Articles | Volume 6, issue 3
https://doi.org/10.5194/gmd-6-643-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-6-643-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
17 May 2013
Model description paper |
| 17 May 2013
A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0)
M. Pfeiffer
ARVE Group, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
A. Spessa
Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany
J. O. Kaplan
ARVE Group, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Ram Singh, Alexander Koch, Allegra N. LeGrande, Kostas Tsigaridis, Riovie D. Ramos, Francis Ludlow, Igor Aleinov, Reto Ruedy, and Jed O. Kaplan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-219, https://doi.org/10.5194/gmd-2024-219, 2024
Preprint under review for GMD
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This study presents and demonstrates an experimental framework for asynchronous land-atmosphere coupling using the NASA GISS ModelE and LPJ-LMfire models for the 2.5ka period. This framework addresses the limitation of NASA ModelE, which does not have a fully dynamic vegetation model component. It also shows the role of model performance metrics, such as model bias and variability, and the simulated climate is evaluated against the multi-proxy paleoclimate reconstructions for the 2.5ka climate.
Basil A. S. Davis, Marc Fasel, Jed O. Kaplan, Emmanuele Russo, and Ariane Burke
Clim. Past, 20, 1939–1988, https://doi.org/10.5194/cp-20-1939-2024, https://doi.org/10.5194/cp-20-1939-2024, 2024
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During the last ice age (21 000 yr BP) in Europe, the composition and extent of forest and its associated climate remain unclear, with models indicating more forest north of the Alps and a warmer and somewhat wetter climate than suggested by the data. A new compilation of pollen records with improved dating suggests greater agreement with model climates but still suggests models overestimate forest cover, especially in the west.
Jed O. Kaplan and Katie Hong-Kiu Lau
Earth Syst. Sci. Data, 14, 5665–5670, https://doi.org/10.5194/essd-14-5665-2022, https://doi.org/10.5194/essd-14-5665-2022, 2022
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Global lightning strokes are recorded continuously by a network of ground-based stations. We consolidated these point observations into a map form and provide these as electronic datasets for research purposes. Here we extend our dataset to include lightning observations from 2021.
Jed O. Kaplan and Katie Hong-Kiu Lau
Earth Syst. Sci. Data, 13, 3219–3237, https://doi.org/10.5194/essd-13-3219-2021, https://doi.org/10.5194/essd-13-3219-2021, 2021
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Lightning is an important atmospheric phenomenon and natural hazard, but few long-term data are freely available on lightning stroke location, timing, and power. Here, we present a new, open-access dataset of lightning strokes covering 2010–2020, based on a network of low-frequency radio detectors. The dataset is comprised of GIS maps and is intended for researchers, government, industry, and anyone for whom knowing when and where lightning is likely to strike is useful information.
Patricio Velasquez, Jed O. Kaplan, Martina Messmer, Patrick Ludwig, and Christoph C. Raible
Clim. Past, 17, 1161–1180, https://doi.org/10.5194/cp-17-1161-2021, https://doi.org/10.5194/cp-17-1161-2021, 2021
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This study assesses the importance of resolution and land–atmosphere feedbacks for European climate. We performed an asynchronously coupled experiment that combined a global climate model (~ 100 km), a regional climate model (18 km), and a dynamic vegetation model (18 km). Modelled climate and land cover agree reasonably well with independent reconstructions based on pollen and other paleoenvironmental proxies. The regional climate is significantly influenced by land cover.
Yang Li, Loretta J. Mickley, and Jed O. Kaplan
Atmos. Chem. Phys., 21, 57–68, https://doi.org/10.5194/acp-21-57-2021, https://doi.org/10.5194/acp-21-57-2021, 2021
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Climate models predict a shift toward warmer, drier environments in southwestern North America. Under future climate, the two main drivers of dust trends play opposing roles: (1) CO2 fertilization enhances vegetation and, in turn, decreases dust, and (2) increasing land use enhances dust emissions from northern Mexico. In the worst-case scenario, elevated dust concentrations spread widely over the domain by 2100 in spring, suggesting a large climate penalty on air quality and human health.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
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To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Matthew J. Rowlinson, Alexandru Rap, Douglas S. Hamilton, Richard J. Pope, Stijn Hantson, Steve R. Arnold, Jed O. Kaplan, Almut Arneth, Martyn P. Chipperfield, Piers M. Forster, and Lars Nieradzik
Atmos. Chem. Phys., 20, 10937–10951, https://doi.org/10.5194/acp-20-10937-2020, https://doi.org/10.5194/acp-20-10937-2020, 2020
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Tropospheric ozone is an important greenhouse gas which contributes to anthropogenic climate change; however, the effect of human emissions is uncertain because pre-industrial ozone concentrations are not well understood. We use revised inventories of pre-industrial natural emissions to estimate the human contribution to changes in tropospheric ozone. We find that tropospheric ozone radiative forcing is up to 34 % lower when using improved pre-industrial biomass burning and vegetation emissions.
Yang Li, Loretta J. Mickley, Pengfei Liu, and Jed O. Kaplan
Atmos. Chem. Phys., 20, 8827–8838, https://doi.org/10.5194/acp-20-8827-2020, https://doi.org/10.5194/acp-20-8827-2020, 2020
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Using a coupled vegetation–fire–climate modeling framework, we show a northward shift in forests and increased lightning fire activity in northern US states, including Idaho, Montana, and Wyoming. Our findings suggest a large climate penalty on ecosystem, air quality, visibility, and human health in a region valued for its national forests and parks. The fine-scale smoke PM predictions provided in this study should prove useful to human health and environmental assessments.
Sandy P. Harrison, Marie-José Gaillard, Benjamin D. Stocker, Marc Vander Linden, Kees Klein Goldewijk, Oliver Boles, Pascale Braconnot, Andria Dawson, Etienne Fluet-Chouinard, Jed O. Kaplan, Thomas Kastner, Francesco S. R. Pausata, Erick Robinson, Nicki J. Whitehouse, Marco Madella, and Kathleen D. Morrison
Geosci. Model Dev., 13, 805–824, https://doi.org/10.5194/gmd-13-805-2020, https://doi.org/10.5194/gmd-13-805-2020, 2020
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The Past Global Changes LandCover6k initiative will use archaeological records to refine scenarios of land use and land cover change through the Holocene to reduce the uncertainties about the impacts of human-induced changes before widespread industrialization. We describe how archaeological data are used to map land use change and how the maps can be evaluated using independent palaeoenvironmental data. We propose simulations to test land use and land cover change impacts on past climates.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
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The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Anina Gilgen, Stiig Wilkenskjeld, Jed O. Kaplan, Thomas Kühn, and Ulrike Lohmann
Clim. Past, 15, 1885–1911, https://doi.org/10.5194/cp-15-1885-2019, https://doi.org/10.5194/cp-15-1885-2019, 2019
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Using the global aerosol–climate model ECHAM-HAM-SALSA, the effect of humans on European climate in the Roman Empire was quantified. Both land use and novel estimates of anthropogenic aerosol emissions were considered. We conducted simulations with fixed sea-surface temperatures to gain a first impression about the anthropogenic impact. While land use effects induced a regional warming for one of the reconstructions, aerosol emissions led to a cooling associated with aerosol–cloud interactions.
Emeline Chaste, Martin P. Girardin, Jed O. Kaplan, Jeanne Portier, Yves Bergeron, and Christelle Hély
Biogeosciences, 15, 1273–1292, https://doi.org/10.5194/bg-15-1273-2018, https://doi.org/10.5194/bg-15-1273-2018, 2018
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A vegetation model was used to reconstruct fire activity from 1901 to 2012 in relation to changes in lightning ignition, climate, and vegetation in eastern Canada's boreal forest. The model correctly simulated the history of fire activity. The results showed that fire activity is ignition limited but is also greatly affected by both climate and vegetation. This research aims to develop a vegetation model that could be used to predict the future impacts of climate changes on fire activity.
Johann H. Jungclaus, Edouard Bard, Mélanie Baroni, Pascale Braconnot, Jian Cao, Louise P. Chini, Tania Egorova, Michael Evans, J. Fidel González-Rouco, Hugues Goosse, George C. Hurtt, Fortunat Joos, Jed O. Kaplan, Myriam Khodri, Kees Klein Goldewijk, Natalie Krivova, Allegra N. LeGrande, Stephan J. Lorenz, Jürg Luterbacher, Wenmin Man, Amanda C. Maycock, Malte Meinshausen, Anders Moberg, Raimund Muscheler, Christoph Nehrbass-Ahles, Bette I. Otto-Bliesner, Steven J. Phipps, Julia Pongratz, Eugene Rozanov, Gavin A. Schmidt, Hauke Schmidt, Werner Schmutz, Andrew Schurer, Alexander I. Shapiro, Michael Sigl, Jason E. Smerdon, Sami K. Solanki, Claudia Timmreck, Matthew Toohey, Ilya G. Usoskin, Sebastian Wagner, Chi-Ju Wu, Kok Leng Yeo, Davide Zanchettin, Qiong Zhang, and Eduardo Zorita
Geosci. Model Dev., 10, 4005–4033, https://doi.org/10.5194/gmd-10-4005-2017, https://doi.org/10.5194/gmd-10-4005-2017, 2017
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Climate model simulations covering the last millennium provide context for the evolution of the modern climate and for the expected changes during the coming centuries. They can help identify plausible mechanisms underlying palaeoclimatic reconstructions. Here, we describe the forcing boundary conditions and the experimental protocol for simulations covering the pre-industrial millennium. We describe the PMIP4 past1000 simulations as contributions to CMIP6 and additional sensitivity experiments.
