Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6495-2022
© Author(s) 2022. 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-15-6495-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
30 Aug 2022
Model evaluation paper |
| 30 Aug 2022
| 30 Aug 2022
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
Johannes Oberpriller
CORRESPONDING AUTHOR
Theoretical Ecology Lab, University of Regensburg,
Universitätsstraße 31, Regensburg, Germany
Christine Herschlein
Department of Atmospheric Environmental Research (IMK-IFU), Karlsruhe
Institute of Technology, Kreuzeckbahnstr. 19, Garmisch-Partenkirchen,
Germany
Peter Anthoni
Department of Atmospheric Environmental Research (IMK-IFU), Karlsruhe
Institute of Technology, Kreuzeckbahnstr. 19, Garmisch-Partenkirchen,
Germany
Almut Arneth
Department of Atmospheric Environmental Research (IMK-IFU), Karlsruhe
Institute of Technology, Kreuzeckbahnstr. 19, Garmisch-Partenkirchen,
Germany
Andreas Krause
School of Life Sciences, Technical University of Munich, Freising,
Germany
Anja Rammig
School of Life Sciences, Technical University of Munich, Freising,
Germany
Mats Lindeskog
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Stefan Olin
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Florian Hartig
Theoretical Ecology Lab, University of Regensburg,
Universitätsstraße 31, Regensburg, Germany
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Zitong Jia, Shouzhi Chen, Yongshuo H. Fu, David Martín Belda, David Wårlind, Stefan Olin, Chongyu Xu, and Jing Tang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4064, https://doi.org/10.5194/egusphere-2025-4064, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Groundwater sustains vegetation and regulates land-atmosphere exchanges, but most Earth system models oversimplify its movement. Our study develops an integrated framework coupling LPJ-GUESS with the 3D hydrological model ParFlow to explicitly represent groundwater-vegetation interactions. Our results add to the evidence that three-dimensional groundwater flow strongly regulates water exchanges, and provides a powerful tool to improve simulations of water cycles in Earth system models.
Rubaya Pervin, Scott Robeson, Mallory Barnes, Stephen Sitch, Anthony Walker, Ben Poulter, Fabienne Maignan, Qing Sun, Thomas Colligan, Sönke Zaehle, Kashif Mahmud, Peter Anthoni, Almut Arneth, Vivek Arora, Vladislav Bastrikov, Liam Bogucki, Bertrand Decharme, Christine Delire, Stefanie Falk, Akihiko Ito, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Michael O’Sullivan, Wenping Yuan, and Natasha MacBean
EGUsphere, https://doi.org/10.5194/egusphere-2025-2841, https://doi.org/10.5194/egusphere-2025-2841, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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Drylands contribute more than a third of the global vegetation productivity. Yet, these regions are not well represented in global vegetation models. Here, we tested how well 15 global models capture annual changes in dryland vegetation productivity. Models that didn’t have vegetation change over time or fire have lower variability in vegetation productivity. Models need better representation of grass cover types and their coverage. Our work highlights where and how these models need to improve.
Benjamin F. Meyer, João P. 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
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025, https://doi.org/10.5194/gmd-18-4643-2025, 2025
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Climate change has increased the likelihood of drought events across Europe, potentially threatening the 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 its ability to capture species-specific evapotranspiration responses to drought and to reproduce flux observations of both gross primary production and evapotranspiration.
Lucia S. Layritz, Konstantin Gregor, Andreas Krause, Stefan Kruse, Benjamin F. Meyer, Thomas A. M. Pugh, and Anja Rammig
Biogeosciences, 22, 3635–3660, https://doi.org/10.5194/bg-22-3635-2025, https://doi.org/10.5194/bg-22-3635-2025, 2025
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Disturbances, such as fire, can change which vegetation grows in a forest, affecting water and carbon flows and, thus, the climate. Disturbances are expected to increase with climate change, but it is uncertain by how much. Using a simulation model, we studied how future climate, disturbances, and their combined effect impact northern (high-latitude) forest ecosystems. Our findings highlight the importance of considering these factors and the need to better understand how disturbances will change in the future.
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025, https://doi.org/10.5194/gmd-18-4317-2025, 2025
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We developed fast machine learning models to predict forest regrowth and carbon dynamics under climate change. These models mimic the outputs of a complex vegetation model but run 95 % faster, enabling global analyses and supporting climate solutions in large modeling frameworks such as LandSyMM.
Jette Elena Stoebke, David Wårlind, Stefan Olin, Annemarie Eckes-Shephard, Bogdan Brzeziecki, Mikko Peltoniemi, and Thomas A. M. Pugh
EGUsphere, https://doi.org/10.5194/egusphere-2025-2995, https://doi.org/10.5194/egusphere-2025-2995, 2025
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Forests are shaped by how trees compete for resources like sunlight. We improved a widely used vegetation model to better capture how light filters through the forest canopy, especially after tree death or harvesting. By assigning trees explicit positions, the model captures forest structure and change more realistically. This advances our understanding of tree competition and forest responses to management, providing a better tool to predict future forest dynamics.
Mateus Dantas de Paula, Tatiana Reichert, Laynara F. Lugli, Erica McGale, Kerstin Pierick, João Paulo Darela-Filho, Liam Langan, Jürgen Homeier, Anja Rammig, and Thomas Hickler
Biogeosciences, 22, 2707–2732, https://doi.org/10.5194/bg-22-2707-2025, https://doi.org/10.5194/bg-22-2707-2025, 2025
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This study explores how plant roots with different forms and functions rely on fungal partnerships for nutrient uptake. This relationship was integrated into a vegetation model and was tested in a tropical forest in Ecuador. The model accurately predicted root traits and showed that without fungi, biomass decreased by up to 80 %. The findings highlight the critical role of fungi in ecosystem processes and suggest that root–fungal interactions should be considered in vegetation models.
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).
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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.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
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An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
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
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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.
Dmitry Otryakhin, David Martín Belda, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2025-1401, https://doi.org/10.5194/egusphere-2025-1401, 2025
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We developed a methodology for comparison of simulation results by a dynamic global vegetation model (DGVM). Using this methodology, we reveal systematic differences between high- and low-resolution DGVM simulations caused by under-representation of climate variability in the low-resolution data and poor representation of shore lines and inland water bodies. In a study area covering European Union, the differences in aggregated output variables were found to be 2 %–10 %.
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
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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.
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
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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.
Prashant Paudel, Stefan Olin, Mark Tjoelker, Mikael Pontarp, Daniel Metcalfe, and Benjamin Smith
EGUsphere, https://doi.org/10.5194/egusphere-2024-3977, https://doi.org/10.5194/egusphere-2024-3977, 2025
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Ecological processes respond to changes in rainfall conditions. Competition and stress created by water availability are two primary components at two ends of the rainfall gradient. In wetter areas, plants compete for resources, while in drier regions, stress limits growth. The complex interaction between plant characters and their response to growth conditions governs ecosystem processes. These findings can be used to understand how future rainfall changes could impact ecosystems.
