Articles | Volume 18, issue 12
https://doi.org/10.5194/bg-18-3861-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-3861-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The importance of antecedent vegetation and drought conditions as global drivers of burnt area
Alexander Kuhn-Régnier
CORRESPONDING AUTHOR
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Department of Physics, Imperial College London, London, SW7 2AZ, UK
Apostolos Voulgarakis
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Department of Physics, Imperial College London, London, SW7 2AZ, UK
School of Environmental Engineering, Technical University of Crete, Chania, Kounoupidiana,
Akrotiri, 73100 Chania, Greece
Peer Nowack
Department of Physics, Imperial College London, London, SW7 2AZ, UK
Grantham Institute and the Data Science Institute, Imperial College London, London, SW7 2AZ, UK
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
Matthias Forkel
Environmental Remote Sensing Group, TU Dresden, Dresden, Germany
I. Colin Prentice
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
Sandy P. Harrison
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Geography and Environmental Science, University of Reading, Reading, RG6 6AB, UK
Related authors
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.
Oliver Perkins, Olivia Haas, Matthew Kasoar, Apostolos Voulgarakis, and James D. A. Millington
EGUsphere, https://doi.org/10.5194/egusphere-2025-3728, https://doi.org/10.5194/egusphere-2025-3728, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Humans impact fire indirectly through climate change, but also directly through land use and different fire management strategies. We compare two recently-developed models of global burned area with very different assumptions about the role of direct human impacts on fire. We contrast their future projections and explore the implications of differences between them for climate change adaptation and fire science more broadly.
Joseph Ovwemuvwose, Ian Colin Prentice, and Heather Graven
EGUsphere, https://doi.org/10.5194/egusphere-2025-3785, https://doi.org/10.5194/egusphere-2025-3785, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This work examines the role of cropland representation and the treatment of photosynthetic pathways in the uncertainties in the carbon flux simulations in Earth System Models (ESMs). Our results show that reducing these uncertainties will require improvement of the representation of C3 and C4 crops and natural vegetation area coverage as well as the theories underpinning the simulation of their carbon uptake and storage processes.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
Atmos. Chem. Phys., 25, 7991–8028, https://doi.org/10.5194/acp-25-7991-2025, https://doi.org/10.5194/acp-25-7991-2025, 2025
Short summary
Short summary
This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight that increases in anthropogenic emissions are the primary driver of ozone increases both in the free troposphere and at the surface.
Joao C. M. Teixeira, Chantelle Burton, Douglas I. Kelley, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-3066, https://doi.org/10.5194/egusphere-2025-3066, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Burnt areas produced by wildfires around the world are decreasing, especially in tropical regions, but many climate models fail to show this trend. Our study looks at whether adding a measure of human development to a fire model could improve its representation of these processes. We found that including these factors helped the model better match observations in many regions. This shows that human activity plays a key role in shaping fire trends.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025, https://doi.org/10.5194/esd-16-607-2025, 2025
Short summary
Short summary
Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.
Amin Hassan, Iain Colin Prentice, and Xu Liang
EGUsphere, https://doi.org/10.5194/egusphere-2025-622, https://doi.org/10.5194/egusphere-2025-622, 2025
Short summary
Short summary
Evapotranspiration (ET) is the evaporation occurring from plants, soil, and water bodies. Separating these components is challenging due to the lack of measurements and uncertainty of existing ET partitioning methods. We propose a method that utilizes hydrological measurements such as streamflow to determine the ratio of transpiration (evaporation from plants) to evapotranspiration. The results provide a better understanding of plant-water interactions and new perspective on a challenging topic.
Anastasios Rovithakis, Eleanor Burke, Chantelle Burton, Matthew Kasoar, Manolis G. Grillakis, Konstantinos D. Seiradakis, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-274, https://doi.org/10.5194/egusphere-2025-274, 2025
Short summary
Short summary
JULES-INFERNO captures observed burned area across Greece fairly well for the present-day. Drastic future changes in burnt area in Eastern continental and southern Greece, especially under severe climate change scenarios. Static vegetation leads to larger burnt area compared to dynamic vegetation due to the lower concentration of flammable needleleaf trees.
Peer Nowack and Duncan Watson-Parris
Atmos. Chem. Phys., 25, 2365–2384, https://doi.org/10.5194/acp-25-2365-2025, https://doi.org/10.5194/acp-25-2365-2025, 2025
Short summary
Short summary
In our article, we review uncertainties in global climate change projections and current methods using Earth observations as constraints, which is crucial for climate risk assessments and for informing society. We then discuss how machine learning can advance the field, discussing recent work that provides potentially stronger and more robust links between observed data and future climate projections. We further discuss the challenges of applying machine learning to climate science.
Jingyu Wang, Gabriel Chiodo, Timofei Sukhodolov, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, and Sandro Vattioni
EGUsphere, https://doi.org/10.5194/egusphere-2025-340, https://doi.org/10.5194/egusphere-2025-340, 2025
Short summary
Short summary
We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then looked at the relations between ozone response and temperature and circulation changes to identify drivers of the ozone change. The climate feedback of ozone is investigated by doing offline calculations and comparing models with and without interactive chemistry. We find that ozone-climate interactions are important for Earth System Models, thus should be considered in future model development.
Philipp Breul, Paulo Ceppi, and Peer Nowack
EGUsphere, https://doi.org/10.5194/egusphere-2025-221, https://doi.org/10.5194/egusphere-2025-221, 2025
Short summary
Short summary
We explore how Pacific low-level clouds influence projections of regional climate change by adjusting a climate model to enhance low cloud response to surface temperatures. We find significant changes in projected warming patterns and circulation changes, under increased CO2 conditions. Our findings are supported by similar relationships across state-of-the-art climate models. These results highlight the importance of accurately representing clouds for predicting regional climate change impacts.
Kieran M. R. Hunt and Sandy P. Harrison
Clim. Past, 21, 1–26, https://doi.org/10.5194/cp-21-1-2025, https://doi.org/10.5194/cp-21-1-2025, 2025
Short summary
Short summary
In this study, we train machine learning models on tree rings, speleothems, and instrumental rainfall to estimate seasonal monsoon rainfall over India over the last 500 years. Our models highlight multidecadal droughts in the mid-17th and 19th centuries, and we link these to historical famines. Using techniques from explainable AI (artificial intelligence), we show that our models use known relationships between local hydroclimate and the monsoon circulation.
