Articles | Volume 7, issue 5
https://doi.org/10.5194/gmd-7-2411-2014
© Author(s) 2014. 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-7-2411-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
16 Oct 2014
Development and technical paper |
| 16 Oct 2014
Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1)
D. I. Kelley
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
S. P. Harrison
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
Geography & Environmental Sciences, School of Archaeology, Geography and Environmental Sciences (SAGES), University of Reading, Whiteknights, Reading, RG6 6AH, UK
I. C. Prentice
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
AXA Chair of Biosphere and Climate Impacts, Grantham Institute for Climate Change and Department of Life Sciences, Imperial College, Silwood Park Campus, Ascot SL5 7PY, UK
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Tyler W. Davis, I. Colin Prentice, Benjamin D. Stocker, Rebecca T. Thomas, Rhys J. Whitley, Han Wang, Bradley J. Evans, Angela V. Gallego-Sala, Martin T. Sykes, and Wolfgang Cramer
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Ning Dong, Iain Colin Prentice, Bradley J. Evans, Stefan Caddy-Retalic, Andrew J. Lowe, and Ian J. Wright
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Corinne Le Quéré, Erik T. Buitenhuis, Róisín Moriarty, Séverine Alvain, Olivier Aumont, Laurent Bopp, Sophie Chollet, Clare Enright, Daniel J. Franklin, Richard J. Geider, Sandy P. Harrison, Andrew G. Hirst, Stuart Larsen, Louis Legendre, Trevor Platt, I. Colin Prentice, Richard B. Rivkin, Sévrine Sailley, Shubha Sathyendranath, Nick Stephens, Meike Vogt, and Sergio M. Vallina
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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
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A. V. Gallego-Sala, D. J. Charman, S. P. Harrison, G. Li, and I. C. Prentice
Clim. Past, 12, 129–136, https://doi.org/10.5194/cp-12-129-2016, https://doi.org/10.5194/cp-12-129-2016, 2016
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M. G. De Kauwe, S.-X. Zhou, B. E. Medlyn, A. J. Pitman, Y.-P. Wang, R. A. Duursma, and I. C. Prentice
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A. Abe-Ouchi, F. Saito, M. Kageyama, P. Braconnot, S. P. Harrison, K. Lambeck, B. L. Otto-Bliesner, W. R. Peltier, L. Tarasov, J.-Y. Peterschmitt, and K. Takahashi
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We describe the creation of boundary conditions related to the presence of ice sheets, including ice-sheet extent and height, ice-shelf extent, and the distribution and altitude of ice-free land, at the Last Glacial Maximum (LGM), for use in LGM experiments conducted as part of the Coupled Modelling Intercomparison Project (CMIP5) and Palaeoclimate Modelling Intercomparison Project (PMIP3). The difference in the ice sheet boundary conditions as well as the climate response to them are discussed.
T.-T. Meng, H. Wang, S. P. Harrison, I. C. Prentice, J. Ni, and G. Wang
Biogeosciences, 12, 5339–5352, https://doi.org/10.5194/bg-12-5339-2015, https://doi.org/10.5194/bg-12-5339-2015, 2015
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By analysing the quantitative leaf-traits along extensive temperature and moisture gradients with generalized linear models, we found that metabolism-related traits are universally acclimated to environmental conditions, rather than being fixed within plant functional types. The results strongly support a move towards Dynamic Global Vegetation Models in which continuous, adaptive trait variation provides the fundamental mechanism for changes in ecosystem properties along environmental gradients.
I. C. Prentice, X. Liang, B. E. Medlyn, and Y.-P. Wang
Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, https://doi.org/10.5194/acp-15-5987-2015, 2015
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Land surface models (LSMs) describe how carbon and water fluxes react to environmental change. They are key component of climate models, yet they differ enormously. Many perform poorly, despite having many parameters. We outline a development strategy emphasizing robustness, reliability and realism, none of which is guaranteed by complexity alone. We propose multiple constraints, benchmarking and data assimilation, and representing unresolved processes stochastically, as tools in this endeavour.