Fredrik Lagergren, Anna Maria Jönsson, Mats Lindeskog, and Thomas A. M. Pugh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-239, https://doi.org/10.5194/gmd-2024-239, 2025
Preprint under review for GMD
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The European spruce bark beetle (SBB) has, in recent years, been the most important disturbance agent in many European forests. We implemented a SBB module in a dynamic vegetation model and calibrated it against observations from Sweden, Switzerland, Austria and France. The start and duration of outbreaks triggered by storm damage and the increased damage driven by recent warm and dry periods were reasonably well simulated, although the spread was reflected in uncertain parameter estimates.
Chansopheaktra Sovann, Torbern Tagesson, Patrik Vestin, Sakada Sakhoeun, Soben Kim, Sothea Kok, and Stefan Olin
EGUsphere, https://doi.org/10.5194/egusphere-2024-3784, https://doi.org/10.5194/egusphere-2024-3784, 2025
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We offer pairwise observed datasets that compare the characteristics of tropical ecosystems, specifically pristine forests, regrowth forests, and cashew plantations. Our findings uncover some key differences in their characteristics, emphasizing the influence of human activities on these ecosystems. By sharing our unique datasets, we hope to improve the knowledge of tropical forest ecosystems in Southeast Asia, advancing tropical research, and tackling global environmental challenges.
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
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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.
Olivier Bouriaud, Ernst-Detlef Schulze, Konstantin Gregor, Issam Bourkhris, Peter Högberg, Roland Irslinger, Phillip Papastefanou, Julia Pongratz, Anja Rammig, Riccardo Valentini, and Christian Körner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3092, https://doi.org/10.5194/egusphere-2024-3092, 2024
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The impact of harvesting on forests' carbon sink capacities is debated. One view is that their sink strength is resilient to harvesting, the other that it disrupts these capacities. Our work shows that leaf area index (LAI) has been overlooked in this discussion. We found that temperate forests' carbon uptake is largely insensitive to variations in LAI beyond about 4 m² m-², but that forests operate at higher levels.
Jens Krause, Peter Anthoni, Mike Harfoot, Moritz Kupisch, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-1646, https://doi.org/10.5194/egusphere-2024-1646, 2024
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While animal biodiversity is facing a global crisis as more and more species are becoming endangered or extinct, the role of animals for the functioning of ecosystems is still not fully understood. We contribute to bridging this gap by coupling a animal population model with a vegetation and thus enable future research in this topic.
Chansopheaktra Sovann, Torbern Tagesson, Patrik Vestin, Sakada Sakhoeun, Soben Kim, Sothea Kok, and Stefan Olin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-98, https://doi.org/10.5194/essd-2024-98, 2024
Revised manuscript not accepted
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We offer pairwise observed datasets that compare the characteristics of tropical ecosystems, specifically pristine forests, regrowth forests, and cashew plantations. Our findings uncover some key differences in their characteristics, emphasizing the influence of human activities on these ecosystems. By sharing our unique datasets, we hope to improve the knowledge of tropical forest ecosystems in Southeast Asia, advancing tropical research, and tackling global environmental challenges.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
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
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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.
Benjamin F. Meyer, Allan Buras, Konstantin Gregor, Lucia S. Layritz, Adriana Principe, Jürgen Kreyling, Anja Rammig, and Christian S. Zang
Biogeosciences, 21, 1355–1370, https://doi.org/10.5194/bg-21-1355-2024, https://doi.org/10.5194/bg-21-1355-2024, 2024
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Late-spring frost (LSF), critically low temperatures when trees have already flushed their leaves, results in freezing damage leaving trees with reduced ability to perform photosynthesis. Forests with a high proportion of susceptible species like European beech are particularly vulnerable. However, this process is rarely included in dynamic vegetation models (DVMs). We show that the effect on simulated productivity and biomass is substantial, warranting more widespread inclusion of LSF in DVMs.
João Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara Figueiredo Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
Earth Syst. Sci. Data, 16, 715–729, https://doi.org/10.5194/essd-16-715-2024, https://doi.org/10.5194/essd-16-715-2024, 2024
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Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it is important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
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
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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.
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
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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.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
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Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Qi Guan, Jing Tang, Lian Feng, Stefan Olin, and Guy Schurgers
Biogeosciences, 20, 1635–1648, https://doi.org/10.5194/bg-20-1635-2023, https://doi.org/10.5194/bg-20-1635-2023, 2023
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Understanding terrestrial sources of nitrogen is vital to examine lake eutrophication changes. Combining process-based ecosystem modeling and satellite observations, we found that land-leached nitrogen in the Yangtze Plain significantly increased from 1979 to 2018, and terrestrial nutrient sources were positively correlated with eutrophication trends observed in most lakes, demonstrating the necessity of sustainable nitrogen management to control eutrophication.
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
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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.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
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We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
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The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
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
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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.
Jianyong Ma, Sam S. Rabin, Peter Anthoni, Anita D. Bayer, Sylvia S. Nyawira, Stefan Olin, Longlong Xia, and Almut Arneth
Biogeosciences, 19, 2145–2169, https://doi.org/10.5194/bg-19-2145-2022, https://doi.org/10.5194/bg-19-2145-2022, 2022
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Improved agricultural management plays a vital role in protecting soils from degradation in eastern Africa. We simulated the impacts of seven management practices on soil carbon pools, nitrogen loss, and crop yield under different climate scenarios in this region. This study highlights the possibilities of conservation agriculture when targeting long-term environmental sustainability and food security in crop ecosystems, particularly for those with poor soil conditions in tropical climates.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Jianyong Ma, Stefan Olin, Peter Anthoni, Sam S. Rabin, Anita D. Bayer, Sylvia S. Nyawira, and Almut Arneth
Geosci. Model Dev., 15, 815–839, https://doi.org/10.5194/gmd-15-815-2022, https://doi.org/10.5194/gmd-15-815-2022, 2022
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The implementation of the biological N fixation process in LPJ-GUESS in this study provides an opportunity to quantify N fixation rates between legumes and to better estimate grain legume production on a global scale. It also helps to predict and detect the potential contribution of N-fixing plants as
green manureto reducing or removing the use of N fertilizer in global agricultural systems, considering different climate conditions, management practices, and land-use change scenarios.
Adrian Gustafson, Paul A. Miller, Robert G. Björk, Stefan Olin, and Benjamin Smith
Biogeosciences, 18, 6329–6347, https://doi.org/10.5194/bg-18-6329-2021, https://doi.org/10.5194/bg-18-6329-2021, 2021
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We performed model simulations of vegetation change for a historic period and a range of climate change scenarios at a high spatial resolution. Projected treeline advance continued at the same or increased rates compared to our historic simulation. Temperature isotherms advanced faster than treelines, revealing a lag in potential vegetation shifts that was modulated by nitrogen availability. At the year 2100 projected treelines had advanced by 45–195 elevational metres depending on the scenario.