Jierong Zhao, Boya Zhou, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2024-3897, https://doi.org/10.5194/egusphere-2024-3897, 2025
Short summary
Short summary
We used eco-evolutionary optimality modelling to examine how climate and CO2 impacted vegetation at the Last Glacial Maximum (LGM, 21,000 years ago) and the mid-Holocene (MH, 6,000 years ago). Low CO2 at the LGM was as important as climate in reducing tree cover and productivity, and increasing C4 plant abundance. Climate had positive effects on MH vegetation, but the low CO2 was a constraint on plant growth. These results show it is important to consider changing CO2 to model ecosystem changes.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
Short summary
Short summary
Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
Luke Fionn Sweeney, Sandy P. Harrison, and Marc Vander Linden
EGUsphere, https://doi.org/10.5194/egusphere-2024-1523, https://doi.org/10.5194/egusphere-2024-1523, 2024
Short summary
Short summary
Changes in tree cover across Europe during the Holocene are reconstructed from fossil pollen data using a model developed with modern observations of tree cover and modern pollen assemblages. There is a rapid increase in tree cover after the last glacial with maximum cover during the mid-Holocene and a decline thereafter; the timing of the maximum and the speed of the increase and subsequent decrease vary regionally likely reflecting differences in climate trajectories and human influence.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Nikita Kaushal, Franziska A. Lechleitner, Micah Wilhelm, Khalil Azennoud, Janica C. Bühler, Kerstin Braun, Yassine Ait Brahim, Andy Baker, Yuval Burstyn, Laia Comas-Bru, Jens Fohlmeister, Yonaton Goldsmith, Sandy P. Harrison, István G. Hatvani, Kira Rehfeld, Magdalena Ritzau, Vanessa Skiba, Heather M. Stoll, József G. Szűcs, Péter Tanos, Pauline C. Treble, Vitor Azevedo, Jonathan L. Baker, Andrea Borsato, Sakonvan Chawchai, Andrea Columbu, Laura Endres, Jun Hu, Zoltán Kern, Alena Kimbrough, Koray Koç, Monika Markowska, Belen Martrat, Syed Masood Ahmad, Carole Nehme, Valdir Felipe Novello, Carlos Pérez-Mejías, Jiaoyang Ruan, Natasha Sekhon, Nitesh Sinha, Carol V. Tadros, Benjamin H. Tiger, Sophie Warken, Annabel Wolf, Haiwei Zhang, and SISAL Working Group members
Earth Syst. Sci. Data, 16, 1933–1963, https://doi.org/10.5194/essd-16-1933-2024, https://doi.org/10.5194/essd-16-1933-2024, 2024
Short summary
Short summary
Speleothems are a popular, multi-proxy climate archive that provide regional to global insights into past hydroclimate trends with precise chronologies. We present an update to the SISAL (Speleothem Isotopes
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
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.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Mengmeng Liu, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-12, https://doi.org/10.5194/cp-2024-12, 2024
Preprint under review for CP
Short summary
Short summary
Dansgaard-Oeschger events were large and rapid warming events that occurred multiple times during the last ice age. We show that changes in the northern extratropics and the southern extratropics were anti-phased, with warming over most of the north and cooling in the south. The reconstructions do not provide evidence for a change in seasonality in temperature. However, they do indicate that warming was generally accompanied by wetter conditions and cooling by drier conditions.
Christopher D. Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis
Atmos. Chem. Phys., 24, 1025–1039, https://doi.org/10.5194/acp-24-1025-2024, https://doi.org/10.5194/acp-24-1025-2024, 2024
Short summary
Short summary
Human-driven emissions of air pollutants, mostly caused by burning fossil fuels, impact both the climate and human health. Millions of deaths each year are caused by air pollution globally, and the future trends are uncertain. Here, we use a global climate model to study the effect of African pollutant emissions on surface level air pollution, and resultant impacts on human health, in several future emission scenarios. We find much lower health impacts under cleaner, lower-emission futures.
Huiying Xu, Han Wang, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 4511–4525, https://doi.org/10.5194/bg-20-4511-2023, https://doi.org/10.5194/bg-20-4511-2023, 2023
Short summary
Short summary
Leaf carbon (C) and nitrogen (N) are crucial elements in leaf construction and physiological processes. This study reconciled the roles of phylogeny, species identity, and climate in stoichiometric traits at individual and community levels. The variations in community-level leaf N and C : N ratio were captured by optimality-based models using climate data. Our results provide an approach to improve the representation of leaf stoichiometry in vegetation models to better couple N with C cycling.
Esmeralda Cruz-Silva, Sandy P. Harrison, I. Colin Prentice, Elena Marinova, Patrick J. Bartlein, Hans Renssen, and Yurui Zhang
Clim. Past, 19, 2093–2108, https://doi.org/10.5194/cp-19-2093-2023, https://doi.org/10.5194/cp-19-2093-2023, 2023
Short summary
Short summary
We examined 71 pollen records (12.3 ka to present) in the eastern Mediterranean, reconstructing climate changes. Over 9000 years, winters gradually warmed due to orbital factors. Summer temperatures peaked at 4.5–5 ka, likely declining because of ice sheets. Moisture increased post-11 kyr, remaining high from 10–6 kyr before a slow decrease. Climate models face challenges in replicating moisture transport.
Olivia Haas, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 3981–3995, https://doi.org/10.5194/bg-20-3981-2023, https://doi.org/10.5194/bg-20-3981-2023, 2023
Short summary
Short summary
We quantify the impact of CO2 and climate on global patterns of burnt area, fire size, and intensity under Last Glacial Maximum (LGM) conditions using three climate scenarios. Climate change alone did not produce the observed LGM reduction in burnt area, but low CO2 did through reducing vegetation productivity. Fire intensity was sensitive to CO2 but strongly affected by changes in atmospheric dryness. Low CO2 caused smaller fires; climate had the opposite effect except in the driest scenario.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
Short summary
Short summary
This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-136, https://doi.org/10.5194/bg-2023-136, 2023
Revised manuscript not accepted
Short summary
Short summary
Representing socio-economic impacts on fires is crucial to underpin the confidence in global fire models. Introducing these into INFERNO, reduces biases and improves the modelled burnt area (BA) trends when compared to observations. Including socio-economic factors in the representation of fires in Earth System Models is important for realistically simulating BA, quantifying trends in the recent past, and for understanding the main drivers of those at regional scales.
