climate Observatory, Atmos. Chem. Phys., 20, 4561–4573, https://doi.org/10.5194/acp-20-4561-2020, 2020. a
Andreae, M. O., Andreae, T. W., Annegarn, H., Beer, J., Cachier, H., Canut, P. L., Elbert, W., Maenhaut, W., Salma, I., Wienhold, F. G., and Zenker, T.: Airborne studies of aerosol emissions from savanna fires in southern Africa: 2. Aerosol chemical composition, Journal of Geophysical Research: Atmospheres, 103, 32119–32128, https://doi.org/10.1029/98JD02280, 1998. a, b, c
Archibald, S., Scholes, R. J., Roy, D. P., Roberts, G., and Boschetti, L.: Southern African fire regimes as revealed by remote sensing, International Journal of Wildland Fire, 19, 861–878, https://doi.org/10.1071/WF10008, 2010. a, b, c, d
Archibald, S., Bond, W. J., Hoffmann, W., Lehmann, C., Staver, C., and Stevens, N.: Distribution and Determinants of Savannas, Wiley, 1–24, https://doi.org/10.1002/9781119081111.ch1, 2019. a
Bassett, R., Young, P., Blair, G., Samreen, F., and Simm, W.: A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model, Journal of Geophysical Research: Atmospheres, 125, https://doi.org/10.1029/2019JD031286, 2020. a, b
Beerling, D. J. and Osborne, C. P.: The origin of the savanna biome, Global Change Biology, 12, 2023–2031, https://doi.org/10.1111/j.1365-2486.2006.01239.x, 2006. a, b
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson‐Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A., Dufresne, J., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Reviews of Geophysics, 58, https://doi.org/10.1029/2019RG000660, 2020. a
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment, Journal of Geophysical Research: Atmospheres, 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013. a, b, c
Borsdorff, T., Hasekamp, O. P., Wassmann, A., and Landgraf, J.: Insights into Tikhonov regularization: application to trace gas column retrieval and the efficient calculation of total column averaging kernels, Atmos. Meas. Tech., 7, 523–535, https://doi.org/10.5194/amt-7-523-2014, 2014. a
Bouvet, A., Mermoz, S., Toan, T. L., Villard, L., Mathieu, R., Naidoo, L., and Asner, G. P.: An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR, Remote Sensing of Environment, 206, 156–173, https://doi.org/10.1016/j.rse.2017.12.030, 2018. a
Buchholz, R., Emmons, L., Tilmes, S., and The CESM2 Development Team: CESM2.1/CAM-chem Instantaneous Output for Boundary Conditions, lat: −45 to 10, Lon: −20 to 60, UCAR/NCAR – Atmospheric Chemistry Observations and Modeling Laboratory [data set], https://doi.org/10.5065/NMP7-EP60, 2019. a
Butz, R. J.: Traditional fire management: historical fire regimes and land use change in pastoral East Africa, International Journal of Wildland Fire, 18, 442, https://doi.org/10.1071/wf07067, 2009. a
Canut, P. L., Andreae, M. O., Harris, G. W., Wienhold, F. G., and Zenker, T.: Airborne studies of emissions from savanna fires in southern Africa: 1. Aerosol emissions measured with a laser optical particle counter, Journal of Geophysical Research: Atmospheres, 101, 23615–23630, https://doi.org/10.1029/95JD02610, 1996. a
Carslaw, K. S., Boucher, O., Spracklen, D. V., Mann, G. W., Rae, J. G. L., Woodward, S., and Kulmala, M.: A review of natural aerosol interactions and feedbacks within the Earth system, Atmos. Chem. Phys., 10, 1701–1737, https://doi.org/10.5194/acp-10-1701-2010, 2010. a
Carter, T. S., Heald, C. L., Cappa, C. D., Kroll, J. H., Campos, T. L., Coe, H., Cotterell, M. I., Davies, N. W., Farmer, D. K., Fox, C., Garofalo, L. A., Hu, L., Langridge, J. M., Levin, E. J. T., Murphy, S. M., Pokhrel, R. P., Shen, Y., Szpek, K., Taylor, J. W., and Wu, H.: Investigating Carbonaceous Aerosol and Its Absorption Properties From Fires in the Western United States (WE‐CAN) and Southern Africa (ORACLES and CLARIFY), Journal of Geophysical Research: Atmospheres, 126, https://doi.org/10.1029/2021JD034984, 2021. a
Case, M. F. and Staver, A. C.: Fire prevents woody encroachment only at higher‐than‐historical frequencies in a South African savanna, Journal of Applied Ecology, 54, 955–962, https://doi.org/10.1111/1365-2664.12805, 2017. a
Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., Coakley, J. A., Hansen, J. E., and Hofmann, D. J.: Climate Forcing by Anthropogenic Aerosols, Science, 255, 423–430, https://doi.org/10.1126/science.255.5043.423, 1992. a