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
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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.
Mats Lindeskog, Benjamin Smith, Fredrik Lagergren, Ekaterina Sycheva, Andrej Ficko, Hans Pretzsch, and Anja Rammig
Geosci. Model Dev., 14, 6071–6112, https://doi.org/10.5194/gmd-14-6071-2021, https://doi.org/10.5194/gmd-14-6071-2021, 2021
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Forests play an important role in the global carbon cycle and for carbon storage. In Europe, forests are intensively managed. To understand how management influences carbon storage in European forests, we implement detailed forest management into the dynamic vegetation model LPJ-GUESS. We test the model by comparing model output to typical forestry measures, such as growing stock and harvest data, for different countries in Europe.
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
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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.
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
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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.
Gilvan Sampaio, Marília H. Shimizu, Carlos A. Guimarães-Júnior, Felipe Alexandre, Marcelo Guatura, Manoel Cardoso, Tomas F. Domingues, Anja Rammig, Celso von Randow, Luiz F. C. Rezende, and David M. Lapola
Biogeosciences, 18, 2511–2525, https://doi.org/10.5194/bg-18-2511-2021, https://doi.org/10.5194/bg-18-2511-2021, 2021
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The impact of large-scale deforestation and the physiological effects of elevated atmospheric CO2 on Amazon rainfall are systematically compared in this study. Our results are remarkable in showing that the two disturbances cause equivalent rainfall decrease, though through different causal mechanisms. These results highlight the importance of not only curbing regional deforestation but also reducing global CO2 emissions to avoid climatic changes in the Amazon.
Anita D. Bayer, Richard Fuchs, Reinhard Mey, Andreas Krause, Peter H. Verburg, Peter Anthoni, and Almut Arneth
Earth Syst. Dynam., 12, 327–351, https://doi.org/10.5194/esd-12-327-2021, https://doi.org/10.5194/esd-12-327-2021, 2021
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Many projections of future land-use/-cover exist. We evaluate a number of these and determine the variability they cause in ecosystems and their services. We found that projections differ a lot in regional patterns, with some patterns being at least questionable in a historical context. Across ecosystem service indicators, resulting variability until 2040 was highest in crop production. Results emphasize that such variability should be acknowledged in assessments of future ecosystem provisions.
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
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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.
Lena R. Boysen, Victor Brovkin, Julia Pongratz, David M. Lawrence, Peter Lawrence, Nicolas Vuichard, Philippe Peylin, Spencer Liddicoat, Tomohiro Hajima, Yanwu Zhang, Matthias Rocher, Christine Delire, Roland Séférian, Vivek K. Arora, Lars Nieradzik, Peter Anthoni, Wim Thiery, Marysa M. Laguë, Deborah Lawrence, and Min-Hui Lo
Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, https://doi.org/10.5194/bg-17-5615-2020, 2020
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We find a biogeophysically induced global cooling with strong carbon losses in a 20 million square kilometre idealized deforestation experiment performed by nine CMIP6 Earth system models. It takes many decades for the temperature signal to emerge, with non-local effects playing an important role. Despite a consistent experimental setup, models diverge substantially in their climate responses. This study offers unprecedented insights for understanding land use change effects in CMIP6 models.
Matthew J. Rowlinson, Alexandru Rap, Douglas S. Hamilton, Richard J. Pope, Stijn Hantson, Steve R. Arnold, Jed O. Kaplan, Almut Arneth, Martyn P. Chipperfield, Piers M. Forster, and Lars Nieradzik
Atmos. Chem. Phys., 20, 10937–10951, https://doi.org/10.5194/acp-20-10937-2020, https://doi.org/10.5194/acp-20-10937-2020, 2020
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Tropospheric ozone is an important greenhouse gas which contributes to anthropogenic climate change; however, the effect of human emissions is uncertain because pre-industrial ozone concentrations are not well understood. We use revised inventories of pre-industrial natural emissions to estimate the human contribution to changes in tropospheric ozone. We find that tropospheric ozone radiative forcing is up to 34 % lower when using improved pre-industrial biomass burning and vegetation emissions.
Cited articles
Augustynczik, A. L. D., Hartig, F., Minunno, F., Kahle, H.-P., Diaconu, D.,
Hanewinkel, M., and Yousefpour, R.: Productivity of Fagus sylvatica under
climate change – A Bayesian analysis of risk and uncertainty using the
model 3-PG, Forest Ecol. Manag., 401, 192–206,
https://doi.org/10.1016/j.foreco.2017.06.061, 2017.
Balaman, Ş. Y.: Chapter 5 – Uncertainty Issues in Biomass-Based
Production Chains, in: Decision-Making for Biomass-Based Production Chains,
edited by: Balaman, Ş. Y., Academic Press, 113–142,
https://doi.org/10.1016/B978-0-12-814278-3.00005-4, 2019.
Barman, R., Jain, A. K., and Liang, M.: Climate-driven uncertainties in
modeling terrestrial gross primary production: a site level to global-scale
analysis, Glob. Change Biol., 20, 1394–1411, https://doi.org/10.1111/gcb.12474, 2014.
Bastos, A., O'Sullivan, M., Ciais, P., Makowski, D., Sitch, S.,
Friedlingstein, P., Chevallier, F., Rödenbeck, C., Pongratz, J., Luijkx,
I. T., Patra, P. K., Peylin, P., Canadell, J. G., Lauerwald, R., Li, W.,
Smith, N. E., Peters, W., Goll, D. S., Jain, A. k., Kato, E., Lienert, S.,
Lombardozzi, D. L., Haverd, V., Nabel, J. E. M. S., Poulter, B., Tian, H.,
Walker, A. P., and Zaehle, S.: Sources of Uncertainty in Regional and Global
Terrestrial CO2 Exchange Estimates, 34, e2019GB006393,
https://doi.org/10.1029/2019GB006393, 2020.
Batjes, N. H.: ISRIC-WISE global data set of derived soil properties on a
0.5 by 0.5 degree grid (ver. 3.0), 24, https://www.isric.org/sites/default/files/isric_report_2005_08.pdf (last access: 10 December 2020), 2005.
Bolnick, D. I., Amarasekare, P., Araújo, M. S., Bürger, R., Levine,
J. M., Novak, M., Rudolf, V. H. W., Schreiber, S. J., Urban, M. C., and
Vasseur, D. A.: Why intraspecific trait variation matters in community
ecology, Trends Ecol. Evol., 26, 183–192,
https://doi.org/10.1016/j.tree.2011.01.009, 2011.
Bolte, A., Czajkowski, T., and Kompa, T.: The north-eastern distribution
range of European beech – a review, Forestry, 80, 413–429,
https://doi.org/10.1093/forestry/cpm028, 2007.
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.
Brazee, R. J. and Amacher, G. S.: Duality and Faustmann: Implications for
the Evaluation of Landowner Behavior, Forest Sci., 46, 132–138,
https://academic.oup.com/forestscience/article/46/1/132/4617376 (last access: 21 November 2021), 2000.