Giulia Mengoli, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2023-1261, https://doi.org/10.5194/egusphere-2023-1261, 2023
Preprint archived
Short summary
Short summary
Soil water availability affects plant carbon uptake by reducing leaf area and/or by closing stomata, which reduces its efficiency. We present a new formulation of how climatic dryness reduces both maximum carbon uptake and the soil-moisture threshold below which it declines further. This formulation illustrates how plants adapt their water conservation strategy to thrive in dry climates, and is step towards a better representation of soil-moisture effects in climate models.
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.
Mengmeng Liu, Yicheng Shen, Penelope González-Sampériz, Graciela Gil-Romera, Cajo J. F. ter Braak, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past, 19, 803–834, https://doi.org/10.5194/cp-19-803-2023, https://doi.org/10.5194/cp-19-803-2023, 2023
Short summary
Short summary
We reconstructed the Holocene climates in the Iberian Peninsula using a large pollen data set and found that the west–east moisture gradient was much flatter than today. We also found that the winter was much colder, which can be expected from the low winter insolation during the Holocene. However, summer temperature did not follow the trend of summer insolation, instead, it was strongly correlated with moisture.
Christopher D. Wells, Matthew Kasoar, Nicolas Bellouin, and Apostolos Voulgarakis
Atmos. Chem. Phys., 23, 3575–3593, https://doi.org/10.5194/acp-23-3575-2023, https://doi.org/10.5194/acp-23-3575-2023, 2023
Short summary
Short summary
The climate is altered by greenhouse gases and air pollutant particles, and such emissions are likely to change drastically in the future over Africa. Air pollutants do not travel far, so their climate effect depends on where they are emitted. This study uses a climate model to find the climate impacts of future African pollutant emissions being either high or low. The particles absorb and scatter sunlight, causing the ground nearby to be cooler, but elsewhere the increased heat causes warming.
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.
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.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
Short summary
Short summary
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Xiang Weng, Grant L. Forster, and Peer Nowack
Atmos. Chem. Phys., 22, 8385–8402, https://doi.org/10.5194/acp-22-8385-2022, https://doi.org/10.5194/acp-22-8385-2022, 2022
Short summary
Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Yicheng Shen, Luke Sweeney, Mengmeng Liu, Jose Antonio Lopez Saez, Sebastián Pérez-Díaz, Reyes Luelmo-Lautenschlaeger, Graciela Gil-Romera, Dana Hoefer, Gonzalo Jiménez-Moreno, Heike Schneider, I. Colin Prentice, and Sandy P. Harrison
Clim. Past, 18, 1189–1201, https://doi.org/10.5194/cp-18-1189-2022, https://doi.org/10.5194/cp-18-1189-2022, 2022
Short summary
Short summary
We present a method to reconstruct burnt area using a relationship between pollen and charcoal abundances and the calibration of charcoal abundance using modern observations of burnt area. We use this method to reconstruct changes in burnt area over the past 12 000 years from sites in Iberia. We show that regional changes in burnt area reflect known changes in climate, with a high burnt area during warming intervals and low burnt area when the climate was cooler and/or wetter than today.
Sandy P. Harrison, Roberto Villegas-Diaz, Esmeralda Cruz-Silva, Daniel Gallagher, David Kesner, Paul Lincoln, Yicheng Shen, Luke Sweeney, Daniele Colombaroli, Adam Ali, Chéïma Barhoumi, Yves Bergeron, Tatiana Blyakharchuk, Přemysl Bobek, Richard Bradshaw, Jennifer L. Clear, Sambor Czerwiński, Anne-Laure Daniau, John Dodson, Kevin J. Edwards, Mary E. Edwards, Angelica Feurdean, David Foster, Konrad Gajewski, Mariusz Gałka, Michelle Garneau, Thomas Giesecke, Graciela Gil Romera, Martin P. Girardin, Dana Hoefer, Kangyou Huang, Jun Inoue, Eva Jamrichová, Nauris Jasiunas, Wenying Jiang, Gonzalo Jiménez-Moreno, Monika Karpińska-Kołaczek, Piotr Kołaczek, Niina Kuosmanen, Mariusz Lamentowicz, Martin Lavoie, Fang Li, Jianyong Li, Olga Lisitsyna, José Antonio López-Sáez, Reyes Luelmo-Lautenschlaeger, Gabriel Magnan, Eniko Katalin Magyari, Alekss Maksims, Katarzyna Marcisz, Elena Marinova, Jenn Marlon, Scott Mensing, Joanna Miroslaw-Grabowska, Wyatt Oswald, Sebastián Pérez-Díaz, Ramón Pérez-Obiol, Sanna Piilo, Anneli Poska, Xiaoguang Qin, Cécile C. Remy, Pierre J. H. Richard, Sakari Salonen, Naoko Sasaki, Hieke Schneider, William Shotyk, Migle Stancikaite, Dace Šteinberga, Normunds Stivrins, Hikaru Takahara, Zhihai Tan, Liva Trasune, Charles E. Umbanhowar, Minna Väliranta, Jüri Vassiljev, Xiayun Xiao, Qinghai Xu, Xin Xu, Edyta Zawisza, Yan Zhao, Zheng Zhou, and Jordan Paillard
Earth Syst. Sci. Data, 14, 1109–1124, https://doi.org/10.5194/essd-14-1109-2022, https://doi.org/10.5194/essd-14-1109-2022, 2022
Short summary
Short summary
We provide a new global data set of charcoal preserved in sediments that can be used to examine how fire regimes have changed during past millennia and to investigate what caused these changes. The individual records have been standardised, and new age models have been constructed to allow better comparison across sites. The data set contains 1681 records from 1477 sites worldwide.
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.
