Articles | Volume 18, issue 3
https://doi.org/10.5194/bg-18-787-2021
© 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-18-787-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
04 Feb 2021
Technical note |
| 04 Feb 2021
| 04 Feb 2021
Technical note: Low meteorological influence found in 2019 Amazonia fires
Douglas I. Kelley
CORRESPONDING AUTHOR
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Chantelle Burton
CORRESPONDING AUTHOR
Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB, UK
Chris Huntingford
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Megan A. J. Brown
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
DISCnet Centre for Doctoral Training, School of Physical Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK
Rhys Whitley
Natural Perils Pricing, Commercial and Consumer Portfolio and Product, Suncorp Group, Sydney, Australia
Ning Dong
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
Related authors
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1775, https://doi.org/10.5194/egusphere-2024-1775, 2024
Short summary
Short summary
As fire seasons in Brazil intensify, understanding what drives these fires becomes crucial. We developed a new model, FLAME, to predict fires using environmental and human factors, while also accounting for uncertainties. We found temperature and rainfall to be key factors, with uncertainties higher in some regions. By customizing the model for different regions, we can improve fire management strategies, making FLAME a valuable tool for protecting Brazil's and other region’s landscapes.
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.
Renata Moura da Veiga, Celso von Randow, Chantelle Burton, Douglas Kelley, Manoel Cardoso, and Fabiano Morelli
EGUsphere, https://doi.org/10.5194/egusphere-2024-2348, https://doi.org/10.5194/egusphere-2024-2348, 2024
Short summary
Short summary
We systematically reviewed 69 papers on fire emissions from the Brazilian Cerrado biome to provide insights into its placement in the atmospheric carbon budget and support future improved estimation. We find that estimating fire emissions in the Cerrado requires a comprehensive approach, combining quantitative and qualitative aspects of fire. A pathway towards this is the inclusion of fire management representation in land surface models and the integration of observational and modelling data.
Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
Short summary
Short summary
We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
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.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
Short summary
Short summary
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
Short summary
Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Chantelle Burton, Richard Betts, Manoel Cardoso, Ted R. Feldpausch, Anna Harper, Chris D. Jones, Douglas I. Kelley, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 12, 179–193, https://doi.org/10.5194/gmd-12-179-2019, https://doi.org/10.5194/gmd-12-179-2019, 2019
Short summary
Short summary
Fire and land-use change are important disturbances within the Earth system, and their inclusion in models is critical to enable the correct simulation of vegetation cover. Here we describe developments to the land surface model JULES to represent explicit land-use change and fire and to assess the effects of each process on present day vegetation compared to observations. Using historical land-use data and the fire model INFERNO, overall model results are improved by the developments.
Sam S. Rabin, Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, Fang Li, Stéphane Mangeon, Daniel S. Ward, Chao Yue, Vivek K. Arora, Thomas Hickler, Silvia Kloster, Wolfgang Knorr, Lars Nieradzik, Allan Spessa, Gerd A. Folberth, Tim Sheehan, Apostolos Voulgarakis, Douglas I. Kelley, I. Colin Prentice, Stephen Sitch, Sandy Harrison, and Almut Arneth
Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, https://doi.org/10.5194/gmd-10-1175-2017, 2017
Short summary
Short summary
Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
Short summary
Short summary
Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
Paul David Longden Ritchie, Chris Huntingford, and Peter Cox
EGUsphere, https://doi.org/10.5194/egusphere-2024-3023, https://doi.org/10.5194/egusphere-2024-3023, 2024
Short summary
Short summary
Climate Tipping Points are not instantaneous upon crossing critical thresholds in global warming, as is often assumed. Instead, it is possible to temporarily overshoot a threshold without causing tipping, provided the duration of the overshoot is short. In this Idea, we demonstrate that restricting the time over 1.5 °C would considerably reduce tipping point risks.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Chris Huntingford, Andrew J. Nicoll, Cornelia Klein, and Jawairia A. Ahmad
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-30, https://doi.org/10.5194/esd-2024-30, 2024
Preprint under review for ESD
Short summary
Short summary
AI is impacting science, providing key data insights, but most algorithms are statistical requiring cautious "out-of-sample" extrapolation. Yet climate research concerns predicting future climatic states. We consider a new method of AI-led equation discovery. Equations offer process interpretation and more robust predictions. We recommend this method for climate analysis, suggesting illustrative application to atmospheric convection, land-atmosphere CO2 flux and global ocean circulation models.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1775, https://doi.org/10.5194/egusphere-2024-1775, 2024
Short summary
Short summary
As fire seasons in Brazil intensify, understanding what drives these fires becomes crucial. We developed a new model, FLAME, to predict fires using environmental and human factors, while also accounting for uncertainties. We found temperature and rainfall to be key factors, with uncertainties higher in some regions. By customizing the model for different regions, we can improve fire management strategies, making FLAME a valuable tool for protecting Brazil's and other region’s landscapes.
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.
Renata Moura da Veiga, Celso von Randow, Chantelle Burton, Douglas Kelley, Manoel Cardoso, and Fabiano Morelli
EGUsphere, https://doi.org/10.5194/egusphere-2024-2348, https://doi.org/10.5194/egusphere-2024-2348, 2024
Short summary
Short summary
We systematically reviewed 69 papers on fire emissions from the Brazilian Cerrado biome to provide insights into its placement in the atmospheric carbon budget and support future improved estimation. We find that estimating fire emissions in the Cerrado requires a comprehensive approach, combining quantitative and qualitative aspects of fire. A pathway towards this is the inclusion of fire management representation in land surface models and the integration of observational and modelling data.