Bugmann, H., Seidl, R., Hartig, F., Bohn, F., Brůna, J., Cailleret, M.,
François, L., Heinke, J., Henrot, A.-J., Hickler, T., Hülsmann, L.,
Huth, A., Jacquemin, I., Kollas, C., Lasch-Born, P., Lexer, M. J.,
Merganič, J., Merganičová, K., Mette, T., Miranda, B. R.,
Nadal-Sala, D., Rammer, W., Rammig, A., Reineking, B., Roedig, E.,
Sabaté, S., Steinkamp, J., Suckow, F., Vacchiano, G., Wild, J., Xu, C.,
and Reyer, C. P. O.: Tree mortality submodels drive simulated long-term
forest dynamics: assessing 15 models from the stand to global scale, Ecosphere, 10,
e02616, https://doi.org/10.1002/ecs2.2616, 2019.
Buras, A. and Menzel, A.: Projecting Tree Species Composition Changes of
European Forests for 2061–2090 Under RCP 4.5 and RCP 8.5 Scenarios, Front.
Plant Sci., 9, https://doi.org/10.3389/fpls.2018.01986, 2019.
Buras, A., Schunk, C., Zeiträg, C., Herrmann, C., Kaiser, L., Lemme, H.,
Straub, C., Taeger, S., Gößwein, S., Klemmt, H.-J., and Menzel, A.:
Are Scots pine forest edges particularly prone to drought-induced
mortality?, Environ. Res. Lett., 13, 025001,
https://doi.org/10.1088/1748-9326/aaa0b4, 2018.
Burkett, V. R., Wilcox, D. A., Stottlemyer, R., Barrow, W., Fagre, D.,
Baron, J., Price, J., Nielsen, J. L., Allen, C. D., Peterson, D. L.,
Ruggerone, G., and Doyle, T.: Nonlinear dynamics in ecosystem response to
climatic change: Case studies and policy implications, Ecol.
Complex., 2, 357–394, https://doi.org/10.1016/j.ecocom.2005.04.010, 2005.
Cailleret, M., Bircher, N., Hartig, F., Hülsmann, L., and Bugmann, H.:
Bayesian calibration of a growth-dependent tree mortality model to simulate
the dynamics of European temperate forests, Ecol. Appl., 30, e02021,
https://doi.org/10.1002/eap.2021, 2020.
Callaway, R. M.: Positive Interactions and Interdependence in Plant
Communities, 1st edn., Springer Netherlands, Hardcover ISBN 978-1-4020-6223-0, Softcover ISBN 978-90-481-7573-4, eBook ISBN 978-1-4020-6224-7,
https://doi.org/10.1007/978-1-4020-6224-7, 2007.
Cariboni, J., Gatelli, D., Liska, R., and Saltelli, A.: The role of
sensitivity analysis in ecological modelling, Ecol. Model., 203,
167–182, https://doi.org/10.1016/j.ecolmodel.2005.10.045, 2007.
Caswell, H.: Introduction: Sensitivity Analysis – What and Why?, in:
Sensitivity Analysis: Matrix Methods in Demography and Ecology, edited by:
Caswell, H., Springer International Publishing, Cham, 3–12, Print ISBN 978-3-030-10533-4, Online ISBN 978-3-030-10534-1, https://doi.org/10.1007/978-3-030-10534-1_1, 2019.
Caudullo, G., Welk, E., and San-Miguel-Ayanz, J.:
Chorological maps for the main European woody species,
Data in Brief, 12, 662–666, https://doi.org/10.1016/j.dib.2017.05.007, 2017.
Charru, M., Seynave, I., Morneau, F., and Bontemps, J.-D.: Recent changes in
forest productivity: An analysis of national forest inventory data for
common beech (Fagus sylvatica L.) in north-eastern France, Forest Ecol.
Manag., 260, 864–874, https://doi.org/10.1016/j.foreco.2010.06.005,
2010.
Chauliac, C., Aragonés, J.-M., Bestion, D., Cacuci, D. G., Crouzet, N.,
Weiss, F.-P., and Zimmermann, M. A.: NURESIM – A European simulation
platform for nuclear reactor safety: Multi-scale and multi-physics
calculations, sensitivity and uncertainty analysis, Nucl. Eng.
Des., 241, 3416–3426, https://doi.org/10.1016/j.nucengdes.2010.09.040,
2011.
Cleveland, C. C., Townsend, A. R., Schimel, D. S., Fisher, H., Howarth, R.
W., Hedin, L. O., Perakis, S. S., Latty, E. F., Fischer, J. C. V., Elseroad,
A., and Wasson, M. F.: Global patterns of terrestrial biological nitrogen
(N2) fixation in natural ecosystems, Global Biogeochem. Cy., 13, 623–645,
https://doi.org/10.1029/1999GB900014, 1999.
Courbaud, B., Lafond, V., Lagarrigues, G., Vieilledent, G., Cordonnier, T.,
Jabot, F., and de Coligny, F.: Applying ecological model evaludation:
Lessons learned with the forest dynamics model Samsara2, Ecol.
Model., 314, 1–14, https://doi.org/10.1016/j.ecolmodel.2015.06.039,
2015.
Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A.,
Brovkin, V., Cox, P. M., Fisher, V., Foley, J. A., Friend, A. D., Kucharik,
C., Lomas, M. R., Ramankutty, N., Sitch, S., Smith, B., White, A., and
Young-Molling, C.: Global response of terrestrial ecosystem structure and
function to CO2 and climate change: results from six dynamic global
vegetation models, Global Change Biol., 7, 357–373,
https://doi.org/10.1046/j.1365-2486.2001.00383.x, 2001.
Cui, E., Huang, K., Arain, M. A., Fisher, J. B., Huntzinger, D. N., Ito, A.,
Luo, Y., Jain, A. K., Mao, J., Michalak, A. M., Niu, S., Parazoo, N. C.,
Peng, C., Peng, S., Poulter, B., Ricciuto, D. M., Schaefer, K. M., Schwalm,
C. R., Shi, X., Tian, H., Wang, W., Wang, J., Wei, Y., Yan, E., Yan, L.,
Zeng, N., Zhu, Q., and Xia, J.: Vegetation Functional Properties Determine
Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in
the East Asian Monsoon Region, Global Biogeochem. Cy., 33, 668–689,
https://doi.org/10.1029/2018GB005909, 2019.
Dietze, M. C.: Ecological Forecasting, 1st edn., Princeton University Press, 284 pp., ISBN 9780691160573,
2017a.
Dietze, M. C.: Prediction in ecology: a first-principles framework, Ecol. Appl., 27,
2048–2060, https://doi.org/10.1002/eap.1589, 2017b.
Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M.
B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L.
G., Loescher, H. W., Lunch, C. K., Pijanowski, B. C., Randerson, J. T.,
Read, E. K., Tredennick, A. T., Vargas, R., Weathers, K. C., and White, E.
