Articles | Volume 9, issue 12
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 et al.
No articles found.
Shamil Maksyutov, Tomohiro Oda, Makoto Saito, Rajesh Janardanan, Dmitry Belikov, Johannes W. Kaiser, Ruslan Zhuravlev, Alexander Ganshin, Vinu K. Valsala, Arlyn Andrews, Lukasz Chmura, Edward Dlugokencky, László Haszpra, Ray L. Langenfelds, Toshinobu Machida, Takakiyo Nakazawa, Michel Ramonet, Colm Sweeney, and Douglas Worthy
Atmos. Chem. Phys., 21, 1245–1266,Short summary
In order to improve the top-down estimation of the anthropogenic greenhouse gas emissions, a high-resolution inverse modelling technique was developed for applications to global transport modelling of carbon dioxide and other greenhouse gases. A coupled Eulerian–Lagrangian transport model and its adjoint are combined with surface fluxes at 0.1° resolution to provide high-resolution forward simulation and inverse modelling of surface fluxes accounting for signals from emission hot spots.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486,Short summary
The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464,Short summary
To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
László Haszpra and Ernő Prácser
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
Most of the tall-tower GHG observatories applies a single gas analyzer for the sequential sampling of several intakes along the tower. The non-continuous sampling at each intake introduces excess uncertainty to the calculated hourly average concentrations used in several applications. Based on real-world measurements the paper systematically assesses this type of uncertainty.
Łukasz Chmura, Michał Gałkowski, Piotr Sekuła, Mirosław Zimnoch, Jarosław Nęcki, Jakub Bartyzel, Damian Zięba, Kazimierz Różański, Wojciech Wołkowicz, and Laszlo Haszpra
Atmos. Chem. Phys. Discuss.,
Revised manuscript not acceptedShort summary
The rise of temperatures across the globe, mainly attributed to the anthropogenic emissions of greenhouse gases, is predicted to have an increased impact on ecosystems over the next century. One of the manifestations of this anthropogenic global warming will be the increased occurrence of prolonged droughts in the temperate climate zones. In the current study we present the evidence of an increased impact of droughts on the annual cycle of carbon dioxide over Central-Eastern Europe.
Jianqiu Zheng, Peter E. Thornton, Scott L. Painter, Baohua Gu, Stan D. Wullschleger, and David E. Graham
Biogeosciences, 16, 663–680,Short summary
Arctic warming exposes soil carbon to increased degradation, increasing CO2 and CH4 emissions. Models underrepresent anaerobic decomposition that predominates wet soils. We simulated microbial growth, pH regulation, and anaerobic carbon decomposition in a new model, parameterized and validated with prior soil incubation data. The model accurately simulated CO2 production and strong influences of water content, pH, methanogen biomass, and competing electron acceptors on CH4 production.
Peter Bergamaschi, Ute Karstens, Alistair J. Manning, Marielle Saunois, Aki Tsuruta, Antoine Berchet, Alexander T. Vermeulen, Tim Arnold, Greet Janssens-Maenhout, Samuel Hammer, Ingeborg Levin, Martina Schmidt, Michel Ramonet, Morgan Lopez, Jost Lavric, Tuula Aalto, Huilin Chen, Dietrich G. Feist, Christoph Gerbig, László Haszpra, Ove Hermansen, Giovanni Manca, John Moncrieff, Frank Meinhardt, Jaroslaw Necki, Michal Galkowski, Simon O'Doherty, Nina Paramonova, Hubertus A. Scheeren, Martin Steinbacher, and Ed Dlugokencky
Atmos. Chem. Phys., 18, 901–920,Short summary
European methane (CH4) emissions are estimated for 2006–2012 using atmospheric in situ measurements from 18 European monitoring stations and 7 different inverse models. Our analysis highlights the potential significant contribution of natural emissions from wetlands (including peatlands and wet soils) to the total European emissions. The top-down estimates of total EU-28 CH4 emissions are broadly consistent with the sum of reported anthropogenic CH4 emissions and the estimated natural emissions.
Henrique F. Duarte, Brett M. Raczka, Daniel M. Ricciuto, John C. Lin, Charles D. Koven, Peter E. Thornton, David R. Bowling, Chun-Ta Lai, Kenneth J. Bible, and James R. Ehleringer
Biogeosciences, 14, 4315–4340,Short summary
We evaluate the Community Land Model (CLM4.5) against observations at an old-growth coniferous forest site that is subjected to water stress each summer. We found that, after calibration, CLM was able to reasonably simulate the observed fluxes of energy and carbon, carbon stocks, carbon isotope ratios, and ecosystem response to water stress. This study demonstrates that carbon isotopes can expose structural weaknesses in CLM and provide a key constraint that may guide future model development.