G. Li, S. P. Harrison, and I. C. Prentice
Biogeosciences Discuss., https://doi.org/10.5194/bgd-12-4769-2015, https://doi.org/10.5194/bgd-12-4769-2015, 2015
Revised manuscript has not been submitted
I. Hessler, S. P. Harrison, M. Kucera, C. Waelbroeck, M.-T. Chen, C. Anderson, A. de Vernal, B. Fréchette, A. Cloke-Hayes, G. Leduc, and L. Londeix
Clim. Past, 10, 2237–2252, https://doi.org/10.5194/cp-10-2237-2014, https://doi.org/10.5194/cp-10-2237-2014, 2014
G. Li, S. P. Harrison, I. C. Prentice, and D. Falster
Biogeosciences, 11, 6711–6724, https://doi.org/10.5194/bg-11-6711-2014, https://doi.org/10.5194/bg-11-6711-2014, 2014
M. Martin Calvo, I. C. Prentice, and S. P. Harrison
Biogeosciences, 11, 6017–6027, https://doi.org/10.5194/bg-11-6017-2014, https://doi.org/10.5194/bg-11-6017-2014, 2014
H. Wang, I. C. Prentice, and T. W. Davis
Biogeosciences, 11, 5987–6001, https://doi.org/10.5194/bg-11-5987-2014, https://doi.org/10.5194/bg-11-5987-2014, 2014
I. Bistinas, S. P. Harrison, I. C. Prentice, and J. M. C. Pereira
Biogeosciences, 11, 5087–5101, https://doi.org/10.5194/bg-11-5087-2014, https://doi.org/10.5194/bg-11-5087-2014, 2014
P. N. Foster, I. C. Prentice, C. Morfopoulos, M. Siddall, and M. van Weele
Biogeosciences, 11, 3437–3451, https://doi.org/10.5194/bg-11-3437-2014, https://doi.org/10.5194/bg-11-3437-2014, 2014
E. Journet, Y. Balkanski, and S. P. Harrison
Atmos. Chem. Phys., 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, https://doi.org/10.5194/acp-14-3801-2014, 2014
A. Perez-Sanz, G. Li, P. González-Sampériz, and S. P. Harrison
Clim. Past, 10, 551–568, https://doi.org/10.5194/cp-10-551-2014, https://doi.org/10.5194/cp-10-551-2014, 2014
G. A. Schmidt, J. D. Annan, P. J. Bartlein, B. I. Cook, E. Guilyardi, J. C. Hargreaves, S. P. Harrison, M. Kageyama, A. N. LeGrande, B. Konecky, S. Lovejoy, M. E. Mann, V. Masson-Delmotte, C. Risi, D. Thompson, A. Timmermann, L.-B. Tremblay, and P. Yiou
Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, https://doi.org/10.5194/cp-10-221-2014, 2014
A. M. Foley, D. Dalmonech, A. D. Friend, F. Aires, A. T. Archibald, P. Bartlein, L. Bopp, J. Chappellaz, P. Cox, N. R. Edwards, G. Feulner, P. Friedlingstein, S. P. Harrison, P. O. Hopcroft, C. D. Jones, J. Kolassa, J. G. Levine, I. C. Prentice, J. Pyle, N. Vázquez Riveiros, E. W. Wolff, and S. Zaehle
Biogeosciences, 10, 8305–8328, https://doi.org/10.5194/bg-10-8305-2013, https://doi.org/10.5194/bg-10-8305-2013, 2013
A. M. Ukkola and I. C. Prentice
Hydrol. Earth Syst. Sci., 17, 4177–4187, https://doi.org/10.5194/hess-17-4177-2013, https://doi.org/10.5194/hess-17-4177-2013, 2013
H. Wang, I. C. Prentice, and J. Ni
Biogeosciences, 10, 5817–5830, https://doi.org/10.5194/bg-10-5817-2013, https://doi.org/10.5194/bg-10-5817-2013, 2013
D. I. Kelley, I. C. Prentice, S. P. Harrison, H. Wang, M. Simard, J. B. Fisher, and K. O. Willis
Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, https://doi.org/10.5194/bg-10-3313-2013, 2013
F. J. Bragg, I. C. Prentice, S. P. Harrison, G. Eglinton, P. N. Foster, F. Rommerskirchen, and J. Rullkötter
Biogeosciences, 10, 2001–2010, https://doi.org/10.5194/bg-10-2001-2013, https://doi.org/10.5194/bg-10-2001-2013, 2013
D. J. Charman, D. W. Beilman, M. Blaauw, R. K. Booth, S. Brewer, F. M. Chambers, J. A. Christen, A. Gallego-Sala, S. P. Harrison, P. D. M. Hughes, S. T. Jackson, A. Korhola, D. Mauquoy, F. J. G. Mitchell, I. C. Prentice, M. van der Linden, F. De Vleeschouwer, Z. C. Yu, J. Alm, I. E. Bauer, Y. M. C. Corish, M. Garneau, V. Hohl, Y. Huang, E. Karofeld, G. Le Roux, J. Loisel, R. Moschen, J. E. Nichols, T. M. Nieminen, G. M. MacDonald, N. R. Phadtare, N. Rausch, Ü. Sillasoo, G. T. Swindles, E.-S. Tuittila, L. Ukonmaanaho, M. Väliranta, S. van Bellen, B. van Geel, D. H. Vitt, and Y. Zhao
Biogeosciences, 10, 929–944, https://doi.org/10.5194/bg-10-929-2013, https://doi.org/10.5194/bg-10-929-2013, 2013
Related subject area
Biogeosciences
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
FESOM2.1-REcoM3-MEDUSA2: an ocean-sea ice-biogeochemistry model coupled to a sediment model
