Articles | Volume 12, issue 11
https://doi.org/10.5194/gmd-12-4781-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4781-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
20 Nov 2019
Model description paper |
| 20 Nov 2019
| 20 Nov 2019
A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996)
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
Silvia Caldararu
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
Jan Engel
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
Melanie Kern
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
International Max Planck Research School (IMPRS) for Global Biogeochemical Cycles, Jena, Germany
Technical University Munich – School of Life Sciences, Weihenstephan, Germany
Marleen Pallandt
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
International Max Planck Research School (IMPRS) for Global Biogeochemical Cycles, Jena, Germany
Reiner Schnur
Max Planck Institute for Meteorology, Hamburg, Germany
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
Max Planck Institute for Biogeochemistry, Hans-Knöll Str. 10, Jena, Germany
Michael Stifel Center Jena for Data-driven and Simulation Science, Jena, Germany
Related authors
Tuuli Miinalainen, Amanda Ojasalo, Holly Croft, Mika Aurela, Mikko Peltoniemi, Silvia Caldararu, Sönke Zaehle, and Tea Thum
EGUsphere, https://doi.org/10.5194/egusphere-2025-2987, https://doi.org/10.5194/egusphere-2025-2987, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Estimating the future carbon budget requires an accurate understanding of the interlinkages between the land carbon and nitrogen cycles. We use a remote sensing leaf chlorophyll product to evaluate a terrestrial biosphere model, QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system). Our study showcases how the latest advancements in remote sensing-based vegetation monitoring can be harnessed for improving and evaluating process-based vegetation models.
Tea Thum, Tuuli Miinalainen, Outi Seppälä, Holly Croft, Cheryl Rogers, Ralf Staebler, Silvia Caldararu, and Sönke Zaehle
Biogeosciences, 22, 1781–1807, https://doi.org/10.5194/bg-22-1781-2025, https://doi.org/10.5194/bg-22-1781-2025, 2025
Short summary
Short summary
Climate change has the potential to influence the carbon sequestration potential of terrestrial ecosystems, and here the nitrogen cycle is also important. We used the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) in a mixed deciduous forest in Canada. We investigated the usefulness of using the leaf area index and leaf chlorophyll content to improve the parameterization of the model. This work paves the way for using spaceborne observations in model parameterizations, also including information on the nitrogen cycle.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Tea Thum, Julia E. M. S. Nabel, Aki Tsuruta, Tuula Aalto, Edward J. Dlugokencky, Jari Liski, Ingrid T. Luijkx, Tiina Markkanen, Julia Pongratz, Yukio Yoshida, and Sönke Zaehle
Biogeosciences, 17, 5721–5743, https://doi.org/10.5194/bg-17-5721-2020, https://doi.org/10.5194/bg-17-5721-2020, 2020
Short summary
Short summary
Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.
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, https://doi.org/10.5194/gmd-12-4075-2019, https://doi.org/10.5194/gmd-12-4075-2019, 2019
Short summary
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.
Yao Gao, Tiina Markkanen, Mika Aurela, Ivan Mammarella, Tea Thum, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences, 14, 4409–4422, https://doi.org/10.5194/bg-14-4409-2017, https://doi.org/10.5194/bg-14-4409-2017, 2017
Short summary
Short summary
We investigated the response of water use efficiency (WUE) to summer drought in a boreal Scots pine forest (Pinus sylvestris) on the daily time scale mainly using EC flux data from the Hyytiälä (southern Finland) flux site. Simulation results from the JSBACH land surface model were also evaluated against the observed results. The performance of three WUE metrics at the ecosystem level (EWUE, IWUE, and uWUE) during the severe summer drought were studied and showed different results.
Tea Thum, Sönke Zaehle, Philipp Köhler, Tuula Aalto, Mika Aurela, Luis Guanter, Pasi Kolari, Tuomas Laurila, Annalea Lohila, Federico Magnani, Christiaan Van Der Tol, and Tiina Markkanen
Biogeosciences, 14, 1969–1987, https://doi.org/10.5194/bg-14-1969-2017, https://doi.org/10.5194/bg-14-1969-2017, 2017
Short summary
Short summary
Modelling seasonal cycle at the coniferous forests poses a challenge. We implemented a model for sun-induced chlorophyll fluorescence (SIF) to a land surface model JSBACH. It was used to study the seasonality of the carbon cycle in the Fenno-Scandinavian region. Comparison was made to direct CO2 flux measurements and satellite observations of SIF. SIF proved to be a better proxy for photosynthesis than the fraction of absorbed photosynthetically active radiation.
Y. Gao, T. Markkanen, T. Thum, M. Aurela, A. Lohila, I. Mammarella, M. Kämäräinen, S. Hagemann, and T. Aalto
Hydrol. Earth Syst. Sci., 20, 175–191, https://doi.org/10.5194/hess-20-175-2016, https://doi.org/10.5194/hess-20-175-2016, 2016
Tuuli Miinalainen, Amanda Ojasalo, Holly Croft, Mika Aurela, Mikko Peltoniemi, Silvia Caldararu, Sönke Zaehle, and Tea Thum
EGUsphere, https://doi.org/10.5194/egusphere-2025-2987, https://doi.org/10.5194/egusphere-2025-2987, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Estimating the future carbon budget requires an accurate understanding of the interlinkages between the land carbon and nitrogen cycles. We use a remote sensing leaf chlorophyll product to evaluate a terrestrial biosphere model, QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system). Our study showcases how the latest advancements in remote sensing-based vegetation monitoring can be harnessed for improving and evaluating process-based vegetation models.
Xiankun Li, Marleen Pallandt, Dilip Naidu, Johannes Rousk, Gustaf Hugelius, and Stefano Manzoni
Biogeosciences, 22, 2691–2705, https://doi.org/10.5194/bg-22-2691-2025, https://doi.org/10.5194/bg-22-2691-2025, 2025
Short summary
Short summary
While laboratory studies have identified many drivers and their effects on the carbon emission pulse after rewetting of dry soils, a validation with field data is still missing. Here, we show that the carbon emission pulse in the laboratory and in the field increases with soil organic carbon and temperature, but their trends with pre-rewetting dryness and moisture increment at rewetting differ. We conclude that the laboratory findings can be partially validated.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2025-1416, https://doi.org/10.5194/egusphere-2025-1416, 2025
Short summary
Short summary
Our results showed substantially varied fertilizer-induced soil NOx emissions in 2019 from 0.84 to 2.2 Tg N yr-1 globally. Such variations further lead to 0.3 to 3.3 ppbv summertime ozone enhancement in agricultural hotspot regions and 7.1 ppbv to 16.6 ppbv reductions in global methane concentrations
Marleen Pallandt, Marion Schrumpf, Holger Lange, Markus Reichstein, Lin Yu, and Bernhard Ahrens
Biogeosciences, 22, 1907–1928, https://doi.org/10.5194/bg-22-1907-2025, https://doi.org/10.5194/bg-22-1907-2025, 2025
Short summary
Short summary
As soils warm due to climate change, soil organic carbon (SOC) decomposes faster due to increased microbial activity, given sufficient available moisture. We modelled the microbial decomposition of plant litter and residue at different depths and found that deep soil layers are more sensitive than topsoils. Warming causes SOC loss, but its extent depends on the litter type and its temperature sensitivity, which can either counteract or amplify losses. Droughts may also counteract warming-induced SOC losses.
Tea Thum, Tuuli Miinalainen, Outi Seppälä, Holly Croft, Cheryl Rogers, Ralf Staebler, Silvia Caldararu, and Sönke Zaehle
Biogeosciences, 22, 1781–1807, https://doi.org/10.5194/bg-22-1781-2025, https://doi.org/10.5194/bg-22-1781-2025, 2025
Short summary
Short summary
Climate change has the potential to influence the carbon sequestration potential of terrestrial ecosystems, and here the nitrogen cycle is also important. We used the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) in a mixed deciduous forest in Canada. We investigated the usefulness of using the leaf area index and leaf chlorophyll content to improve the parameterization of the model. This work paves the way for using spaceborne observations in model parameterizations, also including information on the nitrogen cycle.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Carlos A. Sierra, Ingrid Chanca, Meinrat Andreae, Alessandro Carioca de Araújo, Hella van Asperen, Lars Borchardt, Santiago Botía, Luiz Antonio Candido, Caio S. C. Correa, Cléo Quaresma Dias-Junior, Markus Eritt, Annica Fröhlich, Luciana V. Gatti, Marcus Guderle, Samuel Hammer, Martin Heimann, Viviana Horna, Armin Jordan, Steffen Knabe, Richard Kneißl, Jost Valentin Lavric, Ingeborg Levin, Kita Macario, Juliana Menger, Heiko Moossen, Carlos Alberto Quesada, Michael Rothe, Christian Rödenbeck, Yago Santos, Axel Steinhof, Bruno Takeshi, Susan Trumbore, and Sönke Zaehle
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-151, https://doi.org/10.5194/essd-2025-151, 2025
Preprint under review for ESSD
Short summary
Short summary
We present here a unique dataset of atmospheric observations of greenhouse gases and isotopes that provide key information on land-atmosphere interactions for the Amazon forests of central Brazil. The data show a relatively large level of variability, but also important trends in greenhouse gases, and signals from fires as well as seasonal biological activity.
Martin Thurner, Kailiang Yu, Stefano Manzoni, Anatoly Prokushkin, Melanie A. Thurner, Zhiqiang Wang, and Thomas Hickler
Biogeosciences, 22, 1475–1493, https://doi.org/10.5194/bg-22-1475-2025, https://doi.org/10.5194/bg-22-1475-2025, 2025
Short summary
Short summary
Nitrogen concentrations in tree tissues (leaves, branches, stems, and roots) are related to photosynthesis, growth, and respiration and thus to vegetation carbon uptake. Our novel database allows us to identify the controls of tree tissue nitrogen concentrations in boreal and temperate forests, such as tree age/size, species, and climate. Changes therein will affect tissue nitrogen concentrations and thus also vegetation carbon uptake.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Daniele Peano, Deborah Hemming, Christine Delire, Yuanchao Fan, Hanna Lee, Stefano Materia, Julia E. M .S. Nabel, Taejin Park, David Wårlind, Andy Wiltshire, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2024-4114, https://doi.org/10.5194/egusphere-2024-4114, 2025
Short summary
Short summary
Earth System Models are the principal tools for scientists to study past, present, and future climate changes. This work investigates the ability of a set of them to represent the observed changes in vegetation, which are vital to estimating the impact of future climate mitigation and adaptation strategies. This study highlights the main limitations in correctly representing vegetation variability. These tools still need further development to improve our understanding of future changes.
Nathalie Ylenia Triches, Jan Engel, Abdullah Bolek, Timo Vesala, Maija E. Marushchak, Anna-Maria Virkkala, Martin Heimann, and Mathias Göckede
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-203, https://doi.org/10.5194/amt-2024-203, 2025
Revised manuscript accepted for AMT
Short summary
Short summary
This study explores nitrous oxide (N2O) fluxes from a nutrient-poor, sub-Arctic peatland. N2O is a potent greenhouse gas; understanding its fluxes is essential for addressing global warming. Using a new instrument and flux chambers, we introduce a system to reliably detect low N2O fluxes and provide recommendations on chamber closure times and flux calculation methods to better quantify N2O fluxes. We encourage researchers to further investigate N2O fluxes in low-nutrient environments.