Foad Foolad, Trenton E. Franz, Tiejun Wang, Justin Gibson, Ayse Kilic, Richard G. Allen, and Andrew Suyker
Hydrol. Earth Syst. Sci., 21, 1263–1277,Short summary
Estimates of evapotranspiration are vital for validation of models. However, those datasets are often limited to research applications. Here, we explore using vadose zone modeling with widespread and readily available soil water content monitoring networks. While this work focused on one agricultural site, the framework can be used everywhere there is basic data. The resulting evapotranspiration and soil water content measurements are valuable benchmarks for evaluation of land surface models.
Jitendra Kumar, Nathan Collier, Gautam Bisht, Richard T. Mills, Peter E. Thornton, Colleen M. Iversen, and Vladimir Romanovsky
The Cryosphere, 10, 2241–2274,Short summary
Microtopography of the low-gradient polygonal tundra plays a critical role in these ecosystem; however, patterns and drivers are poorly understood. A modeling-based approach was developed in this study to characterize and represent the permafrost soils in the model and simulate the thermal dynamics using a mechanistic high-resolution model. Results shows the ability of the model to simulate the patterns and variability of thermal regimes and improve our understanding of polygonal tundra.
János Balogh, Marianna Papp, Krisztina Pintér, Szilvia Fóti, Katalin Posta, Werner Eugster, and Zoltán Nagy
Biogeosciences, 13, 5171–5182,Short summary
In the dry grassland investigated in this study the components of the soil CO2 efflux decreased at different rates under drought conditions. During drought the contribution made by the heterotrophic components was the highest and the rhizospheric component was the most sensitive to soil drying. According to our results, the heterotrophic component of soil respiration is the major contributor to the respiration activities during drought events.
William Alexander Avery, Catherine Finkenbiner, Trenton E. Franz, Tiejun Wang, Anthony L. Nguy-Robertson, Andrew Suyker, Timothy Arkebauer, and Francisco Muñoz-Arriola
Hydrol. Earth Syst. Sci., 20, 3859–3872,Short summary
Here we present a strategy to use globally available datasets in the calibration function used to convert observed moderated neutron counts into volumetric soil water content. While local sampling protocols are well documented for fixed probes, the use of roving probes presents new calibration challenges. With over 200 fixed probes and 10 roving probes in use globally, we anticipate this paper will serve as a keystone for the growing cosmic-ray neutron probe and hydrologic community.
Brett Raczka, Henrique F. Duarte, Charles D. Koven, Daniel Ricciuto, Peter E. Thornton, John C. Lin, and David R. Bowling
Biogeosciences, 13, 5183–5204,Short summary
We use carbon isotopes of CO2 to improve the performance of a land surface model, a component with earth system climate models. We found that isotope observations can provide important information related to the exchange of carbon and water from vegetation driven by environmental stress from low atmospheric moisture and nitrogen limitation. It follows that isotopes have a unique potential to improve model performance and provide insight into land surface model development.
Guoping Tang, Jianqiu Zheng, Xiaofeng Xu, Ziming Yang, David E. Graham, Baohua Gu, Scott L. Painter, and Peter E. Thornton
Biogeosciences, 13, 5021–5041,Short summary
We extend the Community Land Model coupled with carbon and nitrogen (CLM-CN) decomposition cascade to include simple organic substrate turnover, fermentation, Fe(III) reduction, and methanogenesis reactions, and assess the efficacy of various temperature and pH response functions. Incorporating the Windermere Humic Aqueous Model (WHAM) describes the observed pH evolution. Fe reduction can increase pH toward neutral pH to facilitate methanogenesis.
Xiaofeng Xu, Fengming Yuan, Paul J. Hanson, Stan D. Wullschleger, Peter E. Thornton, William J. Riley, Xia Song, David E. Graham, Changchun Song, and Hanqin Tian
Biogeosciences, 13, 3735–3755,Short summary
Accurately projecting future climate change requires a good methane modeling. However, how good the current models are and what are the key improvements needed remain unclear. This paper reviews the 40 published methane models to characterize the strengths and weakness of current methane models and further lay out the roadmap for future model improvements.
Boris Bonn, Erika von Schneidemesser, Dorota Andrich, Jörn Quedenau, Holger Gerwig, Anja Lüdecke, Jürgen Kura, Axel Pietsch, Christian Ehlers, Dieter Klemp, Claudia Kofahl, Rainer Nothard, Andreas Kerschbaumer, Wolfgang Junkermann, Rüdiger Grote, Tobias Pohl, Konradin Weber, Birgit Lode, Philipp Schönberger, Galina Churkina, Tim M. Butler, and Mark G. Lawrence
Atmos. Chem. Phys., 16, 7785–7811,Short summary
The distribution of air pollutants (gases and particles) have been investigated in different environments in Potsdam, Germany. Remarkable variations of the pollutants have been observed for distances of tens of meters by bicycles, vans and aircraft. Vegetated areas caused reductions depending on the pollutants, the vegetation type and dimensions. Our measurements show the pollutants to be of predominantly local origin, resulting in a huge challenge for common models to resolve.