João C. Teixeira, Gerd A. Folberth, Fiona M. O'Connor, Nadine Unger, and Apostolos Voulgarakis
Geosci. Model Dev., 14, 6515–6539, https://doi.org/10.5194/gmd-14-6515-2021, https://doi.org/10.5194/gmd-14-6515-2021, 2021
Short summary
Short summary
Fire constitutes a key process in the Earth system, being driven by climate as well as affecting climate. However, studies on the effects of fires on atmospheric composition and climate have been limited to date. This work implements and assesses the coupling of an interactive fire model with atmospheric composition, comparing it to an offline approach. This approach shows good performance at a global scale. However, regional-scale limitations lead to a bias in modelling fire emissions.
Peer Nowack, Lev Konstantinovskiy, Hannah Gardiner, and John Cant
Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, https://doi.org/10.5194/amt-14-5637-2021, 2021
Short summary
Short summary
Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, https://doi.org/10.5194/wcd-2-581-2021, 2021
Short summary
Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Sarah E. Parker, Sandy P. Harrison, Laia Comas-Bru, Nikita Kaushal, Allegra N. LeGrande, and Martin Werner
Clim. Past, 17, 1119–1138, https://doi.org/10.5194/cp-17-1119-2021, https://doi.org/10.5194/cp-17-1119-2021, 2021
Short summary
Short summary
Regional trends in the oxygen isotope (δ18O) composition of stalagmites reflect several climate processes. We compare stalagmite δ18O records from monsoon regions and model simulations to identify the causes of δ18O variability over the last 12 000 years, and between glacial and interglacial states. Precipitation changes explain the glacial–interglacial δ18O changes in all monsoon regions; Holocene trends are due to a combination of precipitation, atmospheric circulation and temperature changes.
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
Short summary
Short summary
Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, https://doi.org/10.5194/cp-17-1065-2021, 2021
Short summary
Short summary
The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
Yawei Qu, Apostolos Voulgarakis, Tijian Wang, Matthew Kasoar, Chris Wells, Cheng Yuan, Sunil Varma, and Laura Mansfield
Atmos. Chem. Phys., 21, 5705–5718, https://doi.org/10.5194/acp-21-5705-2021, https://doi.org/10.5194/acp-21-5705-2021, 2021
Short summary
Short summary
The meteorological effect of aerosols on tropospheric ozone is investigated using global atmospheric modelling. We found that aerosol-induced meteorological effects act to reduce modelled ozone concentrations over China, which brings the simulation closer to observed levels. Our work sheds light on understudied processes affecting the levels of tropospheric gaseous pollutants and provides a basis for evaluating such processes using a combination of observations and model sensitivity experiments.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
Short summary
Short summary
Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Abdul Malik, Peer J. Nowack, Joanna D. Haigh, Long Cao, Luqman Atique, and Yves Plancherel
Atmos. Chem. Phys., 20, 15461–15485, https://doi.org/10.5194/acp-20-15461-2020, https://doi.org/10.5194/acp-20-15461-2020, 2020
Short summary
Short summary
Solar geoengineering has been introduced to mitigate human-caused global warming by reflecting sunlight back into space. This research investigates the impact of solar geoengineering on the tropical Pacific climate. We find that solar geoengineering can compensate some of the greenhouse-induced changes in the tropical Pacific but not all. In particular, solar geoengineering will result in significant changes in rainfall, sea surface temperatures, and increased frequency of extreme ENSO events.
Laia Comas-Bru, Kira Rehfeld, Carla Roesch, Sahar Amirnezhad-Mozhdehi, Sandy P. Harrison, Kamolphat Atsawawaranunt, Syed Masood Ahmad, Yassine Ait Brahim, Andy Baker, Matthew Bosomworth, Sebastian F. M. Breitenbach, Yuval Burstyn, Andrea Columbu, Michael Deininger, Attila Demény, Bronwyn Dixon, Jens Fohlmeister, István Gábor Hatvani, Jun Hu, Nikita Kaushal, Zoltán Kern, Inga Labuhn, Franziska A. Lechleitner, Andrew Lorrey, Belen Martrat, Valdir Felipe Novello, Jessica Oster, Carlos Pérez-Mejías, Denis Scholz, Nick Scroxton, Nitesh Sinha, Brittany Marie Ward, Sophie Warken, Haiwei Zhang, and SISAL Working Group members
Earth Syst. Sci. Data, 12, 2579–2606, https://doi.org/10.5194/essd-12-2579-2020, https://doi.org/10.5194/essd-12-2579-2020, 2020
Short summary
Short summary
This paper presents an updated version of the SISAL (Speleothem Isotope Synthesis and Analysis) database. This new version contains isotopic data from 691 speleothem records from 294 cave sites and new age–depth models, including their uncertainties, for 512 speleothems.
Chris M. Brierley, Anni Zhao, Sandy P. Harrison, Pascale Braconnot, Charles J. R. Williams, David J. R. Thornalley, Xiaoxu Shi, Jean-Yves Peterschmitt, Rumi Ohgaito, Darrell S. Kaufman, Masa Kageyama, Julia C. Hargreaves, Michael P. Erb, Julien Emile-Geay, Roberta D'Agostino, Deepak Chandan, Matthieu Carré, Partrick J. Bartlein, Weipeng Zheng, Zhongshi Zhang, Qiong Zhang, Hu Yang, Evgeny M. Volodin, Robert A. Tomas, Cody Routson, W. Richard Peltier, Bette Otto-Bliesner, Polina A. Morozova, Nicholas P. McKay, Gerrit Lohmann, Allegra N. Legrande, Chuncheng Guo, Jian Cao, Esther Brady, James D. Annan, and Ayako Abe-Ouchi
Clim. Past, 16, 1847–1872, https://doi.org/10.5194/cp-16-1847-2020, https://doi.org/10.5194/cp-16-1847-2020, 2020
Short summary
Short summary
This paper provides an initial exploration and comparison to climate reconstructions of the new climate model simulations of the mid-Holocene (6000 years ago). These use state-of-the-art models developed for CMIP6 and apply the same experimental set-up. The models capture several key aspects of the climate, but some persistent issues remain.