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1914, https://doi.org/10.5194/egusphere-2024-1914, 2024
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Under climate change, the conditions for wildfires to form are becoming more frequent in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a crucial platform for future developments.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Juliette Bernard, Marielle Saunois, Elodie Salmon, Philippe Ciais, Shushi Peng, Antoine Berchet, Penélope Serrano-Ortiz, Palingamoorthy Gnanamoorthy, and Joachim Jansen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1331, https://doi.org/10.5194/egusphere-2024-1331, 2024
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Despite their importance, uncertainties remain in estimating methane emissions from wetlands. Here, a simplified model that operates at a global scale is developed. Taking advantage of advances in remote sensing data and in situ observations, the model effectively reproduces the spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights for sensitivity analyses.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land Surface Models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-181, https://doi.org/10.5194/gmd-2023-181, 2023
Revised manuscript accepted for GMD
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change the storage of carbon in sediments slows down carbon cycling and influences feedbacks in the atmosphere-ocean-sediment system. Here we coupled a sediment model to an ocean biogeochemistry model and found a shift of carbon storage from the atmosphere to the ocean-sediment system.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Cited articles
Albini, F. A.: Estimating wildfire behavior and effects, Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture, 1976.
Anderson, D. H., Catchpole, E. A., De Mestre, N. J., and Parkes, T.: Modelling the spread of grass fires, J. Aust. Math. Soc., 23, 451–466, 1982.
Archibald, S., 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.
Archibald, S., Staver, A. C., and Levin, S. A.: Evolution of human-driven fire regimes in Africa, P. Natl. Acad. Sci. USA, 109, 847–852, https://doi.org/10.1073/pnas.1118648109, 2012.
Arora, V. K., and Boer, G. J.: Fire as an interactive component of dynamic vegetation models, J. Geophys. Res., 110, G02008, https://doi.org/10.1029/2005JG000042, 2005.
Barrett, D. J.: NPP multi-biome: VAST calibration data, 1965–1998, available at: http://www.daac.ornl.gov (last access: 13 January 2011), 2001.
Bellingham, P. J. and Sparrow, A. D.: Resprouting as a life history strategy in woody plant communities, Oikos, 89, 409–416, 2000.
Biganzoli, F., Wiegand, T., and Batista, W. B.: Fire-mediated interactions between shrubs in a South American temperate savannah, Oikos, 118, 1383–1395, https://doi.org/10.1111/j.1600-0706.2009.17349.x, 2009.
Bistinas, I., Harrison, S. P., Prentice, I. C., and Pereira, J. M. C.: Causal relationships vs. emergent patterns in the global controls of fire frequency, Biogeosciences Discuss., 11, 3865–3892, https://doi.org/10.5194/bgd-11-3865-2014, 2014.
Baldocchi, D., Cummins, K. L., Christian, H. J., and Goodman, S. J.: Combined satellite- and surface-based estimation of the intracloud – cloud-to-ground lightning ratio over the continental United States, Mon. Weather Rev., 129, 108–122, 2001.
Bond, W. J. and Midgley, J. J.: Ecology of sprouting in woody plants: the persistence niche, Trends Ecol. Evol., 16, 45–51, 2001.
Bond, W. J. and Van Wilgen, B. W.: Fire and Plants, Population and Community Biology Series, 14, 1–15, 1996.
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., Krawchuk, 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, 481–484, https://doi.org/10.1126/science.1163886, 2009.
Bradstock, R., Williams, R., and Gill, A.: Flammable Australia: Fire Regimes, Biodiversity and Ecosystems in a Changing World, CSIRO Publishing, 2012.
Bradstock, R., Penman, T., Boer, M., Price, O., and Clarke, H.: Divergent responses of fire to recent warming and drying across south-eastern Australia, Glob. Change Biol., 20, 1412–1428, https://doi.org/10.1111/gcb.12449, 2014.