Philipp S. Sommer and Jed O. Kaplan
Geosci. Model Dev., 10, 3771–3791, https://doi.org/10.5194/gmd-10-3771-2017, https://doi.org/10.5194/gmd-10-3771-2017, 2017
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We present GWGEN, a computer program for converting monthly climate data into estimates of daily weather, using statistical methods. The GWGEN weather generator program was developed using a global database of more than 5 million observations of daily weather, and it simulates daily values of minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models.
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
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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
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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.
M. Clare Smith, Joy S. Singarayer, Paul J. Valdes, Jed O. Kaplan, and Nicholas P. Branch
Clim. Past, 12, 923–941, https://doi.org/10.5194/cp-12-923-2016, https://doi.org/10.5194/cp-12-923-2016, 2016
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We used climate modelling to estimate the biogeophysical impacts of agriculture on the climate over the last 8000 years of the Holocene. Our results show statistically significant surface temperature changes (mainly cooling) from as early as 7000 BP in the JJA season and throughout the entire annual cycle by 2–3000 BP. The changes were greatest in the areas of land use change but were also seen in other areas. Precipitation was also affected, particularly in Europe, India, and the ITCZ region.
Zhen Zhang, Niklaus E. Zimmermann, Jed O. Kaplan, and Benjamin Poulter
Biogeosciences, 13, 1387–1408, https://doi.org/10.5194/bg-13-1387-2016, https://doi.org/10.5194/bg-13-1387-2016, 2016
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This study investigates improvements and uncertainties associated with estimating global inundated area and wetland CH4 emissions using TOPMODEL. Different topographic information and catchment aggregation schemes are evaluated against seasonal and permanently inundated wetland observations. Reducing uncertainty in prognostic wetland dynamics modeling must take into account forcing data as well as topographic scaling schemes.
M. J. McGrath, S. Luyssaert, P. Meyfroidt, J. O. Kaplan, M. Bürgi, Y. Chen, K. Erb, U. Gimmi, D. McInerney, K. Naudts, J. Otto, F. Pasztor, J. Ryder, M.-J. Schelhaas, and A. Valade
Biogeosciences, 12, 4291–4316, https://doi.org/10.5194/bg-12-4291-2015, https://doi.org/10.5194/bg-12-4291-2015, 2015
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Studying century-scale ecological processes and their legacy effects requires taking forest management into account. In this study we produce spatially and temporally explicit maps of European forest management from 1600 to 2010. The most important changes between 1600 and 2010 are an increase of 593 000km2 in conifers at the expense of deciduous forest, a 612 000km2 decrease in unmanaged forest, a 152 000km2 decrease in coppice management and a 818 000km2 increase in high stand management.
P. Achakulwisut, L. J. Mickley, L. T. Murray, A. P. K. Tai, J. O. Kaplan, and B. Alexander
Atmos. Chem. Phys., 15, 7977–7998, https://doi.org/10.5194/acp-15-7977-2015, https://doi.org/10.5194/acp-15-7977-2015, 2015
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The atmosphere’s oxidative capacity determines the lifetime of many trace gases important to climate, chemistry, and human health. Yet uncertainties remain about its past variations, its controlling factors, and the radiative forcing of short-lived species it influences. To reduce these uncertainties, we must better quantify the natural emissions and chemical reaction mechanisms of organic compounds in the atmosphere, which play a role in governing the oxidative capacity.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
A. Mauri, B. A. S. Davis, P. M. Collins, and J. O. Kaplan
Clim. Past, 10, 1925–1938, https://doi.org/10.5194/cp-10-1925-2014, https://doi.org/10.5194/cp-10-1925-2014, 2014
L. T. Murray, L. J. Mickley, J. O. Kaplan, E. D. Sofen, M. Pfeiffer, and B. Alexander
Atmos. Chem. Phys., 14, 3589–3622, https://doi.org/10.5194/acp-14-3589-2014, https://doi.org/10.5194/acp-14-3589-2014, 2014
G. Strandberg, E. Kjellström, A. Poska, S. Wagner, M.-J. Gaillard, A.-K. Trondman, A. Mauri, B. A. S. Davis, J. O. Kaplan, H. J. B. Birks, A. E. Bjune, R. Fyfe, T. Giesecke, L. Kalnina, M. Kangur, W. O. van der Knaap, U. Kokfelt, P. Kuneš, M. Lata\l owa, L. Marquer, F. Mazier, A. B. Nielsen, B. Smith, H. Seppä, and S. Sugita
Clim. Past, 10, 661–680, https://doi.org/10.5194/cp-10-661-2014, https://doi.org/10.5194/cp-10-661-2014, 2014
T. Hoffmann, S. M. Mudd, K. van Oost, G. Verstraeten, G. Erkens, A. Lang, H. Middelkoop, J. Boyle, J. O. Kaplan, J. Willenbring, and R. Aalto
Earth Surf. Dynam., 1, 45–52, https://doi.org/10.5194/esurf-1-45-2013, https://doi.org/10.5194/esurf-1-45-2013, 2013
M. Scherstjanoi, J. O. Kaplan, E. Thürig, and H. Lischke
Geosci. Model Dev., 6, 1517–1542, https://doi.org/10.5194/gmd-6-1517-2013, https://doi.org/10.5194/gmd-6-1517-2013, 2013
V. Beck, C. Gerbig, T. Koch, M. M. Bela, K. M. Longo, S. R. Freitas, J. O. Kaplan, C. Prigent, P. Bergamaschi, and M. Heimann
Atmos. Chem. Phys., 13, 7961–7982, https://doi.org/10.5194/acp-13-7961-2013, https://doi.org/10.5194/acp-13-7961-2013, 2013
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
J. R. Melton, R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan
Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, https://doi.org/10.5194/bg-10-753-2013, 2013
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COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
The Development and Application of an Arctic Sea Ice Emulator v.1
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)
Architectural insights into and training methodology optimization of Pangu-Weather
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
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We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
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NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
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We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
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The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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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 to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
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Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Sian Megan Chilcott and Malte Meinshausen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-203, https://doi.org/10.5194/gmd-2024-203, 2025
Revised manuscript accepted for GMD
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Climate models are expensive to run and often underestimate how sensitive Arctic sea ice is to climate change. To address this, we developed a simple model that emulates the response of sea ice to global warming. We find the remaining carbon dioxide (CO2) emissions that will avoid a seasonally ice-free Arctic Ocean is lower than previous estimates of 821 Gigatonnes of CO2. Our model also provides insights into the future of winter sea ice, examining a larger ensemble than previously possible.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Revised manuscript accepted for GMD
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We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Cited articles
Ahlenius, H.: Human impact, year 2002 (Miller cylindrical projection), GLOBIO-2 model, http://www.grida.no/graphicslib/detail/human-impact-year-2002-miller-cylindrical-projection_7006, last access: 10 May 2013, 2005.
Akanvou, R., Becker, M., Chano, M., Johnson, D. E., Gbaka-Tcheche, H., and Toure, A.: Fallow residue management effects on upland rice in three agroecological zones of West Africa, Biol. Fert. Soils, 31, 501–507, https://doi.org/10.1007/s003740000199, 2000.
Akselsson, C., B., B., Meentemeyer, V., and Westling, O.: Carbon sequestration rates in organic layers of boreal and temperate forest soils – Sweden as a case study, Global Ecol. Biogeogr., 14, 77–84, 2005.
Alaska Bureau of Land Management: Alaska Lightning Detection System, http://afsmaps.blm.gov/imf/imf.jsp?site=lightning(last access: 10 May 2013), 2013.
Alaska Fire Service: Alaska Fire Service polygon maps of burned area, http://afsmaps.blm.gov/imf/imf.jsp?site=firehistory(last access: 10 May 2013), 2013.
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-minute Global Relief Model: Procedures, Data Sources and Analysis, Noaa technical memorandum nesdis ngdc-24, NOAA, 2009.
Anderson, M. K.: Prehistoric anthropogenic wildland burning by hunter-gatherer societies in the temperate regions: A net source, sink, or neutral to the global carbon budget?, Chemosphere, 29, 913–934, https://doi.org/10.1016/0045-6535(94)90160-0, 1994.
Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, https://doi.org/10.1029/2000GB001382, 2001.
Andrews, P. L.: BEHAVE: Fire Behavior Prediction and Fuel Modeling System - Burn Subsystem, Part 1, United States Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT 84401, General Technical Report INT-194, 1986.
Andrews, P. L.: BehavePlus Fire Modeling System: Past, Present, and Future, in: Proceedings of the 7th Symposium on Fire and Forest Meteorological Society, American Meteorological Society, Bar Harbor, ME, 2007.
Andrews, P. L. and Chase, C. H.: BEHAVE: fire behavior prediction and fuel modeling system – BURN subsystem, Part 2, United States Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT 84401, General Technical Report INT-260, 1989.
Andrews, P. L., Bevins, C. D., and Seli, R. C.: BehavePlus fire modeling system, version 2.0: Users Guide, General technical report, United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT, 2003.
Andrews, P. L., Bevins, C. D., and Seli, R. C.: BehavePlus Fire Modeling System, version 4.0: User's Guide, United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT, General Technical Report RMRS-GTR-106WWW Revised, 2008.
Archibald, S. A., Roy, D. P., van Wilgen, B. W., and Scholes, R. J.: What limits fire? An examination of drivers of burnt area in Southern Africa, Glob. Change Biol., 15, 613–630, https://doi.org/10.1111/j.1365-2486.2008.01754.x, 2009.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nature Climate Change Letters, 2, 182–185, https://doi.org/10.1038/nclimate1354, 2012.
Balshi, M. S., McGuire, A. D., Zhuang, Q., Melillo, J., Kicklighter, D. W., Kasischke, E., Wirth, C., Flannigan, M., Harden, J., Clein, J. S., Burnside, T. J., McAllister, J., Kurz, W. A., Apps, M., and Shvidenko, A.: The role of historical fire disturbance in the carbon dynamics of the pan-boreal region: A process-based analysis, J. Geophys. Res., 112, G02029, https://doi.org/10.1029/2006JG000380, 2007.
Barney, R. J.: Wildfires in Alaska – some historical and projected effects and aspects, in: Proceedings – Fire in the Northern Environment, A Symposium, US Forest Service: Portland, OR, College AK, 13-14 April 1971, 51–59, 1971.
Berg, B.: Litter decomposition and organic matter turnover in northern forest soils, Forest Ecol. Manag., 133, 13–22, https://doi.org/10.1016/S0378-1127(99)00294-7, 2000.
Berg, B., McGlaugherty, C., De Santo, A. V., and Johnson, D.: Humus buildup in boreal forests: effects of litter fall and its N concentration, Canadian J. Forest Res., 31, 988–998, https://doi.org/10.1139/x01-031, 2001.
Bergner, B., Johnstone, J., and Treseder, K. K.: Experimental warming and burn severity alter CO2 flux and soil functional groups in recently burned boreal forest, Glob. Change Biol., 10, 1996–2004, https://doi.org/10.1111/j.1365-2486.2004.00868.x, 2004.
Bliss, L. C.: Adaptations of Arctic and Alpine Plants to Environmental Conditions, Arctic, 15, 117–144, 1962.
Boles, S. H. and Verbyla, D. L.: Comparison of Three AVHRR-Based Fire Detection Algorithms for Interior Alaska, Remote Sens. Environ., 72, 1–16, https://doi.org/10.1016/S0034-4257(99)00079-6, 2000.
Bond, W. J. and Keeley, J. E.: Fire as a global 'herbivore': the ecology and evolution of flammable ecosystems, Trends Ecol. Evol., 20, 387–394, 2005.
Bond, W. J. and Midgley, J. J.: Fire and the Angiosperm Revolutions, Int. J. Plant Sci., 173, 569–583, 2012.
Bond, W. J. and Scott, A. C.: Fire and the spread of flowering plants in the Cretaceous, New Phytol., 188, 1137–1150, https://doi.org/10.1111/j.1469-8137.2010.03418.x, 2010.
Bond, W. J. and van Wilgen, B. W.: Fire and Plants, Chapman & Hall, London, UK, 1996.
Bond, W. J., Woodward, F. I., and Midgley, G. F.: The global distribution of ecosystems in a world without fire, New Phytol., 165, 525–538, https://doi.org/10.1111/j.1469-8137.2004.01252.x, 2005.
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze-Campen, H., M{ü}ller, C., Reichstein, M., and Smith, B.: Modelling the role of agriculture for the 20th century global terrestrial carbon balance, Glob. Change Biol., 13, 679–706, https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007.
Bowman, D. M. J. S.: Tansley Review No. 101 – The impact of Aboriginal landscape burning on the Australian biota, New Phytol., 140, 385–410, 1998.
Bowman, D. M. J. S. and Prior, L. D.: Impact of Aboriginal landscape burning on woody vegetation in Eucalyptus tetrodonta savanna in Arnhem Land, northern Australia, J. Biogeogr., 31, 807–817, https://doi.org/10.1111/j.1365-2699.2004.01077.x, 2004.
Bowman, D. M. J. S., Walsh, A., and Prior, L. D.: Landscape analysis of Aboriginal fire management in Central Arnhem Land, north Australia, J. Biogeogr., 31, 207–223, https://doi.org/10.1046/j.0305-0270.2003.00997.x, 2004.
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D'Antonio, C. M., DeFries, R. S., Doyle, J. C., Harrison, S. P., Johnston, F. H., Keeley, J. E., Krawchuck, M. A., Kull, C. A., Marston, J. B., Moritz, M. A., Prentice, I. C., Roos, C. I., Scott, A. C., Swetnam, T. W., van der Werf, G. R., and Pyne, S. J.: Fire in the Earth System, Science, 324, 481–485, https://doi.org/10.1126/science.1163886, 2009.
Breckle, S. W.: Walter's Vegetation of the Earth: The Ecological Systems of the Geo-Biosphere, Springer Verlag, Berlin, Heidelberg, 2002.
Brubaker, L., Higuera, P. E., Rupp, T. S., Olson, M. A., Anderson, P. M., and Hu, F. S.: Linking sediment-charcoal records and ecological modeling to understand causes of fire-regime change in boreal forests, Ecology, 90, 1788–1801, https://doi.org/10.1890/08-0797.1, 2009.
Burgan, R. E.: Concepts and Interpreted Examples In Advanced Fuel Modeling, United States Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT 84401, General Technical Report INT-283, 1987.
Burgan, R. E. and Rothermel, R. C.: BEHAVE: Fire Behavior Prediction and Fuel Modeling System – Fuel Subsystem, National Wildfire Coordinating Group, United States Department of Agriculture, United States Department of the Interior, Intermountain Forest and Range Experiment Station, Ogden, UT 84401, General Technical Report INT-167, 1984.
Cairns, M. and Garrity, D. P.: Improving shifting cultivation in Southeast Asia by building on indigenous fallow management strategies, Agroforest. Syst., 47, 37–48, 1999.
Calkin, D. E., Gebert, K. M., Jones, J. G., and Neilson, R. P.: Forest Service Large Fire Area Burned and Suppression Expenditure Trends, 1970–2002, J. Forest., 103, 179–183, 2005.
Carcaillet, C., Almquist, H., Asnong, H., Bradshaw, R. H. W., Carri{ó}n, J. S., Gaillard, M.-J., Gajewski, K., Haas, J. N., Haberle, S. G., Hadorn, P., M{ü}ller, S. D., Richard, P. J. H., Richoz, I., R{ö}sch, M., S{á}nchez Go{ñ}i, M. F., von Stedingk, H., Stevenson, A. C., Talon, B., Tardy, C., Tinner, W., Tryterud, E., Wick, L., and Willis, K. J.: Holocene biomass burning and global dynamics of the carbon cycle, Chemosphere, 49, 845–863, 2002.
Cheney, P. and Sullivan, A.: Grassfires: Fuel, Weather and Fire Behavior, 2nd Edn., CSIRO Publishing, 2008.
Christian, H. J., Blakeslee, R. J., Boccippio, D. J., Boeck, W. L., Buechler, D. E., Driscoll, K. T., Goodman, S. J., Hall, J. M., Koshak, W. J., Mach, D. M., and Stewart, M. F.: Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, J. Geophys. Res., 108, 4005, https://doi.org/10.1029/2002JD002347, 2003.
Collins, S. L.: Fire Frequency and Community Heterogeneity in Tallgrass Prairie Vegetation, Ecology, 73, 2001–2006, 1992.
Compo, G. P., Whitacker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason Jr., B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Br{ö}nnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.
Conklin, H. C.: The Study of Shifting Cultivation, Curr. Anthropol., 2, 27–61, 1961.
Connell, J. H.: Diversity in Tropical Rain Forests and Coral Reefs, Science, 199, 1302–1310, https://doi.org/10.1126/science.199.4335.1302, 1978.