Cited articles
Abarca, S. F., Corbosiero, K. L., and Galarneau, T. J.: An Evaluation of the
Worldwide Lightning Location Network (WWLLN) Using the National
Lightning Detection Network (NLDN) as Ground Truth, J. Geophys. Res.,
115, D18206, https://doi.org/10.1029/2009JD013411, 2010. a
Abatzoglou, J. T. and Kolden, C. A.: Relationships between Climate and
Macroscale Area Burned in the Western United States, Int. J. Wildland
Fire, 22, 1003–1020, https://doi.org/10.1071/WF13019, 2013. a, b, c
Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M., and Kolden,
C. A.: Global Patterns of Interannual Climate-Fire Relationships, Glob. Change
Biol., 24, 5164–5175, https://doi.org/10.1111/gcb.14405, 2018. a
Abatzoglou, J. T., Williams, A. P., and Barbero, R.: Global Emergence of
Anthropogenic Climate Change in Fire Weather Indices, Geophys. Res.
Lett., 46, 326–336, https://doi.org/10.1029/2018GL080959, 2019. a
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N.,
Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From
Near-Surface to Root-Zone Soil Moisture Using an Exponential Filter: An
Assessment of the Method Based on in-Situ Observations and Model Simulations,
Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008,
2008. a, b
Aldersley, A., Murray, S. J., and Cornell, S. E.: Global and Regional Analysis
of Climate and Human Drivers of Wildfire, Sci. Total Environ.,
409, 3472–3481, https://doi.org/10.1016/j.scitotenv.2011.05.032, 2011. a
Alvarado, S. T., Andela, N., Silva, T. S. F., and Archibald, S.: Thresholds of
Fire Response to Moisture and Fuel Load Differ between Tropical Savannas and
Grasslands across Continents, Global Ecol. Biogeogr., 29, 331–344,
https://doi.org/10.1111/geb.13034, 2020. 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, b
Apley, D. W. and Zhu, J.: Visualizing the Effects of Predictor Variables in
Black Box Supervised Learning Models, J. R. Stat. Soc. Ser. B, 82, 1059–1086, https://doi.org/10.1111/rssb.12377, 2020. 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, Glob. Change Biol., 22,
1406–1420, https://doi.org/10.1111/gcb.13139, 2016. a, b
Barbero, R., Abatzoglou, J. T., Larkin, N. K., Kolden, C. A., and Stocks, B.:
Climate Change Presents Increased Potential for Very Large Fires in the
Contiguous United States, Int. J. Wildland Fire, 24, 892–899,
https://doi.org/10.1071/WF15083, 2015. a
Beck, P. S. A., Atzberger, C., Høgda, K. A., Johansen, B., and Skidmore,
A. K.: Improved Monitoring of Vegetation Dynamics at Very High Latitudes:
A New Method Using MODIS NDVI, Remote Sens. Environ., 100,
321–334, https://doi.org/10.1016/j.rse.2005.10.021, 2006. a
Bedia, J., Herrera, S., Gutiérrez, J. M., Benali, A., Brands, S., Mota, B.,
and Moreno, J. M.: Global Patterns in the Sensitivity of Burned Area to
Fire-Weather: Implications for Climate Change, Agr. Forest
Meteorol., 214-215, 369–379, https://doi.org/10.1016/j.agrformet.2015.09.002, 2015. a
Bessie, W. C. and Johnson, E. A.: The Relative Importance of Fuels and
Weather on Fire Behavior in Subalpine Forests, Ecology, 76,
747–762, https://doi.org/10.2307/1939341, 1995. a
Bistinas, I., Harrison, S. P., Prentice, I. C., and Pereira, J. M. C.: Causal
Relationships versus Emergent Patterns in the Global Controls of Fire
Frequency, Biogeosciences, 11, 5087–5101, https://doi.org/10.5194/bg-11-5087-2014,
2014. a, b
Boer, M. M., Dios, V. R. D., Stefaniak, E., and Bradstock, R. A.: A
Hydroclimatic Model for the Distribution of Fire on Earth, Environ. Res.
Commun., 3, 035001, https://doi.org/10.1088/2515-7620/abec1f, 2021. a, b
Bowman, D. M. J. S., Balch, J., Artaxo, P., Bond, W. J., Cochrane, M. A.,
D'Antonio, C. M., DeFries, R., Johnston, F. H., Keeley, J. E., Krawchuk,
M. A., Kull, C. A., Mack, M., Moritz, M. A., Pyne, S., Roos, C. I., Scott,
A. C., Sodhi, N. S., and Swetnam, T. W.: The Human Dimension of Fire Regimes
on Earth, J. Biogeogr., 38, 2223–2236,
https://doi.org/10.1111/j.1365-2699.2011.02595.x, 2011. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Bürgesser, R. E.: Assessment of the World Wide Lightning Location
Network (WWLLN) Detection Efficiency by Comparison to the Lightning
Imaging Sensor (LIS): WWLLN Detection Efficiency Relative to
LIS, Q. J. R. Meteorol. Soc, 143, 2809–2817, https://doi.org/10.1002/qj.3129, 2017. a
Burton, C., Betts, R. A., Jones, C. D., and Williams, K.: Will Fire Danger Be
Reduced by Using Solar Radiation Management to Limit Global Warming
to 1.5 ∘C Compared to 2.0 ∘C?, Geophys. Res.
Lett., 45, 3644–3652, https://doi.org/10.1002/2018GL077848, 2018. a
Dankers, C. and Pfisterer, F.: Chapter 11 PFI: Training vs. Test
Data, Limitations of Interpretable Machine Learning Methods, LMU Munich, Munich, 2020. a
Dask Development Team: Dask: Library for Dynamic Task Scheduling, 2016. a
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G.,
Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J.,
Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B.,
Schröder, B., Skidmore, A. K., Zurell, D., and Lautenbach, S.:
Collinearity: A Review of Methods to Deal with It and a Simulation Study
Evaluating Their Performance, Ecography, 36, 27–46,
https://doi.org/10.1111/j.1600-0587.2012.07348.x, 2013. 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, c, d, e, f, g, h
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, d, e
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
Fox, E. W., Hill, R. A., Leibowitz, S. G., Olsen, A. R., Thornbrugh, D. J., and
Weber, M. H.: Assessing the Accuracy and Stability of Variable Selection
Methods for Random Forest Modeling in Ecology, Environ. Monit. Assess., 189,
316, https://doi.org/10.1007/s10661-017-6025-0, 2017. a
Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of Daily,
Monthly, and Annual Burned Area Using the Fourth-Generation Global Fire
Emissions Database (GFED4), J. Geophys. Res.-Biogeo., 118,
317–328, https://doi.org/10.1002/jgrg.20042, 2013. a, b, c, d
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
Sens. Environ., 217, 72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018. a, b
Goss, M., Swain, D. L., Abatzoglou, J. T., Sarhadi, A., Kolden, C., Williams,
A. P., and Diffenbaugh, N. S.: Climate Change Is Increasing the Risk of
Extreme Autumn Wildfire Conditions across California, Environ. Res.