P.: Iterative near-term ecological forecasting: Needs, opportunities, and
challenges, P. Natl. Acad. Sci. USA, 115, 1424–1432,
https://doi.org/10.1073/pnas.1710231115, 2018.
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1,
2013.
EUFORGEN: Distribution map of norway
spruce (Picea abies),
https://www.euforgen.org/species/pinus-sylvestris/ (last access: 20 August 2020), 2013.
EUFORGEN: Distribution map of scots pine
(Pinus sylvestris), https://www.euforgen.org/species/picea-abies/ (last access: 21 August 2020), 2008.
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., and Dietze, M. C.: Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation, Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, 2018.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O.,
Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G.,
Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Global Change Biol., 24, 35–54,
https://doi.org/10.1111/gcb.13910, 2018.
Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F.,
Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M., Haverd,
V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn, B. E.,
Mercado, L. M., Norby, R. J., Pak, B., von Randow, C., Quesada, C. A.,
Schaap, K. J., Valverde-Barrantes, O. J., Wang, Y.-P., Yang, X., Zaehle, S.,
Zhu, Q., and Lapola, D. M.: Amazon forest response to CO2 fertilization
dependent on plant phosphorus acquisition, Nat. Geosci., 12, 736–741,
https://doi.org/10.1038/s41561-019-0404-9, 2019.
Forrest, M., Tost, H., Lelieveld, J., and Hickler, T.: Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53), Geosci. Model Dev., 13, 1285–1309, https://doi.org/10.5194/gmd-13-1285-2020, 2020.
Galbraith, D., Levy, P. E., Sitch, S., Huntingford, C., Cox, P., Williams,
M., and Meir, P.: Multiple mechanisms of Amazonian forest biomass losses in
three dynamic global vegetation models under climate change, New Phytol., 187, 647–665,
https://doi.org/10.1111/j.1469-8137.2010.03350.x, 2010.
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.:
Terrestrial vegetation and water balance – hydrological evaluation of a
dynamic global vegetation model, J. Hydrol., 286, 249–270,
https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.
Gosling, J. P.: SHELF: The Sheffield Elicitation Framework, in: Elicitation:
The Science and Art of Structuring Judgement, 1st edn., edited by: Dias, L. C.,
Morton, A., and Quigley, J., Springer International Publishing, Cham,
61–93, ISBN 978-3-319-65052-4, https://doi.org/10.1007/978-3-319-65052-4_4, 2018.
Grimm, V.: Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons
from Ecology, Science, 310, 987–991, https://doi.org/10.1126/science.1116681, 2005.
Gustafson, E. J., Miranda, B. R., and Sturtevant, B. R.: Can Future CO2
Concentrations Mitigate the Negative Effects of High Temperature and Longer
Droughts on Forest Growth?, Forests, 9, 664, https://doi.org/10.3390/f9110664, 2018.
Hamby, D. M.: A review of techniques for parameter sensitivity analysis of
environmental models, Environ. Monit. Assess., 32, 135–154,
https://doi.org/10.1007/BF00547132, 1994.
Hardiman, B. S., Bohrer, G., Gough, C. M., Vogel, C. S., and Curtis, P. S.:
The role of canopy structural complexity in wood net primary production of a
maturing northern deciduous forest, Ecology, 92, 1818–1827,
https://doi.org/10.1890/10-2192.1, 2011.
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., and Huth, A.:
Statistical inference for stochastic simulation models – theory and
application, Ecol. Lett., 14, 816–827, https://doi.org/10.1111/j.1461-0248.2011.01640.x,
2011.
Haverd, V., Smith, B., Nieradzik, L. P., and Briggs, P. R.: A stand-alone tree demography and landscape structure module for Earth system models: integration with inventory data from temperate and boreal forests, Biogeosciences, 11, 4039–4055, https://doi.org/10.5194/bg-11-4039-2014, 2014.
Herrero de Aza, C., Turrión, M. B., Pando, V., and Bravo, F.: Carbon in
heartwood, sapwood and bark along the stem profile in three Mediterranean
Pinus species, Ann. For. Sci., 68, 1067,
https://doi.org/10.1007/s13595-011-0122-y, 2011.
Hickler, T., Smith, B., Sykes, M. T., Davis, M. B., Sugita, S., and Walker,
K.: USING A GENERALIZED VEGETATION MODEL TO SIMULATE VEGETATION DYNAMICS IN
NORTHEASTERN USA, Ecology, 85, 519–530, https://doi.org/10.1890/02-0344,
2004.
Hickler, T., Smith, B., Prentice, I. C., Mjöfors, K., Miller, P.,
Arneth, A., and Sykes, M. T.: CO2 fertilization in temperate FACE
experiments not representative of boreal and tropical forests, Global Change Biol., 14,
1531–1542, https://doi.org/10.1111/j.1365-2486.2008.01598.x, 2008.
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L.,
Giesecke, T., Fronzek, S., Carter, T. R., Cramer, W., Kühn, I., and
Sykes, M. T.: Projecting the future distribution of European potential
natural vegetation zones with a generalized, tree species-based dynamic
vegetation model, Global Ecol. Biogeogr., 21, 50–63,
https://doi.org/10.1111/j.1466-8238.2010.00613.x, 2012.
Huang, S., Titus, S. J., and Wiens, D. P.: Comparison of nonlinear
height–diameter functions for major Alberta tree species, Can. J. Forest Res., 22, 1297–1304,
https://doi.org/10.1139/x92-172, 1992.
Huber, N., Bugmann, H., and Lafond, V.: Global sensitivity analysis of a
dynamic vegetation model: Model sensitivity depends on successional time,
climate and competitive interactions, Ecol. Model., 368, 377–390,
https://doi.org/10.1016/j.ecolmodel.2017.12.013, 2018.
Huntzinger, D. N., Michalak, A. M., Schwalm, C., Ciais, P., King, A. W.,
Fang, Y., Schaefer, K., Wei, Y., Cook, R. B., Fisher, J. B., Hayes, D.,
Huang, M., Ito, A., Jain, A. K., Lei, H., Lu, C., Maignan, F., Mao, J.,
Parazoo, N., Peng, S., Poulter, B., Ricciuto, D., Shi, X., Tian, H., Wang,
W., Zeng, N., and Zhao, F.: Uncertainty in the response of terrestrial
carbon sink to environmental drivers undermines carbon-climate feedback
predictions, Sci. Rep., 7, 4765, https://doi.org/10.1038/s41598-017-03818-2, 2017.
IPCC: Climate Change 2014: Synthesis Report, Contribution of Working
Groups I, II and III to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and Meyer, L. A., IPCC, Geneva, Switzerland, 151 pp., https://epic.awi.de/id/eprint/37530/1/IPCC_AR5_SYR_Final.pdf (last access: 13 January 2021), 2014.
