Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5311-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5311-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
05 Nov 2020
Model evaluation paper |
| 05 Nov 2020
| 05 Nov 2020
Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Christopher P. O. Reyer
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Martin Gutsch
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Chris Kollas
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
DB Services GmbH, Elisabeth-Schwarzhaupt-Platz 1, 10115 Berlin, Germany
Franz-Werner Badeck
Research Centre for Genomics and Bioinformatics, Council for Agricultural Research and Economics, via S. Protaso, 302, 29017 Fiorenzuola d'Arda PC, Italy
Harald K. M. Bugmann
Forest Ecology, Department of Environmental Systems Science, ETH Zürich, Universitätstrasse 16, 8092 Zürich, Switzerland
Rüdiger Grote
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Cornelia Fürstenau
Friedrich-Schiller-Universität Jena, Institut für Informatik, Heinz Nixdorf Chair for Distributed Information Systems, Ernst-Abbe-Platz 1–4, 07743 Jena, Germany
Marcus Lindner
European Forest Institute, Resilience Programme, Platz der Vereinten Nationen 7, 53113 Bonn, Germany
Jörg Schaber
EXCO GmbH, Adam-Opel-Str. 9–11, 67227 Frankenthal, Germany
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Jaber Rahimi, Expedit Evariste Ago, Augustine Ayantunde, Sina Berger, Jan Bogaert, Klaus Butterbach-Bahl, Bernard Cappelaere, Jean-Martial Cohard, Jérôme Demarty, Abdoul Aziz Diouf, Ulrike Falk, Edwin Haas, Pierre Hiernaux, David Kraus, Olivier Roupsard, Clemens Scheer, Amit Kumar Srivastava, Torbern Tagesson, and Rüdiger Grote
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Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
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Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
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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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Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
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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.
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.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
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Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
Short summary
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Cited articles
Anderegg, W. R. L., Martinez-Vilalta, J., Cailleret, M., Camarero, J. J., Ewers, B. E., Galbraith, D., Gessler, A., Grote, R., Huang, C.-y., Levick, S. R., Powell, T. L., Rowland, L., Sánchez-Salguero, R., and Trotsiuk, V.:
When a Tree Dies in the Forest: Scaling Climate-Driven Tree Mortality to Ecosystem Water and Carbon Fluxes,
Ecosystems,
19, 1133–1147, https://doi.org/10.1007/s10021-016-9982-1, 2016.
Badeck, F. W., Beese, F., Berthold, D., Einert, P., Jochheim, H., Kallweit, R., Konopatzky, A., Lasch, P., Meesenburg, H., Meiwes, K.-J., Puhlmann, M., Raspe, S., Schulte-Bisping, H., Schulz, C., and Suckow, F.:
Parametrisierung, Kalibrierung und Validierung von Modellen des Kohlenstoffumsatzes in Waldökosystemen und deren Böden,
Bayerische Landesanstalt für Wald und Forstwirtschaft (LWF), Institut für Bodenkunde und Waldernährung der Universität Göttingen (IBW), Landesforstanstalt Eberswalde (LFE), Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Nordwestdeutsche Forstliche Versuchsanstalt (NW-FVA), Potsdam-Institut für Klimafolgenforschung (PIK), 110, 2007.
Baldocchi, D., Chu, H., and Reichstein, M.:
Inter-annual variability of net and gross ecosystem carbon fluxes: A review,
Agr. Forest Meteorol.,
249, 520–533, https://doi.org/10.1016/j.agrformet.2017.05.015, 2018.
Berninger, F., Coll, L., Vanninen, P., Mäkelä, A., Palmroth, S., and Nikinmaa, E.:
Effects of tree size and position on pipe model ratios in Scots pine,
Can. J. Forest Res.,
35, 1294–1304, https://doi.org/10.1139/X05-055, 2005.
Borys, A., Lasch, P., Suckow, F., and Reyer, C.:
Kohlenstoffspeicherung in Buchenbeständen in Abhängigkeit von Waldpflege und Klimawandel,
Allg. Forst Jagdztg.,
184, 26–35, 2013.
Borys, A., Suckow, F., Reyer, C., Gutsch, M., and Lasch-Born, P.:
The impact of climate change under different thinning regimes on carbon sequestration in a German forest district,
Mitig. Adapt. Strat. Gl.,
21, 861–881, https://doi.org/10.1007/s11027-014-9628-6, 2016.
Botkin, D.:
Forest Dynamics: An Ecological Model,
Oxford University Press, Oxford & New York, 309 pp., 1993.
Bugmann, H., Grote, R., Lasch, P., Lindner, M., and Suckow, F.:
A new forest gap model to study the effects of environmental change on forest structure and functioning,
in: Impacts of Global Change of Tree Physiology and Forest Ecosystem,
Proceedings of the International Conference on Impacts of Global Change on Tree Physiology and Forest Ecosystems, held 26–29 November 1996, Wageningen,
edited by: Mohren, G. M. J., Kramer, K., and Sabate, S.,
Forestry Science, Kluwer Academic Publisher, Dordrecht, 255–261, 1997.