Chen, G., Guo, Y., Yue, X., Tong, S., Gasparrini, A., Bell, M. L., Armstrong, B., Schwartz, J., Jaakkola, J. J. K., Zanobetti, A., Lavigne, E., Saldiva, P. H. N., Kan, H., Royé, D., Milojevic, A., Overcenco, A., Urban, A., Schneider, A., Entezari, A., Vicedo-Cabrera, A. M., Zeka, A., Tobias, A., Nunes, B., Alahmad, B., Forsberg, B., Pan, S.-C., Íñiguez, C., Ameling, C., la Cruz Valencia, C. D., Åström, C., Houthuijs, D., Dung, D. V., Samoli, E., Mayvaneh, F., Sera, F., Carrasco-Escobar, G., Lei, Y., Orru, H., Kim, H., Holobaca, I.-H., Kyselý, J., Teixeira, J. P., Madureira, J., Katsouyanni, K., Hurtado-Díaz, M., Maasikmets, M., Ragettli, M. S., Hashizume, M., Stafoggia, M., Pascal, M., Scortichini, M., de Sousa Zanotti Stagliorio Coêlho, M., Ortega, N. V., Ryti, N. R. I., Scovronick, N., Matus, P., Goodman, P., Garland, R. M., Abrutzky, R., Garcia, S. O., Rao, S., Fratianni, S., Dang, T. N., Colistro, V., Huber, V., Lee, W., Seposo, X., Honda, Y., Guo, Y. L., Ye, T., Yu, W., Abramson, M. J., Samet, J. M., and Li, S.: Mortality risk attributable to wildfire-related PM2.5 pollution: a global time series study in 749 locations, The Lancet Planetary Health, 5, e579–e587, https://doi.org/10.1016/S2542-5196(21)00200-X, 2021. a
Chen, Y., Hall, J., van Wees, D., Andela, N., Hantson, S., Giglio, L., van der Werf, G. R., Morton, D. C., and Randerson, J. T.: Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5), Earth Syst. Sci. Data, 15, 5227–5259, https://doi.org/10.5194/essd-15-5227-2023, 2023a. a, b
Chen, Y., Hall, J., van Wees, D., Andela, N., Hantson, S., Giglio, L., van der Werf, G. R., Morton, D. C., and Randerson, J. T.: Global Fire Emissions Database (GFED5) Burned Area, Zenodo [data set], https://doi.org/10.5281/zenodo.7668424, 2023b. a
Chipperfield, M. P., Liang, Q., Strahan, S. E., Morgenstern, O., Dhomse, S. S., Abraham, N. L., Archibald, A. T., Bekki, S., Braesicke, P., Genova, G. D., Fleming, E. L., Hardiman, S. C., Iachetti, D., Jackman, C. H., Kinnison, D. E., Marchand, M., Pitari, G., Pyle, J. A., Rozanov, E., Stenke, A., and Tummon, F.: Multimodel estimates of atmospheric lifetimes of long‐lived ozone‐depleting substances: Present and future, Journal of Geophysical Research: Atmospheres, 119, 2555–2573, https://doi.org/10.1002/2013JD021097, 2014. a
Ciais, P., Sabine, C., Bala, G., and Peters, W.: Carbon and Other Biogeochemical Cycles, Cambridge University Press, United Kingdom, 465–570, ISBN 9781107661820, https://doi.org/10.1017/CBO9781107415324.015, 2013. a
Clean Energy Regulator Australia: Carbon credits (carbon farming initiative – emissions abatement through savanna fire management) methodology determination, http://www.cleanenergyregulator.gov.au (last access: 22 November 2024), 2015. a
Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J., Grassi, G., Rossi, S., and Vignati, E.: GHG emissions of all world countries, https://doi.org/10.2760/173513 (online), https://doi.org/10.2760/074804 (print), 2021. a
Crutzen, P. J.: A discussion of the chemistry of some minor constituents in the stratosphere and troposphere, Pure and Applied Geophysics, 106–108, 1385–1399, https://doi.org/10.1007/BF00881092, 1973. a
DOC/NOAA/NWS/NCEP: National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, NSF National Center for Atmospheric Research [data set], https://doi.org/10.5065/D6M043C6, 2000. a
Eames, T.: Input and output files for prescribed burning emissions simulations in southern African savannas, Zenodo [data set], https://doi.org/10.5281/zenodo.15578063, 2025. a
Eames, T., Russell-Smith, J., Yates, C., Edwards, A., Vernooij, R., Ribeiro, N., Steinbruch, F., and van der Werf, G. R.: Instantaneous pre-fire biomass and fuel load measurements from multi-spectral UAS mapping in southern African Savannas, Fire, 4, https://doi.org/10.3390/fire4010002, 2021. a
Eames, T., Vernooij, R., Russell-Smith, J., Yates, C., Edwards, A., and van der Werf, G. R.: Division of the tropical savanna fire season into early and late dry season burning using MODIS active fires, International Journal of Applied Earth Observation and Geoinformation, 125, 103575, https://doi.org/10.1016/j.jag.2023.103575, 2023. a, b, c, d, e
Eames, T., Kaluka, A., Vernooij, R., Yates, C., Russell-Smith, J., and van der Werf, G. R.: A bottom–up savanna fire fuel consumption inventory and its application to savanna burning in Kafue National Park, Zambia, International Journal of Wildland Fire, 34, https://doi.org/10.1071/WF24121, 2025. a, b