Mark S. Williamson, Peter M. Cox, Chris Huntingford, and Femke J. M. M. Nijsse
Earth Syst. Dynam., 15, 829–852, https://doi.org/10.5194/esd-15-829-2024, https://doi.org/10.5194/esd-15-829-2024, 2024
Short summary
Short summary
Emergent constraints on equilibrium climate sensitivity (ECS) have generally got statistically weaker in the latest set of state-of-the-art climate models (CMIP6) compared to past sets (CMIP5). We look at why this weakening happened for one particular study (Cox et al, 2018) and attribute it to an assumption made in the theory that when corrected for restores there is a stronger relationship between predictor and ECS.
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.
Camilla Therese Mathison, Eleanor Burke, Eszter Kovacs, Gregory Munday, Chris Huntingford, Chris Jones, Chris Smith, Norman Steinert, Andy Wiltshire, Laila Gohar, and Rebecca Varney
EGUsphere, https://doi.org/10.5194/egusphere-2023-2932, https://doi.org/10.5194/egusphere-2023-2932, 2024
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emission scenarios and rapidly provide regional impacts information. PRIME allows large ensembles to be run on multi-centennial timescales including analysis of many important variables for impacts assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios giving confidence in using PRIME for novel scenarios.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, https://doi.org/10.5194/amt-17-407-2024, 2024
Short summary
Short summary
The paper addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilizes a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regard.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
Short summary
Short summary
We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
Short summary
Short summary
CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
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.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023, https://doi.org/10.5194/esd-14-433-2023, 2023
Short summary
Short summary
Emergent constraints (ECs) reduce the spread of projections between climate models. ECs estimate changes to climate features impacting adaptation policy, and with this high profile, the method is under scrutiny. Asking
What is an EC?, we suggest they are often the discovery of parameters that characterise hidden large-scale equations that climate models solve implicitly. We present this conceptually via two examples. Our analysis implies possible new paths to link ECs and physical processes.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023, https://doi.org/10.5194/gmd-16-1277-2023, 2023
Short summary
Short summary
Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
Louise J. Slater, Chris Huntingford, Richard F. Pywell, John W. Redhead, and Elizabeth J. Kendon
Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, https://doi.org/10.5194/esd-13-1377-2022, 2022
Short summary
Short summary
This work considers how wheat yields are affected by weather conditions during the three main wheat growth stages in the UK. Impacts are strongest in years with compound weather extremes across multiple growth stages. Future climate projections are beneficial for wheat yields, on average, but indicate a high risk of unseen weather conditions which farmers may struggle to adapt to and mitigate against.
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.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
Short summary
Short summary
We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
Short summary
Short summary
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
Short summary
Short summary
We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Garry D. Hayman, Edward Comyn-Platt, Chris Huntingford, Anna B. Harper, Tom Powell, Peter M. Cox, William Collins, Christopher Webber, Jason Lowe, Stephen Sitch, Joanna I. House, Jonathan C. Doelman, Detlef P. van Vuuren, Sarah E. Chadburn, Eleanor Burke, and Nicola Gedney
Earth Syst. Dynam., 12, 513–544, https://doi.org/10.5194/esd-12-513-2021, https://doi.org/10.5194/esd-12-513-2021, 2021
Short summary
Short summary
We model greenhouse gas emission scenarios consistent with limiting global warming to either 1.5 or 2 °C above pre-industrial levels. We quantify the effectiveness of methane emission control and land-based mitigation options regionally. Our results highlight the importance of reducing methane emissions for realistic emission pathways that meet the global warming targets. For land-based mitigation, growing bioenergy crops on existing agricultural land is preferable to replacing forests.
Arthur P. K. Argles, Jonathan R. Moore, Chris Huntingford, Andrew J. Wiltshire, Anna B. Harper, Chris D. Jones, and Peter M. Cox
Geosci. Model Dev., 13, 4067–4089, https://doi.org/10.5194/gmd-13-4067-2020, https://doi.org/10.5194/gmd-13-4067-2020, 2020
Short summary
Short summary
The Robust Ecosystem Demography (RED) model simulates cohorts of vegetation through mass classes. RED establishes a framework for representing demographic changes through competition, growth, and mortality across the size distribution of a forest. The steady state of the model can be solved analytically, enabling initialization. When driven by mean growth rates from a land-surface model, RED is able to fit the observed global vegetation map, giving a map of implicit mortality rates.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
Short summary
Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Jonathan R. Moore, Arthur P. K. Argles, Kai Zhu, Chris Huntingford, and Peter M. Cox
Biogeosciences, 17, 1013–1032, https://doi.org/10.5194/bg-17-1013-2020, https://doi.org/10.5194/bg-17-1013-2020, 2020
Short summary
Short summary
The distribution of tree sizes across Amazonia can be fitted very well (for both trunk diameter and tree mass) by a simple equilibrium model assuming power law growth and size-independent mortality. We find tree growth to mirror some aspects of metabolic scaling theory and that there may be a trade-off between fast-growing, short-lived and longer-lived, slow-growing ones. Our Amazon mortality-to-growth ratio is very similar to US temperate forests, hinting at a universal property for trees.