Bugmann, H. K. M.:
A Simplified Forest Model to Study Species Composition Along Climate Gradients,
Ecology,
77, 2055–2074, https://doi.org/10.2307/2265700, 1996.
Bugmann, H., Seidl, R., Hartig, F., Bohn, F., Brůna, J., Cailleret, M., François, L., Heinke, J., Henrot, A.-J., Hickler, T., Hülsmann, L., Huth, A., Jacquemin, I., Kollas, C., Lasch-Born, P., Lexer, M. J., Merganič, J., Merganičová, K., Mette, T., Miranda, B. R., Nadal-Sala, D., Rammer, W., Rammig, A., Reineking, B., Roedig, E., Sabaté, S., Steinkamp, J., Suckow, F., Vacchiano, G., Wild, J., Xu, C., and Reyer, C. P. O.: Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale, Ecosphere, 10, e02616, https://doi.org/10.1002/ecs2.2616, 2019.
Cameron, D. R., Van Oijen, M., Werner, C., Butterbach-Bahl, K., Grote, R., Haas, E., Heuvelink, G. B. M., Kiese, R., Kros, J., Kuhnert, M., Leip, A., Reinds, G. J., Reuter, H. I., Schelhaas, M. J., De Vries, W., and Yeluripati, J.: Environmental change impacts on the C- and N-cycle of European forests: a model comparison study, Biogeosciences, 10, 1751–1773, https://doi.org/10.5194/bg-10-1751-2013, 2013.
Cannell, M. G. R. and Smith, R.:
Thermal time, chill days and prediction of budburst in Picea sitchensis,
J. Appl. Ecol.,
20, 951–963, 1983.
Coelho, M. T. P., Diniz, J. A., and Rangel, T. F.:
A parsimonious view of the parsimony principle in ecology and evolution,
Ecography,
42, 968–976, https://doi.org/10.1111/ecog.04228, 2019.
Collalti, A., Marconi, S., Ibrom, A., Trotta, C., Anav, A., D'Andrea, E., Matteucci, G., Montagnani, L., Gielen, B., Mammarella, I., Grünwald, T., Knohl, A., Berninger, F., Zhao, Y., Valentini, R., and Santini, M.: Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for 10 European forest sites, Geosci. Model Dev., 9, 479–504, https://doi.org/10.5194/gmd-9-479-2016, 2016.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.:
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.
Constable, J. V. H. and Friend, A. L.:
Suitability of process-based tree growth models for addressing tree response to climate change,
Environ. Pollut.,
110, 47–59, https://doi.org/10.1016/S0269-7491(99)00289-4, 2000.
Davidson, R. L.:
Effect of root/leaf temperature differentials on root∕shoot ratios in some pasture grasses and clover,
Ann. Bot.,
33, 561–569, https://doi.org/10.1093/oxfordjournals.aob.a084308, 1969.
Dietze, M. C. and Matthes, J. H.:
A general ecophysiological framework for modelling the impact of pests and pathogens on forest ecosystems,
Ecol. Lett.,
17, 1418–1426, https://doi.org/10.1111/ele.12345, 2014.
DVWK:
Ermittlung der Verdunstung von Land- und Wasserflächen, DVWK-Merkblätter zur Wasserwirtschaft,
edited by: Deutscher Verband für Wasserwirtschaft und Kulturbau e.V.,
Wirtschafts- und Verlagsgesellschaft Gas und Wasser mbH Bonn, Bonn, 134 pp., 1996.
Dyck, S. and Peschke, G.:
Grundlagen der Hydrologie, 3 edn.,
Verlag für Bauwesen GmbH, Berlin, 536 pp., 1995.
Eggers, T.:
The impacts of manufacturing and utilization of wood products on the European carbon budget,
European Forest Institute, Joensuu, Internal report 9, 90 pp., 2002.
Ellenberg, M., Mayer, R., and Schauermann, J. (eds.):
Ökosystemforschung, Ergebnisse des Sollingprojekts, 1966–1986,
Ulmer Eugen Verlag, 1991.
Farquhar, G. D., Caemmerer, S. V., and Berry, J. A.:
A Biochemical-Model of Photosynthetic CO2 Assimilation in Leaves of C-3 Species,
Planta,
149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence, D., and Bonan, G.: Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015.
Fontes, L., Bontemps, J.-D., Bugmann, H., van Oijen, M., Gracia, C. A., Kramer, K., Lindner, M., Rötzer, T., and Skovsgaard, J. P.:
Models for supporting forest management in a changing environment,
For. Syst.,
19, 8–9, 2010.
Forrester, D. I.:
A stand-level light interception model for horizontally and vertically heterogeneous canopies,
Ecol. Model.,
276, 14–22, https://doi.org/10.1016/j.ecolmodel.2013.12.021, 2014.
Franko, U.:
C- und N-Dynamik beim Umsatz organischer Substanz im Boden,
Akademie der Landwirtschaftswissenschaften der DDR, Berlin, 1990.