Brando, P. M., Nepstad, D. C., Balch, J. K., Bolker, B., Christman, M. C., Coe, M., and Putz, F. E.: Fire-induced tree mortality in a neotropical forest: the roles of bark traits, tree size, wood density and fire behavior, Glob. Change Biol., 18, 630–641, https://doi.org/10.1111/j.1365-2486.2011.02533.x, 2012.
Brovkin, V., van Bodegom, P. M., Kleinen, T., Wirth, C., Cornwell, W. K., Cornelissen, J. H. C., and Kattge, J.: Plant-driven variation in decomposition rates improves projections of global litter stock distribution, Biogeosciences, 9, 565–576, https://doi.org/10.5194/bg-9-565-2012, 2012.
Calvo, L., Santalla, S., Marcos, E., Valbuena, L., Tárrega, R., and Luis, E.: Regeneration after wildfire in communities dominated by Pinus pinaster, an obligate seeder, and in others dominated by Quercus pyrenaica, a typical resprouter, Forest Ecol. Manag., 184, 209–223, https://doi.org/10.1016/S0378-1127(03)00207-X, 2003.
Casady, G.: Examining Drivers of Post-Wildfire Vegetation Dynamics Across Multiple Scales Using Time-Series Remote Sensing, ProQuest, 2008.
Casady, G. M., van Leeuwen, W. J. D., and Marsh, S. E.: Evaluating post-wildfire vegetation regeneration as a response to multiple environmental determinants, Environ. Model. Assess., 15, 295–307, https://doi.org/10.1007/s10666-009-9210-x, 2009.
Cha, S.-H.: Comprehensive survey on distance/similarity measures between probability density functions, Int. J. Math. Model. Met. Appl. Sci., 4, 300–307, 2007.
Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., and Zanne, A. E.: Towards a worldwide wood economics spectrum, Ecol. Lett., 12, 351–366, https://doi.org/10.1111/j.1461-0248.2009.01285.x, 2009.
Christian, H. J.: Optical detection of lightning from space, in: Proceedings of the 11th International Conference on Atmospheric Electricity, Guntersville, Alabama, 715–718, 1999.
Christian, H. J., Blakeslee, R. J., Goodman, S. J., Mach, D. A., Stewart, M. F., Buechler, D. E., Koshak, W. J., Hall, J. M., Boeck, W. L., Driscoll, K. T., and Bocippio, D. J.: The lightning imaging sensor, in: Proceedings of the 11th International Conference On Atmospheric Electricity, Guntersville, Alabama, 746–749, 1999.
Clarke, P. J., Lawes, M. J., Midgley, J. J., Lamont, B. B., Ojeda, F., Burrows, G. E., Enright, N. J., and Knox, K. J. E.: Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire, New Phytol., 197, 19–35, https://doi.org/10.1111/nph.12001, 2013.
Climent, J., Tapias, R., Pardos, J. A., and Gil, L.: Fire adaptations in the Canary Islands pine (Pinus canariensis), Plant Ecol., 171, 185–196, 2004.
Cochrane, M. A.: Fire science for rainforests, Nature, 421, 913–919, https://doi.org/10.1038/nature01437, 2003.
Cornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., Hobbie, S. E., Hoorens, B., Kurokawa, H., Pérez-Harguindeguy, N., Quested, H. M., Santiago, L. S., Wardle, D. A., Wright, I. J., Aerts, R., Allison, S. D., van Bodegom, P., Brovkin, V., Chatain, A., Callaghan, T. V., Díaz, S., Garnier, E., Gurvich, D. E., Kazakou, E., Klein, J. A., Read, J., Reich, P. B., Soudzilovskaia, N. A., Vaieretti, M. V., and Westoby, M.: Plant species traits are the predominant control on litter decomposition rates within biomes worldwide, Ecol. Lett., 11, 1065–71, https://doi.org/10.1111/j.1461-0248.2008.01219.x, 2008.
Cornwell, W. K., Cornelissen, J. H. C., Allison, S. D., Bauhus, J., Eggleton, P., Preston, C. M., Scarff, F., Weedon, J. T., Wirth, C., and Zanne, A. E.: Plant traits and wood fates across the globe: rotted, burned, or consumed?, Glob. Change Biol., 15, 2431–2449, https://doi.org/10.1111/j.1365-2486.2009.01916.x, 2009.
Crisp, M. D., Burrows, G. E., Cook, L. G., Thornhill, A. H., and Bowman, D. M. J. S.: Flammable biomes dominated by eucalypts originated at the Cretaceous-Palaeogene boundary, Nature Com., 2, 193, https://doi.org/10.1038/ncomms1191, 2011.