Lett., 15, 094016. https://doi.org/10.1088/1748-9326/ab83a7, 2020. a
Hantson, S., Kelley, D. I., Arneth, A., Harrison, S. P., Archibald, S., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Nieradzik, L., Rabin, S. S., Prentice, I. C., Sheehan, T., Sitch, S., Teckentrup, L., Voulgarakis, A., and Yue, C.: Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, 2020. a, b, c, d
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated High-resolution
Grids of Monthly Climatic Observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014. a
Higuera, P. E., Abatzoglou, J. T., Littell, J. S., and Morgan, P.: The
Changing Strength and Nature of Fire-Climate Relationships in
the Northern Rocky Mountains, USA, 1902–2008, PLOS ONE,
10, e0127563, https://doi.org/10.1371/journal.pone.0127563, 2015. a
Hooker, G. and Mentch, L.: Please Stop Permuting Features: An
Explanation and Alternatives, ArXiv190503151 Cs Stat, arXiv, 2019. a
Hunter, J. D.: Matplotlib: A 2D Graphics Environment, Comput. Sci. Eng., 9,
90–95, 2007. a
Jenkins, M. E., Bedward, M., Price, O., and Bradstock, R. A.: Modelling
Bushfire Fuel Hazard Using Biophysical Parameters, Forests, 11, 925,
https://doi.org/10.3390/f11090925, 2020. a, b
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
Kaplan, J. O. and Lau, H.-K.: The WGLC Global Gridded Monthly Lightning
Stroke Density and Climatology, PANGAEA [Dataset], https://doi.org/10.1594/PANGAEA.904253, 2019. a, b
Keane, R. E., Burgan, R., and van Wagtendonk, J.: Mapping Wildland Fuels for
Fire Management across Multiple Scales: Integrating Remote Sensing,
GIS, and Biophysical Modeling, Int. J. Wildland Fire, 10, 301,
https://doi.org/10.1071/WF01028, 2001. a
Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews, T. R., and
Dong, N.: How Contemporary Bioclimatic and Human Controls Change Global Fire
Regimes, Nat. Clim. Change, 9, 690–696, https://doi.org/10.1038/s41558-019-0540-7,
2019. a, b
Klein Goldewijk, C.: Anthropogenic Land-Use Estimates for the Holocene,
HYDE 3.2 DANS [Dataset], https://doi.org/10.17026/DANS-25G-GEZ3, 2017. a, b
Kloster, S. and Lasslop, G.: Historical and Future Fire Occurrence (1850 to
2100) Simulated in CMIP5 Earth System Models, Glob. Planet.
Change, 150, 58–69, https://doi.org/10.1016/j.gloplacha.2016.12.017, 2017. a
Kloster, S., Mahowald, N. M., Randerson, J. T., and Lawrence, P. J.: The
Impacts of Climate, Land Use, and Demography on Fires during the 21st Century
Simulated by CLM-CN, Biogeosciences, 9, 509–525,
https://doi.org/10.5194/bg-9-509-2012, 2012. a
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M.,
Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P.,
Avila, D., Abdalla, S., and Willing, C.: Jupyter Notebooks –
a Publishing Format for Reproducible Computational Workflows, in: Positioning
and Power in Academic Publishing: Players, Agents and Agendas, edited by:
Loizides, F. and Schmidt, B., IOS Press, the Netherlands, 87–90, 2016. a
Knorr, W., Jiang, L., and Arneth, A.: Climate, CO2 and
Human Population Impacts on Global Wildfire Emissions, Biogeosciences, 13,
267–282, https://doi.org/10.5194/bg-13-267-2016, 2016. a
Köhler, P., Guanter, L., and Joiner, J.: A Linear Method for the Retrieval
of Sun-Induced Chlorophyll Fluorescence from GOME-2 and SCIAMACHY
Data, Atmos. Meas. Tech., 8, 2589–2608, https://doi.org/10.5194/amt-8-2589-2015,
2015. a, b
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, b
Kuhn-Régnier, A.: era5analysis (Version 0.2.1), Zenodo [Dataset],
https://doi.org/10.5281/zenodo.4173493, 2020. a
Kuhn-Régnier, A.: empirical-fire-modelling (Version 0.1.2), Zenodo [Dataset],
https://doi.org/10.5281/zenodo.4778777, 2021a. a
Kuhn-Régnier, A.: wildfires (Version 0.10.2.1), Zenodo [Dataset],
https://doi.org/10.5281/zenodo.4778770, 2021b. a
Kuhn-Régnier, A., Jumelle, M., and Rajaratnam, S.: ALEPython
(Version 0.5.5), Zenodo [Dataset], https://doi.org/10.5281/zenodo.4739201, 2021. a
Lasslop, G., Coppola, A. I., Voulgarakis, A., Yue, C., and Veraverbeke, S.:
Influence of Fire on the Carbon Cycle and Climate, Curr. Clim.