Edwards, A., Archer, R., Bruyn, P. D., Evans, J., Lewis, B., Vigilante, T., Whyte, S., and Russell-Smith, J.: Transforming fire management in northern Australia through successful implementation of savanna burning emissions reductions projects, Journal of Environmental Management, 290, 112568, https://doi.org/10.1016/j.jenvman.2021.112568, 2021. a
Emmons, L. K., Schwantes, R. H., Orlando, J. J., Tyndall, G., Kinnison, D., Lamarque, J., Marsh, D., Mills, M. J., Tilmes, S., Bardeen, C., Buchholz, R. R., Conley, A., Gettelman, A., Garcia, R., Simpson, I., Blake, D. R., Meinardi, S., and Pétron, G.: The Chemistry Mechanism in the Community Earth System Model Version 2 (CESM2), Journal of Advances in Modeling Earth Systems, 12, https://doi.org/10.1029/2019MS001882, 2020. a
Evans, J. and Russell-Smith, J.: Delivering effective savanna fire management for defined biodiversity conservation outcomes: an Arnhem Land case study, International Journal of Wildland Fire, 29, 386–400, https://doi.org/10.1071/WF18126, 2020. a
Fan, Z., Neff, J. C., and Hanan, N. P.: Modeling pulsed soil respiration in an African savanna ecosystem, Agricultural and Forest Meteorology, 200, 282–292, https://doi.org/10.1016/j.agrformet.2014.10.009, 2015. a, b
Fast, J. D., Gustafson, W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology‐chemistry‐aerosol model, Journal of Geophysical Research: Atmospheres, 111, https://doi.org/10.1029/2005JD006721, 2006. a
Feng, Y., Ramanathan, V., and Kotamarthi, V. R.: Brown carbon: a significant atmospheric absorber of solar radiation?, Atmos. Chem. Phys., 13, 8607–8621, https://doi.org/10.5194/acp-13-8607-2013, 2013. a, b
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., Landschützer, P., Le Quéré, C., Luijkx, I. T., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Barbero, L., Bates, N. R., Becker, M., Bellouin, N., Decharme, B., Bopp, L., Brasika, I. B. M., Cadule, P., Chamberlain, M. A., Chandra, N., Chau, T.-T.-T., Chevallier, F., Chini, L. P., Cronin, M., Dou, X., Enyo, K., Evans, W., Falk, S., Feely, R. A., Feng, L., Ford, D. J., Gasser, T., Ghattas, J., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Joos, F., Kato, E., Keeling, R. F., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Lan, X., Lefèvre, N., Li, H., Liu, J., Liu, Z., Ma, L., Marland, G., Mayot, N., McGuire, P. C., McKinley, G. A., Meyer, G., Morgan, E. J., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K. M., Olsen, A., Omar, A. M., Ono, T., Paulsen, M., Pierrot, D., Pocock, K., Poulter, B., Powis, C. M., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Séférian, R., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tans, P. P., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., van Ooijen, E., Wanninkhof, R., Watanabe, M., Wimart-Rousseau, C., Yang, D., Yang, X., Yuan, W., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, 2023. a
Garde, M., Nadjamerrek, B. L., Kolkkiwarra, M., Kalarriya, J., Djandjomerr, J., Birriyabirriya, B., Bilindja, R., Kubarkku, M., and Biless, P.: The language of fire: seasonality, resources and landscape burning on the Arnhem Land plateau, edited by: Russell-Smith, J., Whitehead, P., and Cooke, P., Culture, Ecology and Economy of Fire Management in North Australian Savannas : Rekindling the Wurrk Tradition, CSIRO Publishing, 85–164, ISBN 9780643094024, 2009. a
Giglio, L., Justice, C., Boschetti, L., and Roy, D. P.: MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006, distributed by NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD64A1.006, 2015. a
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The Collection 6 MODIS burned area mapping algorithm and product, Remote Sensing of Environment, 217, 72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018. a, b
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019. a
Granier, C., Darras, S., van der Gon, H. D., Doubalova, J., Elguindi, N., Galle, B., Gauss, M., Guevara, M., Jalkanen, J.-P., Kuenen, J., Liousse, C., Quack, B., Simpson, D., and Sindelarova, K.: The Copernicus Atmosphere Monitoring Service global and regional emissions (April 2019 version), Copernicus Atmosphere Monitoring Service (CAMS) report [data set], https://doi.org/10.24380/d0bn-kx16, 2019. a, b