Crowley, G. M. and Garnett, S. T.: Changing Fire Management in the Pastoral Lands of Cape York Peninsula of northeast Australia, 1623 to 1996, Aust. Geogr. Stud., 38, 10–26, https://doi.org/10.1111/1467-8470.00097, 2000.
Crutzen, P. J. and Andreae, M. O.: Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles, Science, 250, 1669–1678, https://doi.org/10.1126/science.250.4988.1669, 1990.
Dagpunar, J.: Principles of Random Variate Generation, Oxford Science Publications, Clarendon Press, Oxford, 1988.
DeFries, R. S., Hansen, M. C., Townshend, J. R. G., Janetos, A. C., and Loveland, T. R.: A new global 1-km dataset of percentage tree cover derived from remote sensing, Glob. Change Biol., 6, 247–254, https://doi.org/10.1046/j.1365-2486.2000.00296.x, 2000.
Desiles, S. L. E., Nijssen, B., Ekwurzel, B., and Ferr{é}, T. P. A.: Post-wildfire changes in suspended sediment rating curves: Sabino Canyon, Arizona, Hydrological Processes, 21, 1413–1423, https://doi.org/10.1002/hyp.6352, 2007.
de Souza, R. A., Miziara, F., and De Marco Junior, P.: Spatial variation of deforestation rated in the Brazilian Amazon: A complex theater for agrarian technology, agrarian structure and governance by surveillance, Land Use Policy, 30, 915–924, 2013.
Diaz-Avalos, C., Peterson, D. L., Alvarado, E., Ferguson, S. A., and Besag, J. E.: Spacetime modelling of lightning-caused ignitions in the Blue Mountains, Oregon, Can. J. Forest Res., 31, 1579–1593, 2001.
Dodgshon, R. A. and Olsson, G. A.: Heather moorland in the Scottish Highlands: the history of a cultural landscape, 1600-1880, J. Hist. Geogr., 32, 21–37, 2006.
Dove, M. R.: Swidden agriculture in Indonesia: the subsistence strategies of the Kalimantan Kantu, Mouton de Gruyter, Berlin, Germany, 1985.
Dregne, H. E.: Land Degradation in the Drylands, Arid Land Res. Manag., 16, 99–132, 2002.
Dumond, D. E.: Swidden agriculture and the rise of the Maya civilization, Southwest. J. Anthrop., 17, 301–316, 1961.
Dwyer, E., Pinnock, S., Gr{é}goire, J.-M., and Pereira, J. M. C.: Global spatial and temporal distribution of vegetation fire as determined from satellite observations, Int. J. Remote Sens., 21, 1289–1302, https://doi.org/10.1080/014311600210182, 2000.
Dyer, R.: The Role of Fire on Pastoral Lands in Northern Australia; in: Fire and Sustainable Agricultural and Forestry Development in Eastern Indonesia and Northern Australia, ACIAR Proc., 91, 108–113, 1999.
Eriksen, C.: Why do they burn the 'bush'? Fire, rural livelihoods, and conservation in Zambia, Geogr. J., 173, 242–256, 2007.
Essery, R., Best, M., and Cox, P.: MOSES 2.2 Technical Documentation, Tech. rep., Hadley Center Technical Note 30, Hadley Center, Met Office, Bracknell, UK, 2001.
Eva, H. D., Malingreau, J. P., Gregoire, J. M., Belward, A. S., and Mutlow, C. T.: Cover The advance of burnt areas in Central Africa as detected by ERS-1 ATSR-1, Int. J. Remote Sens., 19, 1635–1637, 1998.
Faivre, N., P., R., Boer, M. M., McCaw, L., and Grierson, P. F.: Characterization of landscape pyrodiversity in Mediterranean environments: contrasts and similarities between south-western Australia nd south-eastern France, Landscape Ecol., 26, 557–571, https://doi.org/10.1007/s10980-011-9582-6, 2011.
FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.0), 2008.
Finney, M. A.: FARSITE: Fire Area Simulator – Model Development and Evaluation, USDA Forest Service Research Paper, Missoula, MT, RMRS-RP-4 Revised, 52, 1998.
Fisher, J. B., Sitch, S., Malhi, Y., Fisher, R. A., Hungtingford, C., and Tan, S.-Y.: Carbon cost of plant nitrogen acquisition: A mechanistic, globally applicable model of plant nitrogen uptake, retranslocation, and fixation, Global Biogeochem. Cy., 24, GB1014, https://doi.org/10.1029/2009gb003621, 2010.
Fox, J. M.: How Blaming 'Slash and Burn' Farmers is Deforesting Mainland Southeast Asia, AsiaPacific Issues, 47, 1–8, 2000.
Gebert, K. M., Calkins, D. E., and Yoder, J.: Estimating Suppression Expenditures for Individual Large Wildland Fires, West. J. Appl. For., 22, 188–196, 2007.
Gebert, K. M., Calkin, D. E., Huggett, R. J., and Abt, K. L.: Economic analysis of federal wildfire management programs; in: The economics of forest disturbance: wildfires, storms and invasive species, Springer Verlag, Dordrecht, The Netherlands, 2008.
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.: Terrestrial vegetation and water balance - hydrological evaluation of a dynamic global vegetation model, J. Hydrol., 286, 249–270, https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004.
Gibson, D. J.: Grasses and grassland ecology, Oxford University Press, Oxford, UK, 2009.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.
Gomez-Dans, J., Spessa, A., Wooster, M., and Lewis, P.: A sensitivity analysis study of the coupled vegetation-fire model, LPJ-SPITFIRE, Ecological Modeling, in review, 2013.
Government of Western Australia, Department for Agriculture and Food: Fire Management Guidelines for Kimberley Pastoral Rangelands: Best Management Practice Guide, 2005.
Grime, J. P.: Control of species density in herbaceous vegetation, J. Environ. Manage., 1, 151–167, 1973.
Guyette, R. P., Muzika, R. M., and Dey, D. C.: Dynamics of an Anthropogenic Fire Regime, Ecosystems, 5, 472–486, https://doi.org/10.1007/s10021-002-0115-7, 2002.
Hadlow, A. M.: Changes in Fire Season Precipitation in Idaho and Montana from 1982–2006, Ph.D. thesis, Colorado Sate University, Fort Collins, Colorado, 2009.
Hall, B. L.: Precipitation associcated with lightning-ignited wildfires in Arizona and New Mexico, Int. J. Wildland Fire, 16, 242–254, https://doi.org/10.1071/WF06075, 2007.
Hamilton, M. J.: The complex structure of hunter-gatherer social networks, P. R. Soc. B, 274, 2195–2203, https://doi.org/10.1098/rspb.2007.0564, 2007.
Harden, J. W., Trumbore, S. E., Stocks, B. J., Hirsch, A., Gower, S. T., O'Neill, K. P., and Kasischke, E. S.: The role of fire in the boreal carbon budget, Glob. Change Biol., 6, 174–184, https://doi.org/10.1046/j.1365-2486.2000.06019.x, 2000.
Head, L. M.: Landscapes socialised by fire: post-contact changes in Aboriginal fire use in northern Australia, and implications for prehistory, Archaeol. Ocean, 29, 172–181, 1994.
Heinsch, F. A. and Andrews, P. L.: BehavePlus fire modeling system, version 5.0: design and features, General Technical Report RMRS-GTR-249, United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, 2010.
Hickler, T., Prentice, I. C., Smith, B., Sykes, M. T., and Zaehle, S.: Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model, Global Ecol. Biogeogr., 15, 567–577, 2006.
Higuera, P. E., Brubaker, L. B., Anderson, P. M., Brown, T. A., Kennedy, A. T., and Hu, F. S.: Frequent Fires in Ancient Shrub Tundra: Implications of Paleorecords for Arctic Environmental Change, PLoS One, 3, e0001744, https://doi.org/10.1371/journal.pone.0001744, 2008.
Higuera, P. E., Brubaker, L. B., Anderson, P. M., Hu, F. S., and Brown, T. A.: Vegeation mediated the impacts of postglacial climate change on fire regimes in the south-central Brooks Range, Alaska, Ecol. Monogr., 79, 201–219, https://doi.org/10.1890/07-2019.1, 2009.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965–1978, https://doi.org/10.1002/joc.1276, 2005.
Holle, R. L., Cummins, K. L., and Demetriades, N. W. S.: Monthly distribution of NLDN and GLD360 cloud-to-ground lightning, Tech. rep., Vaisala Inc., Tucson, Arizona 85756, 2011.
Houghton, R. A., Lawrence, K. T., Hackler, J. L., and Brown, S.: The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates, Glob. Change Biol., 7, 731–746, 2001.
Hu, F. S., Higuera, P. E., Walsh, J. E., Chapman, W. L., Duffy, P. A., Brubaker, L. B., and Chipman, M. L.: Tundra burning in Alaska: Linkages to climatic change and sea ice retreat, J. Geophys. Res., 115, G04002, https://doi.org/10.1029/2009JG001270, 2010.