Tuula Aalto, Aki Tsuruta, Jarmo Mäkelä, Jurek Müller, Maria Tenkanen, Eleanor Burke, Sarah Chadburn, Yao Gao, Vilma Mannisenaho, Thomas Kleinen, Hanna Lee, Antti Leppänen, Tiina Markkanen, Stefano Materia, Paul A. Miller, Daniele Peano, Olli Peltola, Benjamin Poulter, Maarit Raivonen, Marielle Saunois, David Wårlind, and Sönke Zaehle
Biogeosciences, 22, 323–340, https://doi.org/10.5194/bg-22-323-2025, https://doi.org/10.5194/bg-22-323-2025, 2025
Short summary
Short summary
Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in northern Europe using ecosystem models, atmospheric inversions, and upscaled flux observations. The ecosystem models differed in their responses to temperature and precipitation and in their seasonality. However, multi-model means, inversions, and upscaled fluxes had similar seasonality, and they suggested co-limitation by temperature and precipitation.
Fortunat Joos, Sebastian Lienert, and Sönke Zaehle
Biogeosciences, 22, 19–39, https://doi.org/10.5194/bg-22-19-2025, https://doi.org/10.5194/bg-22-19-2025, 2025
Short summary
Short summary
How plants regulate their exchange of CO2 and water with the atmosphere under global warming is critical for their carbon uptake and their cooling influence. We analyze the isotope ratio of atmospheric CO2 and detect no significant decadal trends in the seasonal cycle amplitude. The data are consistent with the regulation towards leaf CO2 and intrinsic water use efficiency growing proportionally to atmospheric CO2, in contrast to recent suggestions of downregulation of CO2 and water fluxes.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Bettina K. Gier, Manuel Schlund, Pierre Friedlingstein, Chris D. Jones, Colin Jones, Sönke Zaehle, and Veronika Eyring
Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, https://doi.org/10.5194/bg-21-5321-2024, 2024
Short summary
Short summary
This study investigates present-day carbon cycle variables in CMIP5 and CMIP6 simulations. Overall, CMIP6 models perform better but also show many remaining biases. A significant improvement in the simulation of photosynthesis in models with a nitrogen cycle is found, with only small differences between emission- and concentration-based simulations. Thus, we recommend using emission-driven simulations in CMIP7 by default and including the nitrogen cycle in all future carbon cycle models.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Gabriela Sophia, Silvia Caldararu, Benjamin David Stocker, and Sönke Zaehle
Biogeosciences, 21, 4169–4193, https://doi.org/10.5194/bg-21-4169-2024, https://doi.org/10.5194/bg-21-4169-2024, 2024
Short summary
Short summary
Through an extensive global dataset of leaf nutrient resorption and a multifactorial analysis, we show that the majority of spatial variation in nutrient resorption may be driven by leaf habit and type, with thicker, longer-lived leaves having lower resorption efficiencies. Climate, soil fertility and soil-related factors emerge as strong drivers with an additional effect on its role. These results are essential for comprehending plant nutrient status, plant productivity and nutrient cycling.
Pin-Hsin Hu, Christian H. Reick, Reiner Schnur, Axel Kleidon, and Martin Claussen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-111, https://doi.org/10.5194/gmd-2024-111, 2024
Revised manuscript under review for GMD
Short summary
Short summary
We introduce the new plant functional diversity model JeDi-BACH, a novel tool that integrates the Jena Diversity Model (JeDi) within the land component of the ICON Earth System Model. JeDi-BACH captures a richer set of plant trait variations based on environmental filtering and functional tradeoffs without a priori knowledge of the vegetation types. JeDi-BACH represents a significant advancement in modeling the complex interactions between plant functional diversity and climate.
Dieu Anh Tran, Christoph Gerbig, Christian Rödenbeck, and Sönke Zaehle
Atmos. Chem. Phys., 24, 8413–8440, https://doi.org/10.5194/acp-24-8413-2024, https://doi.org/10.5194/acp-24-8413-2024, 2024
Short summary
Short summary
The analysis of the atmospheric CO2 record from the Zotino Tall Tower Observatory (ZOTTO) in central Siberia shows significant increases in the length and amplitude of the CO2 uptake and release in the 2010–2021 period. The trend shows a stronger increase in carbon release amplitude compared to the uptake, suggesting that, despite enhanced growing season uptake, during this period climate warming did not elevate the annual net CO2 uptake as cold-season respirations also responded to the warming.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Melanie A. Thurner, Silvia Caldararu, Jan Engel, Anja Rammig, and Sönke Zaehle
Biogeosciences, 21, 1391–1410, https://doi.org/10.5194/bg-21-1391-2024, https://doi.org/10.5194/bg-21-1391-2024, 2024
Short summary
Short summary
Due to their crucial role in terrestrial ecosystems, we implemented mycorrhizal fungi into the QUINCY terrestrial biosphere model. Fungi interact with mineral and organic soil to support plant N uptake and, thus, plant growth. Our results suggest that the effect of mycorrhizal interactions on simulated ecosystem dynamics is minor under constant environmental conditions but necessary to reproduce and understand observed patterns under changing conditions, such as rising atmospheric CO2.
Christian Rödenbeck, Karina E. Adcock, Markus Eritt, Maksym Gachkivskyi, Christoph Gerbig, Samuel Hammer, Armin Jordan, Ralph F. Keeling, Ingeborg Levin, Fabian Maier, Andrew C. Manning, Heiko Moossen, Saqr Munassar, Penelope A. Pickers, Michael Rothe, Yasunori Tohjima, and Sönke Zaehle
Atmos. Chem. Phys., 23, 15767–15782, https://doi.org/10.5194/acp-23-15767-2023, https://doi.org/10.5194/acp-23-15767-2023, 2023
Short summary
Short summary
The carbon dioxide content of the Earth atmosphere is increasing due to human emissions from burning of fossil fuels, causing global climate change. The strength of the fossil-fuel emissions is estimated by inventories based on energy data, but independent validation of these inventories has been recommended by the Intergovernmental Panel on Climate Change. Here we investigate the potential to validate inventories based on measurements of small changes in the atmospheric oxygen content.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Chenwei Xiao, Sönke Zaehle, Hui Yang, Jean-Pierre Wigneron, Christiane Schmullius, and Ana Bastos
Earth Syst. Dynam., 14, 1211–1237, https://doi.org/10.5194/esd-14-1211-2023, https://doi.org/10.5194/esd-14-1211-2023, 2023
Short summary
Short summary
Ecosystem resistance reflects their susceptibility during adverse conditions and can be changed by land management. We estimate ecosystem resistance to drought and temperature globally. We find a higher resistance to drought in forests compared to croplands and an evident loss of resistance to drought when primary forests are converted to secondary forests or they are harvested. Old-growth trees tend to be more resistant in some forests and crops benefit from irrigation during drought periods.
Richard Nair, Yunpeng Luo, Tarek El-Madany, Victor Rolo, Javier Pacheco-Labrador, Silvia Caldararu, Kendalynn A. Morris, Marion Schrumpf, Arnaud Carrara, Gerardo Moreno, Markus Reichstein, and Mirco Migliavacca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2434, https://doi.org/10.5194/egusphere-2023-2434, 2023
Preprint archived
Short summary
Short summary
We studied a Mediterranean ecosystem to understand carbon uptake efficiency and its controls. These ecosystems face potential nitrogen-phosphorus imbalances due to pollution. Analysing six years of carbon data, we assessed controls at different timeframes. This is crucial for predicting such vulnerable regions. Our findings revealed N limitation on C uptake, not N:P imbalance, and strong influence of water availability. whether drought or wetness promoted net C uptake depended on timescale.
Silvia Caldararu, Victor Rolo, Benjamin D. Stocker, Teresa E. Gimeno, and Richard Nair
Biogeosciences, 20, 3637–3649, https://doi.org/10.5194/bg-20-3637-2023, https://doi.org/10.5194/bg-20-3637-2023, 2023
Short summary
Short summary
Ecosystem manipulative experiments are large experiments in real ecosystems. They include processes such as species interactions and weather that would be omitted in more controlled settings. They offer a high level of realism but are underused in combination with vegetation models used to predict the response of ecosystems to global change. We propose a workflow using models and ecosystem experiments together, taking advantage of the benefits of both tools for Earth system understanding.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
Short summary
Short summary
Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Aparnna Ravi, Dhanyalekshmi Pillai, Christoph Gerbig, Stephen Sitch, Sönke Zaehle, Vishnu Thilakan, and Chandra Shekhar Jha
EGUsphere, https://doi.org/10.5194/egusphere-2023-817, https://doi.org/10.5194/egusphere-2023-817, 2023
Preprint archived
Short summary
Short summary
We derive high-resolution terrestrial CO2 fluxes over India from 2012 to 2020. This is achieved by utilizing satellite-based vegetation indices and meteorological data in a data-driven biospheric model. The model simulations are improved by incorporating soil variables and SIF retrievals from satellite instruments and relate them to ecosystem productivity across different biomes. The derived flux products better explain the flux variability compared to other existing model estimates.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, Joachim Biercamp, Hendryk Bockelmann, Renate Brokopf, Nils Brüggemann, Lucas Casaroli, Fatemeh Chegini, George Datseris, Monika Esch, Geet George, Marco Giorgetta, Oliver Gutjahr, Helmuth Haak, Moritz Hanke, Tatiana Ilyina, Thomas Jahns, Johann Jungclaus, Marcel Kern, Daniel Klocke, Lukas Kluft, Tobias Kölling, Luis Kornblueh, Sergey Kosukhin, Clarissa Kroll, Junhong Lee, Thorsten Mauritsen, Carolin Mehlmann, Theresa Mieslinger, Ann Kristin Naumann, Laura Paccini, Angel Peinado, Divya Sri Praturi, Dian Putrasahan, Sebastian Rast, Thomas Riddick, Niklas Roeber, Hauke Schmidt, Uwe Schulzweida, Florian Schütte, Hans Segura, Radomyra Shevchenko, Vikram Singh, Mia Specht, Claudia Christine Stephan, Jin-Song von Storch, Raphaela Vogel, Christian Wengel, Marius Winkler, Florian Ziemen, Jochem Marotzke, and Bjorn Stevens
Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
Short summary
Short summary
Models of the Earth system used to understand climate and predict its change typically employ a grid spacing of about 100 km. Yet, many atmospheric and oceanic processes occur on much smaller scales. In this study, we present a new model configuration designed for the simulation of the components of the Earth system and their interactions at kilometer and smaller scales, allowing an explicit representation of the main drivers of the flow of energy and matter by solving the underlying equations.
Lin Yu, Silvia Caldararu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, Julian Helfenstein, Chiara Pistocchi, and Sönke Zaehle
Biogeosciences, 20, 57–73, https://doi.org/10.5194/bg-20-57-2023, https://doi.org/10.5194/bg-20-57-2023, 2023
Short summary
Short summary
In this study, we addressed a key weakness in current ecosystem models regarding the phosphorus exchange in the soil and developed a new scheme to describe this process. We showed that the new scheme improved the model performance for plant productivity, soil organic carbon, and soil phosphorus content at five beech forest sites in Germany. We claim that this new model could be used as a better tool to study ecosystems under future climate change, particularly phosphorus-limited systems.