Karina E. Williams, Anna B. Harper, Chris Huntingford, Lina M. Mercado, Camilla T. Mathison, Pete D. Falloon, Peter M. Cox, and Joon Kim
Geosci. Model Dev., 12, 3207–3240, https://doi.org/10.5194/gmd-12-3207-2019, https://doi.org/10.5194/gmd-12-3207-2019, 2019
Short summary
Short summary
Data from the First ISLSCP Field Experiment, 1987–1989, is used to assess how well the JULES land-surface model simulates water stress in tallgrass prairie vegetation. We find that JULES simulates a decrease in key carbon and water cycle variables during the dry period, as expected, but that it does not capture the shape of the diurnal cycle on these days. These results will be used to inform future model development as part of wider evaluation efforts.
Chantelle Burton, Richard Betts, Manoel Cardoso, Ted R. Feldpausch, Anna Harper, Chris D. Jones, Douglas I. Kelley, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 12, 179–193, https://doi.org/10.5194/gmd-12-179-2019, https://doi.org/10.5194/gmd-12-179-2019, 2019
Short summary
Short summary
Fire and land-use change are important disturbances within the Earth system, and their inclusion in models is critical to enable the correct simulation of vegetation cover. Here we describe developments to the land surface model JULES to represent explicit land-use change and fire and to assess the effects of each process on present day vegetation compared to observations. Using historical land-use data and the fire model INFERNO, overall model results are improved by the developments.
Chris Huntingford, Rebecca J. Oliver, Lina M. Mercado, and Stephen Sitch
Biogeosciences, 15, 5415–5422, https://doi.org/10.5194/bg-15-5415-2018, https://doi.org/10.5194/bg-15-5415-2018, 2018
Short summary
Short summary
Raised ozone levels impact plant stomatal opening and thus photosynthesis. Most models describe this as a suppression of stomata opening. Field evidence suggests more complexity, as ozone damage may make stomatal response
sluggish. In some circumstances, this causes stomata to be more open – a concern during drought conditions – by increasing transpiration. To guide interpretation and modelling of field measurements, we present an equation for sluggish effects, via a single tau parameter.
Hui Yang and Chris Huntingford
Nat. Hazards Earth Syst. Sci., 18, 491–497, https://doi.org/10.5194/nhess-18-491-2018, https://doi.org/10.5194/nhess-18-491-2018, 2018
Short summary
Short summary
Major drought in East Africa at the end of 2016 caused severe famine and loss of life. We pose the following question: are increasing levels of atmospheric greenhouse gas concentrations, due to human activity, making droughts such as this more likely? Computer models of the climate system are powerful tools to answer this. Scanning across a full set of these, we find that how future climate change will impact on East Africa ASO drought risk remains uncertain, in any particular year.
Przemyslaw Zelazowski, Chris Huntingford, Lina M. Mercado, and Nathalie Schaller
Geosci. Model Dev., 11, 541–560, https://doi.org/10.5194/gmd-11-541-2018, https://doi.org/10.5194/gmd-11-541-2018, 2018
Short summary
Short summary
This paper describes the calibration of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) impact modelling system against 22 global climate models (GCMs) in the CMIP3 database.
IMOGEN uses "pattern scaling" to emulate GCMs, and with such linearity enables projections to be made for alternative future scenarios of atmospheric greenhouse gas concentrations. It is also coupled to the JULES land surface model, to allow impact assessments.
IMOGEN uses "pattern scaling" to emulate GCMs, and with such linearity enables projections to be made for alternative future scenarios of atmospheric greenhouse gas concentrations. It is also coupled to the JULES land surface model, to allow impact assessments.
Chris Huntingford, Hui Yang, Anna Harper, Peter M. Cox, Nicola Gedney, Eleanor J. Burke, Jason A. Lowe, Garry Hayman, William J. Collins, Stephen M. Smith, and Edward Comyn-Platt
Earth Syst. Dynam., 8, 617–626, https://doi.org/10.5194/esd-8-617-2017, https://doi.org/10.5194/esd-8-617-2017, 2017
Short summary
Short summary
Recent UNFCCC climate meetings have placed much emphasis on constraining global warming to remain below 2 °C. The 2015 Paris meeting went further and gave an aspiration to fulfil a 1.5 °C threshold. We provide a flexible set of algebraic global temperature profiles that stabilise to either target. This will potentially allow the climate research community to estimate local climatic implications for these temperature profiles, along with emissions trajectories to fulfil them.
Eleanor J. Burke, Altug Ekici, Ye Huang, Sarah E. Chadburn, Chris Huntingford, Philippe Ciais, Pierre Friedlingstein, Shushi Peng, and Gerhard Krinner
Biogeosciences, 14, 3051–3066, https://doi.org/10.5194/bg-14-3051-2017, https://doi.org/10.5194/bg-14-3051-2017, 2017
Short summary
Short summary
There are large reserves of carbon within the permafrost which might be released to the atmosphere under global warming. Our models suggest this release may cause an additional global temperature increase of 0.005 to 0.2°C by the year 2100 and 0.01 to 0.34°C by the year 2300. Under climate mitigation scenarios this is between 1.5 and 9 % (by 2100) and between 6 and 16 % (by 2300) of the global mean temperature change. There is a large uncertainty associated with these results.
