Articles | Volume 9, issue 12
https://doi.org/10.5194/gmd-9-4405-2016
© Author(s) 2016. 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-9-4405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
07 Dec 2016
Model description paper |
| 07 Dec 2016
Terrestrial ecosystem process model Biome-BGCMuSo v4.0: summary of improvements and new modeling possibilities
Dóra Hidy
CORRESPONDING AUTHOR
MTA-SZIE Plant Ecology Research Group, Szent István University,
Páter K. u.1., 2103 Gödöllő, Hungary
Zoltán Barcza
Department of Meteorology, Eötvös Loránd University,
Pázmány P. sétány 1/A., 1117 Budapest, Hungary
Hrvoje Marjanović
Croatian Forest Research Institute, Department for Forest Management
and Forestry Economics, Cvjetno naselje 41, 10450 Jastrebarsko, Croatia
Maša Zorana Ostrogović Sever
Croatian Forest Research Institute, Department for Forest Management
and Forestry Economics, Cvjetno naselje 41, 10450 Jastrebarsko, Croatia
Laura Dobor
Department of Meteorology, Eötvös Loránd University,
Pázmány P. sétány 1/A., 1117 Budapest, Hungary
Czech University of Life Sciences, Faculty of Forestry and Wood Sciences, Department of Forest Protection and Entomology, Kamýcká 129, 165 21 Prague 6, Czech Republic
Györgyi Gelybó
Institute for Soil Sciences and Agricultural Chemistry, Centre for
Agricultural Research, Hungarian Academy of Sciences, Herman O. út 15.,
1163 Budapest, Hungary
Nándor Fodor
Agricultural Institute, Centre for Agricultural Research, Hungarian
Academy of Sciences, Brunszvik u. 2., 2462 Martonvásár, Hungary
Krisztina Pintér
MTA-SZIE Plant Ecology Research Group, Szent István University,
Páter K. u.1., 2103 Gödöllő, Hungary
Galina Churkina
Institute for Advanced Sustainability Studies e.V., Berliner Strasse
130, 14467 Potsdam, Germany
Geography Department, Humboldt University of Berlin, Berlin, Germany
Steven Running
Numerical Terradynamic Simulation Group, Department of Ecosystem and
Conservation Sciences University of Montana, Missoula, MT 59812, USA
Peter Thornton
Climate Change Science Institute/Environmental Sciences Division,
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Gianni Bellocchi
UREP, INRA, 63000 Clermont-Ferrand, France
László Haszpra
Hungarian Meteorological Service, P.O. Box 39, 1675 Budapest,
Hungary
Geodetic and Geophysical Institute, MTA Research Centre for
Astronomy and Earth Sciences, Csatkai Endre utca 6-8, 9400 Sopron, Hungary
Ferenc Horváth
Institute of Ecology and Botany, Centre for Ecological Research,
Hungarian Academy of Sciences, Alkotmány u. 2-4., 2163
Vácrátót, Hungary
Andrew Suyker
School of Natural Resources, University of Nebraska-Lincoln, 806
Hardin Hall, Lincoln, NE 68588, USA
Zoltán Nagy
MTA-SZIE Plant Ecology Research Group, Szent István University,
Páter K. u.1., 2103 Gödöllő, Hungary
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W. D. Collins, A. P. Craig, J. E. Truesdale, A. V. Di Vittorio, A. D. Jones, B. Bond-Lamberty, K. V. Calvin, J. A. Edmonds, S. H. Kim, A. M. Thomson, P. Patel, Y. Zhou, J. Mao, X. Shi, P. E. Thornton, L. P. Chini, and G. C. Hurtt
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Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, https://doi.org/10.5194/gmd-8-1899-2015, 2015
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Atmos. Meas. Tech., 8, 1657–1671, https://doi.org/10.5194/amt-8-1657-2015, https://doi.org/10.5194/amt-8-1657-2015, 2015
A. V. Di Vittorio, L. P. Chini, B. Bond-Lamberty, J. Mao, X. Shi, J. Truesdale, A. Craig, K. Calvin, A. Jones, W. D. Collins, J. Edmonds, G. C. Hurtt, P. Thornton, and A. Thomson
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Economic models provide scenarios of land use and greenhouse gas emissions to earth system models to project global change. We found, and partially addressed, inconsistencies in land cover between an economic and an earth system model that effectively alter a prescribed scenario, causing significant differences in projected terrestrial carbon and atmospheric CO2 between prescribed and altered scenarios. We outline a solution to this current problem in scenario-based global change projections.
B. Bond-Lamberty, K. Calvin, A. D. Jones, J. Mao, P. Patel, X. Y. Shi, A. Thomson, P. Thornton, and Y. Zhou
Geosci. Model Dev., 7, 2545–2555, https://doi.org/10.5194/gmd-7-2545-2014, https://doi.org/10.5194/gmd-7-2545-2014, 2014
R. L. Thompson, K. Ishijima, E. Saikawa, M. Corazza, U. Karstens, P. K. Patra, P. Bergamaschi, F. Chevallier, E. Dlugokencky, R. G. Prinn, R. F. Weiss, S. O'Doherty, P. J. Fraser, L. P. Steele, P. B. Krummel, A. Vermeulen, Y. Tohjima, A. Jordan, L. Haszpra, M. Steinbacher, S. Van der Laan, T. Aalto, F. Meinhardt, M. E. Popa, J. Moncrieff, and P. Bousquet
Atmos. Chem. Phys., 14, 6177–6194, https://doi.org/10.5194/acp-14-6177-2014, https://doi.org/10.5194/acp-14-6177-2014, 2014
X. Yang, P. E. Thornton, D. M. Ricciuto, and W. M. Post
Biogeosciences, 11, 1667–1681, https://doi.org/10.5194/bg-11-1667-2014, https://doi.org/10.5194/bg-11-1667-2014, 2014
G. Broquet, F. Chevallier, F.-M. Bréon, N. Kadygrov, M. Alemanno, F. Apadula, S. Hammer, L. Haszpra, F. Meinhardt, J. A. Morguí, J. Necki, S. Piacentino, M. Ramonet, M. Schmidt, R. L. Thompson, A. T. Vermeulen, C. Yver, and P. Ciais
Atmos. Chem. Phys., 13, 9039–9056, https://doi.org/10.5194/acp-13-9039-2013, https://doi.org/10.5194/acp-13-9039-2013, 2013
X. Yang, W. M. Post, P. E. Thornton, and A. Jain
Biogeosciences, 10, 2525–2537, https://doi.org/10.5194/bg-10-2525-2013, https://doi.org/10.5194/bg-10-2525-2013, 2013
T. W. Hudiburg, B. E. Law, and P. E. Thornton
Biogeosciences, 10, 453–470, https://doi.org/10.5194/bg-10-453-2013, https://doi.org/10.5194/bg-10-453-2013, 2013
Related subject area
Biogeosciences
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
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
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
Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Exploring the Potential of History Matching for Land Surface Model Calibration
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
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
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
Assimilation of Carbonyl Sulfide (COS) fluxes within the adjoint-based data assimilation system–Nanjing University Carbon Assimilation System (NUCAS v1.0)
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
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.