Guoping Tang, Fengming Yuan, Gautam Bisht, Glenn E. Hammond, Peter C. Lichtner, Jitendra Kumar, Richard T. Mills, Xiaofeng Xu, Ben Andre, Forrest M. Hoffman, Scott L. Painter, and Peter E. Thornton
Geosci. Model Dev., 9, 927–946,Short summary
We demonstrate that CLM-PFLOTRAN predictions are consistent with CLM4.5 for Arctic, temperate, and tropical sites. A tight relative tolerance may be needed to avoid false convergence when scaling, clipping, or log transformation is used to avoid negative concentration in implicit time stepping and Newton-Raphson methods. The log transformation method is accurate and robust while relaxing relative tolerance or using the clipping or scaling method can result in efficient solutions.
J. Mao, D. M. Ricciuto, P. E. Thornton, J. M. Warren, A. W. King, X. Shi, C. M. Iversen, and R. J. Norby
Biogeosciences, 13, 641–657,Short summary
The aim of this study is to implement, calibrate and evaluate the CLM4 against carbon and hydrology observations from a shading and labeling experiment in a stand of young loblolly pines. We found a combination of parameters measured on-site and calibration targeting biomass, transpiration, and 13C discrimination gave good agreement with pretreatment measurements. We also used observations from the experiment to develop a conceptual model of short-term photosynthate storage and transport.
X. Shi, P. E. Thornton, D. M. Ricciuto, P. J. Hanson, J. Mao, S. D. Sebestyen, N. A. Griffiths, and G. Bisht
Biogeosciences, 12, 6463–6477,
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
Geosci. Model Dev., 8, 2203–2219,Short summary
The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human-climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. By introducing heretofore-omitted feedbacks between natural and societal drivers in iESM, we can improve scientific understanding of the human-Earth system dynamics.
C. Safta, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, and P. E. Thornton
Geosci. Model Dev., 8, 1899–1918,Short summary
In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
L. Haszpra, Z. Barcza, T. Haszpra, Zs. Pátkai, and K. J. Davis
Atmos. Meas. Tech., 8, 1657–1671,
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
Biogeosciences, 11, 6435–6450,Short summary
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,
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,
X. Yang, P. E. Thornton, D. M. Ricciuto, and W. M. Post
Biogeosciences, 11, 1667–1681,
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,
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Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656,Short summary
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
Yunfei Wang, Yijian Zeng, Lianyu Yu, Peiqi Yang, Christiaan Van der Tol, Qiang Yu, Xiaoliang Lü, Huanjie Cai, and Zhongbo Su
Geosci. Model Dev., 14, 1379–1407,Short summary
This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved by considering the root water uptake, especially when vegetation was experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer in simulating ecosystem functioning.
Hongxing He, Per-Erik Jansson, and Annemieke I. Gärdenäs
Geosci. Model Dev., 14, 735–761,Short summary
This study presents the integration of the phosphorus (P) cycle into CoupModel (v6.0, Coup-CNP). The extended Coup-CNP, which explicitly considers the symbiosis between soil microbes and plant roots, enables simulations of coupled C, N, and P dynamics for terrestrial ecosystems. Simulations from the new Coup-CNP model provide strong evidence that P fluxes need to be further considered in studies of how ecosystems and C turnover react to climate change.
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601,Short summary
In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Kazuyuki Saito, Hirokazu Machiya, Go Iwahana, Tokuta Yokohata, and Hiroshi Ohno
Geosci. Model Dev., 14, 521–542,Short summary
Soil organic carbon (SOC) and ground ice (ICE) are essential but under-documented information to assess the circum-Arctic permafrost degradation impacts. A simple numerical model of essential SOC and ICE dynamics, developed and integrated north of 50° N for 125,000 years since the last interglacial, reconstructed the history and 1° distribution of SOC and ICE consistent with current knowledge, together with successful demonstration of climatic and topographical controls on SOC evolution.
Felix Leung, Karina Williams, Stephen Sitch, Amos P. K. Tai, Andy Wiltshire, Jemma Gornall, Elizabeth A. Ainsworth, Timothy Arkebauer, and David Scoby
Geosci. Model Dev., 13, 6201–6213,Short summary
Ground-level ozone (O3) is detrimental to plant productivity and crop yield. Currently, the Joint UK Land Environment Simulator (JULES) includes a representation of crops (JULES-crop). The parameters for O3 damage in soybean in JULES-crop were calibrated against photosynthesis measurements from the Soybean Free Air Concentration Enrichment (SoyFACE). The result shows good performance for yield, and it helps contribute to understanding of the impacts of climate and air pollution on food security.