Daniel S. Goll, Alexander J. Winkler, Thomas Raddatz, Ning Dong, Ian Colin Prentice, Philippe Ciais, and Victor Brovkin
Geosci. Model Dev., 10, 2009–2030, https://doi.org/10.5194/gmd-10-2009-2017, https://doi.org/10.5194/gmd-10-2009-2017, 2017
Short summary
Short summary
The response of soil organic carbon decomposition to warming and the interactions between nitrogen and carbon cycling affect the feedbacks between the land carbon cycle and the climate. In the model JSBACH carbon–nitrogen interactions have only a small effect on the feedbacks, whereas modifications of soil organic carbon decomposition have a large effect. The carbon cycle in the improved model is more resilient to climatic changes than in previous version of the model.
Sam S. Rabin, Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, Fang Li, Stéphane Mangeon, Daniel S. Ward, Chao Yue, Vivek K. Arora, Thomas Hickler, Silvia Kloster, Wolfgang Knorr, Lars Nieradzik, Allan Spessa, Gerd A. Folberth, Tim Sheehan, Apostolos Voulgarakis, Douglas I. Kelley, I. Colin Prentice, Stephen Sitch, Sandy Harrison, and Almut Arneth
Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, https://doi.org/10.5194/gmd-10-1175-2017, 2017
Short summary
Short summary
Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
Ning Dong, Iain Colin Prentice, Bradley J. Evans, Stefan Caddy-Retalic, Andrew J. Lowe, and Ian J. Wright
Biogeosciences, 14, 481–495, https://doi.org/10.5194/bg-14-481-2017, https://doi.org/10.5194/bg-14-481-2017, 2017
Short summary
Short summary
The nitrogen content of leaves is a key quantity for understanding ecosystem function. We analysed variations in nitrogen per unit leaf area among species at sites along a transect across Australia including many climates and ecosystem types. The data could be explained by the idea that leaf nitrogen comprises two parts, one proportional to leaf mass, the other (metabolic) part proportional to light intensity and declining with CO2 drawdown and temperature, as optimal allocation theory predicts.
Stefan C. Dekker, Margriet Groenendijk, Ben B. B. Booth, Chris Huntingford, and Peter M. Cox
Earth Syst. Dynam., 7, 525–533, https://doi.org/10.5194/esd-7-525-2016, https://doi.org/10.5194/esd-7-525-2016, 2016
Short summary
Short summary
Our analysis allows us to infer maps of changing plant water-use efficiency (WUE) for 1901–2010, using atmospheric observations of temperature, humidity and CO2. Our estimated increase in global WUE is consistent with the tree-ring and eddy covariance data, but much larger than the historical WUE increases simulated by Earth System Models (ESMs). We therefore conclude that the effects of increasing CO2 on plant WUE are significantly underestimated in the latest climate projections.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
Short summary
Short summary
Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
G. D. Hayman, F. M. O'Connor, M. Dalvi, D. B. Clark, N. Gedney, C. Huntingford, C. Prigent, M. Buchwitz, O. Schneising, J. P. Burrows, C. Wilson, N. Richards, and M. Chipperfield
Atmos. Chem. Phys., 14, 13257–13280, https://doi.org/10.5194/acp-14-13257-2014, https://doi.org/10.5194/acp-14-13257-2014, 2014
Short summary
Short summary
Globally, wetlands are a major source of methane, which is the second most important greenhouse gas. We find the JULES wetland methane scheme to perform well in general, although there is a tendency for it to overpredict emissions in the tropics and underpredict them in northern latitudes. Our study highlights novel uses of satellite data as a major tool to constrain land-atmosphere methane flux models in a warming world.
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
Related subject area
Earth System Science/Response to Global Change: Evolution of System Earth
Understanding tropical forest abiotic response to hurricanes using experimental manipulations, field observations, and satellite data
Towards a global understanding of vegetation–climate dynamics at multiple timescales
Evaluating and improving the Community Land Model's sensitivity to land cover
The extant shore platform stromatolite (SPS) facies association: a glimpse into the Archean?
Historic carbon burial spike in an Amazon floodplain lake linked to riparian deforestation near Santarém, Brazil
Nonlinear thermal and moisture response of ice-wedge polygons to permafrost disturbance increases heterogeneity of high Arctic wetland
Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products
Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site
A red tide alga grown under ocean acidification upregulates its tolerance to lower pH by increasing its photophysiological functions
Short- and long-term thermo-erosion of ice-rich permafrost coasts in the Laptev Sea region
High-latitude cooling associated with landscape changes from North American boreal forest fires
Life-cycle evaluation of nitrogen-use in rice-farming systems: implications for economically-optimal nitrogen rates
Tephrostratigraphy and tephrochronology of lakes Ohrid and Prespa, Balkans
Ashley E. Van Beusekom, Grizelle González, Sarah Stankavich, Jess K. Zimmerman, and Alonso Ramírez
Biogeosciences, 17, 3149–3163, https://doi.org/10.5194/bg-17-3149-2020, https://doi.org/10.5194/bg-17-3149-2020, 2020
Short summary
Short summary
This study looks at forest abiotic responses to canopy openness and debris deposition that follow a hurricane. We find that recovery to full canopy may take over half a decade and that recovery of humidity, soil moisture, and leaf saturation under the canopy is not monotonic and may temporarily look recovered before the response is over. Furthermore, we find that satellite data show a quicker recovery than field data, necessitating caution when looking at responses to hurricanes with satellites.