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.
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.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-6, https://doi.org/10.5194/gmd-2024-6, 2024
Revised manuscript accepted for GMD
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This study introduces a new scheme to simulate 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 response 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.
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
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2024-464, https://doi.org/10.5194/egusphere-2024-464, 2024
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Our study employs Long Short-Term Memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. 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 unlocking the secrets of vegetation phenology responses to climate change with deep learning techniques.
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.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996, https://doi.org/10.5194/egusphere-2023-2996, 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. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
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.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
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This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
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.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-242, https://doi.org/10.5194/gmd-2023-242, 2024
Revised manuscript accepted for GMD
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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 as 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.
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.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
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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. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
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.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1955, https://doi.org/10.5194/egusphere-2023-1955, 2023
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In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.
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.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
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Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
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Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Cited articles
Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink, Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668, 2015.
Alberti, G., Vicca, S., Inglima, I., Belelli-Marchesini, L., Genesio, L., Miglietta, F., Marjanovic, H., Martinez, C., Matteucci, G., D'Andrea, E., Peressotti, A., Petrella, F., Rodeghiero, M., and Cotrufo, M. F.: Soil C:N stoichiometry controls carbon sink partitioning between above-ground tree biomass and soil organic matter in high fertility forests, Iforest 8, 195–206, https://doi.org/10.3832/ifor1196-008, 2014.
Atkin, O. K., Atkinson, L. J., Fisher, R. A., Campbell, C. D., Zaragoza-Castells, J., Pitchford, J. W., Woodward, F. I., and Hurry, V.: Using temperature-dependent changes in leaf scaling relationships to quantitatively account for thermal acclimation of respiration in a coupled global climate-vegetation model, Glob. Change Biol., 14, 1–18, https://doi.org/10.1111/j.1365-2486.2008.01664.x, 2008.
Aubinet, M., Grelle, G., Ibrom, A., Rannik, U., Moncrieff, J., Foken, T., Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, C., Clement, R., Elbers, J., Granier, A., Grunwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., and Vesala, T.: Estimates of the annual net carbon and water exchange of European forests: the EUROFLUX methodology, Adv. Ecol. Res., 30, 113–175, https://doi.org/10.1016/S0065-2504(08)60018-5, 2000.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in: Progress in Photosynthesis Research, edited by: Biggins, J., Springer Netherlands, 221–224, 1987.
Balogh, J., Pintér, K., Fóti, S., Cserhalmi, D., Papp, M., and Nagy, Z.: Dependence of soil respiration on soil moisture, clay content, soil organic matter, and CO2 uptake in dry grasslands, Soil Biol. Biochem., 43, 1006–1013, https://doi.org/10.1016/j.soilbio.2011.01.017, 2011.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009.
Barcza, Z., Kern, A., Haszpra, L., and Kljun, N.: Spatial representativeness of tall tower eddy covariance measurements using remote sensing and footprint analysis, Agr. Forest Meteorol., 149, 795–807, https://doi.org/10.1016/j.agrformet.2008.10.021, 2009.
Barcza, Z., Bondeau, A., Churkina, G., Ciais, P., Czóbel, S., Gelybó, G., Grosz, B., Haszpra, L., Hidy, D., Horváth, L., Machon, A., Pásztor, L., Somogyi, Z., and Van Oost, K.: Model-based biospheric greenhouse gas balance of Hungary, in: Atmospheric Greenhouse Gases: The Hungarian Perspective, edited by: Haszpra, L., Springer, 295–330, 2011.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Bond-Lamberty, B., Gower, S. T., Ahl, D. E., and Thornton, P. E.: Reimplementation of the Biome-BGC model to simulate successional change, Tree Physiol., 25, 413–424, https://doi.org/10.1093/treephys/25.4.413, 2005.
Bond-Lamberty, B., Gower, S. T., and Ahl, D. E.: Improved simulation of poorly drained forests using Biome-BGC, Tree Physiol., 27, 703–715, https://doi.org/10.1093/treephys/27.5.703, 2007a.
Bond-Lamberty, B., Peckham, S. D., Ahl, D. E., and Gower, S. T.: Fire as the dominant driver of central Canadian boreal forest carbon balance, Nature, 450, 89–92, https://doi.org/10.1038/nature06272, 2007b.
Campbell, G. S. and Diaz, R.: Simplified soil water balance models to predict crop transpiration, in: Drought Research Priorities for the Drylands, 15–26, 1988.
Campioli, M., Vicca, S., Luyssaert, S., Bilcke, J., Ceschia, E., Chapin III, F. S., Ciais, P., Fernández-Martínez, M., Malhi, Y., Obersteiner, M., Olefeldt, D., Papale, D., Piao, S. L., Peñuelas, J., Sullivan, P. F., Wang, X., Zenone, T., and Janssens, I. A.: Biomass production efficiency controlled by management in temperate and boreal ecosystems, Nat. Geosci., 8, 843–846, https://doi.org/10.1038/ngeo2553, 2015.
Cannell, M. G. R. and Thornley, J. H. M.: Modelling the components of plant respiration: Some guiding principles, Ann. Bot.-London, 85, 45–54, https://doi.org/10.1006/anbo.1999.0996, 2000.
