Articles | Volume 6, issue 3
https://doi.org/10.5194/gmd-6-643-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-6-643-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
17 May 2013
Model description paper |
| 17 May 2013
A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0)
M. Pfeiffer
ARVE Group, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
A. Spessa
Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany
J. O. Kaplan
ARVE Group, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Related authors
No articles found.
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
Short summary
Short summary
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 has not been submitted
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
Short summary
Short summary
Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Related subject area
Climate and Earth system modeling
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Truly Conserving with Conservative Remapping Methods
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Earth system modeling on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON 2.6.6-rc
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
WRF (v4.0)-SUEWS (v2018c) Coupled System: Development, Evaluation and Application
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
Resolving the mesoscale at reduced computational cost with FESOM 2.5: efficient modeling approaches applied to the Southern Ocean
Modeling and evaluating the effects of irrigation on land-atmosphere interaction in South-West Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Deep Learning Model based on Multi-scale Feature Fusion for Precipitation Nowcasting
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Karl E. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-177, https://doi.org/10.5194/gmd-2023-177, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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 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.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
Short summary
Short summary
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 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
Short summary
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
Short summary
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-1263, https://doi.org/10.5194/egusphere-2023-1263, 2023
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the E3SMv2 to document model performance on clouds and understand what updates in E3SMv2 have caused the 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 re-tuning of cloud macrophysics parameters. This study offers additional insights about clouds simulated in E3SMv2 and will benefit the future E3SM developments.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-117, https://doi.org/10.5194/gmd-2023-117, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1496, https://doi.org/10.5194/egusphere-2023-1496, 2023
Short summary
Short summary
Ocean models struggle to simulate small-scale ocean flows due to the computational cost of high-resolution simulations. Several cost-reducing strategies are applied to simulations of the Southern Ocean and evaluated with respect to observations and traditional, lower-resolution modelling methods. The high-resolution simulations effectively reproduce small-scale flows seen in satellite data and are largely consistent with traditional model simulations regarding their response to climate change.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
EGUsphere, https://doi.org/10.5194/egusphere-2023-890, https://doi.org/10.5194/egusphere-2023-890, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The caused effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which is simulating the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in 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.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
Short summary
Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-109, https://doi.org/10.5194/gmd-2023-109, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
1. This study present a deep learning architecture MFF to improve the forecast skills of precipitations especially for heavy precipitations. 2. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. 3. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors, so that heavy precipitations are produced.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
Short summary
Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio Bento, and Angelina Bushenkova
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-136, https://doi.org/10.5194/gmd-2023-136, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
Short summary
Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
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.