Nora Linscheid, Lina M. Estupinan-Suarez, Alexander Brenning, Nuno Carvalhais, Felix Cremer, Fabian Gans, Anja Rammig, Markus Reichstein, Carlos A. Sierra, and Miguel D. Mahecha
Biogeosciences, 17, 945–962, https://doi.org/10.5194/bg-17-945-2020, https://doi.org/10.5194/bg-17-945-2020, 2020
Short summary
Short summary
Vegetation typically responds to variation in temperature and rainfall within days. Yet seasonal changes in meteorological conditions, as well as decadal climate variability, additionally shape the state of ecosystems. It remains unclear how vegetation responds to climate variability on these different timescales. We find that the vegetation response to climate variability depends on the timescale considered. This scale dependency should be considered for modeling land–atmosphere interactions.
Ronny Meier, Edouard L. Davin, Quentin Lejeune, Mathias Hauser, Yan Li, Brecht Martens, Natalie M. Schultz, Shannon Sterling, and Wim Thiery
Biogeosciences, 15, 4731–4757, https://doi.org/10.5194/bg-15-4731-2018, https://doi.org/10.5194/bg-15-4731-2018, 2018
Short summary
Short summary
Deforestation not only releases carbon dioxide to the atmosphere but also affects local climatic conditions by altering energy fluxes at the land surface and thereby the local temperature. Here, we evaluate the local impact of deforestation in a widely used land surface model. We find that the model reproduces the daytime warming effect of deforestation well. On the other hand, the warmer temperatures observed during night in forests are not present in this model.
Alan Smith, Andrew Cooper, Saumitra Misra, Vishal Bharuth, Lisa Guastella, and Riaan Botes
Biogeosciences, 15, 2189–2203, https://doi.org/10.5194/bg-15-2189-2018, https://doi.org/10.5194/bg-15-2189-2018, 2018
Short summary
Short summary
Growing shore-platform stromatolites are increasingly found on modern rocky coasts. Stromatolites are very similar to Archean and Proterozoic stromatolites. A study of modern stromatolites may shed light on the conditions that existed on the early Earth and other planets and possibly provide information on how life began.
Luciana M. Sanders, Kathryn Taffs, Debra Stokes, Christian J. Sanders, Alex Enrich-Prast, Leonardo Amora-Nogueira, and Humberto Marotta
Biogeosciences, 15, 447–455, https://doi.org/10.5194/bg-15-447-2018, https://doi.org/10.5194/bg-15-447-2018, 2018
Short summary
Short summary
The Amazon rainforest produce large quantities of carbon, a portion of which is deposited in floodplain lakes. This research shows a potentially important spatial dependence of the carbon deposition in the Amazon lacustrine sediments in relation to deforestation rates in the catchment. The findings presented here highlight the effects of abrupt and temporary events in which some of the biomass released by the deforestation reach the depositional environments in the Amazon floodplains.
Etienne Godin, Daniel Fortier, and Esther Lévesque
Biogeosciences, 13, 1439–1452, https://doi.org/10.5194/bg-13-1439-2016, https://doi.org/10.5194/bg-13-1439-2016, 2016
Short summary
Short summary
Bowl-shaped ice-wedge polygons in permafrost regions can retain snowmelt water and moisture in their center. On Bylot Island (NU, CA), a rapidly developing thermal erosion gully eroded the polygons' ridges, impacting the polygon centers' ground moisture and temperature, plant cover and species. An intact polygon was homogeneous in its center for the aforementioned elements, whereas eroded polygons had a varying response following the breach, with heterogeneity as their new equilibrium state.
M. Marshall, E. Okuto, Y. Kang, E. Opiyo, and M. Ahmed
Biogeosciences, 13, 625–639, https://doi.org/10.5194/bg-13-625-2016, https://doi.org/10.5194/bg-13-625-2016, 2016
Short summary
Short summary
We compared two new Earth observation based long-term global vegetation index products used in global change research (Global Inventory Modeling and Mapping Studies and Vegetation Index and Phenology Lab- VIP version 3). The two products showed a high level of consistency throughout the primary growing season and were less consistent during green-up and brown-down that impacted trends in phenology. VIP was generally higher and more variable leading to poorer correlations with in situ data
G. Mendiguren, M. Pilar Martín, H. Nieto, J. Pacheco-Labrador, and S. Jurdao
Biogeosciences, 12, 5523–5535, https://doi.org/10.5194/bg-12-5523-2015, https://doi.org/10.5194/bg-12-5523-2015, 2015
S. Chen, J. Beardall, and K. Gao
Biogeosciences, 11, 4829–4837, https://doi.org/10.5194/bg-11-4829-2014, https://doi.org/10.5194/bg-11-4829-2014, 2014
F. Günther, P. P. Overduin, A. V. Sandakov, G. Grosse, and M. N. Grigoriev
Biogeosciences, 10, 4297–4318, https://doi.org/10.5194/bg-10-4297-2013, https://doi.org/10.5194/bg-10-4297-2013, 2013
B. M. Rogers, J. T. Randerson, and G. B. Bonan
Biogeosciences, 10, 699–718, https://doi.org/10.5194/bg-10-699-2013, https://doi.org/10.5194/bg-10-699-2013, 2013
Y. Xia and X. Yan
Biogeosciences, 8, 3159–3168, https://doi.org/10.5194/bg-8-3159-2011, https://doi.org/10.5194/bg-8-3159-2011, 2011
R. Sulpizio, G. Zanchetta, M. D'Orazio, H. Vogel, and B. Wagner
Biogeosciences, 7, 3273–3288, https://doi.org/10.5194/bg-7-3273-2010, https://doi.org/10.5194/bg-7-3273-2010, 2010
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.:
The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present),
J. Hydrometeorol.,
4, 1147–1167, 2003.