Rainer Schneck, Veronika Gayler, Julia E. M. S. Nabel, Thomas Raddatz, Christian H. Reick, and Reiner Schnur
Geosci. Model Dev., 15, 8581–8611, https://doi.org/10.5194/gmd-15-8581-2022, https://doi.org/10.5194/gmd-15-8581-2022, 2022
Short summary
Short summary
The versions of ICON-A and ICON-Land/JSBACHv4 used for this study constitute the first milestone in the development of the new ICON Earth System Model ICON-ESM. JSBACHv4 is the successor of JSBACHv3, and most of the parameterizations of JSBACHv4 are re-implementations from JSBACHv3. We assess and compare the performance of JSBACHv4 and JSBACHv3. Overall, the JSBACHv4 results are as good as JSBACHv3, but both models reveal the same main shortcomings, e.g. the depiction of the leaf area index.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
Short summary
Short summary
This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Taraka Davies-Barnard, Sönke Zaehle, and Pierre Friedlingstein
Biogeosciences, 19, 3491–3503, https://doi.org/10.5194/bg-19-3491-2022, https://doi.org/10.5194/bg-19-3491-2022, 2022
Short summary
Short summary
Biological nitrogen fixation is the largest natural input of new nitrogen onto land. Earth system models mainly represent global total terrestrial biological nitrogen fixation within observational uncertainties but overestimate tropical fixation. The model range of increase in biological nitrogen fixation in the SSP3-7.0 scenario is 3 % to 87 %. While biological nitrogen fixation is a key source of new nitrogen, its predictive power for net primary productivity in models is limited.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
Short summary
Short summary
The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
Short summary
Short summary
Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
Short summary
Short summary
Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Martina Franz and Sönke Zaehle
Biogeosciences, 18, 3219–3241, https://doi.org/10.5194/bg-18-3219-2021, https://doi.org/10.5194/bg-18-3219-2021, 2021
Short summary
Short summary
The combined effects of ozone and nitrogen deposition on the terrestrial carbon uptake and storage has been unclear. Our simulations, from 1850 to 2099, show that ozone-related damage considerably reduced gross primary production and carbon storage in the past. The growth-stimulating effect induced by nitrogen deposition is offset until the 2050s. Accounting for nitrogen deposition without considering ozone effects might lead to an overestimation of terrestrial carbon uptake and storage.
Wolfgang A. Obermeier, Julia E. M. S. Nabel, Tammas Loughran, Kerstin Hartung, Ana Bastos, Felix Havermann, Peter Anthoni, Almut Arneth, Daniel S. Goll, Sebastian Lienert, Danica Lombardozzi, Sebastiaan Luyssaert, Patrick C. McGuire, Joe R. Melton, Benjamin Poulter, Stephen Sitch, Michael O. Sullivan, Hanqin Tian, Anthony P. Walker, Andrew J. Wiltshire, Soenke Zaehle, and Julia Pongratz
Earth Syst. Dynam., 12, 635–670, https://doi.org/10.5194/esd-12-635-2021, https://doi.org/10.5194/esd-12-635-2021, 2021
Short summary
Short summary
We provide the first spatio-temporally explicit comparison of different model-derived fluxes from land use and land cover changes (fLULCCs) by using the TRENDY v8 dynamic global vegetation models used in the 2019 global carbon budget. We find huge regional fLULCC differences resulting from environmental assumptions, simulated periods, and the timing of land use and land cover changes, and we argue for a method consistent across time and space and for carefully choosing the accounting period.
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021, https://doi.org/10.5194/acp-21-6663-2021, 2021
Short summary
Short summary
NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite observes atmospheric CO2 globally. We use a multiple regression and inverse model to quantify the relationships between OCO-2 and environmental drivers within individual years for 2015–2018 and within seven global biomes. Our results point to limitations of current space-based observations for inferring environmental relationships but also indicate the potential to inform key relationships that are very uncertain in process-based models.
Andrew J. Wiltshire, Eleanor J. Burke, Sarah E. Chadburn, Chris D. Jones, Peter M. Cox, Taraka Davies-Barnard, Pierre Friedlingstein, Anna B. Harper, Spencer Liddicoat, Stephen Sitch, and Sönke Zaehle
Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, https://doi.org/10.5194/gmd-14-2161-2021, 2021
Short summary
Short summary
Limited nitrogen availbility can restrict the growth of plants and their ability to assimilate carbon. It is important to include the impact of this process on the global land carbon cycle. This paper presents a model of the coupled land carbon and nitrogen cycle, which is included within the UK Earth System model to improve projections of climate change and impacts on ecosystems.
Daniele Peano, Deborah Hemming, Stefano Materia, Christine Delire, Yuanchao Fan, Emilie Joetzjer, Hanna Lee, Julia E. M. S. Nabel, Taejin Park, Philippe Peylin, David Wårlind, Andy Wiltshire, and Sönke Zaehle
Biogeosciences, 18, 2405–2428, https://doi.org/10.5194/bg-18-2405-2021, https://doi.org/10.5194/bg-18-2405-2021, 2021
Short summary
Short summary
Global climate models are the scientist’s tools used for studying past, present, and future climate conditions. This work examines the ability of a group of our tools in reproducing and capturing the right timing and length of the season when plants show their green leaves. This season, indeed, is fundamental for CO2 exchanges between land, atmosphere, and climate. This work shows that discrepancies compared to observations remain, demanding further polishing of these tools.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Tea Thum, Julia E. M. S. Nabel, Aki Tsuruta, Tuula Aalto, Edward J. Dlugokencky, Jari Liski, Ingrid T. Luijkx, Tiina Markkanen, Julia Pongratz, Yukio Yoshida, and Sönke Zaehle
Biogeosciences, 17, 5721–5743, https://doi.org/10.5194/bg-17-5721-2020, https://doi.org/10.5194/bg-17-5721-2020, 2020
Short summary
Short summary
Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.
Taraka Davies-Barnard, Johannes Meyerholt, Sönke Zaehle, Pierre Friedlingstein, Victor Brovkin, Yuanchao Fan, Rosie A. Fisher, Chris D. Jones, Hanna Lee, Daniele Peano, Benjamin Smith, David Wårlind, and Andy J. Wiltshire
Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, https://doi.org/10.5194/bg-17-5129-2020, 2020
Shufen Pan, Naiqing Pan, Hanqin Tian, Pierre Friedlingstein, Stephen Sitch, Hao Shi, Vivek K. Arora, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Julia E. M. S. Nabel, Catherine Ottlé, Benjamin Poulter, Sönke Zaehle, and Steven W. Running
Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, https://doi.org/10.5194/hess-24-1485-2020, 2020
Short summary
Short summary
Evapotranspiration (ET) links global water, carbon and energy cycles. We used 4 remote sensing models, 2 machine-learning algorithms and 14 land surface models to analyze the changes in global terrestrial ET. These three categories of approaches agreed well in terms of ET intensity. For 1982–2011, all models showed that Earth greening enhanced terrestrial ET. The small interannual variability of global terrestrial ET suggests it has a potential planetary boundary of around 600 mm yr-1.
Lin Yu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 13, 783–803, https://doi.org/10.5194/gmd-13-783-2020, https://doi.org/10.5194/gmd-13-783-2020, 2020
Short summary
Short summary
In this paper, we have developed a new soil organic carbon model that describes the formation and turnover of soil organic matter in a more mechanistic manner. With this model, we are able to better represent how microorganisms and nutrient processes influence the below-ground carbon storage and better explain some observed features of soil organic matter. We hope this model can increase our confidence in predictions of future climate change, particularly on how soil can mitigate the process.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
Short summary
Short summary
The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Nicolas Vuichard, Palmira Messina, Sebastiaan Luyssaert, Bertrand Guenet, Sönke Zaehle, Josefine Ghattas, Vladislav Bastrikov, and Philippe Peylin
Geosci. Model Dev., 12, 4751–4779, https://doi.org/10.5194/gmd-12-4751-2019, https://doi.org/10.5194/gmd-12-4751-2019, 2019
Short summary
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.
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, https://doi.org/10.5194/gmd-12-4075-2019, https://doi.org/10.5194/gmd-12-4075-2019, 2019
Short summary
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.
Karel Castro-Morales, Gregor Schürmann, Christoph Köstler, Christian Rödenbeck, Martin Heimann, and Sönke Zaehle
Biogeosciences, 16, 3009–3032, https://doi.org/10.5194/bg-16-3009-2019, https://doi.org/10.5194/bg-16-3009-2019, 2019
Short summary
Short summary
To obtain nearly 30 years of global terrestrial carbon fluxes, we simultaneously incorporated in a land surface model three different time periods of two observational data sets: absorbed photosynthetic active radiation and atmospheric CO2 concentrations. One decade of data is enough to improve the modeled long-term trends and seasonal amplitudes of the assimilated variables, particularly in boreal regions. This model has the potential to provide short-term predictions of land carbon fluxes.
Mingkai Jiang, Sönke Zaehle, Martin G. De Kauwe, Anthony P. Walker, Silvia Caldararu, David S. Ellsworth, and Belinda E. Medlyn
Geosci. Model Dev., 12, 2069–2089, https://doi.org/10.5194/gmd-12-2069-2019, https://doi.org/10.5194/gmd-12-2069-2019, 2019
Short summary
Short summary
Here we used a simple analytical framework developed by Comins and McMurtrie (1993) to investigate how different model assumptions affected plant responses to elevated CO2. This framework is useful in revealing both the consequences and the mechanisms through which different assumptions affect predictions. We therefore recommend the use of this framework to analyze the likely outcomes of new assumptions before introducing them to complex model structures.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Judith Hauck, Julia Pongratz, Penelope A. Pickers, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Almut Arneth, Vivek K. Arora, Leticia Barbero, Ana Bastos, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Scott C. Doney, Thanos Gkritzalis, Daniel S. Goll, Ian Harris, Vanessa Haverd, Forrest M. Hoffman, Mario Hoppema, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Truls Johannessen, Chris D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Peter Landschützer, Nathalie Lefèvre, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Craig Neill, Are Olsen, Tsueno Ono, Prabir Patra, Anna Peregon, Wouter Peters, Philippe Peylin, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Matthias Rocher, Christian Rödenbeck, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Tobias Steinhoff, Adrienne Sutton, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, Rebecca Wright, Sönke Zaehle, and Bo Zheng
Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, https://doi.org/10.5194/essd-10-2141-2018, 2018
Short summary
Short summary
The Global Carbon Budget 2018 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Martina Franz, Rocio Alonso, Almut Arneth, Patrick Büker, Susana Elvira, Giacomo Gerosa, Lisa Emberson, Zhaozhong Feng, Didier Le Thiec, Riccardo Marzuoli, Elina Oksanen, Johan Uddling, Matthew Wilkinson, and Sönke Zaehle
Biogeosciences, 15, 6941–6957, https://doi.org/10.5194/bg-15-6941-2018, https://doi.org/10.5194/bg-15-6941-2018, 2018
Short summary
Short summary
Four published ozone damage functions previously used in terrestrial biosphere models were evaluated regarding their ability to simulate observed biomass dose–response relationships using the O-CN model. Neither damage function was able to reproduce the observed ozone-induced biomass reductions. Calibrating a plant-functional-type-specific relationship between accumulated ozone uptake and leaf-level photosynthesis did lead to a good agreement between observed and modelled ozone damage.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
Short summary
Short summary
We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Johannes Meyerholt and Sönke Zaehle
Biogeosciences, 15, 5677–5698, https://doi.org/10.5194/bg-15-5677-2018, https://doi.org/10.5194/bg-15-5677-2018, 2018
Short summary
Short summary
Terrestrial biosphere models employ various representations of ecosystem nitrogen loss, some based on soil N availability, some based on net N mineralization. We show in local and global simulations that this variety leads to pronounced uncertainty in the predicted magnitude and sign of ecosystem N loss change under elevated CO2. Suprisingly, this uncertainty barely affects predicted carbon storage responses to elevated CO2, illustrating the need for new benchmarks especially in the boreal zone.
Werner von Bloh, Sibyll Schaphoff, Christoph Müller, Susanne Rolinski, Katharina Waha, and Sönke Zaehle
Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, https://doi.org/10.5194/gmd-11-2789-2018, 2018
Short summary
Short summary
The dynamics of the terrestrial carbon cycle are of central importance for Earth system science. Nutrient limitations, especially from nitrogen, are important constraints on vegetation growth and the terrestrial carbon cycle. We extended the well-established global vegetation, hydrology, and crop model LPJmL with a nitrogen cycle. We find significant improvement in global patterns of crop productivity. Regional differences in crop productivity can now be largely reproduced by the model.