Chapin, F. S., Woodwell, G. M., Randerson, J. T., Rastetter, E. B., Lovett, G. M., Baldocchi, D. D., Clark, D. A., Harmon, M. E., Schimel, D. S., Valentini, R., Wirth, C., Aber, J. D., Cole, J. J., Goulden, M. L., Harden, J. W., Heimann, M. R., Howarth, W., Matson, P. A., McGuire, A. D., Melillo, J. M., Mooney, H. A., Neff, J. C., Houghton, R. A., Pace, M. L., Ryan, M. G., Running, S. W., Sala, O. E., Schlesinger, W. H., and Schulze, E. D.: Reconciling carbon-cycle concepts, terminology, and methods, Ecosystems 9, 1041–1050, https://doi.org/10.1007/s10021-005-0105-7, 2006.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chiesi, M., Maselli, F., Moriondo, M., Fibbi, L., Bindi, M., and Running, S. W.: Application of BIOME-BGC to simulate Mediterranean forest processes, Ecol. Model., 206, 179–190, https://doi.org/10.1016/j.ecolmodel.2007.03.032, 2007.
Churkina, G., Running, S. W., Running, S. W., Schloss, A. L., and the participants of the Potsdam NPP Model Intercomparison: Comparing global models of terrestrial net primary productivity (NPP): the importance of water availability, Glob. Change Biol., 5, 46–55, https://doi.org/10.1046/j.1365-2486.1999.00006.x, 1999.
Churkina, G., Tenhunen, J., Thornton, P., Falge, E. M., Elbers, J. A., Erhard, M., Grünwald, T., Kowalski, A. S., Rannik, Ü., and Sprinz, D.: Analyzing the ecosystem carbon dynamics of four European coniferous forests using a biogeochemistry model, Ecosystems, 6, 168–184, https://doi.org/10.1007/s10021-002-0197-2, 2003.
Churkina, G., Brovkin, V., von Bloh, W., Trusilova, K., Jung, M., and Dentener, F.: Synergy of rising nitrogen depositions and atmospheric CO2 on land carbon uptake moderately offsets global warming, Global Biogeochem. Cy., 23, GB4027, https://doi.org/10.1029/2008GB003291, 2009.
Churkina, G., Zaehle, S., Hughes, J., Viovy, N., Chen, Y., Jung, M., Heumann, B. W., Ramankutty, N., Heimann, M., and Jones, C.: Interactions between nitrogen deposition, land cover conversion, and climate change determine the contemporary carbon balance of Europe, Biogeosciences, 7, 2749–2764, https://doi.org/10.5194/bg-7-2749-2010, 2010.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R. B., Piao S., and Thornton, P.: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
Clapp, R. B. and Hornberger, G. M.: Empirical equations for some soil hydraulic properties, Water Resour. Res., 14, 601–604, https://doi.org/10.1029/WR014i004p00601, 1978.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer, Agr. Forest Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
Damour, G., Simonneau, T., Cochard, H., and Urban, L.: An overview of models of stomatal conductance at the leaf level, Plant. Cell Environ., 33, 1419–1438, https://doi.org/10.1111/j.1365-3040.2010.02181.x, 2010.
Dietze, M., Serbin, S., Davidson, C., Desai, A., Feng, X., Kelly, R., Kooper, R., Lebauer, D., Mantooth, J., Mchenry, K., and Wang, D.: A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes, J. Geophys. Res.-Biogeo., 119, 286–300, https://doi.org/10.1002/2013JG002392, 2014.
Di Vittorio, A. V., Anderson, R. S., White, J. D., Miller, N. L., and Running, S. W.: Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses, Ecol. Model., 221, 2038–2053, https://doi.org/10.1016/j.ecolmodel.2010.05.013, 2010.
Driessen, P., Deckers, J., Spaargaren, O., and Nachtergaele, F.: Lecture notes on the major soils of the world, edited by: Driessen, P., Deckers, J., Spaargaren, O., and Nachtergaele, F., Food and Agriculture Organization (FAO), 2001.
Eastaugh, C. S., Pötzelsberger, E., and Hasenauer, H.: Assessing the impacts of climate change and nitrogen deposition on Norway spruce (Picea abies L. Karst) growth in Austria with BIOME-BGC, Tree Physiol., 31, 262–274, https://doi.org/10.1093/treephys/tpr033, 2011.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A bio-chemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Florides, G. and Kalogirou, S.: Annual ground temperature measurements at various depth, available at: https://www.researchgate.net/publication/30500353 (last access: 10 April 2016), 2016.
Fodor, N. and Rajkai, K.: Computer program (SOILarium 1.0) for estimating the physical and hydrophysical properties of soils from other soil characteristics, Agrokémia és Talajt., 60, 27–40, 2011.
Foken, T. and Vichura, B.: Tools for quality assessment of surface-based flux measurements, Agr. Forest Meteorol., 78, 83–105, https://doi.org/10.1016/0168-1923(95)02248-1, 1996.
Franks, P. J., Adams, M. A., Amthor, J. S., Barbour, M. M., Berry, J. A., Ellsworth, D. S., Farquhar, G. D., Ghannoum, O., Lloyd, J., McDowel, L., Norby, R. J., Tissue, D. T., and von Caemmerer, S.: Sensitivity of plants to changing atmospheric CO2 concentration: from the geological past to the next century, New Phytol., 197, 1077–1094, https://doi.org/10.1111/nph.12104, 2013.
Friedlingstein, P. and Prentice, I. C.: Carbon-climate feedbacks: a review of model and observation based estimates, Curr. Opin. Environ. Sust., 2, 251–257, https://doi.org/10.1016/j.cosust.2010.06.002, 2010.
Friedlingstein, P., Joel, G., Field, C. B., and Fung, I. Y.: Toward an allocation scheme for global terrestrial carbon models, Glob. Change Biol., 5, 755–770, https://doi.org/10.1046/j.1365-2486.1999.00269.x, 1999.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., Von Bloh, W., Brovkin, V., Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J. J., Jones, C. J., Joos, F., Kato, T. K., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H. D., Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K. G., Schnur, R., Strassmann, K., Weawer, A. J., Yoshikawa, C., and Zeng, N.: Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison, J. Climate, 19, 3338–3353, https://doi.org/10.1175/JCLI3800.1, 2006.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks, J. Climate, 27, 511–526, https://doi.org/10.1175/JCLI-D-12-00579.1, 2014.