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, 2011.
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, 2017.
Anon:
2020 Incident Archive, CAL FIRE,
available at: https://www.fire.ca.gov/incidents/2020/ (last access: 10 April 2019), 2020.
Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Rosan, T. M., Vedovato, L. B., Wagner, F. H., Silva, C. V. J., Silva Junior, C. H. L., Arai, E., Aguiar, A. P., Barlow, J., Berenguer, E., Deeter, M. N., Domingues, L. G., Gatti, L., Gloor, M., Malhi, Y., Marengo, J. A., Miller, J. B., Phillips, O. L., and Saatchi, S.:
21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions,
Nat. Commun.,
9, 536, https://doi.org/10.1038/s41467-017-02771-y, 2018.
Arruda, D., Candido, H. G., and Fonseca, R.:
Amazon fires threaten Brazil's agribusiness,
Science,
365, 1387, https://doi.org/10.1126/science.aaz2198, 2019.
BBC news:
Amazon fires: Brazil bans land clearance blazes for 60 days,
BBC news,
available at: https://www.bbc.co.uk/news/world-latin-america-49507405 (last access: 3 November 2020), 2019.
Bivand, R., Keitt, T., and Rowlingson, B.:
rgdal: Bindings for the Geospatial Data Abstraction Library,
available at: http://CRAN.R-project.org/package=rgdal, last access: 10 November 2016.
Boer, M. M., de Dios, V. R., and Bradstock, R. A.:
Unprecedented burn area of Australian mega forest fires,
Nat. Clim. Change,
10, 171–172, 2020.
Brando, P. M., Balch, J. K., and Nepstad, D. C.:
Abrupt increases in Amazonian tree mortality due to drought–fire interactions,
P. Natl. Acad. Sci. USA, 111, 6347–6352, https://doi.org/10.1073/pnas.1305499111, 2014.
Burton, C., Betts, R. A., and Jones, C. D.:
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.
Butler, R.:
Calculating deforestation figures for the Amazon,
Rio de Janeiro, available at: https://rainforests.mongabay.com/amazon/deforestation_calculations.html (last access: 29 February 2020), 2017.
Cochrane, M. A. and Schulze, M. D.:
Fire as a Recurrent Event in Tropical Forests of the Eastern Amazon: Effects on Forest Structure, Biomass, and Species Composition1,
Biotropica,
31, 2–16, 1999.
De Groot, W. J., Goldammer, J. G., Justice, C. O., and Lynham, T. J.:
Implementing a global early warning system for wildland fire,
in: Proc. Virchow. Pirquet. Med. Soc., University of Coimbra, Coimbra, Portugal, 15–18,
2010.
de Groot, W. J., Wotton, B. M., and Flannigan, M. D.:
Chapter 11 – Wildland Fire Danger Rating and Early Warning Systems,
in: Wildfire Hazards, Risks and Disasters,
edited by:
Shroder, J. F. and Paton, D.,
Elsevier, Oxford, 207–228, 2015.
de Oliveira Andrade, R.:
Alarming surge in Amazon fires prompts global outcry,
Nature,
https://doi.org/10.1038/d41586-019-02537-0, 2019.
Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D. H., Kelly, M., and Townshend, J. R. G.:
MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006, NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MODIS/MOD44B.006, 2015.
Escobar, H.:
Amazon fires clearly linked to deforestation, scientists say,
Science,
365, 853, https://doi.org/10.1126/science.365.6456.853, 2019.
Fisher, R. A. and Koven, C. D.:
Perspectives on the future of Land Surface Models and the challenges of representing complex terrestrial systems,
J. Adv. Model. Earth Sy., 12,
e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020.
Fonseca, M. G., Alves, L. M., Aguiar, A. P. D., Arai, E., Anderson, L. O., Rosan, T. M., Shimabukuro, Y. E., and Aragão, L. E. O. C.:
Effects of climate and land-use change scenarios on fire probability during the 21st century in the Brazilian Amazon,
Glob. Change Biol.,
25, 2931–2946, 2019.
Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D.,
Neigh, C., and Reichstein, M.: Trend Change
Detection in NDVI Time Series: Effects of Inter-Annual Variability
and Methodology, Remote Sensing, 5, 2113–2144, 2013.
Forkel, M., Migliavacca, M., Thonicke, K., Reichstein, M., Schaphoff, S., Weber, U., and Carvalhais, N.:
Co-dominant water control on global inter-annual variability
and trends in land surface phenology and greenness, Glob. Change
Biol., 21, 3414–3435, 2015.