Cummins, K. and Murphy, M.: An overview of lightning locating systems: history, techniques, and data uses, with an in-depth look at the US NLDN, electromagnetic compatibility, IEEE, 51, 499–518, 2009.
Dagit, R.: Post-fire monitoring of coast live Oaks (Quercus agrifolia) burned in the 1993 Old Topanga fire, Proceedings of the Fifth Natural Resources, 90290, 243–249, 2002.
DeFries, R. and Hansen, M.: ISLSCP II Continuous fields of vegetation cover, 1992–1993, in: ISLSCP Initiative II Collection, edited by: Hall, F., Collatz, G., Brown de Colstoun, E., Landis, D., Los, S., and Meeson, B., Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/931, 2009.
de Souza, P. E., Pinto, O., Pinto, I. R. C. A., Ferreira, N. J., and Dos Santos, A. F.: The intracloud/cloud-to-ground lightning ratio in southeastern Brazil, Atmos. Res., 91, 491–499, 2009.
Del Tredici, P.: Sprouting in temperate trees: a morphological and ecological review, Bot. Rev., 67, 121–140, 2001.
Foley, J.: An equilibrium model of the terrestrial carbon budget, Tellus B, 47, 310–319, 1995.
Friend, A. D.: Parameterisation of a global daily weather generator for terrestrial ecosystem modelling, Ecol. Model., 109, 121–140, https://doi.org/10.1016/S0304-3800(98)00036-2, 1998.
Fyllas, N. M., Patiño, S., Baker, T. R., Bielefeld Nardoto, G., Martinelli, L. A., Quesada, C. A., Paiva, R., Schwarz, M., Horna, V., Mercado, L. M., Santos, A., Arroyo, L., Jiménez, E. M., Luizão, F. J., Neill, D. A., Silva, N., Prieto, A., Rudas, A., Silviera, M., Vieira, I. C. G., Lopez-Gonzalez, G., Malhi, Y., Phillips, O. L., and Lloyd, J.: Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate, Biogeosciences, 6, 2677–2708, https://doi.org/10.5194/bg-6-2677-2009, 2009.
Gallego-Sala, A. V., Clark, J. M., House, J. I., Orr, H. G., Prentice, I. C., Smith, P., Farewell, T., and Chapman, S. J.: Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain, Clim. Res., 45, 151–162, 2010.
Gavin, D. G., Oswald, W. W., Wahl, E. R., and Williams, J. W.: A statistical approach to evaluating distance metrics and analog assignments for pollen records, Quaternary Res., 60, 356–367, 2003.
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.
Gharun, M., Turnbull, T. L., and Adams, M. A.: Stand water use status in relation to fire in a mixed species eucalypt forest, Forest Ecol. Manag., 304, 162–170, 2013.
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.
Giglio, L., Randerson, J. T., and Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth-generation Global Fire Emissions Database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.
Gobron, N., Pinty, B., Taberner, M., Mélin, F., Verstraete, M. M., and Widlowski, J.-L.: Monitoring the photosynthetic activity of vegetation from remote sensing data, Adv. Space Res., 38, 2196–2202, 2006.
Gouveia, C., DaCamara, C. C., and Trigo, R. M.: Post-fire vegetation recovery in Portugal based on spot/vegetation data, Nat. Hazards Earth Syst. Sci., 10, 673–684, https://doi.org/10.5194/nhess-10-673-2010, 2010.
Greene, D. F., Zasada, J. C., Sirois, L., Kneeshaw, D., Morin, H., Charron, I., and Simard, M.-J.: A review of the regeneration dynamics of north American boreal forest tree species, Can. J. Forest Res., 29, 824–839, 1999.
Groot, W. J., Bothwell, P. M., Carlsson, D. H., and Logan, K. A.: Simulating the effects of future fire regimes on western Canadian boreal forests, J. Veg. Sci., 14, 355–364, 2003.
Hall, F. G., de Colstoun, E., Collatz, G. J., Landis, D., Dirmeyer, P., Betts, A., Huffman, G. J., Bounoua, L., and Meeson, B.: ISLSCP Initiative II global data sets: surface boundary conditions and atmospheric forcings for land-atmosphere studies, J. Geophys. Res.-Atmos., 111, D22S01, https://doi.org/10.1029/2006JD007366, 2006.
Halliwell, D. H. and Apps, M. J.: BOReal Ecosystem-Atmosphere Study (BOREAS) biometry and auxiliary sites: overstory and understory data, Northern Forestry Centre Edmonton, Canada, 1997.