Jiang, Y., Zhuang, Q., Schaphoff, S., Sitch, S., Sokolov, A., Kicklighter,
D., and Melillo, J.: Uncertainty analysis of vegetation distribution in the
northern high latitudes during the 21st century with a dynamic vegetation
model, Ecol. Evol., 2, 593–614, https://doi.org/10.1002/ece3.85, 2012.
Johansson, P. O.: The economics of forestry and natural resources, 1st edn., Basil Blackwell Ltd., ISBN 9780631141624, 1986.
Jørgensen, S. E. and Bendoricchio, G.: Fundamentals of Ecological
Modelling, 4th edn., Elsevier, 544 pp., Hardcover ISBN 9780444535672,
eBook ISBN 9780444535689, 2001.
Keenan, T., Serra, J. M., Lloret, F., Ninyerola, M., and Sabate, S.:
Predicting the future of forests in the Mediterranean under climate change,
with niche- and process-based models: CO2 matters!, Global Change Biol., 17, 565–579,
https://doi.org/10.1111/j.1365-2486.2010.02254.x, 2011.
Körner, C.: Plant CO2 responses: an issue of definition, time and
resource supply, New Phytol., 172, 393–411,
https://doi.org/10.1111/j.1469-8137.2006.01886.x, 2006.
Krause, A., Haverd, V., Poulter, B., Anthoni, P., Quesada, B., Rammig, A.,
and Arneth, A.: Multimodel Analysis of Future Land Use and Climate Change
Impacts on Ecosystem Functioning, Earth's Future, 7, 833–851,
https://doi.org/10.1029/2018EF001123, 2019.
Laberge, Y.: Simulating nature: a philosophical study of computer-simulation
uncertainties and their role in climate science and policy advice, J.
Appl. Stat., 40, 919–920,
https://doi.org/10.1080/02664763.2012.749047, 2013.
Lamarque, J.-F., Dentener, F., McConnell, J., Ro, C.-U., Shaw, M., Vet, R., Bergmann, D., Cameron-Smith, P., Dalsoren, S., Doherty, R., Faluvegi, G., Ghan, S. J., Josse, B., Lee, Y. H., MacKenzie, I. A., Plummer, D., Shindell, D. T., Skeie, R. B., Stevenson, D. S., Strode, S., Zeng, G., Curran, M., Dahl-Jensen, D., Das, S., Fritzsche, D., and Nolan, M.: Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes, Atmos. Chem. Phys., 13, 7997–8018, https://doi.org/10.5194/acp-13-7997-2013, 2013.
Larcher, W.: Ökophysiologische Konstitutionseigenschaften von
Gebirgspflanzen, Ber. Deut. Bot. Ges., 96, 73–85, 1983.
Levin, S. A.: The Problem of Pattern and Scale in Ecology: The Robert H.
MacArthur Award Lecture, Ecology, 73, 1943–1967, https://doi.org/10.2307/1941447,
1992.
Lindeskog, M., Arneth, A., Bondeau, A., Waha, K., Seaquist, J., Olin, S., and Smith, B.: Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa, Earth Syst. Dynam., 4, 385–407, https://doi.org/10.5194/esd-4-385-2013, 2013.
Lindeskog, M., Smith, B., Lagergren, F., Sycheva, E., Ficko, A., Pretzsch, H., and Rammig, A.: Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): implementation and evaluation of simulations for Europe, Geosci. Model Dev., 14, 6071–6112, https://doi.org/10.5194/gmd-14-6071-2021, 2021.
Loehle, C.: Strategy Space and the Disturbance Spectrum: A Life-History
Model for Tree Species Coexistence, Am. Nat., 156, 14–33,
https://doi.org/10.1086/303369, 2000.
Maestre, F. T., Callaway, R. M., Valladares, F., and Lortie, C. J.: Refining
the stress-gradient hypothesis for competition and facilitation in plant
communities, J. Ecol., 97, 199–205, https://doi.org/10.1111/j.1365-2745.2008.01476.x,
2009.
Mahto, A.: splitstackshape: Stack and Reshape Datasets After Splitting
Concatenated Values, CRAN R package, https://github.com/mrdwab/splitstackshape (last access: 22 July 2022), 2019.
Mäkelä, J., Minunno, F., Aalto, T., Mäkelä, A., Markkanen, T., and Peltoniemi, M.: Sensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sites, Biogeosciences, 17, 2681–2700, https://doi.org/10.5194/bg-17-2681-2020, 2020.
Marsili-Libelli, S., Beck, M. B., Brunner, P., Croke, B., Guillaume, J.,
Jakeman, A., Jakeman, J., Keesman, K. J., and Stigter, H.: Practical
identifiability analysis of environmental models, in:
International Environmental Modelling and Software Society (iEMSs) 7th International Congress on Environmental Modelling and Software San Diego, California, USA, 15–19 June 2014, edited by: Ames, D. P. and Quinn, N., http://www.iemss.org/society/index.php/iemss-2014-proceedings (last access: 21 July 2021),
2014.
Matott, L. S., Babendreier, J. E., and Purucker, S. T.: Evaluating
uncertainty in integrated environmental models: A review of concepts and
tools, Water Resour. Res., 45, W06421, https://doi.org/10.1029/2008WR007301, 2009.
Mauri, A., Strona, G., and San-Miguel-Ayanz, J.: EU-Forest, a high-resolution tree occurrence dataset for Europe, Sci. Data, 4, 160123, https://doi.org/10.1038/sdata.2016.123, 2017.
Maxim, L. and van der Sluijs, J. P.: Quality in environmental science for
policy: Assessing uncertainty as a component of policy analysis,
Environ. Sci. Policy, 14, 482–492,
https://doi.org/10.1016/j.envsci.2011.01.003, 2011.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T.,
Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K.,
Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse
gas concentrations and their extensions from 1765 to 2300, Climatic Change,
109, 213, https://doi.org/10.1007/s10584-011-0156-z, 2011.
Mencuccini, M. and Bonosi, L.: Leaf/sapwood area ratios in Scots pine show acclimation across Europe, Can. J. Forest Res., 31, 442–456, 2001.
Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A., and
Watkins, J. W.: A climatic stratification of the environment of Europe, Global Ecol. Biogeogr., 14,
549–563, https://doi.org/10.1111/j.1466-822X.2005.00190.x, 2005.
Oberpriller, J.: JohannesOberpriller/SensitivityAnalysisLPJ: Release for revision (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5873672, 2022.
Oberpriller, J., Cameron, D. R., Dietze, M. C., and Hartig, F.: Towards
robust statistical inference for complex computer models, Ecol. Lett., 24, 1251–1261,
https://doi.org/10.1111/ele.13728, 2021a.
Oberpriller, J., Anthoni, P., Herschlein, C., Arneth, A., Krause, A., Rammig, A., and Hartig, F.: Parameters and model results for sensitivity and uncertainty analysis of LPJ-GUESS, Zenodo [data set], https://doi.org/10.5281/zenodo.4670295, 2021b.