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.
GDAL/OGR contributors:
GDAL/OGR Geospatial Data Abstraction software Library,
Open Source Geospatial Foundation, Version 3.2,
available at: http://gdal.org (last access: March 2020), 2018.
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, 2018.
Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin, S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo, P., Bachelet, D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler, T., Kaplan, J. O., Kloster, S., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Meyn, A., Sitch, S., Spessa, A., van der Werf, G. R., Voulgarakis, A., and Yue, C.: The status and challenge of global fire modelling, Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, 2016.
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.
Hijmans, R. J. and van Etten, J.:
raster: Geographic data analysis and modeling, R package version, available at: http://CRAN.R-project.org/package=raster
(last access: 15 March 2018), 2014.
INPE:
INPE Fire Count Data,
available at: http://queimadas.dgi.inpe.br/queimadas/portal-static/estatisticas_paises/, last access: 29 February 2019.
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.
Jones, M. W., Smith, A., Betts, R., Canadell, J. G., Prentice, I. C., and Le Queìreì, C.:
Climate change increases the risk of wildfires,
ScienceBrief Review, available at: https://ueaeprints.uea.ac.uk/id/eprint/77982/, last access: 29 February 2020.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.:
The NCEP/NCAR 40-Year Reanalysis Project,
B. Am. Meteorol. Soc.,
77, 437–472, 1996.
Kelley, D. I.:
Modelling Australian fire regimes,
PhD thesis, Macquarie, University, Ryde, NSW, Australia, 2014.
Kelley, D. I. and Harrison, S. P.:
Enhanced Australian carbon sink despite increased wildfire during the 21st century,
Environ. Res. Lett.,
9, 104015, https://doi.org/10.1088/1748-9326/9/10/104015, 2014.
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013.
Kelley, D. I., Harrison, S. P., and Prentice, I. C.: Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1), Geosci. Model Dev., 7, 2411–2433, https://doi.org/10.5194/gmd-7-2411-2014, 2014.
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, 2019.
Kelley, D. I., Whitley, R., Brown, M. A. J., and Burton, C.: Scripts used in the submission of “Low Climatic Influence found in 2019 Amazonia Fires”, Zenodo, https://doi.org/10.5281/zenodo.4298760, 2020a.
Kelley, D. I., Whitley, R., Burton, C., Brown, M. A. J., Huntingford, C., and Dong, N.: ConFire Model input/output for South America, Zenodo, https://doi.org/10.5281/zenodo.4298902, 2020b.
Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G., and De Vos, M.:
The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years,
Global Ecol. Biogeogr.,
20, 73–86, 2010.
Krawchuk, M. A. and Moritz, M. A.:
Burning issues: statistical analyses of global fire data to inform assessments of environmental change,
Environmetrics,
25, 472–481, 2014.
Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S., and Kloster, S.:
Multiple stable states of tree cover in a global land surface model due to a fire-vegetation feedback,
Geophys. Res. Lett.,
43, 6324–6331, 2016.
Lenihan, J. M., Daly, C., Bachelet, D., and Neilson, R. P.:
Simulating broad-scale fire severity in a dynamic global vegetation model,
Northwest Sci.,
72, 91–101, 1998.
Lizundia-Loiola, J., Pettinari, M. L., and Chuvieco, E.:
Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis,
Remote Sens.-Basel,
12, 151, https://doi.org/10.3390/rs12010151, 2020.
Lovejoy, T. E. and Nobre, C.:
Amazon tipping point: Last chance for action,
Sci. Adv.,
5, eaba2949, https://doi.org/10.1126/sciadv.aba2949, 2019.
Marengo, J. A. and Espinoza, J. C.:
Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts: EXTREMES IN AMAZONIA,
Int. J. Climatol.,
36, 1033–1050, 2016.
Marengo, J. A., Souza, C. A., Thonicke, K., Burton, C., Halladay, K., Betts, R. A., and Soares, W. R.:
Changes in climate and land use over the Amazon Region: current and future variability and trends,
Front Earth Sci. Chin.,
6, 228, https://doi.org/10.3389/feart.2018.00228, 2018.
Met Office: Iris:
A Python library for analysing and visualising meteorological and oceanographic data sets, v1.2 edn., Exeter, Devon,
available at: http://scitools.org.uk/ (last access: 8 October 2019), 2013.
Morton, D. C., Defries, R. S., Randerson, J. T., Giglio, L., Schroeder, W., and Van Der Werf, G. R.:
Agricultural intensification increases deforestation fire activity in Amazonia,
Glob. Change Biol.,
14, 2262–2275, 2008.
NASA:
Uptick in Amazon Fire Activity in 2019,
NASA Earth Observatory,
available at: https://www.earthobservatory.nasa.gov/images/145498/uptick-in-amazon-fire-activity-in-2019 (last access: 3 November 2020), 2019.
Nauslar, N. J., Brown, T. J., McEvoy, D. J., and Lareau, N. P.:
Record-setting 2018 California wildfires,
in: State of the Climate 2018,
edited by: Blunden, J. A. D. S. A.,
Special Supplement to the Bulletin of the American Meteorological Society Vol. 100, No. 9, September 2019, p. 195, 2019.