Harrell Jr., F. E.: Hmisc: Harrell miscellaneous, available at: http://cran.r-project.org/package=Hmisc (last access: 16 December 2013), 2012.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset, Int. J. Climatol., https://doi.org/10.1002/joc.3711, 2013.
Harrison, S. P., Prentice, I. C., Barboni, D., Kohfeld, K. E., Ni, J., and Sutra, J.-P.: Ecophysiological and bioclimatic foundations for a global plant functional classification, J. Veg. Sci., 21, 300–317, https://doi.org/10.1111/j.1654-1103.2009.01144.x, 2010.
Harrison, S., Kelley, D., Wang, H., Herbert, A., Li, G., Bradstock, R., Fontaine, J., Enright, N., Murphy, B., Penman, T., and Russell-Smith, J.: Patterns in fire-response trait abundances in Australia using a new database of plot-level measurements for fire model evaluation, Global Ecology and Biogeography, submitted, 2014.
Hastie, T. J. and Pregibon, D.: Generalized linear models, Chapter 6 of Statistical Models in S, edited by: Chambers, J. M. and Hastie, T. J., Wadsworth & Brooks/Cole, 1992.
Higgins, R. W. and Centre, U. C. P.: Improved United States precipitation quality control system and analysis, in: NCEP/Climate Prediction Center ATLAS No. 6, NOAA, National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center, Camp Springs, MD, 2000.
Higgins, R. W., Janowiak, J. E., and Yao, Y.-P.: A gridded hourly precipitation data base for the United States (1963–1993), in: NCEP/Climate prediction center atlas 1, p. 46, NOAA, National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center, Camp Springs, MD, 1996.
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, 2005.
Kauffman, J. B.: Survival by sprouting following fire in tropical forests of the eastern Amazon, Biotropica, 23, 219–224, 1991.
Kaufmann, W. F. and Hartmann, K. M.: Earth-based spectroradiometric monitoring of forest decline, J. Plant Physiol., 138, 270–273, 1991.
Keeley, J. E.: Fire severity and plant age in postfire resprouting of woody plants in sage scrub and chaparral, Madrono, 53, 373–379, 2006.
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013.
Kimball, J. S., Running, S. W., and Nemani, R.: An improved method for estimating surface humidity from daily minimum temperature, Agr. Forest Meteorol., 85, 87–98, 1997.
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 12,000 years, Global Ecol. Biogeogr., 20, 73–86, 2011.
Kloster, S., Mahowald, N. M., Randerson, J. T., Thornton, P. E., Hoffman, F. M., Levis, S., Lawrence, P. J., Feddema, J. J., Oleson, K. W., and Lawrence, D. M.: Fire dynamics during the 20th century simulated by the Community Land Model, Biogeosciences, 7, 1877–1902, https://doi.org/10.5194/bg-7-1877-2010, 2010.
Knox, K. J. E. and Clarke, P. J.: Fire season and intensity affect shrub recruitment in temperate sclerophyllous woodlands, Oecologia, 149, 730–739, https://doi.org/10.1007/s00442-006-0480-6, 2006.
Koenker, R.: Package "quantreg": quantile regression, available at: http://cran.r-project.org/package=quantreg (last access: 6 December 2013), 2013.
Kuleshov, Y. and Jayaratne, R.: Estimates of lightning ground flash density in Australia and its relationship to thunder-days, Aust. Meteorol. Mag., 53, 189–196, 2004.
Lawes, M. J., Adie, H., Russell-Smith, J., Murphy, B., and Midgley, J. J.: How do small savanna trees avoid stem mortality by fire? The roles of stem diameter, height and bark thickness, Ecosphere, 2, 42, https://doi.org/10.1890/ES10-00204.1, 2011a.
Lawes, M. J., Richards, A., Dathe, J., and Midgley, J. J.: Bark thickness determines fire resistance of selected tree species from fire-prone tropical savanna in north Australia, Plant Ecol., 212, 2057–2069, https://doi.org/10.1007/s11258-011-9954-7, 2011b.
Lawrence, M. G.: The relationship between relative humidity and the dewpoint temperature in moist air: a simple conversion and applications, B. Am. Meteorol. Soc., 86, 225–233, https://doi.org/10.1175/BAMS-86-2-225, 2005.
Lehmann, C. E. R., Prior, L. D., Williams, R. J., and Bowman, D. M. J. S.: Spatio-temporal trends in tree cover of a tropical mesic savanna are driven by landscape disturbance, J. Appl. Ecol., 45, 1304–1311, https://doi.org/10.1111/j.1365-2664.2008.01496.x, 2008.