Huilin Huang, Yongkang Xue, Fang Li, and Ye Liu
Geosci. Model Dev., 13, 6029–6050,Short summary
We developed a fire-coupled dynamic vegetation model that captures the spatial distribution, temporal variability, and especially the seasonal variability of fire regimes. The fire model is applied to assess the long-term fire impact on ecosystems and surface energy. We find that fire is an important determinant of the structure and function of the tropical savanna. By changing the vegetation composition and ecosystem characteristics, fire significantly alters surface energy balance.
Virginie Moreaux, Simon Martel, Alexandre Bosc, Delphine Picart, David Achat, Christophe Moisy, Raphael Aussenac, Christophe Chipeaux, Jean-Marc Bonnefond, Soisick Figuères, Pierre Trichet, Rémi Vezy, Vincent Badeau, Bernard Longdoz, André Granier, Olivier Roupsard, Manuel Nicolas, Kim Pilegaard, Giorgio Matteucci, Claudy Jolivet, Andrew T. Black, Olivier Picard, and Denis Loustau
Geosci. Model Dev., 13, 5973–6009,Short summary
The model GO+ describes the functioning of managed forests based upon biophysical and biogeochemical processes. It accounts for the impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes versatile descriptions of management operations. Its sensitivity and uncertainty are detailed and predictions are compared with observations about mass and energy exchanges, hydrological data, and tree growth variables from different sites.
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971,Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
Guillaume Le Gland, Sergio M. Vallina, S. Lan Smith, and Pedro Cermeño
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
We present an ecological model called SPEAD where various phytoplankton compete for a nutrient. Phytoplankton in SPEAD is characterized by two continuously distributed traits: optimal temperature and nutrient half-saturation. Trait diversity is sustained by allowing the traits to mutate at each generation. We showed that SPEAD agreed well with a more classical discrete model for only a fraction of its cost. We also identified realistic values for the mutation rates, to be used in future models.
Christopher T. Reinhard, Stephanie L. Olson, Sandra Kirtland Turner, Cecily Pälike, Yoshiki Kanzaki, and Andy Ridgwell
Geosci. Model Dev., 13, 5687–5706,Short summary
We provide documentation and testing of new developments for the oceanic and atmospheric methane cycles in the cGENIE Earth system model. The model is designed to explore Earth's methane cycle across a wide range of timescales and scenarios, in particular assessing the mean climate state and climate perturbations in Earth's deep past. We further document the impact of atmospheric oxygen levels and ocean chemistry on fluxes of methane to the atmosphere from the ocean biosphere.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423,Short summary
We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Petra Lasch-Born, Felicitas Suckow, Christopher P. O. Reyer, Martin Gutsch, Chris Kollas, Franz-Werner Badeck, Harald K. M. Bugmann, Rüdiger Grote, Cornelia Fürstenau, Marcus Lindner, and Jörg Schaber
Geosci. Model Dev., 13, 5311–5343,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.
Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
This study evaluates how well the CLASSIC Land Surface Model reproduces the energy, water, and carbon cycle when compared to a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models, and that failing to do so may potentially misguide future model development.
Zhengang Wang, Jianxiu Qiu, and Kristof Van Oost
Geosci. Model Dev., 13, 4977–4992,Short summary
This study developed a spatially distributed carbon cycling model applicable in an eroding landscape. It includes all three carbon isotopes so that it is able to represent the carbon isotopic compositions. The model is able to represent the observations that eroding area is enriched in 13C and depleted of 14C compared to depositional area. Our simulations show that the spatial variability of carbon isotopic properties in an eroding landscape is mainly caused by the soil redistribution.
Yuan Zhang, Olivier Boucher, Philippe Ciais, Laurent Li, and Nicolas Bellouin
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
We investigated different methods to reconstruct spatio-temporal distribution of the fraction of diffuse radiation (Fdf) to qualtify the aerosol impacts on GPP using ORCHIDEE_DF land surface model. We find that climatologically-averaging methods which dampens the variability of Fdf can cause significant bias in the modeled diffuse radiation impacts on GPP. Better methods to do the reconstruction of Fdf are recommended.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare model against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeast US.
Brian N. Bailey, María A. Ponce de León, and E. Scott Krayenhoff
Geosci. Model Dev., 13, 4789–4808,Short summary
Numerous models of plant radiation interception based on a range of assumptions are available in the literature, but the importance of each assumption is not well understood. In this work, we evaluate several assumptions common in simple models of radiation interception in canopies with widely spaced plants by comparing against a detailed 3-D model. This yielded a simple model based on readily measurable parameters that could accurately predict interception for a wide range of architectures.