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmospheric Environment, 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a, b
Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023. a
Ha, S.: Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme, Geosci. Model Dev., 15, 1769–1788, https://doi.org/10.5194/gmd-15-1769-2022, 2022. a
Hakkarainen, J., Ialongo, I., Maksyutov, S., and Crisp, D.: Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2, Remote Sensing, 11, 850, https://doi.org/10.3390/rs11070850, 2019. a
Holben, B., Eck, T., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote, E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sensing of Environment, 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998. a
Huffman, M. R.: The many elements of traditional fire knowledge: Synthesis, classification, and aids to cross-cultural problem solving in firedependent systems around the world, Ecology and Society, 18, https://doi.org/10.5751/ES-05843-180403, 2013. a
Intergovernmental Panel on Climate Change: Climate Change 2021 – The Physical Science Basis, Cambridge University Press, ISBN 9781009157896, https://doi.org/10.1017/9781009157896, 2023. a, b, c, d
Jin, Y. and Roy, D. P.: Fire‐induced albedo change and its radiative forcing at the surface in northern Australia, Geophysical Research Letters, 32, https://doi.org/10.1029/2005GL022822, 2005. a, b
Johnston, J. M., Johnston, L. M., Wooster, M. J., Brookes, A., McFayden, C., and Cantin, A. S.: Satellite Detection Limitations of Sub-Canopy Smouldering Wildfires in the North American Boreal Forest, Fire, 1, 28, https://doi.org/10.3390/fire1020028, 2018. a
Khalil, M. and Rasmussen, R.: The global cycle of carbon monoxide: Trends and mass balance, Chemosphere, 20, 227–242, https://doi.org/10.1016/0045-6535(90)90098-E, 1990. a
Knowles, T., Stevens, N., Amoako, E. E., Armani, M., Barbosa, C., Beale, C., Bond, W., Chidumayo, E., Courtney-Mustaphi, C., Dintwe, K., Dobson, A., Donaldson, J., Dziba, L., Govender, N., Hempson, G., Humphrey, G. J., Kimuyu, D., Laris, P., N’Dri, A. B., Parr, C. L., Probert, J., Ruecker, G., Smit, I., Strydom, T., Syampungani, S., and Archibald, S.: Viability and desirability of financing conservation in Africa through fire management, Nature Sustainability, 8, 226–233, https://doi.org/10.1038/s41893-024-01490-9, 2025. a, b
Kristiansen, N. I., Stohl, A., Olivié, D. J. L., Croft, B., Søvde, O. A., Klein, H., Christoudias, T., Kunkel, D., Leadbetter, S. J., Lee, Y. H., Zhang, K., Tsigaridis, K., Bergman, T., Evangeliou, N., Wang, H., Ma, P.-L., Easter, R. C., Rasch, P. J., Liu, X., Pitari, G., Di Genova, G., Zhao, S. Y., Balkanski, Y., Bauer, S. E., Faluvegi, G. S., Kokkola, H., Martin, R. V., Pierce, J. R., Schulz, M., Shindell, D., Tost, H., and Zhang, H.: Evaluation of observed and modelled aerosol lifetimes using radioactive tracers of opportunity and an ensemble of 19 global models, Atmos. Chem. Phys., 16, 3525–3561, https://doi.org/10.5194/acp-16-3525-2016, 2016. a
Landgraf, J., aan de Brugh, J., Scheepmaker, R., Borsdorff, T., Hu, H., Houweling, S., Butz, A., Aben, I., and Hasekamp, O.: Carbon monoxide total column retrievals from TROPOMI shortwave infrared measurements, Atmos. Meas. Tech., 9, 4955–4975, https://doi.org/10.5194/amt-9-4955-2016, 2016. a
Landry, J.-S. and Matthews, H. D.: Non-deforestation fire vs. fossil fuel combustion: the source of CO2 emissions affects the global carbon cycle and climate responses, Biogeosciences, 13, 2137–2149, https://doi.org/10.5194/bg-13-2137-2016, 2016. a, b, c
Laris, P.: Burning the Seasonal Mosaic: Preventative Burning Strategies in the Wooded Savanna of Southern Mali, Human Ecology, 30, 155–186, 2002. a
Laris, P., Koné, M., Dembélé, F., Rodrigue, C. M., Yang, L., Jacobs, R., and Laris, Q.: Methane gas emissions from savanna fires: what analysis of local burning regimes in a working West African landscape tell us, Biogeosciences, 18, 6229–6244, https://doi.org/10.5194/bg-18-6229-2021, 2021. a, b
Laris, P., Koné, M., Dembélé, F., Rodrigue, C. M., Yang, L., Jacobs, R., Laris, Q., and Camara, F.: The Pyrogeography of Methane Emissions from Seasonal Mosaic Burning Regimes in a West African Landscape, Fire, 6, 52, https://doi.org/10.3390/fire6020052, 2023. a
Laskin, A., Laskin, J., and Nizkorodov, S. A.: Chemistry of Atmospheric Brown Carbon, Chemical Reviews, 115, 4335–4382, https://doi.org/10.1021/cr5006167, 2015. a