Fürstenau, C., Badeck, F., Lasch, P., Lexer, M., Lindner, M., Mohr, P., and Suckow, F.:
Multiple-use forest management in consideration of climate change and the interests of stakeholder groups,
Eur. J. For. Res.,
126, 225–239, https://doi.org/10.1007/s10342-006-0114-x, 2007.
Gerold, D.:
Modellierung des Wachstums von Waldbeständen auf der Basis der Durchmesserstruktur,
Sektion Forstwirtsch. Tharandt, Technische Universität Dresden, Dresden, 174 pp., 1990.
Glugla, G.:
Berechnungsverfahren zur Ermittlung des aktuellen Wassergehaltes und Gravitationswasserabflusses im Boden,
Albrecht-Thaer-Archiv,
13, 371–376, 1969.
Granier, A., Reichstein, M., Breda, N., Janssens, I. A., Falge, E., Ciais, P., Grunwald, T., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Facini, O., Grassi, G., Heinesch, B., Ilvesniemi, H., Keronen, P., Knohl, A., Kostner, B., Lagergren, F., Lindroth, A., Longdoz, B., Loustau, D., Mateus, J., Montagnani, L., Nys, C., Moors, E., Papale, D., Peiffer, M., Pilegaard, K., Pita, G., Pumpanen, J., Rambal, S., Rebmann, C., Rodrigues, A., Seufert, G., Tenhunen, J., Vesala, T., and Wang, Q.:
Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003,
Agr. Forest Meteorol.,
143, 123–145, https://doi.org/10.1016/j.agrformet.2006.12.004, 2007.
Grote, R. and Suckow, F.:
Integrating dynamic morphological properties into forest growth modeling. I. Effects on water balance and gas exchange,
Forest. Ecol. Manag.,
112, 101–119, https://doi.org/10.1016/S0378-1127(98)00329-6, 1998.
Grote, R., Suckow, F., and Bellmann, K.:
Modelling of carbon-, nitrogen-, and water balances in pine stands under changing air pollution and deposition,
in: Changes of Atmospheric Chemistry and Effects on Forest Ecosystems. A Roof Experiment Without Roof,
edited by: Hüttl, R. F. and Bellmann, K.,
Nutrients in Ecosystems, Kluwer, Dordrecht, 251–281, 1998.
Gutsch, M., Lasch-Born, P., Lüttger, A. B., Suckow, F., Murawski, A., and Pilz, T.:
Uncertainty of biomass contributions from agriculture and forestry to renewable energy resources under climate change,
Meteorol. Z.,
24, 1–11, https://doi.org/10.1127/metz/2015/0532, 2015a.
Gutsch, M., Lasch-Born, P., Suckow, F., and Reyer, C.:
Modeling of Two Different Water Uptake Approaches for Mono- and Mixed-Species Forest Stands,
Forests,
6, 2125–2147, https://doi.org/10.3390/f6062125, 2015b.
Gutsch, M., Lasch-Born, P., Suckow, F., and Reyer, C. P. O.:
Evaluating the productivity of four main tree species in Germany under climate change with static reduced models,
Ann. For. Sci.,
73, 401–410, https://doi.org/10.1007/s13595-015-0532-3, 2016.
Gutsch, M., Lasch-Born, P., Kollas, C., Suckow, F., and Reyer, C. O. P.:
Balancing trade-offs between ecosystem services in Germany's forests under climate change,
Environ. Res. Lett.,
13, 045012, https://doi.org/10.1088/1748-9326/aab4e5, 2018.
Haataja, J. and Vesala, T. (eds.):
SMEAR II. Station for measuring forest ecosystem–atmosphere relation,
University of Helsinki, Department of Forest Ecology, Helsinki, 1997.
Hauskeller-Bullerjahn, K.:
Wachstum junger Eichen unter Schirm,
Berichte des Forschungszentrums Waldökosysteme, Reihe A Bd. 147,
Forschungszentrum Waldökosysteme der Universität Göttingen, Göttingen, 1997.
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.
Haxeltine, A. and Prentice, I. C.:
BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability and competition among plant functional types,
Global Biogeochem. Cy.,
10, 693–709, https://doi.org/10.1029/96GB02344, 1996a.
Haxeltine, A. and Prentice, I. C.:
A general model for the light-use efficiency of primary production,
Funct. Ecol.,
10, 551–561, https://doi.org/10.2307/2390165, 1996b.
Heide, O. M.:
Dormancy release in beech buds (Fagus sylvatica) requires both chilling and long days,
Physiol. Plantarum,
89, 187–191, https://doi.org/10.1111/j.1399-3054.1993.tb01804.x, 1993a.
Heide, O. M.:
Daylength and thermal time responses of budburst during dormancy release in some northern deciduous trees,
Physiol. Plantarum,
88, 531–540, https://doi.org/10.1111/j.1399-3054.1993.tb01368.x, 1993b.
Hoch, G., Richter, A., and Körnner, C.:
Non-structural carbon compounds in temperate forest trees,
Plant Cell Environ.,
26, 1067–1081, https://doi.org/10.1046/j.0016-8025.2003.01032.x, 2003.