Julius Vira, Peter Hess, Jeff Melkonian, and William R. Wieder
Geosci. Model Dev., 13, 4459–4490,Short summary
Mostly emitted by the agricultural sector, ammonia has an important role in atmospheric chemistry. We developed a model to simulate how ammonia emissions respond to changes in temperature and soil moisture, and we evaluated agricultural ammonia emissions globally. The simulated emissions agree with earlier estimates over many regions, but the results highlight the variability of ammonia emissions and suggest that emissions in warm climates may be higher than previously thought.
Emily Kyker-Snowman, William R. Wieder, Serita D. Frey, and A. Stuart Grandy
Geosci. Model Dev., 13, 4413–4434,Short summary
Microbes drive carbon (C) and nitrogen (N) transformations in soil, and soil models have started to include explicit microbial physiology and functioning to try to reduce uncertainty in soil–climate feedbacks. Here, we add N cycling to a microbially explicit soil C model and reproduce C and N dynamics in soil during litter decomposition across a range of sites. We discuss model-generated hypotheses about soil C and N cycling and highlight the need for landscape-scale model evaluation data.
Leonardo Calle and Benjamin Poulter
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
We developed a model to simulate and track the age of ecosystems on Earth. We found that the effect of ecosystem age on net primary production and ecosystem respiration is as important as climate in large areas of every vegetated continent. The LPJ-wsl v2.0 age-class model simulates the upper limit of age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.
Jinxuan Chen, Christoph Gerbig, Julia Marshall, and Kai Uwe Totsche
Geosci. Model Dev., 13, 4091–4106,Short summary
One of the essential challenge for atmospheric CO2 forecasting is predicting CO2 flux variation on synoptic timescale. For CAMS CO2 forecast, a process-based vegetation model is used. In this research we evaluate another type of model (i.e., the light-use-efficiency model VPRM), which is a data-driven approach and thus ideal for realistic estimation, on its ability of flux prediction. Errors from different sources are assessed, and overall the model is capable of CO2 flux prediction.
Yuma Sakai, Hideki Kobayashi, and Tomomichi Kato
Geosci. Model Dev., 13, 4041–4066,Short summary
Chlorophyll fluorescence is one of the energy release pathways of excess incident light in the photosynthetic process. The canopy-scale Sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf-level photosynthesis to global GPP, can be observed from satellites. We develop the three-dimensional Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind SIF emission from complex forest canopies.
Femke Lutz, Stephen Del Grosso, Stephen Ogle, Stephen Williams, Sara Minoli, Susanne Rolinski, Jens Heinke, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 13, 3905–3923,Short summary
Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Tingting Li, Yanyu Lu, Lingfei Yu, Wenjuan Sun, Qing Zhang, Wen Zhang, Guocheng Wang, Zhangcai Qin, Lijun Yu, Hailing Li, and Ran Zhang
Geosci. Model Dev., 13, 3769–3788,Short summary
Reliable models are required to estimate global wetland CH4 emissions, which are the largest and most uncertain source of atmospheric CH4. This paper evaluated CH4MODwetland and TEM models against CH4 measurements from different continents and wetland types. Based on best-model performance, we estimated 117–125 Tg yr−1 of global CH4 emissions from wetlands for the period 2000–2010. Efforts should be made to reduce estimate uncertainties for different wetland types and regions.
Jennifer E. Dentith, Ruza F. Ivanovic, Lauren J. Gregoire, Julia C. Tindall, and Laura F. Robinson
Geosci. Model Dev., 13, 3529–3552,Short summary
We have added a new tracer (13C) into the ocean of the FAMOUS climate model to study large-scale circulation and the marine carbon cycle. The model captures the large-scale spatial pattern of observations but the simulated values are consistently higher than observed. In the first instance, our new tracer is therefore useful for recalibrating the physical and biogeochemical components of the model.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318,Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Yifei Dai, Long Cao, and Bin Wang
Geosci. Model Dev., 13, 3119–3144,Short summary
NESM v3 is one of the CMIP6 registered Earth system models. We evaluate its ocean carbon cycle component and present its present-day and future oceanic CO2 uptake based on the CMIP6 historical and SSP5–8.5 scenarios. We hope that this paper can serve as a documentation of the marine biogeochemical cycle in NESM v3. Also, the model defects found and their underlying causes analyzed in this paper could help users and further model development.
Joe R. Melton, Vivek K. Arora, Eduard Wisernig-Cojoc, Christian Seiler, Matthew Fortier, Ed Chan, and Lina Teckentrup
Geosci. Model Dev., 13, 2825–2850,Short summary
We transitioned the CLASS-CTEM land surface model to an open-source community model format by modernizing the code base to make the model easier to use and understand, providing a complete software environment to run the model within, developing a benchmarking suite for model evaluation, and creating an infrastructure to support community involvement. The new model, the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC), is now available for the community to use and develop.