Laux, P., Nguyen, P. N. B., Cullmann, J., Van, T. P., and Kunstmann, H.: How many RCM ensemble members provide confidence in the impact of land‐use land cover change?, International Journal of Climatology, 37, 2080–2100, https://doi.org/10.1002/joc.4836, 2017. a
Lee, D., Fahey, D., Skowron, A., Allen, M., Burkhardt, U., Chen, Q., Doherty, S., Freeman, S., Forster, P., Fuglestvedt, J., Gettelman, A., León, R. D., Lim, L., Lund, M., Millar, R., Owen, B., Penner, J., Pitari, G., Prather, M., Sausen, R., and Wilcox, L.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmospheric Environment, 244, 117834, https://doi.org/10.1016/j.atmosenv.2020.117834, 2021. a
Lelieveld, J., Gromov, S., Pozzer, A., and Taraborrelli, D.: Global tropospheric hydroxyl distribution, budget and reactivity, Atmos. Chem. Phys., 16, 12477–12493, https://doi.org/10.5194/acp-16-12477-2016, 2016. a
Li, R., Liu, L., Yang, G., Zhang, C., and Wang, B.: Bitwise identical compiling setup: prospective for reproducibility and reliability of Earth system modeling, Geosci. Model Dev., 9, 731–748, https://doi.org/10.5194/gmd-9-731-2016, 2016. a
Logan, T., Dong, X., Xi, B., Zheng, X., Wu, L., Abramowitz, A., Goluszka, A., and Harper, M.: Assessing Radiative Impacts of African Smoke Aerosols Over the Southeastern Atlantic Ocean, Earth and Space Science, 11, https://doi.org/10.1029/2023EA003138, 2024. a
Lohmann, U. and Ferrachat, S.: Impact of parametric uncertainties on the present-day climate and on the anthropogenic aerosol effect, Atmos. Chem. Phys., 10, 11373–11383, https://doi.org/10.5194/acp-10-11373-2010, 2010. a
Lu, Z., Streets, D. G., Winijkul, E., Yan, F., Chen, Y., Bond, T. C., Feng, Y., Dubey, M. K., Liu, S., Pinto, J. P., and Carmichael, G. R.: Light Absorption Properties and Radiative Effects of Primary Organic Aerosol Emissions, Environmental Science & Technology, 49, 4868–4877, https://doi.org/10.1021/acs.est.5b00211, 2015. a
Lyapustin, A. and Wang, Y.: MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1 km SIN Grid V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD19A2.061, 2022. a, b, c
López-Saldaña, G., Bistinas, I., and Pereira, J. M. C.: Global analysis of radiative forcing from fire-induced shortwave albedo change, Biogeosciences, 12, 557–565, https://doi.org/10.5194/bg-12-557-2015, 2015. a
Masarie, K. A. and Tans, P. P.: Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record, Journal of Geophysical Research: Atmospheres, 100, 11593–11610, https://doi.org/10.1029/95JD00859, 1995. a
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017. a
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020. a
Mhawish, A., Banerjee, T., Sorek-Hamer, M., Lyapustin, A., Broday, D. M., and Chatfield, R.: Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia, Remote Sensing of Environment, 224, 12–28, https://doi.org/10.1016/j.rse.2019.01.033, 2019. a
Moubarak, M., Sistla, S., Potter, S., Natali, S. M., and Rogers, B. M.: Carbon emissions and radiative forcings from tundra wildfires in the Yukon–Kuskokwim River Delta, Alaska, Biogeosciences, 20, 1537–1557, https://doi.org/10.5194/bg-20-1537-2023, 2023. a, b, c, d
Myhre, G., Myhre, C. E. L., Samset, B. H., and Storelvmo, T.: Aerosols and their Relation to Global Climate and Climate Sensitivity, Nature Education Knowledge, 4, https://www.researchgate.net/publication/259117107_Aerosols_and_their_Relation_to_Global_Climate_and_Climate_Sensitivity (last access: 28 November 2025), 2013. a
National Centers for Environmental Prediction: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, NSF National Center for Atmospheric Research [data set], https://doi.org/10.5065/D6M043C6, 2000. a
Ntelekos, A. A., Smith, J. A., Donner, L., Fast, J. D., Gustafson, W. I., Chapman, E. G., and Krajewski, W. F.: The effects of aerosols on intense convective precipitation in the northeastern United States, Quarterly Journal of the Royal Meteorological Society, 135, 1367–1391, https://doi.org/10.1002/qj.476, 2009. a
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., and Kassem, K. R.: Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity, BioScience, 51, 933–938, https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2, 2001. a, b