Friend, A. D., Arneth, A., Kiang, N. Y., Lomas, M., Ogée, J., Rödenbeck, C., Running, S. W., Santaren, J. D., Sitch, S., Viovy, N., Woodward, F. I., and Zaehle, S.: FLUXNET and modelling the global carbon cycle, Glob. Change Biol., 1, 1–24, https://doi.org/10.1111/j.1365-2486.2006.01223.x, 2006.
Groffman, P. M. and Tiedje, J. M.: Denitrification in north temperate forest soils: Relationships between denitrification and environmental factors at the landscape scale, Soil Biol. Biochem., 21, 621–626, https://doi.org/10.1016/0038-0717(89)90054-0, 1989
Hartig, F., Dyke, J., Hickler, T., Higgins, S. I., O'Hara, R. B., Scheiter, S., and Huth, A.: Connecting dynamic vegetation models to data - an inverse perspective, J. Biogeogr., 39, 2240–2252, https://doi.org/10.1111/j.1365-2699.2012.02745.x, 2012.
Hashimoto, S., Morishita, T., Sakata, T., Ishizuka, S., Kaneko, S., and Takahashi, M.: Simple models for soil CO2, CH4, and N2O fluxes calibrated using a Bayesian approach and multi-site data, Ecol. Model., 222, 1283–1292, https://doi.org/10.1016/j.ecolmodel.2011.01.013, 2011.
Hidy, D., Barcza, Z., Haszpra, L., Churkina, G., Pintér, K., and Nagy, Z.: Development of the Biome-BGC model for simulation of managed herbaceous ecosystems, Ecol. Model., 226, 99–119, https://doi.org/10.1016/j.ecolmodel.2011.11.008, 2012.
Hidy, D., Barcza, Z., Thornton, P., and Running, S. W.: User's Guide for Biome-BGC MuSo 4.0, available at: http://nimbus.elte.hu/bbgc/files/Manual_BBGC_MuSo_v4.0.pdf (last access: 15 October 2016), 2015.
Hlásny, T., Barcza, Z., Barka, I., Merganicova, K., Sedmak, R., Kern, A, Pajtik, J., Balazs, B., Fabrika, M., and Churkina, G.: Future carbon cycle in mountain spruce forests of Central Europe: Modelling framework and ecological inferences, Forest Ecol. Manage., 328, 55–68, https://doi.org/10.1016/j.foreco.2014.04.038, 2014.
Hunt, E. R., Piper, S. C., Nemani, R., Keeling, C. D., Otto, R. D., and Running, S. W.: Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model, Global Biogeochem. Cy., 10, 431–456, https://doi.org/10.1029/96GB01691, 1996.
Huntzinger, D. N., Schwalm, C., Michalak, A. M., Schaefer, K., King, A. W., Wei, Y., Jacobson, A., Liu, S., Cook, R. B., Post, W. M., Berthier, G., Hayes, D., Huang, M., Ito, A., Lei, H., Lu, C., Mao, J., Peng, C. H., Peng, S., Poulter, B., Riccuito, D., Shi, X., Tian, H., Wang, W., Zeng, N., Zhao, F., and Zhu, Q.: The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design, Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, 2013.
IPCC: Guidelines for National Greenhouse Gas Inventories, edited by: Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K., National Greenhouse Gas Inventories Programme, IGES, Japan, 2006.
Jarvis, N. J.: A simple empirical model of root water uptake, J. Hydrol., 107, 57–72, https://doi.org/10.1016/0022-1694(89)90050-4, 1989.
Jarvis, P. G.: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Philos. Trans. R. Soc. B, 273, 593–610, https://doi.org/10.1098/rstb.1976.0035, 1976.
Jochheim, H., Puhlmann, M., Beese, F., Berthold, D., Einert, P., Kallweit, R., Konopatzky, A., Meesenburg, H., Meiwes, K. J., Raspe, S., Schulte-Bisping, H., and Schulz, C.: Modelling the carbon budget of intensive forest monitoring sites in Germany using the simulation model BIOME-BGC, iForest – Biogeosciences For., 2, 7–10, https://doi.org/10.3832/ifor0475-002, 2009.
Jolly, W., Nemani, R. R., and Running, S. W.: A generalized, bioclimatic index to predict foliar phenology in response to climate, Glob. Change Biol., 11, 619–632, https://doi.org/10.1111/j.1365-2486.2005.00930.x, 2005.
Jung, M., Le Maire, G., Zaehle, S., Luyssaert, S., Vetter, M., Churkina, G., Ciais, P., Viovy, N., and Reichstein, M.: Assessing the ability of three land ecosystem models to simulate gross carbon uptake of forests from boreal to Mediterranean climate in Europe, Biogeosciences, 4, 647–656, https://doi.org/10.5194/bg-4-647-2007, 2007a.
Jung, M., Vetter, M., Herold, M., Churkina, G., Reichstein, M., Zaehle, S., Ciais, P., Viovy, N., Bondeau, A., Chen, Y., Trusilova, K., Feser, F., and Heimann, M.: Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models, Global Biogochem. Cy., 21, GB4021, https://doi.org/10.1029/2006GB002915, 2007b.
Kimball, J. S., White, M. A., and Running, S. W.: BIOME-BGC simulations of stand hydrologic processes for BOREAS, J. Geophys. Res., 102, 29043–29051, https://doi.org/10.1029/97JD02235, 1997.
Knorr, W. and Kattge, J.: Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling, Glob. Change Biol., 11, 1333–1351, https://doi.org/10.1111/j.1365-2486.2005.00977.x, 2005.
Korol, R. L., Milner, K. S., and Running, S. W.: Testing a mechanistic model for predicting stand and tree growth, Forest Sci., 42, 139–153, 1996.
Kulkarni, M. V., Burgin, A. J., Groffman, P. M., and Yavitt, J. B.: Direct flux and N-15 tracer methods for measuring denitrification in forest soils, Biogeochemistry, 117, 359–373, https://doi.org/10.1007/s10533-013-9876-7, 2014.