Magnus Dahler Norling, Leah Amber Jackson-Blake, José-Luis Guerrero Calidonio, and James Edward Sample
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
In order for researchers to more quickly prototype and build models of natural systems we have created the Mobius framework. Such models can for instance be used to ask questions about what the impacts of land use changes are to water quality in a river or lake, or the response of biological systems to climate change etc. The Mobius framework makes it quick to build models that run fast, which enables the user to explore many different scenarios and model formulations.
Giovanni Denaro, Daniela Salvagio Manta, Alessandro Borri, Maria Bonsignore, Davide Valenti, Enza Quinci, Andrea Cucco, Bernardo Spagnolo, Mario Sprovieri, and Andrea De Gaetano
Geosci. Model Dev., 13, 2073–2093,Short summary
The HR3DHG (high-resolution 3D mercury model) investigates the spatiotemporal behavior, in seawater and marine sediments, of three mercury species (elemental, inorganic, and organic mercury) in a highly polluted marine environment (Augusta Bay, southern Italy). The model shows fair agreement with the experimental data collected during six different oceanographic cruises and can possibly be used for a detailed exploration of the effects of climate change on mercury distribution.
Elisa Lovecchio and Timothy M. Lenton
Geosci. Model Dev., 13, 1865–1883,Short summary
We present here the newly developed BPOP box model. BPOP is aimed at studying the impact of large-scale changes in the biological pump, i.e. the cycle of production, export and remineralization of the marine organic matter, on the nutrient and oxygen concentrations in the shelf and open ocean. This model has been developed to investigate the global consequences of the evolution of larger and heavier phytoplankton cells but can be applied to a variety of past and future case studies.
Benjamin D. Stocker, Han Wang, Nicholas G. Smith, Sandy P. Harrison, Trevor F. Keenan, David Sandoval, Tyler Davis, and I. Colin Prentice
Geosci. Model Dev., 13, 1545–1581,Short summary
Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models simulating the efficiency by which light absorbed by vegetation is used for CO2 assimilation. This paper presents the P-model, a light use efficiency model derived from a carbon–water optimality principle, and evaluates its predictions of ecosystem-level photosynthesis against globally distributed observations. The model is implemented and openly accessible as an R package (rpmodel).
Louis de Wergifosse, Frédéric André, Nicolas Beudez, François de Coligny, Hugues Goosse, François Jonard, Quentin Ponette, Hugues Titeux, Caroline Vincke, and Mathieu Jonard
Geosci. Model Dev., 13, 1459–1498,Short summary
Given their key role in the simulation of climate impacts on tree growth, phenological and water balance processes must be integrated in models simulating forest dynamics under a changing environment. Here, we describe these processes integrated in HETEROFOR, a model accounting simultaneously for the functional, structural and spatial complexity to explore the forest response to forestry practices. The model evaluation using phenological and soil water content observations is quite promising.
Arjun Chakrawal, Anke M. Herrmann, John Koestel, Jerker Jarsjö, Naoise Nunan, Thomas Kätterer, and Stefano Manzoni
Geosci. Model Dev., 13, 1399–1429,Short summary
Soils are heterogeneous, which results in a nonuniform spatial distribution of substrates and the microorganisms feeding on them. Our results show that the variability in the spatial distribution of substrates and microorganisms at the pore scale is crucial because it affects how fast substrates are used by microorganisms and thus the decomposition rate observed at the soil core scale. This work provides a methodology to include microscale heterogeneity in soil carbon cycling models.
Victoria Naipal, Ronny Lauerwald, Philippe Ciais, Bertrand Guenet, and Yilong Wang
Geosci. Model Dev., 13, 1201–1222,Short summary
In this study we present the Carbon Erosion DYNAMics model (CE-DYNAM) that links sediment dynamics resulting from water erosion with the soil carbon cycle along a cascade of hillslopes, floodplains, and rivers. The model can simulate the removal of soil and carbon from eroding areas and their destination at regional scale. We calibrated and validated the model for the Rhine catchment, and we show that soil erosion is a potential large net carbon sink over the period 1850–2005.
Kelly Kearney, Albert Hermann, Wei Cheng, Ivonne Ortiz, and Kerim Aydin
Geosci. Model Dev., 13, 597–650,Short summary
We describe an ecosystem model for the Bering Sea. Biological components in the Bering Sea can be found in the water column, on and within the bottom sediments, and within the porous lower layer of seasonal sea ice. This model simulates the exchange of material between nutrients and plankton within all three environments. Here, we thoroughly document the model and assess its skill in capturing key biophysical features across the Bering Sea.