Karel Castro-Morales, Thomas Kleinen, Sonja Kaiser, Sönke Zaehle, Fanny Kittler, Min Jung Kwon, Christian Beer, and Mathias Göckede
Biogeosciences, 15, 2691–2722, https://doi.org/10.5194/bg-15-2691-2018, https://doi.org/10.5194/bg-15-2691-2018, 2018
Short summary
Short summary
We present year-round methane emissions from wetlands in Northeast Siberia that were simulated with a land surface model. Ground-based flux measurements from the same area were used for evaluation of the model results, finding a best agreement with the observations in the summertime emissions that take place in this region predominantly through plants. During winter, methane emissions through the snow contribute 4 % of the total annual methane budget, but these are still underestimated.
Christian Rödenbeck, Sönke Zaehle, Ralph Keeling, and Martin Heimann
Biogeosciences, 15, 2481–2498, https://doi.org/10.5194/bg-15-2481-2018, https://doi.org/10.5194/bg-15-2481-2018, 2018
Short summary
Short summary
In this paper we investigate how the CO2 exchange between the land vegetation and the atmosphere varies from year to year. We quantify the relation between variations in the CO2 exchange and variations in air temperature. For this quantification, we use long-term measurements of CO2 in the air at many locations, a simulation code for the transport of carbon dioxide through the atmosphere, and a data set of air temperature. The results help us to understand the mechanisms of CO2 exchange.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek K. Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Catherine E. Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antonio Padin, Anna Peregon, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, and Dan Zhu
Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, https://doi.org/10.5194/essd-10-405-2018, 2018
Short summary
Short summary
The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Wei Li, Philippe Ciais, Shushi Peng, Chao Yue, Yilong Wang, Martin Thurner, Sassan S. Saatchi, Almut Arneth, Valerio Avitabile, Nuno Carvalhais, Anna B. Harper, Etsushi Kato, Charles Koven, Yi Y. Liu, Julia E.M.S. Nabel, Yude Pan, Julia Pongratz, Benjamin Poulter, Thomas A. M. Pugh, Maurizio Santoro, Stephen Sitch, Benjamin D. Stocker, Nicolas Viovy, Andy Wiltshire, Rasoul Yousefpour, and Sönke Zaehle
Biogeosciences, 14, 5053–5067, https://doi.org/10.5194/bg-14-5053-2017, https://doi.org/10.5194/bg-14-5053-2017, 2017
Short summary
Short summary
We used several observation-based biomass datasets to constrain the historical land-use change carbon emissions simulated by models. Compared to the range of the original modeled emissions (from 94 to 273 Pg C), the observationally constrained global cumulative emission estimate is 155 ± 50 Pg C (1σ Gaussian error) from 1901 to 2012. Our approach can also be applied to evaluate the LULCC impact of land-based climate mitigation policies.
Daniel S. Goll, Nicolas Vuichard, Fabienne Maignan, Albert Jornet-Puig, Jordi Sardans, Aurelie Violette, Shushi Peng, Yan Sun, Marko Kvakic, Matthieu Guimberteau, Bertrand Guenet, Soenke Zaehle, Josep Penuelas, Ivan Janssens, and Philippe Ciais
Geosci. Model Dev., 10, 3745–3770, https://doi.org/10.5194/gmd-10-3745-2017, https://doi.org/10.5194/gmd-10-3745-2017, 2017
Short summary
Short summary
We describe a representation of the terrestrial phosphorus cycle for the ORCHIDEE land surface model. The model is able to reproduce the observed shift from nitrogen to phosphorus limited net primary productivity along a soil formation chronosequence in Hawaii, as well as the contrasting responses of net primary productivity to nutrient addition. However, the simulated nutrient use efficiencies are lower, as observed primarily due to biases in the nutrient content and turnover of woody biomass.
Yao Gao, Tiina Markkanen, Mika Aurela, Ivan Mammarella, Tea Thum, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences, 14, 4409–4422, https://doi.org/10.5194/bg-14-4409-2017, https://doi.org/10.5194/bg-14-4409-2017, 2017
Short summary
Short summary
We investigated the response of water use efficiency (WUE) to summer drought in a boreal Scots pine forest (Pinus sylvestris) on the daily time scale mainly using EC flux data from the Hyytiälä (southern Finland) flux site. Simulation results from the JSBACH land surface model were also evaluated against the observed results. The performance of three WUE metrics at the ecosystem level (EWUE, IWUE, and uWUE) during the severe summer drought were studied and showed different results.
Tea Thum, Sönke Zaehle, Philipp Köhler, Tuula Aalto, Mika Aurela, Luis Guanter, Pasi Kolari, Tuomas Laurila, Annalea Lohila, Federico Magnani, Christiaan Van Der Tol, and Tiina Markkanen
Biogeosciences, 14, 1969–1987, https://doi.org/10.5194/bg-14-1969-2017, https://doi.org/10.5194/bg-14-1969-2017, 2017
Short summary
Short summary
Modelling seasonal cycle at the coniferous forests poses a challenge. We implemented a model for sun-induced chlorophyll fluorescence (SIF) to a land surface model JSBACH. It was used to study the seasonality of the carbon cycle in the Fenno-Scandinavian region. Comparison was made to direct CO2 flux measurements and satellite observations of SIF. SIF proved to be a better proxy for photosynthesis than the fraction of absorbed photosynthetically active radiation.
Martina Franz, David Simpson, Almut Arneth, and Sönke Zaehle
Biogeosciences, 14, 45–71, https://doi.org/10.5194/bg-14-45-2017, https://doi.org/10.5194/bg-14-45-2017, 2017
Short summary
Short summary
Ozone is a toxic air pollutant that can damage plant leaves and impact their carbon uptake from the atmosphere. We extend a terrestrial biosphere model to account for ozone damage of plants and investigate the impact on the terrestrial carbon cycle. Our approach accounts for ozone transport from the free troposphere to leaf level. We find that this substantially affects simulated ozone uptake into the plants. Simulations indicate that ozone damages plants less than expected from previous studies
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
Short summary
Short summary
The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Fang Zhao, Ning Zeng, Ghassem Asrar, Pierre Friedlingstein, Akihiko Ito, Atul Jain, Eugenia Kalnay, Etsushi Kato, Charles D. Koven, Ben Poulter, Rashid Rafique, Stephen Sitch, Shijie Shu, Beni Stocker, Nicolas Viovy, Andy Wiltshire, and Sonke Zaehle
Biogeosciences, 13, 5121–5137, https://doi.org/10.5194/bg-13-5121-2016, https://doi.org/10.5194/bg-13-5121-2016, 2016
Short summary
Short summary
The increasing seasonality of atmospheric CO2 is strongly linked with enhanced land vegetation activities in the last 5 decades, for which the importance of increasing CO2, climate and land use/cover change was evaluated in single model studies (Zeng et al., 2014; Forkel et al., 2016). Here we examine the relative importance of these factors in multiple models. Our results highlight models can show similar results in some benchmarks with different underlying regional dynamics.
Gregor J. Schürmann, Thomas Kaminski, Christoph Köstler, Nuno Carvalhais, Michael Voßbeck, Jens Kattge, Ralf Giering, Christian Rödenbeck, Martin Heimann, and Sönke Zaehle
Geosci. Model Dev., 9, 2999–3026, https://doi.org/10.5194/gmd-9-2999-2016, https://doi.org/10.5194/gmd-9-2999-2016, 2016
Short summary
Short summary
We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS). The system improves the modelled carbon cycle of the terrestrial biosphere by systematically confronting (or assimilating) the model with observations of atmospheric CO2 and fractions of absorbed photosynthetically active radiation. Jointly assimilating both data streams outperforms the single-data stream experiments, thus showing the value of a multi-data stream assimilation.
Chris D. Jones, Vivek Arora, Pierre Friedlingstein, Laurent Bopp, Victor Brovkin, John Dunne, Heather Graven, Forrest Hoffman, Tatiana Ilyina, Jasmin G. John, Martin Jung, Michio Kawamiya, Charlie Koven, Julia Pongratz, Thomas Raddatz, James T. Randerson, and Sönke Zaehle
Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, https://doi.org/10.5194/gmd-9-2853-2016, 2016
Short summary
Short summary
How the carbon cycle interacts with climate will affect future climate change and how society plans emissions reductions to achieve climate targets. The Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) is an endorsed activity of CMIP6 and aims to quantify these interactions and feedbacks in state-of-the-art climate models. This paper lays out the experimental protocol for modelling groups to follow to contribute to C4MIP. It is a contribution to the CMIP6 GMD Special Issue.
Johannes Meyerholt, Sönke Zaehle, and Matthew J. Smith
Biogeosciences, 13, 1491–1518, https://doi.org/10.5194/bg-13-1491-2016, https://doi.org/10.5194/bg-13-1491-2016, 2016
Short summary
Short summary
We investigated how today's state-of-the-art terrestrial biosphere models represent biological nitrogen fixation and what the consequences of varying representation are for model predictions under ambient conditions and under scenarios of elevated atmospheric carbon dioxide concentrations. We found that varying global nitrogen fixation rates are simulated under ambient conditions and that the responses of the simulated carbon and nitrogen cycles are significantly affected under perturbation.
Silvia Caldararu, Drew W. Purves, and Matthew J. Smith
Biogeosciences, 13, 925–941, https://doi.org/10.5194/bg-13-925-2016, https://doi.org/10.5194/bg-13-925-2016, 2016
Short summary
Short summary
The plant functional type (PFT) concept is widely used in global vegetation models but recent studies have attempted to replace this with a more biologically representative formulation by using plant traits. In this study we aim to quantify the performance of a data-constrained leaf phenology model that uses PFTs when compared to one that uses local traits. We show that the PFT model performs relatively poorly but we can identify a small number of traits that improve model performance.
Y. Gao, T. Markkanen, T. Thum, M. Aurela, A. Lohila, I. Mammarella, M. Kämäräinen, S. Hagemann, and T. Aalto
Hydrol. Earth Syst. Sci., 20, 175–191, https://doi.org/10.5194/hess-20-175-2016, https://doi.org/10.5194/hess-20-175-2016, 2016
G. Murray-Tortarolo, P. Friedlingstein, S. Sitch, V. J. Jaramillo, F. Murguía-Flores, A. Anav, Y. Liu, A. Arneth, A. Arvanitis, A. Harper, A. Jain, E. Kato, C. Koven, B. Poulter, B. D. Stocker, A. Wiltshire, S. Zaehle, and N. Zeng
Biogeosciences, 13, 223–238, https://doi.org/10.5194/bg-13-223-2016, https://doi.org/10.5194/bg-13-223-2016, 2016
Short summary
Short summary
We modelled the carbon (C) cycle in Mexico for three different time periods: past (20th century), present (2000-2005) and future (2006-2100). We used different available products to estimate C stocks and fluxes in the country. Contrary to other current estimates, our results showed that Mexico was a C sink and this is likely to continue in the next century (unless the most extreme climate-change scenarios are reached).
D. Fowler, C. E. Steadman, D. Stevenson, M. Coyle, R. M. Rees, U. M. Skiba, M. A. Sutton, J. N. Cape, A. J. Dore, M. Vieno, D. Simpson, S. Zaehle, B. D. Stocker, M. Rinaldi, M. C. Facchini, C. R. Flechard, E. Nemitz, M. Twigg, J. W. Erisman, K. Butterbach-Bahl, and J. N. Galloway
Atmos. Chem. Phys., 15, 13849–13893, https://doi.org/10.5194/acp-15-13849-2015, https://doi.org/10.5194/acp-15-13849-2015, 2015
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
Short summary
Short summary
Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
S. Olin, M. Lindeskog, T. A. M. Pugh, G. Schurgers, D. Wårlind, M. Mishurov, S. Zaehle, B. D. Stocker, B. Smith, and A. Arneth
Earth Syst. Dynam., 6, 745–768, https://doi.org/10.5194/esd-6-745-2015, https://doi.org/10.5194/esd-6-745-2015, 2015
Short summary
Short summary
Croplands are vital ecosystems for human well-being. Properly managed they can supply food, store carbon and even sequester carbon from the atmosphere. Conversely, if poorly managed, croplands can be a source of nitrogen to inland and coastal waters, causing algal blooms, and a source of carbon dioxide to the atmosphere, accentuating climate change. Here we studied cropland management types for their potential to store carbon and minimize nitrogen losses while maintaining crop yields.