Huston, M.: A General Hypothesis of Species Diversity, Am. Nat., 113, 81–101, 1979.
Iversen, J.: Landnam i Danmarks Stenalder. En pollenanalytisk Undersøgelse over det første Landbrugs Indvirkning paa Vegetationsudviklingen, (Land occupation in Denmark's Stone Age, A Pollen-Analytical Study of the Influence of Farmer Culture on the Vegetational Development), Danmarks Geologiske Undersølgelse, Raekke II, 1941 (in Danish).
Jain, A. K., Tao, Z., Yang, X., and Gillespie, C.: Estimates of global biomass burning emissions for reactive greenhouse gases (CO, NMHCs, and NOx) and CO2, J. Geophys. Res., 111, D06304, https://doi.org/10.1029/2005JD006237, 2006.
Jayaratne, E. R. and Kuleshov, Y.: Geographical and seasonal characteristics of the relationship between lightning ground flash density and rainfall within the continent of Australia, Atmos. Res., 79, 1–14, https://doi.org/10.1016/j.atmosres.2005.03.004, 2006.
Johnson, D. W., Susfalk, R. B., Dahlgren, R. A., and Klopatek, J. M.: Fire is more important than water for nitrogen fluxes in semi-arid forests, Environ. Sci. Policy, 1, 79–86, https://doi.org/10.1016/S1462-9011(98)00008-2, 1998.
Johnson, E. A.: Fire and vegetation dynamics: studies from the North American boreal forest, Cambridge University Press, Cambridge, 1992.
Johnston, K. J.: The intensification of pre-industrial cereal agriculture in the tropics: Boserup, cultivation lengthening, and the Classic Maya, J. Anthropol. Archaeol., 22, 126–161, https://doi.org/10.1016/S0278-4165(03)00013-8, 2003.
Jones, B. M., Kolden, C. A., Jandtt, R., Abatzoglout, J. T., Urbans, F., and Arp, C. D.: Fire Behavior, Weather, and Burn Severity of the 2007 Anaktuvuk River Tundra Fire, North Slope, Alaska, Arct. Antarct. Alp. Res., 41, 309–318, https://doi.org/10.1657/l938-4246-41.3.309, 2009.
Kalis, A. J. and Meurers-Balke, J.: Die "Landnam"-Modelle von Iversen und Troels-Smith zur Neolithisierung des westlichen Ostseegebietes – ein Versuch ihrer Aktualisierung, Praehist. Z., 73, 1–24, 1998 (in German).
Kalis, A. J., Merkt, J., and Wunderlich, J.: Environmental changes during the Holocene climatic optimum in central Europe – human impact and natural causes, Quaternary Sci. Rev., 22, 33–79, https://doi.org/10.1016/S0277-3791(02)00181-6, 2003.
Kane, D. L. and Stein, J.: Water Movement Into Seasonally Frozen Soils, Water Resour. Res., 19, 1547–1557, https://doi.org/10.1029/WR019i006p01547, 1983.
Kaplan, J. O., Bigelow, N. H., Prentice, I. C., Harrison, S. P., Bartlein, P. J., Christensen, T. R., Cramer, W., Matveyeva, N. V., McGuire, A. D., Murray, D. F., Razzhivin, V. Y., Smith, B., Walker, D. A., Anderson, P. M., Andreev, A. A., Brubaker, L. B., Edwards, M. E., and Lozhkin, A. V.: Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections, J. Geophys. Res., 108, 8171, https://doi.org/10.1029/2002JD002559, 2003.
Kaplan, J. O., Krumhard, K. M., Ellis, E. C., Ruddiman, W. F., Lemmen, C., and Klein Goldewijk, K.: Holocene carbon emissions as a result of anthropogenic land cover change, Holocene, 21, 775–791, 2011.
Kasischke, E. S., Williams, D., and Barry, D.: Analysis of the patterns of large fires in the boreal forest of Alaska, Int. J. Wildland Fire, 11, 131–144, 2002.
Kasischke, E. S., Hyer, E. J., Novelli, P. C., Bruhwiler, L. P., French, N. H. F., Sukhinin, A. I., Hewson, J. H., and Stocks, B. J.: Influences of boreal fire emissions on Northern Hemisphere atmospheric carbon and carbon monoxide, Global Biogeochem. Cy., 19, GB1012, https://doi.org/10.1029/2004GB002300, 2005.
Katsanos, D., Lagouvardos, K., Kotroni, V., and Argiriou, A. A.: Combined analysis of rainfall and lightning data produced by mesoscale systems in the central and eastern Mediterranean, Atmos. Res., 83, 55–63, https://doi.org/10.1016/j.atmosres.2006.01.012, 2007.
Keeley, J. E., Zedler, P. H., Zammit, C. A., and Stohlgren, T. J.: Fire and Demography, in: The California Chapararal: Paradigms Reexamined, edited by: Keeley, S. C., Science Series, No. 34, Natural History Museum of Los Angeles County, 1989.
Kimmerer, R. W. and Lake, F. K.: The Role of Indigenous Burning in Land Management, J. Forest., 99, 36–41, 2001.
Kleidon, A. and Heimann, M.: Assessing the role of deep rooted vegetation in the climate system with model simulations: mechanisms, comparison to observations and implications for Amazonian deforestation, Clim. Dynam., 16, 183–199, 2000.
Klein Goldewijk, 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 12000 years, Global Ecol. Biogeogr., 20, 73–86, https://doi.org/10.1111/j.1466-8238.2010.00587.x, 2010.
Kleinman, P. J. A., Pimentel, D., and Bryant, R. B.: The ecological sustainability of slash-and-burn agriculture, Agr. Ecosyst. Environ., 52, 235–249, https://doi.org/10.1016/0167-8809(94)00531-I, 1995.
Klop, E. and Prins, H. H. T.: Diversity and species composition of West African ungulate assemblages: effects of fire, climate and soil, Global Ecol. Biogeogr., 17, 778–787, https://doi.org/10.1111/j.1466-8238.2008.00416.x, 2008.
Kotroni, V. and Lagouvardos, K.: Lightning occurrence in relation with elevation, terrain slope, and vegetation cover in the Mediterranean, J. Geophys. Res., 113, D21118, https://doi.org/10.1029/2008JD010605, 2008.
Kourtz, P. and Todd, B.: Predicting the daily occurrence of lightning-caused forest fires, Forestry Canada, Petawawa National Forestry Institute, Information Report, No. PI-X-112, 18 pp., 1992.
Koven, C., Friedlingstein, P., Ciais, P., D., K., Krinner, G., and Tarnocai, C.: On the formation of high-latitude carbon stocks: Effects of cryoturbation and insulation by organic matter in a land surface model, Geophys. Res. Lett., 36, L21501, https://doi.org/10.1029/2009GL040150, 2009.
Krumhardt, K. M. and Kaplan, J. O.: A spline fit to atmospheric CO2 records from Antarctic ice cores and measured concentrations for the last 25000 years, ARVE Technical Report 2, ARVE Group, Environmental Engineering Institute, Ecole Polytechnique Fédérale de Lausanne, EPFL, Station 2, 1015 Lausanne, http://grkapweb1.epfl.ch/pub/ARVE_tech_report2_co2spline.pdf, last access: 10 May 2013, 2012.
Kull, C. A. and Laris, P.: Fire ecology and fire politics in Mali and Madagascar; in: Tropical Fire Ecology, Springer Verlag, Berlin, Heidelberg, 171–226, https://doi.org/10.1007/978-3-540-77381-8_7, 2009.
Kurz, W. A. and Apps, M. J.: A 70-year retrospective analysis of carbon fluxes in the Canadian forest sector, Ecol. Appl., 9, 526–547, https://doi.org/10.1890/1051-0761(1999)009[0526:AYRAOC]2.0.CO;2, 1999.
Lal, D. M. and Pawar, S. D.: Relationship between rainfall and lightning over central Indian region in monsoon and premonsoon seasons, Atmos. Res., 92, 402–410, https://doi.org/10.1016/j.atmosres.2008.12.009, 2009.
Landhaeuser, S. M. and Wein, R. M.: Postfire vegetation recovery and tree establishment at the Arctic treeline: Climatic-change-vegetation-response hypothesis, J. Ecol., 81, 665–672, 1993.
Latham, D. J. and Rothermel, R. C.: Probability of Fire-Stopping Precipitation Events, Tech. rep., U.S. Forest Service, Utah Regional Depository, Paper 354, 8 pp., 1993.
Lehsten, V., Tansey, K., Balzter, H., Thonicke, K., Spessa, A., Weber, U., Smith, B., and Arneth, A.: Estimating carbon emissions from African wildfires, Biogeosciences, 6, 349–360, https://doi.org/10.5194/bg-6-349-2009, 2009.