Matthias J. R. Speich, Massimiliano Zappa, Marc Scherstjanoi, and Heike Lischke
Geosci. Model Dev., 13, 537–564,Short summary
Climate change is expected to substantially affect natural processes, and simulation models are a valuable tool to anticipate these changes. In this study, we combine two existing models that each describe one aspect of the environment: forest dynamics and the terrestrial water cycle. The coupled model better described observed patterns in vegetation structure. We also found that including the effect of water availability on tree height and rooting depth improved the model.
Simon P. K. Bowring, Ronny Lauerwald, Bertrand Guenet, Dan Zhu, Matthieu Guimberteau, Pierre Regnier, Ardalan Tootchi, Agnès Ducharne, and Philippe Ciais
Geosci. Model Dev., 13, 507–520,Short summary
In this second part of the study, we performed simulations of the carbon and water budget of the Lena catchment with the land surface model ORCHIDEE MICT-LEAK, enabled to simulate dissolved organic carbon (DOC) production in soils and its transport and fate in high-latitude inland waters. We compare simulations using this model to existing data sources to show that it is capable of reproducing dissolved carbon fluxes of potentially great importance for the future of the global permafrost.
Carme Font, Francesco Bregoli, Vicenç Acuña, Sergi Sabater, and Rafael Marcé
Geosci. Model Dev., 12, 5213–5228,Short summary
GLOBAL-FATE is an open-source, multiplatform, and flexible model that simulates the fate of pharmaceutical-like compounds in the global river network. The model considers the consumption of pharmaceuticals by humans, differentiates between pharmaceutical load treated in wastewater treatment plants from that directly delivered to streams and rivers, and integrates lakes and reservoirs in calculations. GLOBAL-FATE is a powerful tool for pollutant impact studies at the global scale.
Luke Gregor, Alice D. Lebehot, Schalk Kok, and Pedro M. Scheel Monteiro
Geosci. Model Dev., 12, 5113–5136,Short summary
The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO2). Historically sparse ship-based measurements of surface ocean CO2 make direct estimates of CO2 exchange changes unreliable. We introduce a machine-learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine-learning algorithms' capability.
Markus Drüke, Matthias Forkel, Werner von Bloh, Boris Sakschewski, Manoel Cardoso, Mercedes Bustamante, Jürgen Kurths, and Kirsten Thonicke
Geosci. Model Dev., 12, 5029–5054,Short summary
This work shows the successful application of a systematic model–data integration setup, as well as the implementation of a new fire danger formulation, in order to optimize a process-based fire-enabled dynamic global vegetation model. We have demonstrated a major improvement in the fire representation within LPJmL4-SPITFIRE in terms of the spatial pattern and the interannual variability of burned area in South America as well as in the modelling of biomass and the distribution of plant types.
Nicolas Vuichard, Palmira Messina, Sebastiaan Luyssaert, Bertrand Guenet, Sönke Zaehle, Josefine Ghattas, Vladislav Bastrikov, and Philippe Peylin
Geosci. Model Dev., 12, 4751–4779,Short summary
In this research, we present a new version of the global terrestrial ecosystem model ORCHIDEE in which carbon and nitrogen cycles are coupled. We evaluate its skills at simulating primary production at 78 sites and at a global scale. Based on a set of additional simulations in which carbon and nitrogen cycles are coupled and uncoupled, we show that the functional responses of the model with carbon–nitrogen interactions better agree with our current understanding of photosynthesis.
Tea Thum, Silvia Caldararu, Jan Engel, Melanie Kern, Marleen Pallandt, Reiner Schnur, Lin Yu, and Sönke Zaehle
Geosci. Model Dev., 12, 4781–4802,Short summary
To predict the response of the vegetation to climate change, we need global models that describe the relevant processes taking place in the vegetation. Recently, we have obtained more in-depth understanding of vegetation processes and the role of nutrients in the biogeochemical cycles. We have developed a new global vegetation model that includes carbon, water, nitrogen, and phosphorus cycles. We show that the model is successful in evaluation against a wide range of observations.
Dave van Wees and Guido R. van der Werf
Geosci. Model Dev., 12, 4681–4703,Short summary
For this paper, a novel high spatial-resolution fire emission model based on the Global Fire Emissions Database (GFED) modelling framework was developed and compared to a coarser-resolution version of the same model. Our findings highlight the importance of fine spatial resolution when modelling global-scale fire emissions, especially considering the comparison of model pixels to individual field measurements and the model representation of heterogeneity in the landscape.