M. H. Vermeulen, B. J. Kruijt, T. Hickler, and P. Kabat
Earth Syst. Dynam., 6, 485–503, https://doi.org/10.5194/esd-6-485-2015, https://doi.org/10.5194/esd-6-485-2015, 2015
Short summary
Short summary
We compared a process-based ecosystem model (LPJ-GUESS) with EC measurements to test whether observed interannual variability (IAV) in carbon and water fluxes can be reproduced because it is important to understand the driving mechanisms of IAV. We show that the model's mechanistic process representation for photosynthesis at low temperatures and during drought could be improved, but other process representations are still lacking in order to fully reproduce the observed IAV.
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
C. Le Quéré, G. P. Peters, R. J. Andres, R. M. Andrew, T. A. Boden, P. Ciais, P. Friedlingstein, R. A. Houghton, G. Marland, R. Moriarty, S. Sitch, P. Tans, A. Arneth, A. Arvanitis, D. C. E. Bakker, L. Bopp, J. G. Canadell, L. P. Chini, S. C. Doney, A. Harper, I. Harris, J. I. House, A. K. Jain, S. D. Jones, E. Kato, R. F. Keeling, K. Klein Goldewijk, A. Körtzinger, C. Koven, N. Lefèvre, F. Maignan, A. Omar, T. Ono, G.-H. Park, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. Schwinger, J. Segschneider, B. D. Stocker, T. Takahashi, B. Tilbrook, S. van Heuven, N. Viovy, R. Wanninkhof, A. Wiltshire, and S. Zaehle
Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, https://doi.org/10.5194/essd-6-235-2014, 2014
B. Smith, D. Wårlind, A. Arneth, T. Hickler, P. Leadley, J. Siltberg, and S. Zaehle
Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, https://doi.org/10.5194/bg-11-2027-2014, 2014
S. Caldararu, D. W. Purves, and P. I. Palmer
Biogeosciences, 11, 763–778, https://doi.org/10.5194/bg-11-763-2014, https://doi.org/10.5194/bg-11-763-2014, 2014
D. Dalmonech, A. M. Foley, A. Anav, P. Friedlingstein, A. D. Friend, M. Kidston, M. Willeit, and S. Zaehle
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-2083-2014, https://doi.org/10.5194/bgd-11-2083-2014, 2014
Revised manuscript has not been submitted
B. Guenet, F. E. Moyano, N. Vuichard, G. J. D. Kirk, P. H. Bellamy, S. Zaehle, and P. Ciais
Geosci. Model Dev., 6, 2153–2163, https://doi.org/10.5194/gmd-6-2153-2013, https://doi.org/10.5194/gmd-6-2153-2013, 2013
A. M. Foley, D. Dalmonech, A. D. Friend, F. Aires, A. T. Archibald, P. Bartlein, L. Bopp, J. Chappellaz, P. Cox, N. R. Edwards, G. Feulner, P. Friedlingstein, S. P. Harrison, P. O. Hopcroft, C. D. Jones, J. Kolassa, J. G. Levine, I. C. Prentice, J. Pyle, N. Vázquez Riveiros, E. W. Wolff, and S. Zaehle
Biogeosciences, 10, 8305–8328, https://doi.org/10.5194/bg-10-8305-2013, https://doi.org/10.5194/bg-10-8305-2013, 2013
D. Dalmonech and S. Zaehle
Biogeosciences, 10, 4189–4210, https://doi.org/10.5194/bg-10-4189-2013, https://doi.org/10.5194/bg-10-4189-2013, 2013
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
Related subject area
Biogeosciences
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0)
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Data-Informed Inversion Model (DIIM): a framework to retrieve marine optical constituents in the BOUSSOLE site using a three-stream irradiance model
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025, https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Short summary
Physical–biogeochemical ocean global models are required to analyze difficult oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Felix Nößler, Thibault Moulin, Oksana Buzhdygan, Britta Tietjen, and Felix May
EGUsphere, https://doi.org/10.5194/egusphere-2024-3798, https://doi.org/10.5194/egusphere-2024-3798, 2024
Short summary
Short summary
To predict the response of grassland plant communities to management and climate change, we developed the computer model GrasslandTraitSim.jl. Unlike other models, it uses measurable plant traits such as height, leaf thinness, and root structure as inputs, rather than hard-to-measure species data. This allows realistic simulation of many species. The model tracks daily changes in above- and below-ground biomass, plant height, and soil water, linking plant community composition to biomass supply.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Carlos Enmanuel Soto López, Fabio Anselmi, Mirna Gharbi Dit Kacem, and Paolo Lazzari
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-174, https://doi.org/10.5194/gmd-2024-174, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Our goal was to use an analytical expression to estimate the density of optical constituents, allowing us to have an interpretable formulation consistent with the laws of physics. We focused on a probabilistic approach, optimizing the model and retrieving quantities with their respective uncertainty. Considering future application to Big Data, we also explored a Neural Network based method, retrieving computationally efficient estimates, maintaining consistency with the analytical expression.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Joshua Coupe, Nicole S. Lovenduski, Luise S. Gleason, Michael N. Levy, Kristen Krumhardt, Keith Lindsay, Charles Bardeen, Clay Tabor, Cheryl Harrison, Kenneth G. MacLeod, Siddhartha Mitra, and Julio Sepúlveda
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-94, https://doi.org/10.5194/gmd-2024-94, 2024
Short summary
Short summary
We develop a new feature in the atmosphere and ocean components of the Community Earth System Model version 2. We have implemented ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation. The new feature will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Cited articles
Ahrens, B., Braakhekke, M. C., Guggenberger, G., Schrumpf, M., and Reichstein,
M.: Contribution of sorption, DOC transport and microbial interactions to the
14C age of a soil organic carbon profile: Insights from a calibrated process
model, Soil Biol. Biochem., 88, 390–402, 2015. a
Ammann, C., Flechard, C., Leifeld, J., Neftel, A., and Fuhrer, J.: The carbon
budget of newly established temperate grassland depends on management
intensity, Agr. Ecosyst. Environ., 121, 5–20,
https://doi.org/10.1016/j.agee.2006.12.002,
2007. a
Araújo, A. C., Nobre, A. D., Kruijt, B., Elbers, J. A., Dallarosa, R.,
Stefani, P., von Randow, C., Manzi, A. O., Culf, A. D., Gash, J. H. C.,
Valentini, R., and Kabat, P.: Comparative measurements of carbon dioxide
fluxes from two nearby towers in a central Amazonian rainforest: The Manaus
LBA site, J. Geophys. Res.-Atmos., 107, LBA 58–1–LBA
58–20, https://doi.org/10.1029/2001JD000676,
2002. a
Archibald, S., Nickless, A., Govender, N., Scholes, R. J., and Lehsten, V.:
Climate and the inter-annual variability of fire in southern Africa: a
meta-analysis using long-term field data and satellite-derived burnt area
data, Global Ecol. Biogeogr., 19, 794–809,
https://doi.org/10.1111/j.1466-8238.2010.00568.x,
2010. a
Atkin, O. K., Meir, P., and Turnbull, M. H.: Improving representation of leaf
respiration in large-scale predictive climate–vegetation models, New
Phytol., 202, 743–748, https://doi.org/10.1111/nph.12686,
2014. a, b
Ball, J., Wooddrow, I., 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: Biggens, J., Dordrecht, The Netherlands, 1–4, 1987. a
Berbigier, P., Bonnefond, J.-M., and Mellmann, P.: CO2 and water vapour fluxes
for 2 years above Euroflux forest site, Agr. Forest Meteorol.,
108, 183–197, https://doi.org/10.1016/S0168-1923(01)00240-4,
2001. a
Bergeron, O., Margolis, H. A., Black, T. A., Coursolle, C., Dunn, A. L., Barr,
A. G., and Wofsy, S. C.: Comparison of carbon dioxide fluxes over three
boreal black spruce forests in Canada, Glob. Change Biol., 13, 89–107,
https://doi.org/10.1111/j.1365-2486.2006.01281.x,
2007. a
Beringer, J., Hutley, L. B., Hacker, J. M., Neininger, B., and Paw, U. K. T.:
Patterns and processes of carbon, water and energy cycles across northern
Australian landscapes: From point to region, Agr. Forest
Meteorol., 151, 1409–1416,
https://doi.org/10.1016/j.agrformet.2011.05.003,
2011. a
Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Protis Jr., A. R., and Long,
S. P.: Improved temperature response functions for models of Rubisco-limited
photosynthesis, Plant Cell Environ., 24, 253–259, 2001. a
Beven, K.: On doing better hydrological science, Hydrol. Process., 22,
3549–3553, 2008. a
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate
Benefits of Forests, Science, 320, 1444–1449,
https://doi.org/10.1126/science.1155121,
2008. a
Bonan, G. B. and Doney, S. C.: Climate, ecosystems, and planetary futures: The
challenge to predict life in Earth system models, Science, 359, eaam8328,
https://doi.org/10.1126/science.aam8328,
2018. a
Bradford, M. A., Wieder, W. R., Bonan, G. B., Fierer, N., Raymond, P. A., and
Crowther, T. W.: Managing uncertainty in soil carbon feedbacks to climate
change, Nat. Clim. Change, 6, 751–758, https://doi.org/10.1038/nclimate3071,
2016. a
Brahney, J., Mahowald, N., Ward, D. S., Ballantyne, A. P., and Neff, J. C.: Is
atmospheric phosphorus pollution altering global alpine Lake stoichiometry?,
Global Biogeochem. Cy., 29, 1369–1383, https://doi.org/10.1002/2015GB005137,
2015. a
Campioli, M., Vicca, S., Luyssaert, S., and Bilcke, J.: Biomass production
efficiency controlled by management in temperate and boreal ecosystems,
Nat. Geosci., 8, 843–846, 2015. a
Carrara, A., Janssens, I. A., Yuste, J. C., and Ceulemans, R.: Seasonal changes
in photosynthesis, respiration and NEE of a mixed temperate forest,
Agr. Forest Meteorol., 126, 15–31,
https://doi.org/10.1016/j.agrformet.2004.05.002,
2004. a
Chien, C.-T., Mackey, K. R. M., Dutkiewicz, S., Mahowald, N. M., Prospero,
J. M., and Paytan, A.: Effects of African dust deposition on phytoplankton in
the western tropical Atlantic Ocean off Barbados, Global Biogeochem.
Cy., 30, 716–734, https://doi.org/10.1002/2015GB005334,
2016. a
Chiesi, M., Maselli, F., Bindi, M., Fibbi, L., Cherubini, P., Arlotta, E.,
Tirone, G., Matteucci, G., and Seufert, G.: Modelling carbon budget of
Mediterranean forests using ground and remote sensing measurements,
Agr. Forest Meteorol., 135, 22–34,
https://doi.org/10.1016/j.agrformet.2005.09.011,
2005. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Cleugh, H. A., Leuning, R., Mu, Q., and Running, S. W.: Regional evaporation
estimates from flux tower and MODIS satellite data, Remote Sens.