Horemans, J., A., Henrot, A., Delire, C., Kollas, C., Lasch-Born, P., Reyer, C., Suckow, F., François, L., and Ceulemans, R.:
Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands,
Central European Forestry Journal,
63, 153–172, https://doi.org/10.1515/forj-2017-0025, 2017.
Ibrom, A.:
Die biophysikalische Steuerung der Kohlenstoffbilanz in einem Fichtenbestand im Solling,
Habilitationsschrift, Berichte des Forschungszentrums Waldökosysteme der Universität Göttingen, Reihe A, 236 Seiten, 2001.
Jansson, P.-E.:
Simulation model for soil water and heat conditions. Description of the SOIL model, Report,
Swedish University of Agricultural Sciences, Department of Soil Sciences, Division of Agricultural Hydrotechnics, Uppsala, 1991.
Johnson, I. R. and Thornley, J. H. M.:
Temperature dependance of plant and crop processes,
Ann. Bot.-London,
55, 7–24, 1985.
Karjalainen, T., Kellomäki S., and Pussinen A.:
Role of wood-based products in absorbing atmospheric carbon,
Silva Fenn.,
28, 67–80, 1994.
Kartschall, T., Döring, P., and Suckow, F.:
Simulation of Nitrogen, Water and Temperature Dynamics in Soil,
Syst. Anal. Model. Sim.,
7, 33–40, 1990.
Keane, R. E., Morgan, P., and Running, S. W.:
FIRE-BGC – A mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains,
Research Paper INT-RP-484,
United States Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT, 1996.
Keenan, T. F., Baker, I., Barr, A., Ciais, P., Davis, K., Dietze, M., Dragoni, D., Gough, C. M., Grant, R., Hollinger, D., Hufkens, K., Poulter, B., McCaughey, H., Raczka, B., Ryu, Y., Schaefer, K., Tian, H., Verbeeck, H., Zhao, M., and Richardson, A. D.:
Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange,
Glob. Change Biol.,
18, 1971–1987, https://doi.org/10.1111/j.1365-2486.2012.02678.x, 2012.
Kingston, D. G., Todd, M. C., Taylor, R. G., Thompson, J. R., and Arnell, N. W.:
Uncertainty in the estimation of potential evapotranspiration under climate change,
Geophys. Res. Lett.,
36, L20403, https://doi.org/10.1029/2009GL040267, 2009.
Kint, V., Lasch, P., Lindner, M., and Muys, B.:
Multipurpose conversion management of Scots pine towards mixed oak-birch stands – A long-term simulation approach,
Forest. Ecol. Manag.,
257, 199–214, https://doi.org/10.1016/j.foreco.2008.08.031, 2009.
Koitzsch, R.:
Schätzung der Bodenfeuchte aus meteorologischen Daten, Boden- und Pflanzenparametern mit einem Mehrschichtmodell,
Z. Meteorol.,
27, 302–306, 1977.
Koitzsch, R. and Günther, R.:
Modell zur ganzjährigen Simulation der Verdunstung und der Bodenfeuchte landwirtschaftlicher Nutzflächen mit und ohne Bewuchs,
Arch. Acker Pfl. Boden.,
34, 803–810, 1990.
Kollas, C., Gutsch, M., Hommel, R., Lasch-Born, P., and Suckow, F.:
Mistletoe-induced growth reductions at the forest stand scale,
Tree Physiol.,
38, 1–10, https://doi.org/10.1093/treephys/tpx150, 2018.
Kramer, K.:
Selecting a model to predict the onset of growth of Fagus sylvatica,
J. Appl. Ecol.,
31, 172–181, 1994.
Kramer, K., Leinonen, I., Bartelink, H. H., Berbigier, P., Borghetti, M., Bernhofer, C., Cienciala, E., Dolman, A. J., Froer, O., Gracia, C. A., Granier, A., Grunwald, T., Hari, P., Jans, W., Kellomaki, S., Loustau, D., Magnani, F., Markkanen, T., Matteucci, G., Mohren, G. M. J., Moors, E., Nissinen, A., Peltola, H., Sabate, S., Sanchez, A., Sontag, M., Valentini, R., and Vesala, T.:
Evaluation of six process-based forest growth models using eddy-covariance measurements of CO2 and H2O fluxes at six forest sites in Europe,
Glob. Change Biol.,
8, 213–230, https://doi.org/10.1046/j.1365-2486.2002.00471.x, 2002.
Lagergren, F., Lindroth, A., Dellwik, E., Ibrom, A., Lankreijer, H., Launiainen, S., MÖLder, M., Kolari, P., Pilegaard, K. I. M., and Vesala, T.:
Biophysical controls on CO2 fluxes of three Northern forests based on long-term eddy covariance data,
Tellus B,
60, 143–152, https://doi.org/10.1111/j.1600-0889.2006.00324.x, 2008.
Landsberg, J.:
Modelling forest ecosystems: state of the art, challenges, and future directions,
Can. J. Forest Res.,
33, 385–397, 2003.