Change Rep., 5, 112–123, https://doi.org/10.1007/s40641-019-00128-9, 2019. a
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S.,
Moreau, I., 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, b
Littell, J. S., McKenzie, D., Peterson, D. L., and Westerling, A. L.: Climate
and Wildfire Area Burned in Western U.S. Ecoprovinces,
1916–2003, Ecol. Appl., 19, 1003–1021, https://doi.org/10.1890/07-1183.1,
2009. a, b
Lundberg, S. and Lee, S.-I.: A Unified Approach to Interpreting Model
Predictions, in: Advances in Neural Information Processing Systems,
edited by Guyon, I., Fergus, R., Wallach, H., von Luxburg, U., Garnett, R.,
Vishwanathan, S., and Bengio, S., Neural
information processing systems foundation, Vol. 2017, 4766–4775, 2017. a
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B.,
Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From Local Explanations
to Global Understanding with Explainable AI for Trees, Nat. Mach.
Intell., 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020. a
Mansfield, L. A., Nowack, P. J., Kasoar, M., Everitt, R. G., Collins, W. J.,
and Voulgarakis, A.: Predicting Global Patterns of Long-Term Climate Change
from Short-Term Simulations Using Machine Learning, Npj Clim. Atmos.
Sci., 3, 1–9, https://doi.org/10.1038/s41612-020-00148-5, 2020. a
Marlon, J. R., Bartlein, P. J., Gavin, D. G., Long, C. J., Anderson, R. S.,
Briles, C. E., Brown, K. J., Colombaroli, D., Hallett, D. J., Power, M. J.,
Scharf, E. A., and Walsh, M. K.: Long-Term Perspective on Wildfires in the
Western USA, P. Natl. Acad. Sci. USA, 109, E535–E543, https://doi.org/10.1073/pnas.1112839109, 2012. a
Martínez, J., Vega-Garcia, C., and Chuvieco, E.: Human-Caused Wildfire
Risk Rating for Prevention Planning in Spain, J. Environ.
Manag., 90, 1241–1252, https://doi.org/10.1016/j.jenvman.2008.07.005, 2009. a
Met Office: Iris: A Python Library for Analysing and Visualising
Meteorological and Oceanographic Data Sets, Exeter, Devon, v2.4 Edn., 2010. a
Meyer, H., Reudenbach, C., Wöllauer, S., and Nauss, T.: Importance of
Spatial Predictor Variable Selection in Machine Learning Applications
– Moving from Data Reproduction to Spatial Prediction,
Ecol. Model., 411, 108815, https://doi.org/10.1016/j.ecolmodel.2019.108815,
2019. a
Moesinger, L., Dorigo, W., De Jeu, R., Van der Schalie, R., Scanlon, T.,
Teubner, I., and Forkel, M.: The Global Long-Term Microwave Vegetation
Optical Depth Climate Archive VODCA (Version 1.0) [Data Set],
https://doi.org/10.5281/zenodo.2575599, 2019. a
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T.,
Teubner, I., and Forkel, M.: The Global Long-Term Microwave Vegetation
Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12,
177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. a, b
Mohammed, G. H., Colombo, R., Middleton, E. M., Rascher, U., van der Tol, C.,
Nedbal, L., Goulas, Y., Pérez-Priego, O., Damm, A., Meroni, M., Joiner,
J., Cogliati, S., Verhoef, W., Malenovský, Z., Gastellu-Etchegorry,
J.-P., Miller, J. R., Guanter, L., Moreno, J., Moya, I., Berry, J. A.,
Frankenberg, C., and Zarco-Tejada, P. J.: Remote Sensing of Solar-Induced
Chlorophyll Fluorescence (SIF) in Vegetation: 50 years of Progress,
Remote Sens. Environ., 231, 111177,
https://doi.org/10.1016/j.rse.2019.04.030, 2019. a
Molnar, C.: Interpretable Machine Learning, Lulu
Press, Morrisville, North Carolina, USA, ISBN: 978-0-244-76852-2, available at: https://christophm.github.io/interpretable-ml-book/ (last access: 2 April 2021), 2020. a
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2H MODIS/Terra Leaf Area
Index/FPAR 8-Day L4 Global 500m SIN Grid V006,
https://doi.org/10.5067/MODIS/MOD15A2H.006, 2015. a, b, c
Nowack, P., Braesicke, P., Haigh, J., Abraham, N. L., Pyle, J., and
Voulgarakis, A.: Using Machine Learning to Build Temperature-Based Ozone
Parameterizations for Climate Sensitivity Simulations, Environ. Res. Lett.,
13, 104016, https://doi.org/10.1088/1748-9326/aae2be, 2018. a
Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal Networks for
Climate Model Evaluation and Constrained Projections, Nat. Commun., 11, 1415,
https://doi.org/10.1038/s41467-020-15195-y, 2020. a
O, S., Hou, X., and Orth, R.: Observational Evidence of Wildfire-Promoting Soil
Moisture Anomalies, Sci. Rep., 10, 11008, https://doi.org/10.1038/s41598-020-67530-4,
2020. a
Ogutu, B. O., Dash, J., and Dawson, T. P.: Evaluation of the Influence of Two
Operational Fraction of Absorbed Photosynthetically Active Radiation
(FAPAR) Products on Terrestrial Ecosystem Productivity Modelling, Int. J.
Remote Sens., 35, 321–340, https://doi.org/10.1080/01431161.2013.871083, 2014. a
Oliphant, T. E.: A Guide to NumPy, vol. 1, Trelgol Publishing USA, 2006. a
Parks, S. A., Miller, C., Parisien, M.-A., Holsinger, L. M., Dobrowski, S. Z.,
and Abatzoglou, J.: Wildland Fire Deficit and Surplus in the Western United
States, 1984–2012, Ecosphere, 6, 1–13,
https://doi.org/10.1890/ES15-00294.1, 2015. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-Learn: Machine Learning in Python, J. Mach. Learn. Res., 12,
2825–2830, 2011. a
Pettinari, M. L. and Chuvieco, E.: Generation of a Global Fuel Data Set Using
the Fuel Characteristic Classification System, Biogeosciences, 13,
2061–2076, https://doi.org/10.5194/bg-13-2061-2016, 2016. a
Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N.,
Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., Lyapustin, A.,
Gourlet-Fleury, S., and Pélissier, R.: Spatial Validation Reveals Poor
Predictive Performance of Large-Scale Ecological Mapping Models, Nat.