Environ., 106, 285–304,
https://doi.org/10.1016/j.rse.2006.07.007,
2007. a
Cleveland, C. C., Townsend, A. R., Schimel, D. S., Fisher, H., Howarth, R. W.,
Hedin, L. O., Perakis, S. S., Latty, E. F., Von Fischer, J. C., Elseroad, A.,
and Wasson, M. F.: Global patterns of terrestrial biological nitrogen (N-2)
fixation in natural ecosystems, Global Biogeochem. Cy., 13, 623–645,
1999. a
Cleveland, C. C., Houlton, B. Z., Smith, W. K., Marklein, A. R., Reed, S. C.,
Parton, W. J., Del Grosso, S. J., and Running, S.: Patterns of new versus
recycled primary production in the terrestrial biosphere, P.
Natl. Acad. Sci. USA, 110, 12733–12737, https://doi.org/10.1073/pnas.1302768110, 2013. a
Cook, B. D., Davis, K. J., Wang, W., Desai, A., Berger, B. W., Teclaw, R. M.,
Martin, J. G., Bolstad, P. V., Bakwin, P. S., Yi, C., and Heilman, W.: Carbon
exchange and venting anomalies in an upland deciduous forest in northern
Wisconsin, USA, Agr. Forest Meteorol., 126, 271–295,
https://doi.org/10.1016/j.agrformet.2004.06.008,
2004. a
Cornwell, W., Wright, I., Turner, J., Maire, V., Barbour, M., Cernusak, L.,
Dawson, T., Ellsworth, D., Farquhar, G., Griffiths, H., Keitel, C., Knohl,
A., Reich, P., Williams, D., Bhaskar, R., Cornelissen, J., Richards, A.,
Schmidt, S., Valladares, F., C, K., Schulze, E.-D., Buchmann, N., and
Santiago, L.: Leaf Δ13C database, available at:
https://github.com/wcornwell/leaf13C/tree/v0.1.0 (last access: 31 July 2018),
2018a. a
Cornwell, W. K., Wright, I. J., Turner, J., Maire, V., Barbour, M. M.,
Cernusak, L. A., Dawson, T., Ellsworth, D., Farquhar, G. D., Griffiths, H.,
Keitel, C., Knohl, A., Reich, P. B., Williams, D. G., Bhaskar, R.,
Cornelissen, J. H. C., Richards, A., Schmidt, S., Valladares, F., Körner,
C., Schulze, E.-D., Buchmann, N., and Santiago, L. S.: Climate and soils
together regulate photosynthetic carbon isotope discrimination within C3
plants worldwide, Global Ecol. Biogeogr., 27, 1056–1067,
2018b. a, b, c, d
De Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C.,
Hickler, T., Jain, A. K., Luo, Y., Parton, W. J., Prentice, I. C., Smith, B.,
Thornton, P. E., Wang, S., Wang, Y.-P., Wårlind, D., Weng, E., Crous,
Kristine, Y., Ellsworth, D. S., Hanson, P. J., Seok, K. H., Warren, J. M.,
Oren, R., and Norby, R. J.: Forest water use and water use efficiency at
elevated CO2: a model-data intercomparison at two contrasting temperate
forest FACE sites, Glob. Change Biol., 19, 1759–1779,
https://doi.org/10.1111/gcb.12164,
2013. a, b
De Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C., Wang,
Y.-P., Luo, Y., Jain, A. K., El-Masri, B., Hickler, T., Wårlind, D., Weng,
E., Parton, W. J., Thornton, P. E., Wang, S., Prentice, I. C., Asao, S.,
Smith, B., McCarthy, H. R., Iversen, C. M., Hanson, P. J., Warren, J. M.,
Oren, R., and Norby, R. J.: Where does the carbon go? A model–data
intercomparison of vegetation carbon allocation and turnover processes at two
temperate forest free-air CO2 enrichment sites, New Phytol., 203,
883–899, https://doi.org/10.1111/nph.12847,
2014. a
Desai, A. R., Bolstad, P. V., Cook, B. D., Davis, K. J., and Carey, E. V.:
Comparing net ecosystem exchange of carbon dioxide between an old-growth and
mature forest in the upper Midwest, USA, Agr. Forest Meteorol.,
128, 33–55, https://doi.org/10.1016/j.agrformet.2004.09.005,
2005. a
Dickinson, R. E., Oleson, K. W., Bonan, G., Hoffman, F., Thornton, P.,
Vertenstein, M., Yang, Z.-L., and Zeng, X.: The Community Land Model and Its
Climate Statistics as a Component of the Community Climate System Model,
J. Climate, 19, 2302–2324, https://doi.org/10.1175/JCLI3742.1,
2006. a
Dolman, A., Moors, E., and Elbers, J.: The carbon uptake of a mid latitude pine
forest growing on sandy soil, Agr. Forest Meteorol., 111, 157–170, https://doi.org/10.1016/S0168-1923(02)00024-2,
2002. a
Dunn, A. L., Barford, C. C., Wofsy, S. C., Goulden, M. L., and Daube, B. C.: A
long-term record of carbon exchange in a boreal black spruce forest: means,
responses to interannual variability, and decadal trends, Glob. Change
Biol., 13, 577–590, https://doi.org/10.1111/j.1365-2486.2006.01221.x,
2007. a
Egea, G., Verhoef, A., and Vidale, P. L.: Towards an improved and more
flexible representation of water stress in coupled
photosynthesis–stomatal conductance
models, Agr. Forest Meteorol., 151, 1370–1384, 2011. a
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S. A., Cox, P. M.,
Huntingford, C., Meir, P., and Ian Woodward, F.: Assessing uncertainties in
a second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681, 2010. a
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. a
Friend, A. D.: Terrestrial plant production and climate change, J.
Exp. Bot., 61, 1293–1309, 2010. a
Friend, A. D., Stevens, A. K., Knox, R. G., and Cannell, M. G. R.: A
process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid
v3.0), Ecol. Model., 95, 249–287, 1997. a
Friend, A. D., Geider, R. J., Behrenfeld, M. J., and Still, C. J.:
Photosynthesis in Global-Scale Models, in: Photosynthesis in silico:
Understanding complexity from molecules to ecosystems, edited by: Laisk, A.,
Nedbal, L., and Govindjee, Springer, 465–497, 2009. a
Gerber, S., Hedin, L. O., Oppenheimer, M., Pacala, S. W., and Shevliakova, E.:
Nitrogen cycling and feedbacks in a global dynamic land model, Global
Biogeochem. Cy., 24, GB1001 https://doi.org/10.1029-2008GB003336, 2010. a
Goldstein, A., Hultman, N., Fracheboud, J., Bauer, M., Panek, J., Xu, M., Qi,
Y., Guenther, A., and Baugh, W.: Effects of climate variability on the carbon
dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in
the Sierra Nevada (CA), Agr. Forest Meteorol., 101, 113–129,
https://doi.org/10.1016/S0168-1923(99)00168-9,
2000. a
Goll, D. S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J., Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B., Zaehle, S., Penuelas, J., Janssens, I., and Ciais, P.: A representation of the phosphorus cycle for ORCHIDEE (revision 4520), Geosci. Model Dev., 10, 3745–3770, https://doi.org/10.5194/gmd-10-3745-2017, 2017. a, b, c, d, e
Granier, A., Ceschia, E., Damesin, C., Dufrêne, E., Epron, D., Gross, P.,
Lebaube, S., Le Dantec, V., Le Goff, N., Lemoine, D., Lucot, E., Ottorini,
J. M., Pontailler, J. Y., and Saugier, B.: The carbon balance of a young
Beech forest, Funct. Ecol., 14, 312–325,
https://doi.org/10.1046/j.1365-2435.2000.00434.x,
2000. a
Graven, H., Allison, C. E., Etheridge, D. M., Hammer, S., Keeling, R. F., Levin, I., Meijer, H. A. J., Rubino, M., Tans, P. P., Trudinger, C. M., Vaughn, B. H., and White, J. W. C.: Compiled records of carbon isotopes in atmospheric CO2 for historical simulations in CMIP6, Geosci. Model Dev., 10, 4405–4417, https://doi.org/10.5194/gmd-10-4405-2017, 2017. a
Grünwald, T. and Bernhofer, C.: A decade of carbon, water and energy flux
measurements of an old spruce forest at the Anchor Station Tharandt, Tellus
B, 59, 387–396, https://doi.org/10.1111/j.1600-0889.2007.00259.x,
2007. a
Grünzweig, J. M., Lin, T., Rotenberg, E., Schwartz, A., and Yakir, D.: Carbon
sequestration in arid-land forest, Glob. Change Biol., 9, 791–799,
https://doi.org/10.1046/j.1365-2486.2003.00612.x,
2003. a
Gu, L., Meyers, T., Pallardy, S. G., Hanson, P. J., Yang, B., Heuer, M.,
Hosman, K. P., Riggs, J. S., Sluss, D., and Wullschleger, S. D.: Direct and
indirect effects of atmospheric conditions and soil moisture on surface
energy partitioning revealed by a prolonged drought at a temperate forest
site, J. Geophys. Res.-Atmos., 111, D16102,
https://doi.org/10.1029/2006JD007161,
2006. a
Hararuk, O., Smith, M. J., and Luo, Y.: Microbial models with data-driven
parameters predict stronger soil carbon responses to climate change, Glob.