Lagergren, F., Grelle, A., Lankreijer, H., Mölder, M., and Lindroth, A.: Current carbon balance of the forested area in Sweden and its sensitivity to global change as simulated by Biome-BGC, Ecosystems, 9, 894–908, https://doi.org/10.1007/s10021-005-0046-1, 2006.
Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis model for C3 plants, Plant. Cell Environ., 18, 339–355, https://doi.org/10.1111/j.1365-3040.1995.tb00370.x, 1995.
Liu, H. P., Peters, G., and Foken, T.: New equations for sonic temperature variance and buoyancy heat flux with an omnidirectional sonic anemometer, Bound.-Lay Meteorol., 100, 459–468, https://doi.org/10.1023/A:1019207031397, 2001.
Lombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S., and Fisher, R. A.: Temperature acclimation of photosynthesis and respiration: a key uncertainty in the carbon cycle-climate feedback, Geophys. Res. Lett., 42, 8624–8631, https://doi.org/10.1002/2015GL065934, 2015.
Ma, S., Churkina, G., Wieland, R., and Gessler, A.: Optimization and evaluation of the ANTHRO-BGC model for winter crops in Europe, Ecol. Model., 222, 3662–3679, https://doi.org/10.1016/j.ecolmodel.2011.08.025, 2011.
Ma, S., Acutis, A., Barcza, Z., Touhami, H. B., Doro, L., Hidy, D., Köchy, M., Minet, J., Lellei-Kovács, E., Perego, A., Rolinski, A., Ruget, F., Seddaiu, G., Wu, L., and Bellocchi, G.: The grassland model intercomparison of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) European knowledge hub, in: Proceedings of the 7th International Congress on Environmental Modelling and Software, edited by: Ames, D. P., Quinn, N. W. T., and Rizzoli, A. E., San Diego, California, USA, 2014.
Marjanović, H., Alberti, G., Balogh, J., Czóbel, S., Horváth, L., Jagodics, A., Nagy, Z., Ostrogović, M. Z., Peressotti, A., and Führer, E.: Measurements and estimations of biosphere-atmosphere exchange of greenhouse gases − Grasslands, in: Atmospheric Greenhouse Gases: The Hungarian Perspective, edited by: Haszpra, L., Springer, Dordrecht – Heidelberg – London – New York, 121–156, https://doi.org/10.1007/978-90-481-9950-1_7, 2011a.
Marjanović, H., Ostrogović, M. Z., Alberti, G., Balenović, I., Paladinić, E., Indir, K., Peresotti, A., and Vuletić, D.: Carbon dynamics in younger stands of pedunculate oak during two vegetation periods, Šum. list special issue, 135, 59–73, 2011b (in Croatian with English summary).
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rotter, R. P., Boote, K. J., Ruane, A. C., Thorburn, P. J., Cammarano, D., Hatfield, J. L., Rosenzweig, C., Aggarwal, P. K., Angulo, C., Basso, B., Bertuzzi, P., Biernath, C., Brisson, N., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R. F., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Kersebaum, K. C., Müller, C., Kumar, S. N., Nendel, C., O'leary, G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherback, I., Steduto, P., Stöckle, C. O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., White, J. W., and Wolf, J.: Multimodel ensembles of wheat growth: many models are better than one, Glob. Change Biol., 21, 911–925, https://doi.org/10.1111/gcb.12768, 2015.
Maselli, F., Chiesi, M., Brilli, L., and Moriondo, M.: Simulation of olive fruit yield in Tuscany through the integration of remote sensing and ground data, Ecol. Model., 244, 1–12, https://doi.org/10.1016/j.ecolmodel.2012.06.028, 2012.
Massman, W. J.: The attenuation of concentration fluctuations in turbulent flow through a tube, J. Geophys. Res., 96, 15269–15273, https://doi.org/10.1029/91JD01514, 1991.
Mayer, B.: Hydropedological relations in the region of lowland forests of Pokupsko basin, Radovi Šumarskog instituta Jastrebarsko, 31, 37–89, 1996 (in Croatian with English summary).
Medlyn, B. E., Dreyer, E., Ellsworth, D., Forstreuter, M., Harley, P. C., Kirschbaum, M. U. F., Le Roux, X., Montpied, P., Strassemeyer, J., Walcroft, A., Wang, K., and Loustau, D.: Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data, Plant. Cell Environ., 25, 1167–1179, https://doi.org/10.1046/j.1365-3040.2002.00891.x, 2002.
Merganičová, K., Pietsch, S. A., and Hasenauer, H.: Testing mechanistic modeling to assess impacts of biomass removal, Forest Ecol. Manag., 207, 37–57, https://doi.org/10.1016/j.foreco.2004.10.017, 2005.
Moore, C. J.: Frequency-response corrections for eddy-correlation systems, Bound.-Lay. Meteorol., 37, 17–35, https://doi.org/10.1007/BF00122754, 1986.
Morecroft, M. D. and Roberts, J. M.: Photosynthesis and stomatal conductance of mature canopy Oak and Sycamore trees throughout the growing season, Funct. Ecol., 13, 332–342, 1999.
Mu, Q., Zhao, M., Running, S. W., Liu, M., and Tian, H.: Contribution of increasing CO2 and climate change to the carbon cycle in China's ecosystems, J. Geophys. Res.-Biogeo., 113, G01018, https://doi.org/10.1029/2006JG000316, 2008.
Nagy, Z., Pintér, K., Czóbel, S., Balogh, J., Horváth, L., Fóti, S., Barcza, Z., Weidinger, T., Csintalan, Z., Dinh, N. Q., Grosz, B., and Tuba, Z.: The carbon budget of semi-arid grassland in a wet and a dry year in Hungary, Agr. Ecosyst. Environ., 121, 21–29, https://doi.org/10.1016/j.agee.2006.12.003, 2007.
Nagy, Z., Barcza, Z., Horváth, L., Balogh, J., Hagyó, A., Káposztás, N., Grosz, B., Machon, A., and Pintér, K.: Measurements and estimations of biosphere-atmosphere exchange of greenhouse gases – Grasslands, in: Atmospheric Greenhouse Gases: The Hungarian Perspective, edited by: Haszpra, L., 91–120, Springer, Dordrecht – Heidelberg – London – New York, 2011.