Commun., 11, 4540, https://doi.org/10.1038/s41467-020-18321-y, 2020. a, b, c, d
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R.,
Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S.,
Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters,
M., and Peylin, P.: Plant Functional Type Classification for Earth System
Models: Results from the European Space Agency's Land Cover Climate
Change Initiative, Geosci. Model Dev., 8, 2315–2328,
https://doi.org/10.5194/gmd-8-2315-2015, 2015. a
Randerson, J. T., van der Werf, G. R., Collatz, G. J., Giglio, L., Still,
C. J., Kasibhatla, P., Miller, J. B., White, J. W. C., DeFries, R. S., and
Kasischke, E. S.: Fire Emissions from C3 and C4 Vegetation and Their
Influence on Interannual Variability of Atmospheric CO2 and
δ13CO2, Global Biogeochem. Cy., 19, https://doi.org/10.1029/2004GB002366,
2005. a, b
Rodger, C. J., Brundell, J. B., Dowden, R. L., and Thomson, N. R.: Location
Accuracy of Long Distance VLF Lightning Locationnetwork, Ann. Geophys.,
22, 747–758, https://doi.org/10.5194/angeo-22-747-2004, 2004. a
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.:
Detecting and Quantifying Causal Associations in Large Nonlinear Time Series
Datasets, Sci. Adv., 5, eaau4996, https://doi.org/10.1126/sciadv.aau4996, 2019. a
Ryu, Y., Berry, J. A., and Baldocchi, D. D.: What Is Global Photosynthesis?
History, Uncertainties and Opportunities, Remote Sens. Environ.,
223, 95–114, https://doi.org/10.1016/j.rse.2019.01.016, 2019. a
Sanderson, B. M. and Fisher, R. A.: A Fiery Wake-up Call for Climate Science,
Nat. Clim. Change, 10, 175–177, https://doi.org/10.1038/s41558-020-0707-2, 2020. a
Spessa, A., McBeth, B., and Prentice, C.: Relationships among Fire Frequency,
Rainfall and Vegetation Patterns in the Wet–Dry Tropics of
Northern Australia: An Analysis Based on NOAA-AVHRR Data, Glob.
Ecol. Biogeogr., 14, 439–454, https://doi.org/10.1111/j.1466-822x.2005.00174.x, 2005. a, b
Swetnam, T. W. and Betancourt, J. L.: Mesoscale Disturbance and
Ecological Response to Decadal Climatic Variability in the American
Southwest, J. Clim., 11, 3128–3147,
https://doi.org/10.1175/1520-0442(1998)011<3128:MDAERT>2.0.CO;2, 1998. a, b
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest,
M., Li, F., Yue, C., Arneth, A., Hickler, T., Sitch, S., and Lasslop, G.:
Response of Simulated Burned Area to Historical Changes in Environmental and
Anthropogenic Factors: A Comparison of Seven Fire Models, Biogeosciences, 16,
3883–3910, https://doi.org/10.5194/bg-16-3883-2019, 2019. a, b
Teubner, I. E., Forkel, M., Jung, M., Liu, Y. Y., Miralles, D. G., Parinussa,
R., van der Schalie, R., Vreugdenhil, M., Schwalm, C. R., Tramontana, G.,
Camps-Valls, G., and Dorigo, W. A.: Assessing the Relationship between
Microwave Vegetation Optical Depth and Gross Primary Production, Int. J.
Appl. Earth Obs. Geoinformation, 65, 79–91, https://doi.org/10.1016/j.jag.2017.10.006,
2018. a
Thomas, P. B., Watson, P. J., Bradstock, R. A., Penman, T. D., and Price,
O. F.: Modelling Surface Fine Fuel Dynamics across Climate Gradients in
Eucalypt Forests of South-Eastern Australia, Ecography, 37, 827–837,
https://doi.org/10.1111/ecog.00445, 2014.
a
Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko,
D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S. R., and Schmullius,
C.: Carbon Stock and Density of Northern Boreal and Temperate Forests, Glob.
Ecol. Biogeogr., 23, 297–310, https://doi.org/10.1111/geb.12125, 2014. a, b
Turco, M., Rosa-Cánovas, J. J., Bedia, J., Jerez, S., Montávez,
J. P., Llasat, M. C., and Provenzale, A.: Exacerbated Fires in
Mediterranean Europe Due to Anthropogenic Warming Projected with
Non-Stationary Climate-Fire Models, Nat. Commun., 9, 3821,
https://doi.org/10.1038/s41467-018-06358-z, 2018. a
van Oldenborgh, G. J., Krikken, F., Lewis, S., Leach, N. J., Lehner, F., Saunders, K. R., van Weele, M., Haustein, K., Li, S., Wallom, D., Sparrow, S., Arrighi, J., Singh, R. K., van Aalst, M. K., Philip, S. Y., Vautard, R., and Otto, F. E. L.: Attribution of the Australian bushfire risk to anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 21, 941–960, https://doi.org/10.5194/nhess-21-941-2021, 2021. a, b
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace,
Scotts Valley, CA, 2009. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, İ.,
Feng, Y., Moore, E. W., Vand erPlas, J., Laxalde, D., Perktold, J.,
Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M.,
Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and Contributors, S. . .:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Method., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Voulgarakis, A. and Field, R. D.: Fire Influences on Atmospheric
Composition, Air Quality and Climate, Curr. Pollut. Rep., 1,
70–81, https://doi.org/10.1007/s40726-015-0007-z, 2015. a
Wagner, W., Lemoine, G., and Rott, H.: A Method for Estimating Soil
Moisture from ERS Scatterometer and Soil Data, Remote Sens.
Environ., 70, 191–207, https://doi.org/10.1016/S0034-4257(99)00036-X, 1999. a, b
Westerling, A. L.: Warming and Earlier Spring Increase Western U.S.
Forest Wildfire Activity, Science, 313, 940–943,
https://doi.org/10.1126/science.1128834, 2006. a
Westerling, A. L., Gershunov, A., Brown, T. J., Cayan, D. R., and Dettinger,
M. D.: Climate and Wildfire in the Western United States, Bull. Am.
Meteorol. Soc., 84, 595–604, https://doi.org/10.1175/BAMS-84-5-595, 2003. a, b
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Along with current climate, vegetation, and human influences, long-term accumulation of biomass...
Altmetrics
Final-revised paper
Preprint