Lehsten, V., Arneth, A., Thonicke, K., and Spessa, A.: The effect of fire on tree-grass coexistence in savannas: a simulation study, J. Veg. Sci., in review, 2013.
Le Page, Y., Oom, D., Silva, J. N. M., J{ö}nsson, P., and Pereira, J. M. C.: Seasonality of vegetation fires as modified by human action: observing the deviation from eco-climatic fire regimes, Global Ecol. Biogeogr., 19, 575–588, https://doi.org/10.1111/j.1466-8238.2010.00525.x, 2010.
Lewis, H. T. (Ed.): Why Indians burned: specific versus general reasons, GTR-INT-182, in: Proceedings – Symposium and Workshop on Wilderness Fire, Missoula, Montana, Ogden, UT, USDA Forest Service, Intermountain Forest and Range Experiment Station, 1985.
Lima, A., Freire Silva, T. S., Oliveira, L. E., and de Arag{ã}o, C.: Land use and land cover changes determine the spatial relationship between fire and deforestation in the Brazilian Amazon, Appl. Geogr., 34, 239–246, https://doi.org/10.1016/j.apgeog.2011.10.013, 2012.
L{ü}ning, J.: Steinzeitliche Bauern in Deutschland: die Landwirtschaft im Neolithikum., Universit{ä}tsforschungen zur pr{ä}historischen Arch{ä}ologie, Bonn, Vol. 58, 285 pp., 2000 (in German).
Lynch, J. A., Hollis, J. L., and Hu, F. S.: Climatic and landscape controls of the boreal forest fire regime: Holocene records from Alaska, J. Ecol., 92, 477–489, 2004.
M{ä}kip{ä}{ä}, R.: Effect of nitrogen input on carbon accumulation of boreal forest soils and ground vegetation, Forest Ecol. Manag., 79, 217–226, https://doi.org/10.1016/0378-1127(95)03601-6, 1995.
Malhi, Y., Wood, D., Bakers, T. R., Wright, J., Phillips, O. L., Cochrane, T., Meir, P., Chave, J., Almeida, S., Arroyo, L., Higuchi, N., Killeen, T. J., Laurance, S. G., Laurance, W. F., Lewis, S. L., Monteagudo, A., Neill, D. A., Vargas, P. N., Pitman, N. C. A., Quesada, C. A., Salomao, R., Silva, J. N. M., Lezama, A. T., Terborgh, J., Vasquez-Martinez, R., and Vinceti, B.: The regional variation of aboveground live biomass in old-growth Amazonian forests, Glob. Change Biol., 12, 1107–1138, https://doi.org/10.1111/j.1365-2486.2006.01120.x, 2006.
Marlowe, F. W.: Hunter-Gatherers and Human Evolution, Evolutionary Anthropology, 14, 54–67, https://doi.org/10.1002/evan.20046, 2005.
Marsaglia, G.: Normal (Gaussian) Random Variables for Supercomputers, The J. Supercomput., 5, 49–55, https://doi.org/10.1007/BF00155857, 1991.
Mather, A. S.: Forest transition theory and the reforestation of Scotland, Scot. Geogr. J., 120, 83–98, https://doi.org/10.1080/00369220418737194, 2004.
Mazarakis, N., Kotroni, V., Lagouvardos, K., and Argiriou, A. A.: Storms and Lightning Activity in Greece during the Warm Periods of 2003–06, J. Appl. Meteorol. Clim., 47, 3089–3098, https://doi.org/10.1175/2008JAMC1798.1, 2008.
McKeon, G. M., Day, K. A., Howden, S. M., Mott, J. J., Orr, D. M., and Scattini, W. J.: Northern Australia savannas: management for pastoral production, J. Biogeogr., 17, 355–372, 1990.
Mell, W. E., Charney, J. J., Jenkins, M. A., Cheney, P., and Gould, J.: Numerical Simulations of Grassland Fire Behavior from the LANL – FIRETEC and NIST-WFDS Models; in: Remote Sensing Modeling and Applications to Wildland Fires, Springer Verlag, Berlin, Heidelberg, 2012.
Menaut, J.-C., Abbadie, L., Lavenu, F., Loudjani, P., and Podaire, A.: Biomass burning in West African savannas, MIT Press, Cambridge, Massachusetts, USA, 133–142, 1991.
Michaelides, S. C., Savvidou, K., Nicolaides, K. A., and Charalambous, M.: In search for relationships between lightning and rainfall with a rectangular grid-box methodology, Adv. Geosci., 20, 51–56, https://doi.org/10.5194/adgeo-20-51-2009, 2009.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling vegetation dynamics: the ecosystem demography model (ED), Ecol. Monogr., 71, 557–586, https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2, 2001.
Moreira, A. G.: Effects of Fire Protection on Savanna Structure in Central Brazil, J. Biogeogr., 27, 1021–1029, https://doi.org/10.1046/j.1365-2699.2000.00422.x, 2000.
Morvan, D., M{é}radji, S., and Accary, G.: Physical modeling of fire spread in Grasslands, Fire Safety J., 44, 50–61, https://doi.org/10.1016/j.firesaf.2008.03.004, 2008.
Mouillot, F. and Field, C. B.: Fire history and the global carbon budget: a $1^\circ \times 1^\circ$ fire history reconstruction for the 20th century, Global Change Biol., 11, 398–420, https://doi.org/10.1111/j.1365-2486.2005.00920.x, 2005.
NASA: Understanding Earth Biomass Burning, National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland, Tech. Rep. NP-2011-10-250-GSFC, 2011.
National Interagency Fire Service: 1997–2012 large fires (100,000 + acres), http://www.nifc.gov/fireInfo/fireInfo_stats_lgFires.html(last access: 10 May 2013), 2013.
Nazzaro, R. M.: Wildland Fire – Management Improvements Could Enhance Federal Agencies' Efforts to Contain the Costs of Fighting Fires, Testimony before the Committee on Energy and Natural Re sources, US Senate, GAO-07-922T, 15 pp., 2007.
Neary, D. G., Ryan, K. C., and DeBano, L. F.: Wildland Fire in Ecosystems – Effects of Fire on Soil and Water, United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT 84401, General Technical Report RMRS-GTR-42-volume 4, 2005.
Nesterov, V. G.: Gorimost' lesa i metody eio opredelenia, Goslesbumaga, Moscow, 1949 (in Russian).
New, M., Lister, D., Hulme, M., and Makin, I.: A high-resolution data set of surface climate over global land areas, Climate Res., 21, 1–25, https://doi.org/10.3354/cr021001, 2002.
Newman, M. E. J. and Ziff, R. M.: Efficient Monte Carlo Algorithm and High-Precision Results for Percolation, Phys. Rev. Lett., 85, 4104–4107, https://doi.org/10.1103/PhysRevLett.85.4104, 2000.
Nickey, B. B.: Occurrences of lightning fires – Can they be simulated?, Fire Technol., 12, 321–330, 1976.
NIMA: Vector Map Level 0 database (VMAP0), Digital Chart of the World, 5th Edn., Tech. rep., National Imagery and Mapping Agency, Bethesda, MD, 2000.
Niu, G.-Y. and Yang, Z.-L.: Effects of Frozen Soil on Snowmelt Runoff and Soil Water Storage at a Continental Scale, J. Hydrometeorol., 7, 973–952, 2006.
Ojima, D. S., Schimel, D. S., Parton, W. J., and Owensby, C. E.: Long- and short-term effects of fire on nitrogen cycling in tallgrass prairie, Biogeochemistry, 24, 67–84, https://doi.org/10.1007/BF02390180, 1994.
Oleson, K. W., M., L. D., Bonan, G. B., Flanner, M. G., Kluzek, E., Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker, M., Dickinson, R., Feddema, J. J., Heald, C. L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J. T., Running, S., Sakaguchi, K., Slater, A., St{ö}ckli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, X.: Technical Description of version 4.0 of the Community Land Model (CLM), NCAR TECHNICAL NOTE, NCAR/TN-478+STR, Boulder, CO, 80307-3000, 2010.
Orville, R. E., Huffins, G. R., Burrows, W. R., and Cummins, K. L.: The North American Lightning Detection Network (NALDN) – Analysis of Flash Data: 2001–09, Mon. Weather Rev., 139, 1305–1322, https://doi.org/10.1175/2010MWR3452.1, 2011.
Otto, J. S. and Anderson, N. E.: Slash-and-Burn Cultivation in the Highlands South: A Problem in Comparative Agricultural History, Comp. Stud. Soc. Hist., 24, 131–147, https://doi.org/10.1017/S0010417500009816, 1982.
Page, S., Siegert, F., Boehm, H., Jaya, A., and Limin, S.: The amount of carbon released from peat and forest fires in Indonesia during 1997, Nature, 420, 61–65, https://doi.org/10.1038/nature01131, 2002.