Nakai, T., van der Molen, M. K., Gash, J. H. C., and Kodama, Y: Correction of sonic anemometer angle of attack errors, Agr. Forest Meteorol., 136, 19–30, https://doi.org/10.1016/j.agrformet.2006.01.006, 2006.
Nemani, R. R. and Running, S. W.: Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation, Agr. Forest Meteorol., 44, 245–260, https://doi.org/10.1016/0168-1923(89)90020-8, 1989.
Olin, S., Schurgers, G., Lindeskog, M., Wårlind, D., Smith, B., Bodin, P., Holmér, J., and Arneth, A.: Modelling the response of yields and tissue C : N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe, Biogeosciences, 12, 2489–2515, https://doi.org/10.5194/bg-12-2489-2015, 2015.
Ostrogović, M. Z.: Carbon stocks and carbon balance of an even-aged pedunculate oak (Quercus robur L.) forest in Kupa river basin, doctoral thesis, Faculty of forestry, University of Zagreb, 2013 (in Croatian with English summary).
Petritsch, R., Hasenauer, H., and Pietsch, S. A.: Incorporating forest growth response to thinning within biome-BGC, Forest Ecol. Manag., 242, 324–336, https://doi.org/10.1016/j.foreco.2007.01.050, 2007.
Pietsch, S. A., Hasenauer, H., Kucera, J., and Cermák, J.: Modeling effects of hydrological changes on the carbon and nitrogen balance of oak in floodplains, Tree Physiol., 23, 735–746, 2003.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J. M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Ritchie, J. T.: Soil water balance and plant water stress, in: Understanding Options for Agricultural Production, edited by: Tsuji, G. Y., Hoogenboom, G., and Thornton, P. E., Kluwer Academic Publishers, The Netherlands, 41–54, 1998.
Running, S. W. and Coughlan, J. C.: A general model of forest ecosystem processes for reginonal applications I. Hydrologic balance, canopy gas exchange and primary production processes, Ecol. Model., 42, 125–154, https://doi.org/10.1016/0304-3800(88)90112-3, 1988.
Running, S. W. and Gower, S. T.: FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets, Tree Physiol., 9, 147–160, https://doi.org/10.1093/treephys/9.1-2.147, 1991.
Running, S. W. and Hunt, E. R. J.: Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models, in: Scaling Physiological Processes: Leaf to Globe, edited by: Ehleringer, J. R. and Field, C., Academic Press, San Diego, 141–158, 1993.
Sándor, R. and Fodor, N.: Simulation of soil temperature dynamics with models using different concepts, Sci. World J., 2012, 1–8, https://doi.org/10.1100/2012/590287, 2012.
Sándor, R., Ma, S., Acutis, M., Barcza, Z., Ben Touhami, H., Doro, L., Hidy, D., Köchy, M., Lellei-Kovács, E., Minet, J., Perego, A., Rolinski, S., Ruget, F., Seddaiu, G., Wu, L., and Bellocchi, G.: Uncertainty in simulating biomass yield and carbon-water fluxes from grasslands under climate change, Advances in Animal Biosciences, 6, 49–51, https://doi.org/10.1017/S2040470014000545, 2015.
Sándor, R., Barcza, Z., Hidy, D., Lellei-Kovács, E., Ma, S., and Bellocchi, G.: Modelling of grassland fluxes in Europe: evaluation of two biogeochemical models, Agr. Ecosyst. Environ., 215, 1–19, https://doi.org/10.1016/j.agee.2015.09.001, 2016.
Schmid, S., Zierl, B., and Bugmann, H.: Analyzing the carbon dynamics of central European forests: comparison of Biome-BGC simulations with measurements, Reg. Environ. Chang., 6, 167–180, https://doi.org/10.1007/s10113-006-0017-x, 2006.
Schulze, E. D., Luyssaert, S., Ciais, P., Freibauer, A., Janssens, I. A., Soussana, J. F., Smith, P., Grace, J., Levin, I., Thiruchittampalam, B., Heimann, M., Dolman, A. J., Valentini, R., Bousquet, P., Peylin, P., Peters, W., Rödenbeck, C., Etiope, G., Vuichard, N., Wattenbach, M., Nabuurs, G. J., Poussi, Z., Nieschulze, J., Gash, J. H., and CarboEurope Team: Importance of methane and nitrous oxide for Europe's terrestrial greenhouse-gas balance, Nat. Geosci., 2, 842–850, https://doi.org/10.1038/ngeo686, 2009.
Schwalm, C. R., Williams, C. A., Schaefer, K., Anderson, R., Arain, M. A., Baker, I., Barr, A., Black, T. A., Chen, G., Chen, J. M., Ciais, P., Davis, K. J., Desai, A., Dietze, M., Dragoni, D., Fischer, M. L., Flanagan, L. B., Grant, R., Gu, L., Hollinger, D., Izaurralde, R. C., Kucharik, C., Lafleur, P., Law, B. E., Li, L., Li, Z., Liu, S., Lokupitiya, E., Luo, Y., Ma, S., Margolis, H., Matamala, R., McCaughey, H., Monson, R. K., Oechel, W. C., Peng, C., Poulter, B., Price, D. T., Riciutto, D. M., Riley, W., Sahoo, A. K., Sprintsin, M., Sun, J., Tian, H., Tonitto, C., Verbeeck, H., and Verma, S. B.: A model-data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis, J. Geophys. Res., 115, G00H05, https://doi.org/10.1029/2009JG001229, 2010.
Schwalm, C. R., Huntinzger, D. N., Fisher, J. B., Michalak, A. M., Bowman, K., Cias, P., Cook, R., El-Masri, B., Hayes, D., Huang, M., Ito, A., Jain, A., King, A.W., Lei, H., Liu, J., Lu, C., Mao, J., Peng, S., Poulter, B., Ricciuto, D., Schaefer, K., Shi, X., Tao, B., Tian, H., Wang, W., Wei, Y., Yang, J., and Zeng, N.: Toward “optimal” integration of terrestrial biosphere models, Geophys. Res. Lett., 42, 4418–4428, https://doi.org/10.1002/2015GL064002, 2015.