O'Rourke, P. R., Smith, S. J., Mott, A., Ahsan, H., McDuffie, E. E., Crippa, M., Klimont, Z., McDonald, B., Wang, S., Nicholson, M. B., Feng, L., and Hoesly, R. M.: CEDS v_2021_04_21 Release Emission Data, Zenodo [data set], https://doi.org/10.5281/zenodo.4741285, 2021. a
Peace, A. H., Carslaw, K. S., Lee, L. A., Regayre, L. A., Booth, B. B. B., Johnson, J. S., and Bernie, D.: Effect of aerosol radiative forcing uncertainty on projected exceedance year of a 1.5 °C global temperature rise, Environmental Research Letters, 15, 0940a6, https://doi.org/10.1088/1748-9326/aba20c, 2020. a
Perry, J. J., Cook, G. D., Graham, E., Meyer, C. P., Murphy, H. T., and Vanderwal, J.: Regional seasonality of fire size and fire weather conditions across Australia's northern savanna, International Journal of Wildland Fire, 29, 1–10, https://doi.org/10.1071/WF19031, 2019. a
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A.: Terrestrial ecosystem production: A process model based on global satellite and surface data, Global Biogeochemical Cycles, 7, 811–841, https://doi.org/10.1029/93GB02725, 1993. a
Prather, M. J., Holmes, C. D., and Hsu, J.: Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of atmospheric chemistry, Geophysical Research Letters, 39, https://doi.org/10.1029/2012GL051440, 2012. a, b
Prather, M. J., Hsu, J., DeLuca, N. M., Jackman, C. H., Oman, L. D., Douglass, A. R., Fleming, E. L., Strahan, S. E., Steenrod, S. D., Søvde, O. A., Isaksen, I. S. A., Froidevaux, L., and Funke, B.: Measuring and modeling the lifetime of nitrous oxide including its variability, Journal of Geophysical Research: Atmospheres, 120, 5693–5705, https://doi.org/10.1002/2015JD023267, 2015. a, b
Price, O. F., Russell-Smith, J., and Watt, F.: The influence of prescribed fire on the extent of wildfire in savanna landscapes of western Arnhem Land, Australia, International Journal of Wildland Fire, 21, 297, https://doi.org/10.1071/WF10079, 2012. a, b
Ramo, R., Roteta, E., Bistinas, I., van Wees, D., Bastarrika, A., Chuvieco, E., and van der Werf, G. R.: African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data, Proceedings of the National Academy of Sciences of the United States of America, 118, https://doi.org/10.1073/pnas.2011160118, 2021. a, b
Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M., and Morton, D. C.: Global burned area and biomass burning emissions from small fires, Journal of Geophysical Research: Biogeosciences, 117, https://doi.org/10.1029/2012JG002128, 2012. a
Reid, C. E., Brauer, M., Johnston, F. H., Jerrett, M., Balmes, J. R., and Elliott, C. T.: Critical Review of Health Impacts of Wildfire Smoke Exposure, Environmental Health Perspectives, 124, 1334–1343, https://doi.org/10.1289/ehp.1409277, 2016. a
Richards, A. E., Dathe, J., and Cook, G. D.: Fire interacts with season to influence soil respiration in tropical savannas, Soil Biology and Biochemistry, 53, 90–98, https://doi.org/10.1016/j.soilbio.2012.05.009, 2012. a, b, c, d
Roy, D. P., Jin, Y., Lewis, P., and Justice, C.: Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data, Remote Sensing of Environment, 97, 137–162, https://doi.org/10.1016/j.rse.2005.04.007, 2005. a
Russell-Smith, J., Murphy, B. P., Meyer, C. P., Cook, G. D., Maier, S., Edwards, A. C., Schatz, J., and Brocklehurst, P.: Improving estimates of savanna burning emissions for greenhouse accounting in northern Australia: Limitations, challenges, applications, International Journal of Wildland Fire, 18, 1–18, https://doi.org/10.1071/WF08009, 2009. a
Russell-Smith, J., Monagle, C., Jacobsohn, M., Beatty, R. L., Bilbao, B., Millán, A., Vessuri, H., and Sánchez-Rose, I.: Can savanna burning projects deliver measurable greenhouse emissions reductions and sustainable livelihood opportunities in fire-prone settings?, Climatic Change, 140, 47–61, https://doi.org/10.1007/s10584-013-0910-5, 2013. a, b
Russell-Smith, J., Yates, C., Vernooij, R., Eames, T., van der Werf, G., Ribeiro, N., Edwards, A., Beatty, R., Lekoko, O., Mafoko, J., Monagle, C., and Johnston, S.: Opportunities and challenges for savanna burning emissions abatement in southern Africa, Journal of Environmental Management, 288, 112414, https://doi.org/10.1016/j.jenvman.2021.112414, 2021. a, b, c, d, e, f, g
Sanderfoot, O. V., Bassing, S. B., Brusa, J. L., Emmet, R. L., Gillman, S. J., Swift, K., and Gardner, B.: A review of the effects of wildfire smoke on the health and behavior of wildlife, Environmental Research Letters, 16, 123003, https://doi.org/10.1088/1748-9326/ac30f6, 2021. a