Olin, S., Schurgers, G., Lindeskog, M., Wårlind, D., Smith, B., Bodin, P., Holmér, J., and Arneth, A.: Modelling the response of yields and tissue C:N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe, Biogeosciences, 12, 2489–2515, https://doi.org/10.5194/bg-12-2489-2015, 2015.
Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A., and Burlando, P.:
Sensitivity analysis of a process-based ecosystem model: Pinpointing
parameterization and structural issues, J. Geophys. Res.-Biogeo., 118, 505–528,
https://doi.org/10.1002/jgrg.20035, 2013.
Pearce, D. W.: The Economic Value of Forest Ecosystems, Ecosyst. Health, 7,
284–296, https://doi.org/10.1046/j.1526-0992.2001.01037.x, 2001.
Petter, G., Mairota, P., Albrich, K., Bebi, P., Brůna, J., Bugmann, H.,
Haffenden, A., Scheller, R. M., Schmatz, D. R., Seidl, R., Speich, M.,
Vacchiano, G., and Lischke, H.: How robust are future projections of forest
landscape dynamics? Insights from a systematic comparison of four forest
landscape models, Environ. Modell. Softw., 134, 104844,
https://doi.org/10.1016/j.envsoft.2020.104844, 2020.
Prestele, R., Alexander, P., Rounsevell, M. D. A., Arneth, A., Calvin, K.,
Doelman, J., Eitelberg, D. A., Engström, K., Fujimori, S., Hasegawa, T.,
Havlik, P., Humpenöder, F., Jain, A. K., Krisztin, T., Kyle, P.,
Meiyappan, P., Popp, A., Sands, R. D., Schaldach, R., Schüngel, J.,
Stehfest, E., Tabeau, A., Meijl, H. V., Vliet, J. V., and Verburg, P. H.:
Hotspots of uncertainty in land-use and land-cover change projections: a
global-scale model comparison, Global Change Biol., 22, 3967–3983,
https://doi.org/10.1111/gcb.13337, 2016.
Pretzsch, H., del Río, M., Ammer, Ch., Avdagic, A., Barbeito, I.,
Bielak, K., Brazaitis, G., Coll, L., Dirnberger, G., Drössler, L.,
Fabrika, M., Forrester, D. I., Godvod, K., Heym, M., Hurt, V., Kurylyak, V.,
Löf, M., Lombardi, F., Matović, B., Mohren, F., Motta, R., den
Ouden, J., Pach, M., Ponette, Q., Schütze, G., Schweig, J.,
Skrzyszewski, J., Sramek, V., Sterba, H., Stojanović, D., Svoboda, M.,
Vanhellemont, M., Verheyen, K., Wellhausen, K., Zlatanov, T., and
Bravo-Oviedo, A.: Growth and yield of mixed versus pure stands of Scots pine
(Pinus sylvestris L.) and European beech (Fagus sylvatica L.) analysed along
a productivity gradient through Europe, Eur. J. Forest. Res., 134, 927–947,
https://doi.org/10.1007/s10342-015-0900-4, 2015.
Pugh, T. A. M., Lindeskog, M., Smith, B., Poulter, B., Arneth, A., Haverd,
V., and Calle, L.: Role of forest regrowth in global carbon sink dynamics,
P. Natl. Acad. Sci. USA, 116, 4382–4387, https://doi.org/10.1073/pnas.1810512116, 2019.
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., Voulgarakis, A., Kelley, D. I., Prentice, I. C., Sitch, S., Harrison, S., and Arneth, A.: The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, 2017.
Renwick, K. M., Fellows, A., Flerchinger, G. N., Lohse, K. A., Clark, P. E., Smith, W. K., Emmett, K., and Poulter, B.:
Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems,
Agr. Forest Meteorol.,
274, 85–94, https://doi.org/10.1016/j.agrformet.2019.04.003, 2019.
Reyer, C. P. O., Flechsig, M., Lasch-Born, P., and van Oijen, M.:
Integrating parameter uncertainty of a process-based model in assessments of
climate change effects on forest productivity, Climatic Change, 137,
395–409, https://doi.org/10.1007/s10584-016-1694-1, 2016.
Robichaud, E. and Methven, I. R.: The applicability of the pipe model theory
for the prediction of foliage biomass in trees from natural, untreated black
spruce stands, Can. J. Forest Res., 22, 1118–1123, https://doi.org/10.1139/x92-148, 1992.
Roux, S., Buis, S., Lafolie, F., and Lamboni, M.: Cluster-based GSA: Global
sensitivity analysis of models with temporal or spatial outputs using
clustering, Environ. Modell. Softw., 140, 105046,
https://doi.org/10.1016/j.envsoft.2021.105046, 2021.
Ruiz-Pérez, G. and Vico, G.: Effects of Temperature and Water
Availability on Northern European Boreal Forests, Frontiers in Forests and Global Change, 3, 34,
https://doi.org/10.3389/ffgc.2020.00034, 2020.
Saltelli, A.: Sensitivity Analysis for Importance Assessment, 22, 579–590, Risk. Anal., 22, 579–590,
https://doi.org/10.1111/0272-4332.00040, 2002.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli,
D., Saisana, M., and Tarantola, S. (Eds.): Global sensitivity analysis: the
primer, 1st edn., John Wiley, Chichester, England; Hoboken, NJ, 292 pp., ISBN 978-0-470-05997-5, 2008.
Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst,
N., Li, S., and Wu, Q.: Why so many published sensitivity analyses are
false: A systematic review of sensitivity analysis practices, Environ.
Modell. Softw., 114, 29–39,
https://doi.org/10.1016/j.envsoft.2019.01.012, 2019.
Saraiva, S., Meier, H. E. M., Andersson, H., Höglund, A., Dieterich, C.,
Gröger, M., Hordoir, R., and Eilola, K.: Uncertainties in Projections of
the Baltic Sea Ecosystem Driven by an Ensemble of Global Climate Models,
Front. Earth Sci., 6, https://doi.org/10.3389/feart.2018.00244, 2019.
Schibalski, A., Lehtonen, A., Hickler, T., and Schröder, B.: Identifying important topics for model refinement in a widely used process-based model informed by correlative model analyses in a boreal forest, Silva Fenn., 51, 6977, https://doi.org/10.14214/sf.6977, 2017.
Seebacher, F. and Franklin, C. E.: Determining environmental causes of
biological effects: the need for a mechanistic physiological dimension in
conservation biology, Philos. T. R. Soc. Lond. B Biol. Sci., 367, 1607–1614,
https://doi.org/10.1098/rstb.2012.0036, 2012.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob.
Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x,
2003.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637,
https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x, 2001.
Smith, B., Samuelsson, P., Wramneby, A., and Rummukainen, M.: A model of the
coupled dynamics of climate, vegetation and terrestrial ecosystem
biogeochemistry for regional applications, Tellus A, 63, 87–106, https://doi.org/10.1111/j.1600-0870.2010.00477.x,
2011.