Smith, N. G. and Dukes, J. S.: Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2, Glob. Change Biol., 19, 45–63, https://doi.org/10.1111/j.1365-2486.2012.02797.x, 2012.
Suyker, A. E., Verma, S. B., and Burba, G. G.: Interannual variability in net CO2 exchange of a native tallgrass prairie, Glob. Change Biol., 9, 255–265, https://doi.org/10.1046/j.1365-2486.2003.00567.x, 2003.
Suyker, A. E., Verma, S. B., Burba, G. G., Arkebauer, T. J., Walters, D. T., and Hubbard, K. G.: Growing season carbon dioxide exchange in irrigated and rainfed maize, Agr. Forest Meteorol., 124, 1–13, https://doi.org/10.1016/j.agrformet.2004.01.011, 2004.
Tatarinov, F. A. and Cienciala, E.: Application of BIOME-BGC model to managed forests, Forest Ecol. Manag., 237, 267–279, https://doi.org/10.1016/j.foreco.2006.09.085, 2006.
Thomas, R. Q., Bonan, G. B., and Goodale, C. L.: Insights into mechanisms governing forest carbon response to nitrogen deposition: a model–data comparison using observed responses to nitrogen addition, Biogeosciences, 10, 3869–3887, https://doi.org/10.5194/bg-10-3869-2013, 2013.
Thornton, P., Law, B., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D., Goldstein, A., Monson, R., Hollinger, D., Falk, M., Chen, J., and Sparks, J.: Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests, Agr. Forest Meteorol., 113, 185–222, https://doi.org/10.1016/S0168-1923(02)00108-9, 2002.
Thornton, P. E.: Regional ecosystem simulation: Combining surface- and satellite-based observations to study linkages between terrestrial energy and mass budgets, The University of Montana, 1998.
Thornton, P. E.: User's Guide for Biome-BGC, Version 4.1.1., available at: ftp://daac.ornl.gov/data/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/bgc_users_guide_411.pdf (last access: April 2016), 2000.
Thornton, P. E. and Rosenbloom, N. A.: Ecosystem model spin-up: Estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model, Ecol. Model., 189, 25–48, https://doi.org/10.1016/j.ecolmodel.2005.04.008, 2005.
Thornton, P. E., Lamarque, J. F., Rosenbloom, N. A., and Mahowald, N. M.: Influence of carbon-nitrogen cycle coupling on land model response to CO2 fertilization and climate variability, Global Biogeochem. Cy., 21, GB4018, https://doi.org/10.1029/2006GB002868, 2007.
Tjoelker, M. G., Oleksyn, J., and Reich, P. B.: Modelling respiration of vegetation: evidence for a general temperature-dependent Q10, Glob. Change Biol., 7, 223–230, https://doi.org/10.1046/j.1365-2486.2001.00397.x, 2001.
Trusilova, K. and Churkina, G.: The response of the terrestrial biosphere to urbanization: land cover conversion, climate, and urban pollution, Biogeosciences, 5, 1505–1515, https://doi.org/10.5194/bg-5-1505-2008, 2008.
Trusilova, K., Trembath, J., and Churkina, G.: Parameter estimation and validation of the terrestrial ecosystem model Biome-BGC using eddy-covariance flux measurements, Technical Report 16, MPI for Biogeochemistry, Jena, 2009.
Ťupek, B., Zanchi, G., Verkerk, P. J., Churkina, G., Viovy, N., Hughes, J. K., and Lindner, M.: A comparison of alternative modelling approaches to evaluate the European forest carbon fluxes, Forest Ecol. Manag., 260, 241–251, https://doi.org/10.1016/j.foreco.2010.01.045, 2010.
Turner, D. P., Ritts, W. D., Law, B. E., Cohen, W. B., Yang, Z., Hudiburg, T., Campbell, J. L., and Duane, M.: Scaling net ecosystem production and net biome production over a heterogeneous region in the western United States, Biogeosciences, 4, 597–612, https://doi.org/10.5194/bg-4-597-2007, 2007.
Ueyama, M., Ichii, K., Hirata, R., Takagi, K., Asanuma, J., Machimura, T., Nakai, Y., Ohta, T., Saigusa, N., Takahashi, Y., and Hirano, T.: Simulating carbon and water cycles of larch forests in East Asia by the BIOME-BGC model with AsiaFlux data, Biogeosciences, 7, 959–977, https://doi.org/10.5194/bg-7-959-2010, 2010.
van Bodegom, P. M., Douma, J. C., Witte, J. P. M., Ordonez, J. C., Bartholomeus, R. P., and Aerts, R.: Going beyond limitations of plant functional types when predicting global ecosystem–atmosphere fluxes: exploring the merits of traits-based approaches, Global Ecol. Biogeogr., 21, 625–636, https://doi.org/10.1111/j.1466-8238.2011.00717.x, 2012.
van der Molen, M. K., Gash, J. H. C., and Elbers, J. A.: Sonic anemometer (co)sine response and flux measurement – II. The effect of introducing an angle of attack dependent calibration, Agr. Forest Meteorol., 122, 95–109, 2004.
van der Molen, M. K., Dolman, A. J., Ciais, P., Eglin, T., Gobron, N., Law, B. E., Meir, P., Peters, W., Phillips, O. L., Reichstein, M., Chen, T., Dekker, S. C., Doubková, M., Friedl, M. A., Jung, M., , van den Hurk, B. J. J. M., de Jeu, R. A. M., Kruijt, B., Ohta, T., Rebel, K. T., Plummer, S., Seneviratne, S. I., Sitch, S., Teuling, A. J., van der Werf, G. R., and Wang, G.: Drought and ecosystem carbon cycling, Agr. Forest Meteorol., 151, 765–773, https://doi.org/10.1016/j.agrformet.2011.01.018, 2011.
Verma, S. B., Dobermann, A., Cassman, K. G., Walters, D. T., Knops, J. M. N., Arkebauer, T. J., Suyker, A. E., Burba, G., Amos, B., Yang, H., Ginting, D., Hubbard, K., Gitleson, A. A., and Walter-Shea, E. A.: Annual carbon dioxide exchange in irrigatedrainfed maize based agroecosystems, Agr. Forest Meteorol., 131, 77–96, https://doi.org/10.1016/j.agrformet.2005.05.003, 2005.