Seiler, W. and Crutzen, P. J.: Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning, Climatic Change, 2, 207–247, https://doi.org/10.1007/BF00137988, 1980. a
Sluyter, A. and Duvall, C.: African Fire Cultures, Cattle Ranching, and Colonial Landscape Transformations in the Neotropics, Geographical Review, 106, 294–311, https://doi.org/10.1111/j.1931-0846.2015.12138.x, 2016. a
Smith, A. M. S., Wooster, M. J., Drake, N. A., Dipotso, F. M., and Perry, G. L. W.: Fire in the African Savanna: testing the impact of incomplete combustion on pyrogenic emissions estimates, Ecological Applications, 15, 1074–1082, https://doi.org/10.1890/03-5256, 2005. a
Soulie, A., Granier, C., Darras, S., Zilbermann, N., Doumbia, T., Guevara, M., Jalkanen, J.-P., Keita, S., Liousse, C., Crippa, M., Guizzardi, D., Hoesly, R., and Smith, S. J.: Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses, Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, 2024. a
Spero, T. L., Nolte, C. G., Mallard, M. S., and Bowden, J. H.: A Maieutic Exploration of Nudging Strategies for Regional Climate Applications Using the WRF Model, Journal of Applied Meteorology and Climatology, 57, 1883–1906, https://doi.org/10.1175/JAMC-D-17-0360.1, 2018. a
Stevenson, D. S., Zhao, A., Naik, V., O'Connor, F. M., Tilmes, S., Zeng, G., Murray, L. T., Collins, W. J., Griffiths, P. T., Shim, S., Horowitz, L. W., Sentman, L. T., and Emmons, L.: Trends in global tropospheric hydroxyl radical and methane lifetime since 1850 from AerChemMIP, Atmos. Chem. Phys., 20, 12905–12920, https://doi.org/10.5194/acp-20-12905-2020, 2020. a
Stockwell, W. R., Kirchner, F., Kuhn, M., and Seefeld, S.: A new mechanism for regional atmospheric chemistry modeling, Journal of Geophysical Research Atmospheres, 102, https://doi.org/10.1029/97jd00849, 1997. a
Sánchez-García, C., Santín, C., Doerr, S. H., Strydom, T., and Urbanek, E.: Wildland fire ash enhances short-term CO2 flux from soil in a Southern African savannah, Soil Biology and Biochemistry, 160, 108334, https://doi.org/10.1016/j.soilbio.2021.108334, 2021. a, b, c
Tan, F., Lim, H. S., Abdullah, K., and Holben, B.: Estimation of aerosol optical depth at different wavelengths by multiple regression method, Environmental Science and Pollution Research, 23, 2735–2748, https://doi.org/10.1007/s11356-015-5506-3, 2016. a
Tian, C., Yue, X., Zhu, J., Liao, H., Yang, Y., Lei, Y., Zhou, X., Zhou, H., Ma, Y., and Cao, Y.: Fire–climate interactions through the aerosol radiative effect in a global chemistry–climate–vegetation model, Atmos. Chem. Phys., 22, 12353–12366, https://doi.org/10.5194/acp-22-12353-2022, 2022. a
Tilmes, S., Hodzic, A., Emmons, L. K., Mills, M. J., Gettelman, A., Kinnison, D. E., Park, M., Lamarque, J., Vitt, F., Shrivastava, M., Campuzano‐Jost, P., Jimenez, J. L., and Liu, X.: Climate Forcing and Trends of Organic Aerosols in the Community Earth System Model (CESM2), Journal of Advances in Modeling Earth Systems, 11, 4323–4351, https://doi.org/10.1029/2019MS001827, 2019. a
Tosca, M., Diner, D., Garay, M., and Kalashnikova, O.: Observational evidence of fire-driven reduction of cloud fraction in tropical Africa, Journal of Geophysical Research: Atmospheres, 119, 8418–8432, https://doi.org/10.1002/2014JD021759, 2014. a
Tuccella, P., Curci, G., Grell, G. A., Visconti, G., Crumeyrolle, S., Schwarzenboeck, A., and Mensah, A. A.: A new chemistry option in WRF-Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: evaluation against IMPACT-EUCAARI data, Geosci. Model Dev., 8, 2749–2776, https://doi.org/10.5194/gmd-8-2749-2015, 2015. a, b
Urbanski, S.: Wildland fire emissions, carbon, and climate: Emission factors, Forest Ecology and Management, 317, 51–60, https://doi.org/10.1016/j.foreco.2013.05.045, 2014. a
van der Velde, I. R., van der Werf, G. R., van Wees, D., Schutgens, N. A. J., Vernooij, R., Houweling, S., Tonucci, E., Chuvieco, E., Randerson, J. T., Frey, M. M., Borsdorff, T., and Aben, I.: Small Fires, Big Impact: Evaluating Fire Emission Estimates in Southern Africa Using New Satellite Imagery of Burned Area and Carbon Monoxide, Geophysical Research Letters, 51, https://doi.org/10.1029/2023GL106122, 2024. a, b, c
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, d