Page, S., Rieley, J., Hoscilo, A., Spessa, A., and Weber, U.: Fire and Global Change, Chapter IV, Current Fire Regimes, in: Impacts and Likely Changes in Tropical Southeast Asia, Springer Verlag, Berlin, Heidelberg, 2012.
Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W. B., and Matthews, E.: Interannual variability of surface water extent at the global scale, 1993–2004, J. Geophys. Res., 115, D12111, https://doi.org/10.1029/2009JD012674, 2010.
Parks, S. A., Parisien, M.-A., and Miller, C.: Spatial bottom-up controls on fire likelihood vary across western North America, Ecosphere, 3, art12, https://doi.org/10.1890/ES11-00298.1, 2012.
Pausas, J. G. and Keeley, J. E.: A burning story: The role of fire in the history of life, BioScience, 59, 593–601, https://doi.org/10.1525/bio.2009.59.7.10, 2009.
Penner, J. E., Dickinson, R. E., and O'Neill, C. A.: Effects of Aerosol from Biomass Burning on the Global Radiation Budget, Science, 256, 1432–1434, https://doi.org/10.1126/science.256.5062.1432, 1992.
Perry, D. A., Hessburg, P. F., Skinner, C. N., Spies, T. A., Stephens, S. L., Taylor, A. H., Franklin, J. F., McComb, B., and Riegel, G.: The ecology of mixed severity fire regimes in Washington, Oregon, and Northern California, Forest Ecol. Manag., 262, 703–717, https://doi.org/10.1016/j.foreco.2011.05.004, 2011.
Peterson, D., Wang, J., Ichoku, C., and Remer, L. A.: Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: implications for fire weather forecasting, Atmos. Chem. Phys., 10, 6873–6888, https://doi.org/10.5194/acp-10-6873-2010, 2010.
Peterson, D. L. and Ryan, K. C.: Modeling postfire conifer mortality for long-range planning, Environ. Manage., 10, 797–808, https://doi.org/10.1007/BF01867732, 1986.
Piepgrass, M. V., Krider, E. P., and Moore, C. B.: Lightning and Surface Rainfall During Florida Thunderstorms, J. Geophys. Res., 87, 11193–11201, https://doi.org/10.1029/JC087iC13p11193, 1982.
Poulter, B., Heyder, U., and Cramer, W.: Modeling the Sensitivity of the Seasonal Cycle of GPP to Dynamic LAI and Soil Depth in Tropical Rainforests, Ecosystems, 12, 517–333, https://doi.org/10.1007/s10021-009-9238-4, 2009.
Prairiesource.com: Prescribed Burning 101: An Introduction to Prescribed Burning, Spring 1992, http://www.prairiesource.com/newsletters/92_spr01.htm, last access: 10 May 2013, 1992.
Pregitzer, K. S. and Euskirchen, E. S.: Carbon cycling and storage in world forests: biomae patterns related to forest age, Glob. Change Biol., 10, 2052–2077, https://doi.org/10.1111/j.1365-2486.2004.00866.x, 2004.
Prentice, I. C., Kelley, D. I., Foster, P. N., Friedlingstein, P., Harrison, S. P., and Bartlein, P. J.: Modeling fire and the terrestrial carbon balance, Global Biogeochem. Cy., 25, GB3005, https://doi.org/10.1029/2010GB003906, 2011.
Pyne, S. J.: Fire in America: A Cultural History of Wildland and Rural Fire, Princeton University Press, Princeton, NJ, 1982.
Pyne, S. J.: Maintaining Focus: An Introduction to Anthropogenic Fire, Chemosphere, 29, 889–911, https://doi.org/10.1016/0045-6535(94)90159-7, 1994.
Pyne, S. J.: World Fire: The Culture of Fire on Earth, University of Washington Press, Seattle, WA, 384 pp., 1997.
Pyne, S. J., Andrews, P. L., and Daven, R. D.: Introduction to Wildland Fire, Wiley, London, 1996.
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952, 2008.
Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M., and Morton, D. C.: Global burnedf area and biomass burning emissions from small fires, J. Geophys. Res., 117, G04012, https://doi.org/10.1029/2012JG002128, 2012.
Rasul, G. and Thapa, G. B.: Shifting Cultivation in the Mountains of South and Southeast Asia: Regional patterns and factors influencing the change, Land Degrad. Dev., 14, 495–508, https://doi.org/10.1002/ldr.570, 2003.
Reinhardt, E. D., Keane, R. E., and Brown, J. K.: First Order Fire Effects Model: FOFEM 4.0, United States Department of Agriculture, Forest Service, Missoula, Montana 59807, Intermountain Research Station, User's Guide, General Technical Report INT-GTR-344, 1997.
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Ringeval, B., de Noblet-Ducoudr{é}, N., Ciais, P., Bousquet, P., Prigent, C., Papa, F., and Rossow, W. B.: An attempt to quantify the impact of changes in wetland extent on methane emissions on the seasonal and interannual time scales, Global Biogeochem. Cy., 24, GB2003, https://doi.org/10.1029/2008GB003354, 2010.
Rivas Soriano, L., De Pablo, F., and Garc{\'\i}a D{\'\i}ez, E.: Relationship between Convective Precipitation and Cloud-to-Ground Lightning in the Iberian Peninsula, Mon. Weather Rev., 129, 2998–3003, 2001.
Roos, C. I., Sullivan, A. P., and NcNamee, C.: Paleoecological Evidence for Systematic Indigenous Burning in the Upland Southwest, The Archaeology of Anthropogenic Environments, Southern Illinois University Press, Carbondale, 142–171, 2010.
R{ö}sch, M., Ehrmann, O., Herrmann, L., Schulz, E., Bogenrieder, A., Goldammer, J. P., Hall, M., Page, H., and Schier, W.: An experimental approach to Neolithic shifting cultivation, Veg. Hist. Archaebot., 11, 143–154, 2002.
Rothermel, R. C.: A mathematical model for predicting fire spread in wildland fuels, USDA Forest Service Research Paper, Ogden, UT 84401, INT-115, 48 pp., 1972.
Roxburgh, S. H., Shea, K., and Wilson, J. B.: The Intermediate Disturbance Hypothesis: Patch Dynamics and Mechanisms of Species Coexistence, Ecology, 85, 359–371, https://doi.org/10.1890/03-0266, 2004.
Roy, D. P. and Boschetti, L.: Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products, IEEE T. Geosci. Remote, 47, 1032–1044, 2009.
Roy, D. P., Boschetti, L., Justice, C. O., and Ju, J.: The collection 5 MODIS burned area product – Global evaluation by comparison with the MODIS active fire product, Remote Sens. Environ., 112, 3690–3707, https://doi.org/10.1016/j.rse.2008.05.013, 2008.
Saatchi, S. S., Houghton, R. A., Alves, D., and Nelson, B.: Amazon Basin Aboveground Live Biomass Distribution Map: 1999–2000, Data Set from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA, 2009.
Saatchi, S. S. , Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 1–6, https://doi.org/10.1073/pnas.1019576108, 2011.
Scholes, M. C., Martin, R., Scholes, R. J., Parsons, D., and Winstead, E.: NO and N2O emissions from savanna soils following the first simulated rains of the season, Nutr. Cycl. Agroecosys., 48, 115–122, 1997.
Schulzweida, U., Kornblueh, L., and Quast, R.: CDO User's Guide, 2012.
Seiler, W. and Crutzen, P. J.: Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning, Climatic Change, 2, 207–247, https://doi.org/10.1007/BF00137988, 1980.
Sigaut, F.: Swidden cultivation in Europe. A question for tropical anthropologists, Soc. Sc. Inform., 18, 679–694, https://doi.org/10.1177/053901847901800404, 1979.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
Skinner, C. N. and Chang, C.-R.: Fire Regimes, Past and Present, Sierra Nevada Ecosystem Project: Final Report to Congress, Vol. II, in: Assessments and scientific basis for management options, Sierra Nevada Ecosystem Project, Final Report to Congress, Wildland Resources Center Report No. 37, Centers for Water and Wildland Resources, University of California, Davis, California, USA, 1996.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637, https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x, 2001.
Smittinand, T., Ratanakhon, S., Banijbatana, D., Komkris, T., Zinke, P. J., Hinton, P., Keen, F. B., Charley, J. L., McGarity, J. W., and Pelzer, K. J.: Farmers in the Forest: Economic development and marginal agriculture in Northern Thailand, edited by: Kunstaedter, P., Chapman, E. C., and Sabhasri, S., University of Hawai'i Press, Honolulu, HI 96822, 402 pp., 1978.
Sonesson, M. and Callaghan, T. V.: Strategies of Survival in Plants of the Fennoscandian Tundra, Arctic, 44, 95–105, 1991.
Spessa, A. and Fisher, R.: On the relative role of fire and rainfall in determining vegetation patterns in tropical savannas: a simulation study, Geophysical Research Abstracts, 12, EGU2010-7142-6, 2010.