Landsberg, J. J. and Waring, R. H.:
A Generalised Model of Forest Productivity Using Simplified Concepts of Radiation-Use Efficiency, Carbon Balance and Partitioning,
Forest Ecol. Manag.,
95, 209–228, https://doi.org/10.1016/S0378-1127(97)00026-1, 1997.
Lasch, P., Badeck, F.-W., Lindner, M., and Suckow, F.:
Sensitivity of simulated forest growth to changes in climate and atmospheric CO2,
Forstwiss. Centralbl.,
121, Supplement 1, 155–171, 2002.
Lasch, P., Badeck, F. W., Suckow, F., Lindner, M., and Mohr, P.:
Model-based analysis of management alternatives at stand and regional level in Brandenburg (Germany),
Forest. Ecol. Manag.,
207, 59–74, https://doi.org/10.1016/j.foreco.2004.10.034, 2005.
Lasch, P., Suckow, F., and Badeck, F.-W.:
Analyses of forest ecosystems' response to climate change at level II monitoring sites,
in: Symposium: Forests in a Changing Environment – Results of 20 years ICP Forests Monitoring Göttingen, 25.-28.10.2006,
edited by: Eichhorn, J.,
Schriften aus der Forstlichen Fakultät der Universität Göttingen und der Nordwestdeutschen Forstlichen Versuchsanstalt,
J.D. Sauerländer's Verlag Frankfurt am Main, Göttingen, 136–141, 2007.
Lasch, P., Kollas, C., Rock, J., and Suckow, F.:
Potentials and impacts of short-rotation coppice plantation with aspen in Eastern Germany under conditions of climate change,
Reg. Environ. Change,
10, 83–94, https://doi.org/10.1007/s10113-009-0095-7, 2010.
Lasch-Born, P., Suckow, F., Gutsch, M., Reyer, C., Hauf, Y., Murawski, A., and Pilz, T.:
Forests under climate change: potential risks and opportunities,
Meteorol. Z.,
24, 157–172, https://doi.org/10.1127/metz/2014/0526, 2015.
Lasch-Born, P., Suckow, F., Badeck, F.-W., Schaber, J., Bugmann, H., Fürstenau, C., Gutsch, M., Kollas, C., and Reyer, C. P. O.:
4C model description,
PIK, Potsdam, 133, https://doi.org/10.2312/pik.2018.006, 2018.
Lasch-Born, P., Suckow, F., Gutsch, M., Kollas, C., Badeck, F.-W., Bugmann, H., Grote, R., Fürstenau, C., Schaber, J., Lindner, M., and Reyer, C.:
FORESEE – 4C. V. 2.2. ,
GFZ Data Services, Potsdam, https://doi.org/10.5880/PIK.2019.015, 2019.
Launiainen, S.:
Canopy processes, fluxes and microclimate in a pine forest,
PhD, Department of Physics, University of Helsinki, Helsinki, 55 pp., 2011.
Lexer, M. J. and Hönninger, K.:
A modified 3D-patch model for spatially explicit simulation of vegetation composition in heterogeneous landscapes,
Forest. Ecol. Manag.,
144, 43–65, https://doi.org/10.1016/S0378-1127(00)00386-8, 2001.
Lindner, M.:
Developing adaptive forest management strategies to cope with climate change,
Tree Physiol.,
20, 299–307, https://doi.org/10.1093/treephys/20.5-6.299, 2000.
Lindner, M., Lasch, P., Badeck, F.-W., Beguiristain, P. P., Junge, S., Kellomäki, S., Peltola, H., Gracia, C., Sabate, S., Jäger, D., Lexer, M., and Freeman, M.:
Chapter 4: SilviStrat Model Evaluation Exercises,
in: Management of European Forests under Changing Climatic Conditions,
edited by: Kellomäki, S. and Leinonen, S.,
University of Joensuu, Faculty of Forerstry, Joensuu, 117–157, 2005.
Loague, K. and Green, R. E.:
Statistical and graphical methods for evaluating solute transport models: Overview and application,
J. Contam. Hydrol.,
7, 51, https://doi.org/10.1016/0169-7722(91)90038-3, 1991.
Lüttschwager, D., Rust, S., Wulf, M., Forkert, J., and Hüttl, R. F.:
Tree canopy and herb layer transpiration in three Scots pine stands with different stand structures,
Ann. For. Sci.,
56, 265–274, 1999.
Mäkelä, A.:
Modeling structural-functional relationships in whole-tree growth: resource allocation,
in: Process modeling of forest growth responses to environmental stress,
edited by: Dixon, R. K., Meldahl, R. S., Ruark, G. A., and Warren, W. G.,
Timber Press, Portland, Oregon, 81–95, 1990.
Mäkelä, A., Landsberg, J., Ek, A. R., Burk, T. E., Ter-Mikaelian, M., Agren, G. I., Oliver, C. D., and Puttonen, P.:
Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation,
Tree Physiol.,
20, 289–298, https://doi.org/10.1093/treephys/20.5-6.289, 2000a.