Change Biol., 21, 2439–2453, https://doi.org/10.1111/gcb.12827,
2015. a
Hartmann, H., Adams, H. D., Hammond, W. M., Hoch, G., Landhäusser, S. M.,
Wiley, E., and Zaehle, S.: Identifying differences in carbohydrate dynamics
of seedlings and mature trees to improve carbon allocation in models for
trees and forests, Environ. Exp. Bot., 152, 7–18, 2018. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., and
Kilibarda, M.: SoilGrids250m: global gridded soil information based on
Machine Learning, PLoS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017. a
Hollinger, D. Y., Goltz, S. M., Davidson, E. A., Lee, J. T., Tu, K., and
Valentine, H. T.: Seasonal patterns and environmental control of carbon
dioxide and water vapour exchange in an ecotonal boreal forest, Glob. Change
Biol., 5, 891–902, https://doi.org/10.1046/j.1365-2486.1999.00281.x,
1999. a
Hou, E., Chen, C., Luo, Y., Zhou, G., Kuang, Y., Zhang, Y., Heenan, M., Lu, X.,
and Wen, D.: Effects of climate on soil phosphorus cycle and availability in
natural terrestrial ecosystems, Glob. Change Biol., 24, 3344–3356,
https://doi.org/10.1111/gcb.14093,
2018. a
Hungate, B. A., Dukes, J. S., Shaw, M. R., Luo, Y., and Field, C. B.: Nitrogen
and Climate Change, Science, 302, 1512–1513, https://doi.org/10.1126/science.1091390,
2003. a
Hyvönen, R., Agren, G. I., Linder, S., Persson, T., Cotrufo, M. F., Ekblad,
A., Freeman, M., Grelle, A., Janssens, I. A., Jarvis, P. G., Kellomäki,
S., Lindroth, A., Loustau, D., Lundmark, T., Norby, R. J., Oren, R.,
Pilegaard, K., Ryan, M. G., Sigurdsson, B. D., Strömgren, M., van Oijen,
M., and Wallin, G.: The likely impact of elevated [CO2], nitrogen
deposition, increased temperature and management on carbon sequestration in
temperate and boreal forest ecosystems: a literature review, New
Phytol., 173, 463–480, 2007. a, b
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E.,
and Schulze, E. D.: A global analysis of root distributions for terrestrial
biomes, Oecologia, 108, 389–411, 1996. a
Jiang, M., Caldararu, S., Zaehle, S., Ellsworth, D. S., and Medlyn, B. E.:
Towards a more physiological representation of vegetation phosphorus
processes in land surface models, New Phytol., 222, 1223–1229,
https://doi.org/10.1111/nph.15688,
2019. a
June, T., Evans, J. R., and Farquhar, G. D.: A simple new equation for the
reversible temperature dependence of photosynthetic electron transport: a
study on soybean leaf, Funct. Plant Biol., 31, 275–283 2004. a
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.,
Caverner-Bares, J., Chambers, J. Q., Chapin III, 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., Fand, 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., Thomas, H., 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., Patino, 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,
2011. a, b
Keenan, T., Sabate, S., and Gracia, C.: Soil water stress and coupled
photosynthesis–conductance models: Bridging the gap between conflicting
reports on the relative roles of stomatal, mesophyll conductance and
biochemical limitations to photosynthesis, Agr. Forest
Meteorol., 150, 443–453,
https://doi.org/10.1016/j.agrformet.2010.01.008,
2010. a
Knauer, J., Werner, C., and Zaehle, S.: Evaluating stomatal models and their
atmospheric drought response in a land surface scheme: A multibiome
analysis, J. Geophys. Res.-Biogeo., 120, 1894–1911,
2015. a
Knauer, J., Zaehle, S., Reichstein, M., Medlyn, B. E., Forkel, M., Hagemann,
S., and Werner, C.: The response of ecosystem water-use efficiency to rising
atmospheric CO2 concentrations: sensitivity and large-scale biogeochemical
implications, New Phytol., 213, 1654–1666, https://doi.org/10.1111/nph.14288,
2016. a
Knauer, J., Zaehle, S., De Kauwe, M., Bahar, N. H., Evans, J. R., Medlyn,
B. E., Reichstein, M., and Werner, C.: Effects of mesophyll conductance on
vegetation responses to elevated CO2 concentrations in a land surface model,
Glob. Change Biol., 25, 1820–1838,https://doi.org/10.1111/gcb.14604, 2019. a
Korhonen, J. F. J., Pihlatie, M., Pumpanen, J., Aaltonen, H., Hari, P., Levula, J., Kieloaho, A.-J., Nikinmaa, E., Vesala, T., and Ilvesniemi, H.: Nitrogen balance of a boreal Scots pine forest, Biogeosciences, 10, 1083–1095, https://doi.org/10.5194/bg-10-1083-2013, 2013. a, b
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins, W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4, Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, 2013. a
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J.,
Friedlingstein, P., Ciais, P., Sitch, S. A., and Prentice, I. C.: A dynamic
global vegetation model for studies of the coupled atmosphere-biosphere
system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005. a
Kutsch, W. L., Kolle, O., Rebmann, C., Knohl, A., Ziegler, W., and Schulze,
E.-D.: Advection and resulting CO2 exchange uncertainty in a tall forest in
central Germany, Ecol. App., 18, 1391–1405,
https://doi.org/10.1890/06-1301.1,
2008. a
Lagergren, F., Lindroth, A., Dellwik, E., Ibrom, A., Lankreijer, H.,
Launiainen, S., Mölder, M., Kolari, P., Pilegaard, K., and Vesala, T.:
Biophysical controls on CO2 fluxes of three Northern forests based on
long-term eddy covariance data, Tellus B, 60, 143–152,
https://doi.org/10.1111/j.1600-0889.2006.00324.x,
2008. a, b
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017-7039, https://doi.org/10.5194/acp-10-7017-2010, 2010. a
Lamarque, J.-F., Kyle, G. P., Meinshausen, M., Riahi, K., Smith, S. J., van
Vuuren, D. P., Conley, A. J., and Vitt, F.: Global and regional evolution of
short-lived radiatively-active gases and aerosols in the Representative
Concentration Pathways, Climatic Change, 109, 191–212,
https://doi.org/10.1007/s10584-011-0155-0,
2011. a
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Pongratz, J., Manning, A. C., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Jackson, R. B., Boden, T. A., Tans, P. P., Andrews, O. D., Arora, V. K., Bakker, D. C. E., Barbero, L., Becker, M., Betts, R. A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Cosca, C. E., Cross, J., Currie, K., Gasser, T., Harris, I., Hauck, J., Haverd, V., Houghton, R. A., Hunt, C. W., Hurtt, G., Ilyina, T., Jain, A. K., Kato, E., Kautz, M., Keeling, R. F., Klein Goldewijk, K., Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lima, I., Lombardozzi, D., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Nojiri, Y., Padin, X. A., Peregon, A., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Reimer, J., Rödenbeck, C., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Viovy, N., Vuichard, N., Walker, A. P., Watson, A. J., Wiltshire, A. J., Zaehle, S., and Zhu, D.: Global Carbon Budget 2017, Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, 2018. a
Lindroth, A., Klemedtsson, L., Grelle, A., Weslien, P., and Langvall, O.:
Measurement of net ecosystem exchange, productivity and respiration in three
spruce forests in Sweden shows unexpectedly large soil carbon losses,
Biogeochem., 89, 43–60, https://doi.org/10.1007/s10533-007-9137-8,
2008. a
Lloyd, J. and Taylor, J. A.: On the temperature dependence of soil respiration,
Funct. Ecol., 8, 315–323,
1994. a
Ma, S., Baldocchi, D. D., Xu, L., and Hehn, T.: Inter-annual variability in
carbon dioxide exchange of an oak/grass savanna and open grassland in
California, Agr. Forest Meteorol., 147, 157–171,
https://doi.org/10.1016/j.agrformet.2007.07.008,
2007. a, b
Massoud, E. C., Xu, C., Fisher, R. A., Knox, R. G., Walker, A. P., Serbin, S. P., Christoffersen, B. O., Holm, J. A., Kueppers, L. M., Ricciuto, D. M., Wei, L., Johnson, D. J., Chambers, J. Q., Koven, C. D., McDowell, N. G., and Vrugt, J. A.: Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES), Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, 2019. a, b
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H.,
Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenez
de la Cuesta Otero, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D.,
Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D.,
Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A.,
Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H.,
Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T.,
Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt,
H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I.,
Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., de Vrese, P., Wieners,
K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the
MPI-M Earth System Model version 1.2 (MPI-ESM 1.2) and its response to
increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400,
2019. a
Medlyn, B. E., Zaehle, S., De Kauwe, M. G., Walker, A. P., Dietze, M. C.,
Hanson, P. J., Hickler, T., Jain, A. K., Luo, Y., Parton, W., Prentice,
I. C., Thornton, P. E., Wang, S., Wang, Y.-P., Weng, E., Iversen, C. M.,
McCarthy, H. R., Warren, J. M., Oren, R., and Norby, R. J.: Using ecosystem
experiments to improve vegetation models, Nat. Clim. Change, 5,
528–534, 2015. a
Medlyn, B. E., De Kauwe, M. G., Zaehle, S., Walker, A. P., Duursma, R. A.,
Luus, K., Mishurov, M., Pak, B., Smith, B., Wang, Y.-P., Yang, X., Crous,
K. Y., Drake, J. E., Gimeno, T. E., Macdonald, C. A., Norby, R. J., Power,
S. A., Tjoelker, M. G., and Ellsworth, D. S.: Using models to guide field
experiments: a priori predictions for the CO2 response of a nutrient-
and water-limited native Eucalypt woodland, Glob. Change Biol., 22,
2834–2851, 2016. a
Medlyn, B. E., De Kauwe, M. G., Lin, Y.-S., Knauer, J., Duursma, R. A.,
Williams, C. A., Arneth, A., Clement, R., Isaac, P., Limousin, J. M.,
Linderson, M.-L., Meir, P., Martin-StPaul, N., and Wingate, L.: How do leaf
and ecosystem measures of water-use efficiency compare?, New Phytol.,
216, 758–770, 2017. a, b
Mercado, L. M., Medlyn, B. E., Huntingford, C., Oliver, R. J., Clark, D. B.,
Sitch, S. A., Zelazowski, P., Kattge, J., Harper, A. B., and Cox, P. M.:
Large sensitivity in land carbon storage due to geographical and temporal
variation in the thermal response of photosynthetic capacity, New
Phytol., 218, 1462–1477, 2018. a
Meyerholt, J. and Zaehle, S.: The role of stoichiometric flexibility in
modelling forest ecosystem responses to nitrogen fertilization, New
Phytol., 208, 1042–1055, https://doi.org/10.1111/nph.13547, 2015. a, b
Mkhabela, M., Amiro, B., Barr, A., Black, T., Hawthorne, I., Kidston, J.,
McCaughey, J., Orchansky, A., Nesic, Z., Sass, A., Shashkov, A., and Zha, T.:
Comparison of carbon dynamics and water use efficiency following fire and
harvesting in Canadian boreal forests, Agr. Forest Meteorol.,
149, 783–794, https://doi.org/10.1016/j.agrformet.2008.10.025,
2009. a
Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., and Yakir, D.: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation, Biogeosciences, 3, 571–583, https://doi.org/10.5194/bg-3-571-2006, 2006. a
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes,
R. J., Schimmel, D. S., Kirchner, T., Menaut, J. C., Seastedt, T., Moya,
E. G., Kamnalrut, A., and Kinyamario, J. I.: Observations and modelling of
biomass and soil organic matter dynamics for the grassland biome worldwide,
Global Biogeochem. Cy., 7, 785–809, 1993. a
Peng, J., Dan, L., and Huang, M.: Sensitivity of Global and Regional
Terrestrial Carbon Storage to the Direct CO2 Effect and Climate Change Based
on the CMIP5 Model Intercomparison, PLOS ONE, 9, 1–17,
https://doi.org/10.1371/journal.pone.0095282,
2014. a
Peñuelas, J., Poulter, B., Sardans, J., Ciais, P., van der Velde, M., Bopp,
L., Boucher, O., Godderis, Y., Hinsinger, P., Llusia, Joan Nardin, E., Vicca,
S., Obersteiner, M., and Janssens, I. A.: Human-induced
nitrogen–phosphorus imbalances alter natural and managed ecosystems across
the globe, Nat. Commun., 4, 2934, https://doi.org/10.1038/ncomms3934,
2013. a
Prentice, I. C., Liang, X., Medlyn, B. E., and Wang, Y.-P.: Reliable, robust and realistic: the three R's of next-generation land-surface modelling, Atmos. Chem. Phys., 15, 5987-6005, https://doi.org/10.5194/acp-15-5987-2015, 2015. a
Raddatz, T. J., Reick, C. H., Knorr, W., Kattge, J., Roeckner, E., Schnur, R.,
Schnitzler, K.-G., Wetzel, P., and Jungclaus, J.: Will the tropical land
biosphere dominate the climate–carbon cycle feedback during the twenty-first
century?, Clim. Dynam., 29, 565–574, https://doi.org/10.1007/s00382-007-0247-8,
2007. a
Rebmann, C., Anthoni, P., E., F., Göckede, M., Mangold, A., Subke, J.-A.,
Thomas, C., Wichura, B., Schulze, E.-D., Tenhunen, J. D., and Foken, T.:
Carbon Budget of a Spruce Forest Ecosystem, in: Biogeochemistry of Forested
Catchments in a Changing Environment, edited by: Matzner, E.,
Springer, 143–159, https://doi.org/10.1007/978-3-662-06073-5_8, 2004. a
Reed, S. C., Townsend, A. R., Taylor, P. G., and Cleveland, C. C.: Phosphorus
Cycling in Tropical Forests Growing on Highly Weathered Soils, in: Phosphorus
in Action Biological Processes in Soil Phosphorus Cycling, edited by:
Bünemann, E., Oberson, A., and Frossard, E., Springer Berlin
Heidelberg, Berlin, Heidelberg, 339–369, https://doi.org/10.1007/978-3-642-15271-9, 2011. a
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier,
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T.,
Havránková, 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. a
Roeckner, E., Tompkins, A., Bäuml, G., Bonaventura, L., Brokopf, R., Esch,
M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E.,
Rhodin, E., Schlese, U., and Schulzweida, U.: The atmospheric general
circulation model ECHAM5 – Part I: Model description, Tech. Rep. 349, Max
Planck Institute for Meteorology, Hamburg, 2003. a
Roupsard, O., Bonnefond, J.-M., Irvine, M., Berbigier, P., Nouvellon, Y.,
Dauzat, J., Taga, S., Hamel, O., Jourdan, C., Saint-André, L., Mialet-Serra,
I., Labouisse, J.-P., Epron, D., Joffre, R., Braconnier, S., Rouzière, A.,
Navarro, M., and Bouillet, J.-P.: Partitioning energy and evapo-transpiration
above and below a tropical palm canopy, Agr. Forest Meteorol.,
139, 252–268, https://doi.org/10.1016/j.agrformet.2006.07.006,
2006. a
Sanz, M., Carrara, A., Gimeno, C., Bucher, A., and Lopez, R.: Effects of a dry
and warm summer conditions on CO2 and energy fluxes from three
Mediterranean ecosystems, Geophys. Res. Abstr., 6, 3239–3239,
2004. a
Saxton, K. E. and Rawls, W. J.: Soil Water Characteristic Estimates by Texture
and Organic Matter for Hydrologic Solutions, Soil Sci. Soc. Am.