Yoshiki Kanzaki, Bernard P. Boudreau, Sandra Kirtland Turner, and Andy Ridgwell
Geosci. Model Dev., 12, 4469–4496,Short summary
This paper provides eLABS, an extension of the lattice-automaton bioturbation simulator LABS. In our new model, the benthic animal behavior interacts and changes dynamically with oxygen and organic matter concentrations and the water flows caused by benthic animals themselves, in a 2-D marine-sediment grid. The model can address the mechanisms behind empirical observations of bioturbation based on the interactions between physical, chemical and biological aspects of marine sediment.
Marcos Longo, Ryan G. Knox, David M. Medvigy, Naomi M. Levine, Michael C. Dietze, Yeonjoo Kim, Abigail L. S. Swann, Ke Zhang, Christine R. Rollinson, Rafael L. Bras, Steven C. Wofsy, and Paul R. Moorcroft
Geosci. Model Dev., 12, 4309–4346,Short summary
Our paper describes the Ecosystem Demography model. This computer program calculates how plants and ground exchange heat, water, and carbon with the air, and how plants grow, reproduce and die in different climates. Most models simplify forests to an average big tree. We consider that tall, deep-rooted trees get more light and water than small plants, and that some plants can with shade and drought. This diversity helps us to better explain how plants live and interact with the atmosphere.
Marcos Longo, Ryan G. Knox, Naomi M. Levine, Abigail L. S. Swann, David M. Medvigy, Michael C. Dietze, Yeonjoo Kim, Ke Zhang, Damien Bonal, Benoit Burban, Plínio B. Camargo, Matthew N. Hayek, Scott R. Saleska, Rodrigo da Silva, Rafael L. Bras, Steven C. Wofsy, and Paul R. Moorcroft
Geosci. Model Dev., 12, 4347–4374,Short summary
The Ecosystem Demography model calculates the fluxes of heat, water, and carbon between plants and ground and the air, and the life cycle of plants in different climates. To test if our calculations were reasonable, we compared our results with field and satellite measurements. Our model predicts well the extent of the Amazon forest, how much light forests absorb, and how much water forests release to the air. However, it must improve the tree growth rates and how fast dead plants decompose.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164,Short summary
We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Jarmo Mäkelä, Jürgen Knauer, Mika Aurela, Andrew Black, Martin Heimann, Hideki Kobayashi, Annalea Lohila, Ivan Mammarella, Hank Margolis, Tiina Markkanen, Jouni Susiluoto, Tea Thum, Toni Viskari, Sönke Zaehle, and Tuula Aalto
Geosci. Model Dev., 12, 4075–4098,Short summary
We assess the differences of six stomatal conductance formulations, embedded into a land–vegetation model JSBACH, on 10 boreal coniferous evergreen forest sites. We calibrate the model parameters using all six functions in a multi-year experiment, as well as for a separate drought event at one of the sites, using the adaptive population importance sampler. The analysis reveals weaknesses in the stomatal conductance formulation-dependent model behaviour that we are able to partially amend.
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.
Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I.J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., Van Bodegom, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker, R., Blanco, C. C., Blonder, B., Bond, W. J., Bradstock, R., Bunker, D. E., Casanoves, F., Cavender-Bares, J., Chambers, J. Q., Chapin, F. S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M., Dobrin, B. H., Duarte, L., Durka, W., Elser, J., Esser, G., Estiarte, M., Fagan, W. F., Fang, J., Fernández-Méndez, F., Fidelis, A., Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R. V., Green, W. A., Gutierrez, A. G., Hickler, T., Higgins, S. I., Hodgson, J. G., Jalili, A., Jansen, S., Joly, C. A., Kerkhoff, A. J., Kirkup, D., Kitajima, K., Kleyer, M., Klotz, S., Knops, J. M. H., Kramer, K., Kühn, I., Kurokawa, H., Laughlin, D., Lee, T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S.L., Lloyd, J., Llusià, J., Louault, F., Ma, S., Mahecha, M. D., Manning, P., Massad, T., Medlyn, B. E., Messier, J., Moles, A. T., Müller, S. C., Nadrowski, K., Naeem, S., Niinemets, Ü., Nöllert, S., Nüske, A., Ogaya, R., Oleksyn, J., Onipchenko, V. G., Onoda, Y., Ordoñez, J., Overbeck, G., Ozinga, W. A., Patiño, S., Paula, S., Pausas, J. G., Peñuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L., Poschlod, P., Prinzing, A., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack, L., Salgado-Negret, B., Sardans, J., Shiodera, S., Shipley, B., Siefert, A., Sosinski, E., Soussana, J. F., Swaine, E., Swenson, N., Thompson, K., Thornton, P., Waldram, M., Weiher, E., White, M., White, S., Wright, S.J., Yguel, B., Zaehle, S., Zanne, A. E., and Wirth, C.: TRY – a global database of plant traits, Glob. Change Biol., 17, 2905–2935, https://doi.org/10.1111/j.1365-2486.2011.02451.x, 2011.
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.
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...