Mäkelä, A., Sievänen, R., Lindner, M., and Lasch, P.:
Application of volume growth and survival graphs in the evaluation of four process-based forest growth models,
Tree Physiol.,
20, 347–355, https://doi.org/10.1093/treephys/20.5-6.347, 2000b.
Manusch, C., Bugmann, H., Heiri, C., and Wolf, A.:
Tree mortality in dynamic vegetation models – A key feature for accurately simulating forest properties,
Ecol. Model.,
243, 101–111, https://doi.org/10.1016/j.ecolmodel.2012.06.008, 2012.
Marconi, S., Chiti, T., Nole, A., Valentini, R., and Collalti, A.:
The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model,
Forests,
8, 220, https://doi.org/10.3390/f8060220, 2017.
Mayer, D. G. and Butler, D. G.:
Statistical Validation,
Ecol. Model.,
68, 21–32, https://doi.org/10.1016/0304-3800(93)90105-2, 1993.
Medlyn, B. E., Berbigier, P., Clement, R., Grelle, A., Loustau, D., Linder, S., Wingate, L., Jarvis, P. G., Sigurdsson, B. D., and McMurtrie, R. E.:
Carbon balance of coniferous forests growing in contrasting climates: Model-based analysis,
Agr. Forest Meteorol.,
131, 97–124, https://doi.org/10.1016/j.agrformet.2005.05.004, 2005a.
Medlyn, B. E., Robinson, A. P., Clement, R., and McMurtrie, R. E.:
On the validation of models of forest CO2 exchange using eddy covariance data: some perils and pitfalls,
Tree Physiol.,
25, 839–857, https://doi.org/10.1093/treephys/25.7.839, 2005b.
Medlyn, B. E., Duursma, R. A., and Zeppel, M. J. B.:
Forest productivity under climate change: a checklist for evaluating model studies,
WIREs Clim. Change,
2, 332–355, https://doi.org/10.1002/wcc.108, 2011.
Menzel, A.:
Phänologie von Waldbäumen unter sich ändernden Klimabedingungen – Auswertung der Beobachtungen in den Internationalen Phänologischen Gärten und Möglichkeiten der Modellierung von Phänodaten,
Forstliche Forschungsberichte, Universität München, München, 150 pp., 1997.
Minunno, F., Peltoniemi, M., Launiainen, S., Aurela, M., Lindroth, A., Lohila, A., Mammarella, I., Minkkinen, K., and Mäkelä, A.:
Calibration and validation of a semi-empirical flux ecosystem model for coniferous forests in the Boreal region,
Ecol. Model.,
341, 37–52, https://doi.org/10.1016/j.ecolmodel.2016.09.020, 2016.
Molina-Herrera, S., Grote, R., Santabárbara-Ruiz, I., Kraus, D., Klatt, S., Haas, E., Kiese, R., and Butterbach-Bahl, K.:
Simulation of CO2 Fluxes in European Forest Ecosystems with the Coupled Soil-Vegetation Process Model “LandscapeDNDC”,
Forests,
6, 1779–1809, https://doi.org/10.3390/f6061779, 2015.
Monsi, M. and Saeki, T.:
On the Factor Light in Plant Communities and its Importance for Matter Production,
Ann. Bot.-London,
95, 549–567, https://doi.org/10.1093/aob/mci052, 2005.
Monteith, J. L. and Unsworth, M. H.:
Principles of environmental physics, second edn.,
Edward Arnold, London, 1990.
Naudts, K., Ryder, J., McGrath, M. J., Otto, J., Chen, Y., Valade, A., Bellasen, V., Berhongaray, G., Bönisch, G., Campioli, M., Ghattas, J., De Groote, T., Haverd, V., Kattge, J., MacBean, N., Maignan, F., Merilä, P., Penuelas, J., Peylin, P., Pinty, B., Pretzsch, H., Schulze, E. D., Solyga, D., Vuichard, N., Yan, Y., and Luyssaert, S.: A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes, Geosci. Model Dev., 8, 2035–2065, https://doi.org/10.5194/gmd-8-2035-2015, 2015.
Neumann, M., Mues, V., Moreno, A., Hasenauer, H., and Seidl, R.:
Climate variability drives recent tree mortality in Europe,
Glob. Change Biol.,
23, 4788–4797, https://doi.org/10.1111/gcb.13724, 2017.
Nitsch, J. P.:
Photoperiodism in woody plants,
P. Am. Soc. Hortic. Sci.,
79, 526–544, 1957.
Peltoniemi, M., Pulkkinen, M., Aurela, M., Pumpanen, J., Kolari, P., and Makela, A.:
A semi-empirical model of boreal-forest gross primary production, evapotranspiration, and soil water – calibration and sensitivity analysis,
Boreal Environ. Res.,
20, 151–171, 2015.
Perry, T. O.:
Dormancy of trees in winter,
Science,
171, 29–36, https://doi.org/10.1126/science.171.3966.29, 1971.
Pilegaard, K., Hummelshoj, P., Jensen, N. O., and Chen, Z.:
Two years of continuous CO2 eddy-flux measurements over a Danish beech forest,
Agr. Forest Meteorol.,
107, 29–41, https://doi.org/10.1016/s0168-1923(00)00227-6, 2001.