J., 70, 1569–1578, 2006. a
Scherer-Lorenzen, M., Schulze, E.-D., Don, A., Schumacher, J., and Weller, E.:
Exploring the functional significance of forest diversity: A new long-term
experiment with temperate tree species (BIOTREE), Perspect. Plant
Ecol., 9, 53–70,
https://doi.org/10.1016/j.ppees.2007.08.002,
2007. a
Schmid, H. P., Grimmond, C. B., Cropley, F., Offerle, B., and Su, H.-B.:
Measurements of CO2 and energy fluxes over a mixed hardwood forest in the
mid-western United States, Agr. Forest Meteorol., 103, 357–374, https://doi.org/10.1016/S0168-1923(00)00140-4,
2000. a
Scott, R. L., Jenerette, G. D., Potts, D. L., and Huxman, T. E.: Effects of
seasonal drought on net carbon dioxide exchange from a woody-plant-encroached
semiarid grassland, J. Geophys. Res.-Biogeo., 114, G04004,
https://doi.org/10.1029/2008JG000900,
2009. a
Shinozaki, K., Yoda, K., Hozumi, K., and Kira, T.: A quantitative analysis of
the plant form – the pipe model theory, Japanese Journal of Ecology, 14,
98–104, 1964. a
Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford, C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G., Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, 2015. a
Sitch, S. A., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., and Venevsky, S.:
Evaluation of ecosystem dynamics, plant geography and terrestrial carbon
cycling in the LPJ Dynamic Global Vegetation Model, Glob. Change Biol.,
9, 161–185, 2003. a, b, c, d
Suni, T., Rinne, J., Reissell, A., Altimir, N., Keronen, P., Rannik, Ü.,
Dal Maso, M., Kulmala, M., and Vesala, T.: Long-term measurements of surface
fluxes above a Scots pine forest in Hyytiälä, southern Finland, 1996–2001,
Boreal Environ. Res., 8, 287–301, 2003. a
Swann, A. L. S., Hoffman, F. M., Koven, C. D., and Randerson, J. T.: Plant
responses to increasing CO2 reduce estimates of climate impacts on drought
severity, P. Natl. Acade. Sci. USA, 113,
10019–10024, https://doi.org/10.1073/pnas.1604581113,
2016. a
Tedeschi, V., Rey, A., Manca, G., Valentini, R., Jarvis, P. G., and Borghetti,
M.: Soil respiration in a Mediterranean oak forest at different developmental
stages after coppicing, Glob. Change Biol., 12, 110–121,
https://doi.org/10.1111/j.1365-2486.2005.01081.x,
2006. a
Thornton, P., 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. a
Thum, T., Zaehle, S., Köhler, P., Aalto, T., Aurela, M., Guanter, L., Kolari, P., Laurila, T., Lohila, A., Magnani, F., Van Der Tol, C., and Markkanen, T.: Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe, Biogeosciences, 14, 1969–1987, https://doi.org/10.5194/bg-14-1969-2017, 2017. a
Tirone, G., Dore, S., Matteucci, G., Greco, S., and Valentini, R.: Evergreen
Mediterranean Forests. Carbon and Water Fluxes, Balances, Ecological and
Ecophysiological Determinants, in: Fluxes of Carbon, Water and Energy of
European Forests, edited by: Valentini, R., Springer Berlin
Heidelberg, Berlin, Heidelberg,125–149, https://doi.org/10.1007/978-3-662-05171-9_7,
2003. a
Urbanski, S., Barford, C., Wofsy, S., Kucharik, C., Pyle, E., Budney, J.,
McKain, K., Fitzjarrald, D., Czikowsky, M., and Munger, J. W.: Factors
controlling CO2 exchange on timescales from hourly to decadal at Harvard
Forest, J. Geophys. Res.-Biogeo., 112, G02020,
https://doi.org/10.1029/2006JG000293,
2007. a
Vargas, R., Sonnentag, O., Abramowitz, G., Carrara, A., Chen, J. M., Ciais, P.,
Correia, A., Keenan, T. F., Kobayashi, H., Ourcival, J.-M., Papale, D.,
Pearson, D., Pereira, J. S., Piao, S., Rambal, S., and Baldocchi, D. D.:
Drought Influences the Accuracy of Simulated Ecosystem Fluxes: A Model-Data
Meta-analysis for Mediterranean Oak Woodlands, Ecosystems, 16, 749–764,
https://doi.org/10.1007/s10021-013-9648-1,
2013. a
Vicca, S., Luyssaert, S., Peñuelas, J., Campioli, M., Chapin III, F. S.,
Ciais, P., Heinemeyer, A., Hogberg, P., Kutsch, W. L., Law, B. E., Malhi, Y.,
Papale, D., Piao, S. L., Reichstein, M., Schulze, E. D., and Janssens, I. A.:
Fertile forests produce biomass more efficiently, Ecol. Lett., 15,
520–526, 2012. a, b, c, d
Viovy, N.: CRU-NCEP dataset, v7, available at:
https://vesg.ipsl.upmc.fr/thredds/catalog/store/p529viov/cruncep/V7_1901_2015/catalog.html (last access: 27 June 2017),
2016. a
Vitousek, P. M. and Howarth, R. W.: Nitrogen limitation on land and in the sea
– how it can occur, Biogeochem., 13, 87–115, 1991. a
Vitousek, P. M., Porder, S., Houlton, B. Z., and Chadwick, O. A.: Terrestrial
phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus
interactions, Ecol. Appl., 20, 5–15, https://doi.org/10.1890/08-0127.1,
2010. a, b
Walker, T. W. and Syers, J. K.: The fate of phosphorus during pedogenesis,
Geoderma, 15, 1–19, https://doi.org/10.1016/0016-7061(76)90066-5,
1976. a
Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7, 2261–2282, https://doi.org/10.5194/bg-7-2261-2010, 2010. a, b, c, d
Wårlind, D., Smith, B., Hickler, T., and Arneth, A.: Nitrogen feedbacks increase future terrestrial ecosystem carbon uptake in an individual-based dynamic vegetation model, Biogeosciences, 11, 6131–6146, https://doi.org/10.5194/bg-11-6131-2014, 2014. a
White, R. E.: Principles and Practice of Soil Science The soil as a Natural
Resource, vol. 32, Blackwell Publishing, Malden, 4th Edn.,
https://doi.org/10.1177/0340035206070163, 2006. a
Wohlfahrt, G., Anderson-Dunn, M., Bahn, M., Balzarolo, M., Berninger, F.,
Campbell, C., Carrara, A., Cescatti, A., Christensen, T., Dore, S., Eugster,
W., Friborg, T., Furger, M., Gianelle, D., Gimeno, C., Hargreaves, K., Hari,
P., Haslwanter, A., Johansson, T., Marcolla, B., Milford, C., Nagy, Z.,
Nemitz, E., Rogiers, N., Sanz, M. J., Siegwolf, R. T., Susiluoto, S., Sutton,
M., Tuba, Z., Ugolini, F., Valentini, R., Zorer, R., and Cernusca, A.:
Biotic, Abiotic, and Management Controls on the Net Ecosystem CO2 Exchange of
European Mountain Grassland Ecosystems, Ecosystems, 11, 1338–1351,
https://doi.org/10.1007/s10021-008-9196-2,
2008a. a
Wohlfahrt, G., Hammerle, A., Haslwanter, A., Bahn, M., Tappeiner, U., and
Cernusca, A.: Seasonal and inter-annual variability of the net ecosystem CO2
exchange of a temperate mountain grassland: Effects of weather and
management, J. Geophys. Res.-Atmos., 113, D08110,
https://doi.org/10.1029/2007JD009286,
2008b. a
Wramneby, A., Smith, B., Zaehle, S., and Sykes, M. T.: Parameter uncertainties
in the modelling of vegetation dynamics – Effects on tree community structure
and ecosystem functioning in European forest biomes, Ecol. Model.,
216, 277–290, 2008. a
Yang, X., Post, W. M., Thornton, P. E., and Jain, A.: The distribution of soil
phosphorus for global biogeochemical modeling, Biogeosciences, 10,
2525–2537, https://doi.org/10.5194/bg-10-2525-2013, 2013.
a, b
Zaehle, S. and Dalmonech, D.: Carbon–nitrogen interactions on land
at global scales: current understanding in modelling climate biosphere
feedbacks, Curr. Opin. Env. Sust., 3, 311–320,
2011. a
Zaehle, S. and Friend, A. D.: Carbon and nitrogen cycle dynamics in the O-CN
land surface model: 1. Model description, site-scale evaluation, and
sensitivity to parameter estimates, Global Biogeochem. Cy., 24, GB1005, https://doi.org/10.1029/2009GB003521, 2010. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Zaehle, S., Sitch, S. A., Smith, B., and Hatterman, F.: Effects of parameter
uncertainties on the modeling of terrestrial biosphere dynamics, Global
Biogeochem. Cy., 19, GB3020,
https://doi.org/10.1029/2004GB002395, 2005. a, b, c, d
Zaehle, S., Friedlingstein, P., and Friend, A. D.: Terrestrial nitrogen
feedbacks may accelerate future climate change, Geophys. Res. Lett.,
37, L01401, https://doi.org/10.1029/2009GL041345,
2010. a
Zaehle, S., Ciais, P., Friend, A. D., and Prieur, V.: Carbon benefits of
anthropogenic reactive nitrogen offset by nitrous oxide emissions, Nat.
Geosci., 4, 601–605, 2011. a
Zaehle, S., Medlyn, B. E., De Kauwe, M. G., Walker, A. P., Dietze, M. C.,
Hickler, T., Luo, Y., Wang, Y.-P., El-Masri, B., Thornton, P., Jain, A.,
Wang, S., Warlind, D., Weng, E., Parton, W., Iversen, C. M., Gallet-Budynek,
A., McCarthy, H., Finzi, A., Hanson, P. J., Prentice, I. C., Oren, R., and
Norby, R. J.: Evaluation of 11 terrestrial carbon–nitrogen cycle models
against observations from two temperate Free-Air CO2 Enrichment studies, New
Phytol., 202, 803–822, https://doi.org/10.1111/nph.12697,
2014. a, b
Zerihun, A., McKenzie, B. A., and Morton, J. D.: Photosynthate costs associated
with the utilization of different nitrogen–forms: influence on the carbon
balance of plants and shoot–root biomass partitioning, New Phytol.,
138, 1–11, 1998. a
Zheng, M., Zhou, Z., Luo, Y., Zhao, P., and Mo, J.: Global pattern and
controls of biological nitrogen fixation under nutrient enrichment: A
meta-analysis, Glob. Change Biol., 25, 3018–3030, 2019. a
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.
To predict the response of the vegetation to climate change, we need global models that describe...