Spessa, A., van der Werf, G., Thonicke, K., Gomez-Dans, J., Fisher, R., and Forrest, M.: Fire and Global Change, in: Modeling Vegetation Fires and Emissions, Chapter XIV, Springer publishers, 2012.
Stephens, S. L. and Ruth, L. W.: Federal Forest-Fire Policy in the United States, Ecol. Appl., 15, 532–542, 2005.
Stewart, O. C., Lewis, H. T., and Anderson, K.: Forgotten Fires: Native Americans and the Transient Wilderness, University of Oklahoma Press, Norman, OK 73069, 364 pp., 2002.
Stocks, B. J., Mason, J. A., Todd, J. B., Bosch, E. M., Wotton, B. M., Amiro, B. D., Flannigan, M. D., Hirsch, K. G., Logan, K. A., Martell, D. L., and Skinner, W. R.: Large forest fires in Canada, 1959–1997, J. Geophys. Res., 108, 8149, https://doi.org/10.1029/2001JD000484, 2003.
Sturm, M., McFadden, J. P., Liston, G. E., Chapin, F. S., Racine, C. H., and Holmgren, J.: Snow-Shrub Interactions in Arctic Tundra: A Hypothesis with Climatic Implications, J. Climate, 14, 336–344, https://doi.org/10.1175/1520-0442(2001)014\textless 0336:SSIIAT\textgreater 2.0.CO;2, 2000.
Tansey, K., Gr{é}goire, J.-M., Stroppiana, D., Sousa, A., Silva, J., Pereira, J. M. C., Boschetti, L., Maggi, M., Brivio, P. A., Praser, R., Flasse, S., Ershov, D., Binaghi, E., Graetz, D., and Peduzzi, P.: Vegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATIOM data, J. Geophys. Res., 109, D14S03, https://doi.org/10.1029/2003JD003598, 2004.
Tansey, K., Gr{é}goire, J.-M., Defourny, P., Leigh, R., Pekel, J., van Bogaert, J. F. O., van Bogaert, E., and Bartholom{é}, E.: A new global, multi-annual (2000-2007) burnt area product at 1 km resolution, Geophys. Res. Lett., 35, L01401, https://doi.org/10.1029/2007GL031567, 2008.
Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G., and Zimov, S.: Soil organic carbon pools in the northern circumpolar permafrost region, Global Biogeochem. Cy., 23, GB2023, https://doi.org/10.1029/2008GB003327, 2009.
Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences Discuss., 7, 697–743, https://doi.org/10.5194/bgd-7-697-2010, 2010.
Tinner, W., Conedera, M., Ammann, B., and Lotter, A. F.: Fire ecology north and south of the Alps since the last ice age, Holocene, 15, 1214–1226, https://doi.org/10.1191/0959683605hl892rp, 2005.
Tinner, W., Hu, F. S., Beer, R., Kaltenrieder, P., Scheurer, B., and Kr{ä}henb{ü}hl, U.: Postglacial vegetational and fire history: pollen, plant macrofossil and charcoal records from two Alaskan lakes, Veg. Hist. Archaebot., 15, 279–293, https://doi.org/10.1007/s00334-006-0052-z, 2006.
Todd, S. K. and Jewkes, H. A.: Wildland Fire in Alaska: A History of Organized Fire Suppression and Management in the Last Frontier,Agricultural and Forestry Experiment Station, University of Alaska, Fairbanks, Tech. Rep. Bulletin No. 114, 2006.
Turetsky, M., Wieder, K., Halsey, L., and Vitt, D.: Current disturbance and the diminishing peatland carbon sink, Geophys. Res. Lett., 29, 279–293, https://doi.org/10.1007/s00334-006-0052-z, 2002.
Uhl, C. and Kauffman, J. B.: Deforestation, Fire Susceptibility, and Potential Tree Responses to Fire in the Eastern Amazon, Ecology, 71, 437–449, https://doi.org/10.2307/1940299, 1990.
Uman, M. A.: The Art and Science of Lightning Protection, Cambridge University Press, Cambridge, 2010.
Unruh, J. D., Treacy, J. M., Alcorn, J. B., and Flores Pait{á}n, S.: Swidden-fallow agroforestry in the Peruvian Amazon, Vol. 5, New York Botanical Garden PressDept, 1987.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010.
Van Reuler, H. and Janssen, B. H.: Comparison of the fertilizing effects of ash from burnt secondary vegetation and of mineral fertilizers on upland rice in south-west Cote d'Ivoire, Fert. Res., 45, 1–11, https://doi.org/10.1007/BF00749875, 1996.
van Wilgen, B. W., Everson, C. S., and Trollope, W. S. W.: Fire management in southern Africa: some examples of current objectives, practices and problems; in: Fire Management in Southern Africa: Some Examples of Current Objectives, Practices and Problems, Springer Verlag, Berlin, 79–212, 1990.
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.
Virts, K. S., Wallace, J. M., Hutchins, M. L., and Holzworth, R. H.: Highlights of a new ground-based, hourly global lightning climatology, B. Amer. Meteorol. Soc., https://doi.org/http://dx.doi.org/10.1175/BAMS-D-12-00082.1, accepted, 2013.
Wan, S., Hui, D., and Luo, Y.: Fire Effects on Nitrogen Pools and Dynamics in Terrestrial Ecosystems: A Meta-Analysis, Ecol. Appl., 11, 1349–1365, https://doi.org/10.1890/1051-0761(2001)011[1349:FEONPA]2.0.CO;2, 2001.
Wang, T., Hamann, A., Spittlehouse, D. L., and Murdock, T. Q.: ClimateWNA – High-Resolution Spatial Climate Data for Western North America, J. Appl. Meteorol. Clim., 51, 16–29, https://doi.org/10.1175/JAMC-D-11-043.1, 2011.
Wania, R., Ross, I., and Prentice, I. C.: Integrating peatlands and permafrost into a dynamic global vegetation model: 1. Evaluation and sensitivity of physical land surface processes, Global Biogeochem. Cy., 23, GB3014, https://doi.org/10.1029/2008GB003412, 2009.
Warneke, C., Bahreini, R., Brock, C. A., de Gouw, J. A., Fahey, D. W., Froyd, K. D., Holloway, J. S., Middlebrook, A., Miller, L., Montzka, S., Murphy, D. M., Peischl, J., Ryerson, T. B., Schwarz, J. P., Spackman, J. R., and Veres, P.: Biomass burning in Siberia and Kazakhstan as important source for haze over the Alaskan Arctic in April 2008, Geophys. Res. Lett., 36, L02813, https://doi.org/10.1029/2008GL036194, 2009.
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., and Swetnam, T. W.: Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity, Science, 313, 940–943, https://doi.org/10.1126/science.1128834, 2006.
Whiten, A. and Erdal, D.: The human socio-cognitive niche and its evolutionary origins, Philos. T. R. Soc. B, 367, 2119–2129, https://doi.org/10.1098/rstb.2012.0114, 2012.
Williams, G. W.: Introduction to Aboriginal Fire Use in North America, Fire Management Today, 60, 8–12, 2000.
Williams, G. W.: Aboriginal use of fire: are there any "natural" plant communities?, in: Wilderness and Political Ecology: Aboriginal Land Management – Myths and Reality, University of Utah Press, Logan, UT, 2002a.
Williams, M.: Deforesting the Earth: From Prehistory to Global Crisis, University of Chicago Press, Chicago, IL, 2002b.
Wylie, D., Jackson, D. L., Menzel, W. P., and Bates, J. J.: Trends in Global Cloud Cover in Two Decades of HIRS Observations, J. Climate, 18, 3021–3031, https://doi.org/10.1175/JCLI3461.1, 2005.
Yevich, R. and Logan, J. A.: An assessment of biofuel use and burning of agricultural waste in the developing world, Global Biogeochem. Cy., 17, 1095, https://doi.org/10.1029/2002GB001952, 2003.
Yibarbuk, D., Whitehead, P. J., Russell-Smith, J., Jackson, D., Godjuwa, C., Fisher, A., Cooke, P., D., C., and Bowman, D. M. J. S.: Fire ecology and Aboriginal land management in central Arnhem Land, northern Austalia: a tradition of ecosystem management, J. Biogeogr., 28, 325–343, https://doi.org/10.1046/j.1365-2699.2001.00555.x, 2002.
Zhang, X., Drake, N. A., Wainwright, J., and Mulligan, M.: Comparison of slope estimates from low resolution DEMS: scaling issues and a fractal method for their solution, Earth Surf. Proc. Land. 24, 763–779, 1999.
Zhou, Y., Qie, X., and Soula, S.: A study of the relationship between cloud-to-ground lightning and precipitation in the convective weather system in China, Ann. Geophys., 20, 107–113, https://doi.org/10.5194/angeo-20-107-2002, 2002.
