Articles | Volume 19, issue 21
https://doi.org/10.5194/gmd-19-10365-2026
© Author(s) 2026. 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-19-10365-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Leveraging leaf-level optimality processes with explicit acclimation improves global GPP representation in an individual-based DGVM (LPJ-GUESS v4.1.1)
Matthew Forrest
CORRESPONDING AUTHOR
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, Frankfurt, Germany
Mateus Dantas de Paula
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, Frankfurt, Germany
Filipe Gomes de Almeida
Department of Earth and Environmental Sciences, Lund University, Lund, Sweden
Sandy P. Harrison
Geography and Environmental Science, University of Reading, Reading, United Kingdom
I. Colin Prentice
Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park Campus, London, United Kingdom
Thomas Hickler
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, Frankfurt, Germany
Department of Physical Geography, Johann Wolfgang Goethe University of Frankfurt, Frankfurt, Germany
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Biogeosciences, 22, 7001–7030, https://doi.org/10.5194/bg-22-7001-2025, https://doi.org/10.5194/bg-22-7001-2025, 2025
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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.
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.
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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
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Ryan Vella, Matthew Forrest, Andrea Pozzer, Alexandra P. Tsimpidi, Thomas Hickler, Jos Lelieveld, and Holger Tost
Atmos. Chem. Phys., 25, 243–262, https://doi.org/10.5194/acp-25-243-2025, https://doi.org/10.5194/acp-25-243-2025, 2025
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This study examines how land cover changes influence biogenic volatile organic compound (BVOC) emissions and atmospheric states. Using a coupled chemistry–climate–vegetation model, we compare present-day land cover (deforested for crops and grazing) with natural vegetation and an extreme reforestation scenario. We find that vegetation changes significantly impact global BVOC emissions and organic aerosols but have a relatively small effect on total aerosols, clouds, and radiative effects.
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Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
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We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
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Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
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Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
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Joseph Ovwemuvwose, Ian Colin Prentice, and Heather Graven
Biogeosciences, 23, 5593–5605, https://doi.org/10.5194/bg-23-5593-2026, https://doi.org/10.5194/bg-23-5593-2026, 2026
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Wenyao Gan, Martin Best, Pier Luigi Vidale, I. Colin Prentice, and Sandy P. Harrison
EGUsphere, https://doi.org/10.5194/egusphere-2026-2804, https://doi.org/10.5194/egusphere-2026-2804, 2026
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Daniel J. Lunt, Nicky M. Wright, Bram Vaes, Ulrich Salzmann, James W. B. Rae, Thomas Hickler, David K. Hutchinson, Julia Brugger, Jiang Zhu, Sebastian Steinig, A. Nele Meckler, Gordon N. Inglis, David Evans, Agatha M. de Boer, Bette L. Otto-Bliesner, Natalie Burls, Yurui Zhang, Appy Sluijs, Tammo Reichgelt, Igor Niezgodzki, Katrin Meissner, Jean-Baptiste Ladant, Fanni D. Kelemen, Matthew Huber, David R. Greenwood, Mattias Green, Flavia Boscolo-Galazzo, Manuel Tobias Blau, and Michiel Baatsen
Geosci. Model Dev., 19, 6143–6166, https://doi.org/10.5194/gmd-19-6143-2026, https://doi.org/10.5194/gmd-19-6143-2026, 2026
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The early Eocene, about 50 million years ago, was a super-warm period of Earth's history, with high concentrations of carbon dioxide in the atmosphere. Here, we provide a framework and experimental design for climate modellers to carry out a coordinated project, simulating this period. This is the second phase of this project, and here we provide updated maps of the Earth's mountains and ocean floor, and vegetation, to enable more accurate modelling.
Jiahe Zheng, Zhengsen Xu, Rossella Arcucci, Sandy P. Harrison, Lincoln Linlin Xu, and Sibo Cheng
Nat. Hazards Earth Syst. Sci., 26, 2871–2895, https://doi.org/10.5194/nhess-26-2871-2026, https://doi.org/10.5194/nhess-26-2871-2026, 2026
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We introduce the first AI model that predicts wildfire spread with the placement of both permanent and temporary firebreaks. Our spatiotemporal model learns from simulation data to capture how fire interacts with changing suppression efforts over time. Our model runs fast enough for near real-time use and performs well across different wildfire events. This approach could lead to better tools for helping decision-makers understand where and when firebreaks are most effective.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada-Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Linn Hamester, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, Matthieu Lengaigne, Rohini Kumar, and Maryna Strokal
Geosci. Model Dev., 19, 4095–4135, https://doi.org/10.5194/gmd-19-4095-2026, https://doi.org/10.5194/gmd-19-4095-2026, 2026
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This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
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, Qing Sun, Peter E. Thornton, and Anja Rammig
Geosci. Model Dev., 19, 2407–2436, https://doi.org/10.5194/gmd-19-2407-2026, https://doi.org/10.5194/gmd-19-2407-2026, 2026
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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.