Li, F., Zeng, X. D., and Levis, S.: A process-based fire parameterization of intermediate complexity in a Dynamic Global Vegetation Model, Biogeosciences, 9, 2761–2780, https://doi.org/10.5194/bg-9-2761-2012, 2012.
Lloyd, J. and Taylor, J.: On the temperature dependence of soil respiration, Funct. Ecol., 8, 315–323, 1994.
Loehle, C.: Strategy space and the disturbance spectrum: a life-history model for tree species coexistence, Am. Nat., 156, 14–33, 2000.
Lu, L. and Shuttleworth, J. W.: Incorporating NDVI-Derived LAI into the climate version of RAMS and its impact on regional climate, J. Hydrometeorol., 3, 347–362, 2002.
Lunt, I. D., Zimmer, H. C., and Cheal, D. C.: The tortoise and the hare? Post-fire regeneration in mixed Eucalyptus–Callitris forest, Aust. J. Bot., 59, 575–581, 2011.
Malanson, G. P. and Westman, W. E.: Postfire succession in Californian coastal Sage scrub: the role of continual Basal sprouting, Am. Midl. Nat., 113, 309–318, https://doi.org/10.2307/2425576, 1985.
Michna, P.: RNetCDF: R Interface to NetCDF Datasets, available at: http://cran.r-project.org/package=RNetCDF (last access: 6 December 2013), 2012.
Midgley, J. J., Lawes, M. J., and Chamaillé-Jammes, S.: Turner Review No. 19. Savanna woody plant dynamics: the role of fire and herbivory, separately and synergistically, Austr. J. Bot., 58, 1–11, https://doi.org/10.1071/BT09034, 2010.
Midgley, J., Kruger, L., and Skelton, R.: How do fires kill plants? The hydraulic death hypothesis and Cape Proteaceae "fire-resisters", S. Afr. J. Bot., 77, 381–386, https://doi.org/10.1016/j.sajb.2010.10.001, 2011.
Mitchell, T. D. and Jones, P. D.: An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25, 693–712, https://doi.org/10.1002/joc.1181, 2005.
Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C., and Koshak, W. J.: Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data, J. Geophys. Res.-Atmos., 117, D20307, https://doi.org/10.1029/2012JD017934, 2012.
Nesterov, V. G.: Combustibility of the forest and methods for its determination, Goslesbumaga, Moscow, 1949.
Paine, C. E. T., Stahl, C., Courtois, E. A., Patiño, S., Sarmiento, C., and Baraloto, C.: Functional explanations for variation in bark thickness in tropical rain forest trees, Funct. Ecol., 24, 1202–1210, https://doi.org/10.1111/j.1365-2435.2010.01736.x, 2010.
Paula, S., Arianoutsou, M., Kazanis, D., Tavsanoglu, C., Lloret, F., Buhk, C., Ojeda, F., Luna, B., Moreno, J. M., Rodrigo, A., Espelta, J. M., Pausas, J. G., Fernández-Santos, B., Fernandes, P. M., and Pausas, J. G.: Fire-related traits for plant species of the Mediterranean Basin, Ecol., 90, 1420 pp., 2009.
Pausas, J. G.: Resprouting of Quercus suber in NE Spain after fire, J. Veg. Sci., 8, 703–706, 1997.
Pausas, J. G., Bradstock, Ross A., and Keith, D. A., and Keeley, J. E.: Plant functional traits in relation to fire in crown-fire ecosystems, Ecol., 85, 1085–1100, 2004.
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.
Pfeiffer, M., Spessa, A., and Kaplan, J. O.: A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0), Geosci. Model Dev., 6, 643–685, https://doi.org/10.5194/gmd-6-643-2013, 2013.
Pierce, E. T.: Latitudinal variation of lightning parameters, J. Appl. Meteorol., 9, 194–195, https://doi.org/10.1175/1520-0450(1970)009<0194:LVOLP>2.0.CO;2, 1970.
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, 1–13, https://doi.org/10.1029/2010GB003906, 2011.
Prentice, S. and Mackerras, D.: The ratio of cloud to cloud-ground lightning flashes in thunderstorms, J. Appl. Meteorol., 16, 545–550, 1977.
Price, C. and Rind, D.: What determines the cloud-to-ground lighting fraction, 20, 463–466, https://doi.org/10.1029/93gl00226, 1993.
Purevdorj, T., Tateishi, R., Ishiyama, T., and Honda, Y.: Relationships between percent vegetation cover and vegetation indices, Int. J. Remote Sens., 19, 3519–3535, https://doi.org/10.1080/014311698213795, 1998.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.r-project.org/, last access: 6 December 2013.
