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.
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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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.
Basil Andrew Stansfield Davis, Marc Fasel, Jed O. Kaplan, Emmanuele Russo, and Ariane Burke
Clim. Past Discuss., https://doi.org/10.5194/cp-2022-59, https://doi.org/10.5194/cp-2022-59, 2022
Revised manuscript under review for CP
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During the last Ice Age 21 k BP, Northern Europe was covered in ice and steppe, and forests were restricted to sheltered regions to the south. However, the composition and extent of forest and its associated climate remains unclear, with models indicating more forest north of the Alps than suggested by the data. A new compilation of pollen records with improved dating suggests greater agreement with model climate, but still suggests models over estimate forest cover especially in the west.
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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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Revised manuscript accepted for GMD
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
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.