Vernooij, R.: Measurements of savanna landscap fire emission factors for CO)2, CO, CH4 and N2O using a UAV-based sampling methodology, Zenodo [data set], https://doi.org/10.5281/zenodo.7695798, 2023. a, b
Vernooij, R., Giongo, M., Borges, M. A., Costa, M. M., Barradas, A. C. S., and van der Werf, G. R.: Intraseasonal variability of greenhouse gas emission factors from biomass burning in the Brazilian Cerrado, Biogeosciences, 18, 1375–1393, https://doi.org/10.5194/bg-18-1375-2021, 2021. a
Vernooij, R., Winiger, P., Wooster, M., Strydom, T., Poulain, L., Dusek, U., Grosvenor, M., Roberts, G. J., Schutgens, N., and van der Werf, G. R.: A quadcopter unmanned aerial system (UAS)-based methodology for measuring biomass burning emission factors, Atmos. Meas. Tech., 15, 4271–4294, https://doi.org/10.5194/amt-15-4271-2022, 2022. a, b, c, d, e, f, g, h
Vernooij, R., Eames, T., Russell-Smith, J., Yates, C., Beatty, R., Evans, J., Edwards, A., Ribeiro, N., Wooster, M., Strydom, T., Giongo, M. V., Borges, M. A., Menezes Costa, M., Barradas, A. C. S., van Wees, D., and Van der Werf, G. R.: Dynamic savanna burning emission factors based on satellite data using a machine learning approach, Earth Syst. Dynam., 14, 1039–1064, https://doi.org/10.5194/esd-14-1039-2023, 2023. a, b, c, d, e, f, g, h, i, j
Vidot, J., Landgraf, J., Hasekamp, O., Butz, A., Galli, A., Tol, P., and Aben, I.: Carbon monoxide from shortwave infrared reflectance measurements: A new retrieval approach for clear sky and partially cloudy atmospheres, Remote Sensing of Environment, 120, 255–266, https://doi.org/10.1016/j.rse.2011.09.032, 2012. a
Ward, D. E., Hao, W. M., Susott, R. A., Babbitt, R. E., Shea, R. W., Kauffman, J. B., and Justice, C. O.: Effect of fuel composition on combustion efficiency and emission factors for African savanna ecosystems, Journal of Geophysical Research: Atmospheres, 101, 23569–23576, https://doi.org/10.1029/95JD02595, 1996. a
Wiedinmyer, C., Kimura, Y., McDonald-Buller, E. C., Emmons, L. K., Buchholz, R. R., Tang, W., Seto, K., Joseph, M. B., Barsanti, K. C., Carlton, A. G., and Yokelson, R.: The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications, Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, 2023. a
Williams, R. J., Gill, A. M., and Moore, P. H. R.: Fire in Tropical Savannas: the Kapalga Experiment, chap. 3, Springer-Verlag: New York, 33–46, https://doi.org/10.1007/b97225, 2003. a
Xu, L., Zhu, Q., Riley, W. J., Chen, Y., Wang, H., Ma, P.-L., and Randerson, J. T.: The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM), Journal of Climate, 1–60, https://doi.org/10.1175/JCLI-D-21-0193.1, 2021. a
Yokelson, R. J., Burling, I. R., Gilman, J. B., Warneke, C., Stockwell, C. E., de Gouw, J., Akagi, S. K., Urbanski, S. P., Veres, P., Roberts, J. M., Kuster, W. C., Reardon, J., Griffith, D. W. T., Johnson, T. J., Hosseini, S., Miller, J. W., Cocker III, D. R., Jung, H., and Weise, D. R.: Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires, Atmos. Chem. Phys., 13, 89–116, https://doi.org/10.5194/acp-13-89-2013, 2013. a
Zheng, B., Chevallier, F., Yin, Y., Ciais, P., Fortems-Cheiney, A., Deeter, M. N., Parker, R. J., Wang, Y., Worden, H. M., and Zhao, Y.: Global atmospheric carbon monoxide budget 2000–2017 inferred from multi-species atmospheric inversions, Earth Syst. Sci. Data, 11, 1411–1436, https://doi.org/10.5194/essd-11-1411-2019, 2019. a
Zhong, Q., Schutgens, N., van der Werf, G. R., Takemura, T., van Noije, T., Mielonen, T., Checa-Garcia, R., Lohmann, U., Kirkevåg, A., Olivié, D. J., Kokkola, H., Matsui, H., Kipling, Z., Ginoux, P., Le Sager, P., Rémy, S., Bian, H., Chin, M., Zhang, K., Bauer, S. E., and Tsigaridis, K.: Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols, Science Advances, 9, https://doi.org/10.1126/sciadv.adi3568, 2023. a
Short summary
Prescribed burning is used as a landscape management tool in southern African savannas. By deliberately changing the timing of fires in this region, the climate effect (radiative forcing) of a fire season can be altered. We show that by burning earlier in the dry season a small climate cooling effect can be achieved, similar to that of a 10 % reduction in global commercial aviation emissions. Local effects must be considered before implementing a fire regime shift for climate change mitigation.
Prescribed burning is used as a landscape management tool in southern African savannas. By...
Altmetrics
Final-revised paper
Preprint