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg, J., and Zaehle, S.: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, 2014.
Snell, R. S., Huth, A., Nabel, J. E. M. S., Bocedi, G., Travis, J. M. J.,
Gravel, D., Bugmann, H., Gutiérrez, A. G., Hickler, T., Higgins, S. I.,
Reineking, B., Scherstjanoi, M., Zurbriggen, N., and Lischke, H.: Using
dynamic vegetation models to simulate plant range shifts, Ecography, 37, 1184–1197,
https://doi.org/10.1111/ecog.00580, 2014.
Snell, R. S., Elkin, C., Kotlarski, S., and Bugmann, H.: Importance of
climate uncertainty for projections of forest ecosystem services, Reg.
Environ. Change, 18, 2145–2159, https://doi.org/10.1007/s10113-018-1337-3,
2018.
Sobie, E. A.: Parameter Sensitivity Analysis in Electrophysiological Models
Using Multivariable Regression, Biophys. J., 96, 1264–1274,
https://doi.org/10.1016/j.bpj.2008.10.056, 2009.
Strengers, B. J., Müller, C., Schaeffer, M., Haarsma, R. J., Severijns,
C., Gerten, D., Schaphoff, S., van den Houdt, R., and Oostenrijk, R.:
Assessing 20th century climate–vegetation feedbacks of land-use change and
natural vegetation dynamics in a fully coupled vegetation–climate model, Int. J. Climatol.,
30, 2055–2065, https://doi.org/10.1002/joc.2132, 2010.
Tegel, W., Seim, A., Hakelberg, D., Hoffmann, S., Panev, M., Westphal, T.,
and Büntgen, U.: A recent growth increase of European beech (Fagus sylvatica L.) at its Mediterranean distribution limit contradicts drought
stress, Eur. J. Forest Res., 133, 61–71,
https://doi.org/10.1007/s10342-013-0737-7, 2014.
Tian, S., Youssef, M. A., Amatya, D. M., and Vance, E. D.: Global
sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem
model: Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model, Hydrol. Process., 28,
4389–4410, https://doi.org/10.1002/hyp.9948, 2014.
Tian, W., Heo, Y., de Wilde, P., Li, Z., Yan, D., Park, C. S., Feng, X., and
Augenbroe, G.: A review of uncertainty analysis in building energy
assessment, Renew. Sust. Energ. Rev., 93, 285–301,
https://doi.org/10.1016/j.rser.2018.05.029, 2018.
Tomlin, A. S.: The role of sensitivity and uncertainty analysis in
combustion modelling, P. Combust. Inst., 34, 159–176,
https://doi.org/10.1016/j.proci.2012.07.043, 2013.
Trotsiuk, V., Hartig, F., Cailleret, M., Babst, F., Forrester, D. I.,
Baltensweiler, A., Buchmann, N., Bugmann, H., Gessler, A., Gharun, M.,
Minunno, F., Rigling, A., Rohner, B., Stillhard, J., Thürig, E.,
Waldner, P., Ferretti, M., Eugster, W., and Schaub, M.: Assessing the
response of forest productivity to climate extremes in Switzerland using
model–data fusion, Global Change Biol., 26, 2463–2476, https://doi.org/10.1111/gcb.15011, 2020.
Tylianakis, J. M., Didham, R. K., Bascompte, J., and Wardle, D. A.: Global
change and species interactions in terrestrial ecosystems, Ecol. Lett., 11, 1351–1363,
https://doi.org/10.1111/j.1461-0248.2008.01250.x, 2008.
Vermeulen, M. H., Kruijt, B. J., Hickler, T., and Kabat, P.: Modelling short-term variability in carbon and water exchange in a temperate Scots pine forest, Earth Syst. Dynam., 6, 485–503, https://doi.org/10.5194/esd-6-485-2015, 2015.
Vitousek, P. M. and Howarth, R. W.: Nitrogen limitation on land and in the
sea: How can it occur?, Biogeochemistry, 13, 87–115,
https://doi.org/10.1007/BF00002772, 1991.
Walentowski, H., Falk, W., Mette, T., Kunz, J., Bräuning, A., Meinardus,
C., Zang, C., Sutcliffe, L. M. E., and Leuschner, C.: Assessing future
suitability of tree species under climate change by multiple methods: a case
study in southern Germany, Ann. For. Res., 60, 101–126,
https://doi.org/10.15287/afr.2016.789, 2017.
Wallach, D. and Genard, M.: Effect of uncertainty in input and parameter
values on model prediction error, Ecol. Model., 105, 337–345,
https://doi.org/10.1016/S0304-3800(97)00180-4, 1998.
Wang, F., Mladenoff, D. J., Forrester, J. A., Keough, C., and Parton, W. J.:
Global sensitivity analysis of a modified CENTURY model for simulating
impacts of harvesting fine woody biomass for bioenergy, Ecol.
Model., 259, 16–23, https://doi.org/10.1016/j.ecolmodel.2013.03.008,
2013.
Waring, R. H.: Estimating Forest Growth and Efficiency in Relation to Canopy
Leaf Area, in: Advances in Ecological Research, vol. 13, 1st edn., edited by:
MacFadyen, A. and Ford, E. D., Academic Press, 327–354, ISSN 0065-2504,
ISBN 9780120139132,
https://doi.org/10.1016/S0065-2504(08)60111-7, 1983.
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,
https://doi.org/10.1016/j.ecolmodel.2008.04.013, 2008.
Wu, Z., Ahlström, A., Smith, B., Ardö, J., Eklundh, L., Fensholt,
R., and Lehsten, V.: Climate Data Induced Uncertainty in Model-Based
Estimations of Terrestrial Primary Productivity, Environ. Res. Lett., 12,
064013, https://doi.org/10.1088/1748-9326/aa6fd8, 2017.
Wu, Z., Boke-Olén, N., Fensholt, R., Ardö, J., Eklundh, L., and
Lehsten, V.: Effect of climate dataset selection on simulations of
terrestrial GPP: Highest uncertainty for tropical regions, PLOS ONE, 13,
e0199383, https://doi.org/10.1371/journal.pone.0199383, 2018.
Xiao, C.-W., Janssens, I. A., Curiel Yuste, J., and Ceulemans, R.: Variation of specific leaf area and upscaling to leaf area index in mature Scots pine, Trees, 20, 304, https://doi.org/10.1007/s00468-005-0039-x, 2006.
Zaehle, S., Sitch, S., 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.
Zeide, B.: Analysis of Growth Equations, Forest Sci., 39, 594–616,
https://doi.org/10.1093/forestscience/39.3.594, 1993.
Zhang, H., Yuan, W., Dong, W., and Liu, S.: Seasonal patterns of
litterfall in forest ecosystem worldwide, Ecol. Complex., 20, 240–247,
https://doi.org/10.1016/j.ecocom.2014.01.003, 2014.
Short summary
Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Understanding uncertainties of projected ecosystem dynamics under environmental change is of...