Pilegaard, K., Ibrom, A., Courtney, M. S., Hummelshoj, P., and Jensen, N. O.:
Increasing net CO2 uptake by a Danish beech forest during the period from 1996 to 2009,
Agr. Forest Meteorol.,
151, 934–946, https://doi.org/10.1016/j.agrformet.2011.02.013, 2011.
Porte, A. and Bartelink, H. H.:
Modelling mixed forest growth: a review of models for forest management,
Ecol. Model.,
150, 141–188, https://doi.org/10.1016/S0304-3800(01)00476-8, 2002.
Post, J., Krysanova, V., Suckow, F., Mirschel, W., Rogasik, J., and Merbach, I.:
Integrated ecohydrological modelling of soil organic matter dynamics for the assessment of environmental change impacts in meso- to macro-scale river basins,
Ecol. Model.,
206, 93–109, https://doi.org/10.1016/j.ecolmodel.2007.03.028, 2007.
Pretzsch, H.:
Forest Dynamics, Growth and Yield,
Springer Berlin, Germany, 664 pp., 2010.
Pretzsch, H., Grote, R., Reineking, B., Rotzer, T., and Seifert, S.:
Models for forest ecosystem management: A European perspective,
Ann. Bot.-London,
101, 1065–1087, https://doi.org/10.1093/aob/mcm246, 2008.
Priestley, C. H. B. and Taylor, R. J.:
On the assessment of surface heat flux and evaporation using large-scale parameters,
Mon. Weather Rev.,
100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972.
Reyer, C.:
Forest Productivity Under Environmental Change – a Review of Stand-Scale Modeling Studies,
Current Forestry Reports,
1, 53–68, https://doi.org/10.1007/s40725-015-0009-5, 2015.
Reyer, C., Lasch, P., Mohren, G. M. J., and Sterck, F. J.:
Inter-specific competition in mixed forests of Douglas-fir (Pseudotsuga menziesii) and common beech (Fagus sylvatica) under climate change – a model-based analysis,
Ann. For. Sci.,
67, 805, https://doi.org/10.1051/forest/2010041, 2010.
Reyer, C., Lasch-Born, P., Suckow, F., Gutsch, M., Murawski, A., and Pilz, T.:
Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide,
Ann. For. Sci.,
71, 211–225, https://doi.org/10.1007/s13595-013-0306-8, 2014.
Reyer, C., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Matteucci, G., Collalti, A., D'Andrea, E., Krupkova, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios, A., Cicuendez, V., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J., Martel, S., Bohn, F., Steinkamp, J., Suckow, F., Weedon, G., Sheffield, J., Chikalanov, A., and Frieler, K.:
The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests V. 0.1.12,
GFZ Data Services, https://doi.org/10.5880/PIK.2019.008, 2019.
Reyer, C. P. O., Flechsig, M., Lasch-Born, P., and van Oijen, M.:
Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity,
Climatic Change,
137, 395–409, https://doi.org/10.1007/s10584-016-1694-1, 2016.
Reyer, C. P. O., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T. G. M., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Collalti, A., Trotta, C., Matteucci, G., D'Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J. A., Bohn, F., Steinkamp, J., Chikalanov, A., Weedon, G. P., Sheffield, J., Babst, F., Vega del Valle, I., Suckow, F., Martel, S., Mahnken, M., Gutsch, M., and Frieler, K.: The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests, Earth Syst. Sci. Data, 12, 1295–1320, https://doi.org/10.5194/essd-12-1295-2020, 2020.
Robinson, A. P., Duursma, R. A., and Marshall, J. D.:
A regression-based equivalence test for model validation: shifting the burden of proof,
Tree Physiol.,
25, 903–913, https://doi.org/10.1093/treephys/25.7.903, 2005.
Russ, A. and Riek, W.:
Pedotransferfunktionen zur Ableitung der nutzbaren Feldkapazität – Validierung für Waldböden des nordostdeutschen Tieflands,
Waldökologie, Landschaftsforschung und Naturschutz,
5–17, 2011.
Schaber, J.:
Phenology in Germany in the 20th century: methods, analyses and models,
Math.-Nat. Fakultät, Universität Potsdam, Potsdam, 164 pp., 2002.
Schaber, J. and Badeck, F.-W.:
Physiology based phenology models for forest tree species in Germany,
Int. J. Biometeorol.,
47, 193–201, https://doi.org/10.1007/s00484-003-0171-5, 2003.
Schall, P.:
Ein Ansatz zur Modellierung der Naturverjüngungsprozesse im Bergmischwald der östlichen bayrischen Alpen,
Forschungszentrum Waldökosysteme, Göttingen, Reihe A Bd 155, 1998.
Seidl, R., Rammer, W., Lasch, P., Badeck, F. W., and M.J., L.:
Does conversion of even-aged, secondary coniferous forest affect carbon sequestration? A simulation study under changing environmental conditions,
Silva Fenn.,
42, 369–386, https://doi.org/10.14214/sf.243, 2008.