Patel, K.:
Arctic Fires Fill the Skies with Soot,
NASA Earth Observatory,
available at: https://earthobservatory.nasa.gov/images/145380/arctic-fires-fill-the-skies-with-soot, last access: 1 December 2019.
Pellegrini, A. F. A., Anderegg, W. R. L., Paine, C. E. T., Hoffmann, W. A., Kartzinel, T., Rabin, S. S., Sheil, D., Franco, A. C., and Pacala, S. W.:
Convergence of bark investment according to fire and climate structures ecosystem vulnerability to future change,
Ecol. Lett.,
20, 307–316, 2017.
Perry, J. J., Cook, G. D., Graham, E., Meyer, C. P. M., Murphy, H. T., and VanDerWal, J.:
Regional seasonality of fire size and fire weather conditions across Australia's northern savanna,
Int. J. Wildland Fire,
29, 1–10, 2020.
Prentice, I. C., Harrison, S. P., and Bartlein, P. J.:
Global vegetation and terrestrial carbon cycle changes after the last ice age,
New Phytol.,
189, 988–998, 2011a.
Prentice, I. C., Kelley, D. I., Foster, P. N., Friedlingstein, P., Harrison, S. P., and Bartlein, P. J.:
Modeling fire and the terrestrial carbon balance,
Global Biogeochem. Cy.,
25, GB3005, https://doi.org/10.1029/2010gb003906, 2011b.
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., Voulgarakis, A., Kelley, D. I., Prentice, I. C., Sitch, S., Harrison, S., and Arneth, A.: The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, 2017.
R Core Team:
R: A Language and Environment for Statistical Computing,
available at: http://www.R-project.org/, last access: 16 April 2015.
RFS: New South Wales Rural Fire Service,
available at: https://www.rfs.nsw.gov.au/fire-information/fires-near-me, last access: 29 February 2019.
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C.:
Probabilistic programming in Python using PyMC3,
PeerJ. Comput. Sci.,
2, e55, https://doi.org/10.7717/peerj-cs.55, 2016.
Sanderson, B. M. and Fisher, R. A.:
A fiery wake-up call for climate science,
Nat. Clim. Change,
10, 175–177, 2020.
Staver, A. C., Brando, P. M., Barlow, J., Morton, D. C., Paine, C. E. T., Malhi, Y., Araujo Murakami, A., and Del Aguila Pasquel, J.:
Thinner bark increases sensitivity of wetter Amazonian tropical forests to fire,
Ecol. Lett.,
23, 99–106, 2020.
Tollefson, J.:
ECOLOGY Huge wildfires defy explanation,
Nature,
561, 16–17, 2018.
Van Der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N., and Dolman, A. J.:
Climate controls on the variability of fires in the tropics and subtropics,
Global Biogeochem. Cy.,
22, GB3028, https://doi.org/10.1029/2007GB003122, 2008.
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. P., 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. Discuss. [preprint], https://doi.org/10.5194/nhess-2020-69, in review, 2020.
Venevsky, S., Thonicke, K., Sitch, S., and Cramer, W.:
Simulating fire regimes in human-dominated ecosystems: Iberian Peninsula case study,
Glob. Change Biol.,
8, 984–998, 2002.
Williams, A. P. and Abatzoglou, J. T.:
Recent Advances and Remaining Uncertainties in Resolving Past and Future Climate Effects on Global Fire Activity,
Current Climate Change Reports,
2, 1–14, 2016.
Wiltshire, J., Hekman, J., and Milan, B. F.:
Carbon loss and economic impacts of a peatland wildfire in north-east Sutherland, Scotland, in: Report on impact of wildfire on the peatland and
released carbon from the peatland,
12–17 May 2019, Ricardo Energy &
Environment for WWF-UK, 2019.
Witze, A.:
The Arctic is burning like never before – and that's bad news for climate change,
Nature,
585, 336–337, 2020.
Zeppel, M. J. B., Harrison, S. P., Adams, H. D., Kelley, D. I., Li, G., Tissue, D. T., Dawson, T. E., Fensham, R., Medlyn, B. E., Palmer, A., West, A. G., and McDowell, N. G.:
Drought and resprouting plants,
New Phytol.,
206, 583–589, 2015.
Zhang, K., de Almeida Castanho, A. D., Galbraith, D. R., Moghim, S., Levine, N. M., Bras, R. L., Coe, M. T., Costa, M. H., Malhi, Y., Longo, M., Knox, R. G., McKnight, S., Wang, J., and Moorcroft, P. R.:
The fate of Amazonian ecosystems over the coming century arising from changes in climate, atmospheric CO2, and land use,
Glob. Change Biol.,
21, 2569–2587, https://doi.org/10.1111/gcb.12903, 2015.
Short summary
Initial evidence suggests human ignitions or landscape changes caused most Amazon fires during August 2019. However, confirmation is needed that meteorological conditions did not have a substantial role. Assessing the influence of historical weather on burning in an uncertainty framework, we find that 2019 meteorological conditions alone should have resulted in much less fire than observed. We conclude socio-economic factors likely had a strong role in the high recorded 2019 fire activity.
Initial evidence suggests human ignitions or landscape changes caused most Amazon fires during...
Altmetrics
Final-revised paper
Preprint