R Development Core Team: R: A language and environment for statistical computing, available at: http://www.r-project.org/, last access: 6 December 2013.
Rothermel, R. C.: A mathematical model for predicting fire spread in wildland fuels, USFS, 1972.
Running, S.: Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis, Can. J. Forest Res., 17, 472–483, 1987.
Schenk, H. J. and Jackson, R. B.: Rooting depths, lateral root spreads and belowground aboveground allometries of plants in water limited ecosystems, J. Ecol., 90, 480–494, 2002a.
Schenk, H. J. and Jackson, R. B.: The global biogeography of roots, Ecol. Monogr., 72, 311–328, 2002b.
Schenk, H. J. and Jackson, R. B.: Mapping the global distribution of deep roots in relation to climate and soil characteristics, Geoderma, 126, 129–140, https://doi.org/10.1016/j.geoderma.2004.11.018, 2005.
Sellers, P. J., Tucker, C. J., Collatz, G. J., Los, S. O., Justice, C. O., Dazlich, D. A., and Randall, D. A.: A revised land surface parameterization (SiB2) for atmospheric GCMs, Part II: The generation of global fields of terrestrial biophysical parameters from satellite data, J. Climate, 4, 706–737, 1996.
Sharples, J. J., McRae, R. H. D., Weber, R. O., and Gill, A. M.: A simple index for assessing fuel moisture content, Environ. Modell. Softw., 24, 637–646, https://doi.org/10.1016/j.envsoft.2008.10.012, 2009.
Simard, M., Baccini, A., Fisher, J. B., and Pinto, N.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
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, 2003.
Tapias, R., Climent, J., Pardos, J. A., and Gil, L.: Life histories of Mediterranean pines, Plant Ecol., 171, 53–68, 2004.
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, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010.
van der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N., and Dolman, A. J.: Climate controls on the variability of fires in the tropics and subtropics, Global Biogeochem. Cy., 22, GB3028, https://doi.org/10.1029/2007GB003122, 2008.
van Etten, J. and Hijmans, R. J.: raster: Geographic data analysis and modeling, available at: http://cran.r-project.org/package=raster (last access: 16 December 2013), 2013.
van Leeuwen, W. J. D., Casady, G. M., Neary, D. G., Bautista, S., Alloza, J. A., Carmel, Y., Wittenberg, L., Malkinson, D., and Orr, B. J.: Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel, Int. J. Wildland Fire, 19, 75–93, https://doi.org/10.1071/WF08078, 2010.
Van Wagner, C. E.: Equilibrium moisture contents of some fine forest fuels in eastern Canada, Information Report, Petawawa Forest Experiment Station, 1972.
Van Wagner, C. E. and Pickett, T. L.: Equations and FORTRAN program for the Canadian forest fire weather index system, in: Canadian Forestry Service, Petawawa national forestry institute, Chalk River, Ontario, Vol. 33, 1985.
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, https://doi.org/10.1046/j.1365-2486.2002.00528.x, 2002.
Viedma, O., Meliá, J., Segarra, D., and García-Haro, J.: Modeling rates of ecosystem recovery after fires by using landsat TM data, Remote Sens., 61, 383–398, 1997.
Viney, N.: A review of fine fuel moisture modelling, Int. J. Wildland Fire, 1, 215–234, https://doi.org/10.1071/WF9910215, 1991.
Weedon, J. T., Cornwell, W. K., Cornelissen, J. H. C., Zanne, A. E., Wirth, C., and Coomes, D. A.: Global meta-analysis of wood decomposition rates: a role for trait variation among tree species?, Ecol. Lett., 12, 45–56, https://doi.org/10.1111/j.1461-0248.2008.01259.x, 2009.
Williams, P. R.: Fire-stimulated rainforest seedling recruitment and vegetative regeneration in a densely grassed wet sclerophyll forest of north-eastern Australia, Aust. J. Bot., 48, 651–658, 2000.
Williams, R. J., Griffiths, A. D., and Allan, G.: Fire regimes and biodiversity in the wet–dry tropical savanna landscapes of northern Australia, in: Flammable Australia: Fire Regimes and Biodiversity of a Continent, edited by: Bradstock, R., Williams, J. A., and Gill, A., 281–304, Cambridge University Press, Cambridge, 2002.
Xiao, J. and Moody, A.: A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA, Remote Sens. Environ., 98, 237–250, https://doi.org/10.1016/j.rse.2005.07.011, 2005.
Zammit, C. and Westoby, M.: Seedling recruitment strategies in obligate-seeding and resprouting Banksia shrubs, Ecology, 1984–1992, 1987.