Seidl, R., Rammer, W., Scheller, R. M., and Spies, T. A.:
An individual-based process model to simulate landscape-scale forest ecosystem dynamics,
Ecol. Model.,
231, 87–100, https://doi.org/10.1016/j.ecolmodel.2012.02.015, 2012.
Shinozaki, K., Yoda, K., Hozumi, K., and Kira, T.:
A quantitative analysis of plant form – the pipe model theory. I. Basic analysis,
Jap. J. Ecology,
14, 97–105, 1964.
Smith, P., Smith, J. U., Powlson, D. S., McGill, W. B., Arah, J. R. M., Chertov, O. G., Coleman, K., Franko, U., Frolking, S., Jenkinson, D. S., Jensen, L. S., Kelly, R. H., Klein-Gunnewiek, H., Komarov, A. S., Li, C., Molina, J. A. E., Mueller, T., Parton, W. J., Thornley, J. H. M., and Whitmore, A. P.:
A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments,
Geoderma,
81, 153–225, https://doi.org/10.1016/S0016-7061(97)00087-6, 1997.
Suckow, F.:
A Model Serving The Calculation Of Soil Temperatures,
Z. Meteorol.,
35, 66–70, 1985.
Suckow, F.: Ein
Modell zur Berechnung der Bodentemperatur unter Brache und unter Pflanzenbestand,
Akademie der Landwirtschaftswissenschaften der DDR, Berlin, 1986.
Suckow, F., Badeck, F.-W., Lasch, P., and Schaber, J.:
Nutzung von Level-II-Beobachtungen für Test und Anwendungen des Sukzessionsmodells FORESEE,
Beiträge für Forstwirtschaft und Landschaftsökologie,
35, 84–87, 2001.
Suckow, F., Lasch-Born, P., Gerstengarbe, F.-W., Werner, P., and Reyer, C. P. O.:
Climate change impacts on a pine stand in Central Siberia,
Reg. Environ. Change,
16, 1671–1683, https://doi.org/10.1007/s10113-015-0915-x, 2016.
van't Hoff, J. H.:
Etudes de dynamique chimique,
Muller, Amsterdam, 214 pp., 1884.
Van Hees, A. F. M.:
Growth and Morphology of Pedunculate Oak (Quercus robur L.) and Beech (Fagus sylvatica L.) Seedlings in Relation to Shading and Drought,
Ann. Sci. Forest.,
54, 9–18, https://doi.org/10.1051/forest:19970102, 1997.
van Oijen, M., Reyer, C., Bohn, F. J., Cameron, D. R., Deckmyn, G., Flechsig, M., Härkönen, S., Hartig, F., Huth, A., Kiviste, A., Lasch, P., Mäkelä, A., Mette, T., Minunno, F., and Rammer, W.:
Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe,
Forest. Ecol. Manag.,
289, 255–268, https://doi.org/10.1016/j.foreco.2012.09.043, 2013.
Vegis, A.:
Dependence of growth processes on temperature,
in: Temperature and life,
edited by: Precht, H., Christophersen, J., H.Hensel, and Larcher, W.,
Springer-Verlag, Berlin, 145–169, 1973.
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.
Wang, J. Y.:
A critique of the heat unit approach to plant response studies,
Ecology,
41, 785–790, 1960.
Wareing, P. F.:
Photoperiodism in woody plants,
Annu. Rev. Plant Phys.,
7, 191–214, https://doi.org/10.1146/annurev.pp.07.060156.001203, 1956.
Waring, R. H., Landsberg, J. J., and Williams, M.:
Net Primary Production of Forests – a Constant Fraction of Gross Primary Production?,
Tree Physiol.,
18, 129–134, https://doi.org/10.1093/treephys/18.2.129, 1998.
Wösten, J. H. M., Pachepsky, Y. A., and Rawls, W. J.:
Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics,
J. Hydrol., 251, 123–150, https://doi.org/10.1016/S0022-1694(01)00464-4, 2001.
Wu, S. H., Jansson, P.-E., and Kolari, P.:
Modeling seasonal course of carbon fluxes and evapotranspiration in response to low temperature and moisture in a boreal Scots pine ecosystem,
Ecol. Model.,
222, 3103–3119, https://doi.org/10.1016/j.ecolmodel.2011.05.023, 2011.
Wu, S. H., Jansson, P.-E., and Kolari, P.:
The role of air and soil temperature in the seasonality of photosynthesis and transpiration in a boreal Scots pine ecosystem,
Agr. Forest Meteorol.,
156, 85–103, https://doi.org/10.1016/j.agrformet.2012.01.006, 2012.
Short summary
The process-based model 4C has been developed to study climate impacts on forests and is now freely available as an open-source tool. This paper provides a comprehensive description of the 4C version (v2.2) for scientific users of the model and presents an evaluation of 4C. The evaluation focused on forest growth, carbon water, and heat fluxes. We conclude that 4C is widely applicable, reliable, and ready to be released to the scientific community to use and further develop the model.
The process-based model 4C has been developed to study climate impacts on forests and is now...