Vetter, M., Wirth, C., Böttcher, H., Churkina, G., Schulze, E.-D., Wutzler, T., and Weber, G.: Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories, Glob. Change Biol., 11, 810–827, https://doi.org/10.1111/j.1365-2486.2005.00932.x, 2005.
Vetter, M., Churkina, G., Jung, M., Reichstein, M., Zaehle, S., Bondeau, A., Chen, Y., Ciais, P., Feser, F., Freibauer, A., Geyer, R., Jones, C., Papale, D., Tenhunen, J., Tomelleri, E., Trusilova, K., Viovy, N., and Heimann, M.: Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models, Biogeosciences, 5, 561–583, https://doi.org/10.5194/bg-5-561-2008, 2008.
Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for tower and aircraft data, J. Atmos. Ocean. Tech., 14, 512–526, https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997.
Vitousek, P., Edin, L. O., Matson, P. A., Fownes, J. H., and Neff, J.: Within-system element cycles, input-output budgets, and nutrient limitations, in: Success, Limitations, and Frontiers in Ecosystem Science, edited by: Pace, M. and Groffman, P., Springer, New York, 432–451, 1998.
Vitousek, P. M., Fahey, T., Johnson, D. W., and Swift, M. J.: Element interactions in forest ecosystems: succession, allometry and input-output budgets, Biogeochemistry, 5, 7–34, https://doi.org/10.1007/BF02180316, 1988.
Wang, Q., Watanabe, M., and Ouyang, Z.: Simulation of water and carbon fluxes using BIOME-BGC model over crops in China, Agr. Forest Meteorol., 131, 209–224, https://doi.org/10.1016/j.agrformet.2005.06.002, 2005.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water-vapor transfer, Q. J. Roy. Meteor. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980.
White, A., Melvin, G. R., Cannell, A., and Friend, D.: Climate change impacts on ecosystems and the terrestrial carbon sink: a new assessment, Global Environ. Change, 9, 21–30, https://doi.org/10.1016/S0959-3780(99)00016-3, 1999.
White, M., Thornton, P. E., Running, S. W., and Nemani, R. R.: Parameterization and sensitivity analysis of the BIOME–BGC terrestrial ecosystem model: Net primary production controls, Earth Interact., 4, 1–85, https://doi.org/10.1175/1087-3562(2000)004<0003:PASAOT>2.0.CO;2, 2000.
Wilczak, J. M., Oncley, S. P., and Stage, S. A.: Sonic anemometer tilt correction algorithms, Bound.-Lay. Meteor., 99, 127–150, https://doi.org/10.1023/A:1018966204465, 2001.
Williams, J. R.: Runoff and water erosion, in Modeling plant and soil systems, Agronomy Monograph nr. 31, edited by: Hanks, R. J. and Ritchie, J. T., American Society of Agronomy, Madison, Wisconsin, USA, 439–455, 1991.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Woodrow, I. E. and Berry, J. A.: Enzymatic regulation of photosynthetic CO2 fixation in C3 plants, Annu. Rev. Plant Phys., 39, 533–594, https://doi.org/10.1146/annurev.pp.39.060188.002533, 1988.
WRB: World Reference Base for Soil Resources (WRB), World Soil Resources Reports No. 103, IX+128 pp., ISSS, ISRIC and United Nations Food and Agriculture Organization (FAO), Rome, 2006.
Yi, C., Ricciuto, D., Li, R., Wolbeck, J., Xu, X., Nilsson, M., Aires, L., Albertson, J. D., Amman, C., Arain, M. A., de Araujo, A. C., Aubinet, M., Aurela, M., Barcza, Z., Barr, A., Berbigier, P., Beringer, J., Bernhofer, C., Black, A. T., Bolstad, P. V., Bosveld, F. C., Broadmeadow, M. S. J., Buchmann, N., Burns, S. P., Cellier, P., Chen, J., Chen, J., Ciais, P., Clement, R., Cook, B. D., Curtis, P. S., Dail, D. B., Dellwik, E., Delpierre, N., Desai, A. R., Dore, S., Dragoni, D., Drake, B. G., Dufrêne, E., Dunn, A., Elbers, J., Eugster, W., Falk, M., Feigenwinter, C., Flanagan, L. B., Foken, T., Frank, J., Fuhrer, J., Gianelle, D., Golstein, A., Goulden, M., Granier, A., Grunwald, T., Gu, L., Guo, H., Hammerle, A., Han, S., Hanan, N. P., Haszpra, L., Heinesch, B., Helfter, C., Hendriks, D., Hutley, L. B., Ibrom, A., Jacobs, C., Johansson, T., Jongen, M., Katul, G., Kiely, G., Klumpp, K., Knohl, A., Kolb, T., Kutsch, W. L., Lafleur, P., Laurila, T., Leuning, R., Lindroth, A., Liu, H., Loubet, B., Manca, G., Marek, M., Margolis, H. A., Martin, T. A., Massman, W. J., Matamala, R., Matteucci, G., McCaughey, H., Merbold, L., Meyers, T., Migliavacca, M., Miglietta, F., Misson, L., Molder, M., Moncrieff, J., Monson, R. K., Montagnani, L., Montes-Helu, M., Moors, E., Moureaux, C., et al.: Climate control of terrestrial carbon exchange across biomes and continents, Environ. Res. Lett., 5, 034007, https://doi.org/10.1088/1748-9326/5/3/034007, 2010.
Zheng, D., Raymond, H., and Running, S. W.: A daily soil temperature model based on air temperature and precipitation for continental applications, Clim. Res., 2, 183–191, https://doi.org/10.3354/cr002183, 1993.
Short summary
This paper provides detailed documentation on the changes implemented in the widely used biogeochemical model Biome-BGC. The version containing all improvements is referred to as Biome-BGCMuSo (Biome-BGC with multilayer soil module). Case studies on forest, cropland, and grassland are presented to demonstrate the effect of developments on the simulation. By using Biome-BGCMuSo, it became possible to analyze the effects of different environmental conditions and human activities on the ecosystems.
This paper provides detailed documentation on the changes implemented in the widely used...