Mengmeng Liu, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past, 22, 205–226, https://doi.org/10.5194/cp-22-205-2026, https://doi.org/10.5194/cp-22-205-2026, 2026
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Dansgaard-Oeschger events were large and rapid climate transitions that occurred multiple times during the last glacial period. We reconstructed the global pattern during these events using fossil pollen records, and found a north-south antiphasing of temperature change, and reduced seasonality in the northern extratropics.
Amin Hassan, Iain Colin Prentice, and Xu Liang
Hydrol. Earth Syst. Sci., 30, 317–341, https://doi.org/10.5194/hess-30-317-2026, https://doi.org/10.5194/hess-30-317-2026, 2026
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Evapotranspiration is evaporation that occurs from plants, soil, and water bodies, but determining these components is difficult. We developed a new method that uses measurements such as streamflow and rainfall to determine the portion of plant evaporation. We found differences among vegetation types and climate conditions, and showed that plant water use peaks in moderately dry regions. The results improve understanding of plant-water interactions and can help improve water and climate models.
Blessing Kavhu, Matthew Forrest, and Thomas Hickler
Biogeosciences, 22, 7001–7030, https://doi.org/10.5194/bg-22-7001-2025, https://doi.org/10.5194/bg-22-7001-2025, 2025
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We developed a statistical model to predict global wildfire patterns based on a parsimonious set of weather, vegetation, and anthropogenic variables. This model is designed within a DGVM (Dynamic Global Vegetation Model)-compatible framework and helps to forecast seasonal fire patterns across diverse regions. It's simplicity makes it valuable for climate and fire management planning, helping communities better prepare for and adapt to rising wildfire threats.
Jierong Zhao, Boya Zhou, Sandy P. Harrison, and Colin Prentice
Earth Syst. Dynam., 16, 1655–1669, https://doi.org/10.5194/esd-16-1655-2025, https://doi.org/10.5194/esd-16-1655-2025, 2025
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We used eco-evolutionary optimality modelling to examine how climate and CO2 impacted vegetation at the Last Glacial Maximum (LGM; 21 000 years ago) and the mid-Holocene (MH; 6000 years ago). Low CO2 at the LGM was as important as climate in reducing tree cover and productivity and in increasing C4 plant abundance. Climate had positive effects on MH vegetation, but the low CO2 was a constraint on plant growth. These results show it is important to consider changing CO2 to model ecosystem changes.
Luke Sweeney, Sandy P. Harrison, and Marc Vander Linden
Biogeosciences, 22, 4903–4922, https://doi.org/10.5194/bg-22-4903-2025, https://doi.org/10.5194/bg-22-4903-2025, 2025
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Changes in tree cover across Europe during the Holocene are reconstructed from fossil pollen data using a model developed with modern observations of tree cover and modern pollen assemblages. There is a rapid increase in tree cover after the last glacial period, with maximum cover during the mid-Holocene and a decline thereafter; the timing of the maximum and the speed of the increase and subsequent decrease vary regionally, likely reflecting differences in climate trajectories and human influence.
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.
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
Short summary
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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.
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.
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.
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
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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.
Ryan Vella, Matthew Forrest, Andrea Pozzer, Alexandra P. Tsimpidi, Thomas Hickler, Jos Lelieveld, and Holger Tost
Atmos. Chem. Phys., 25, 243–262, https://doi.org/10.5194/acp-25-243-2025, https://doi.org/10.5194/acp-25-243-2025, 2025
Short summary
Short summary
This study examines how land cover changes influence biogenic volatile organic compound (BVOC) emissions and atmospheric states. Using a coupled chemistry–climate–vegetation model, we compare present-day land cover (deforested for crops and grazing) with natural vegetation and an extreme reforestation scenario. We find that vegetation changes significantly impact global BVOC emissions and organic aerosols but have a relatively small effect on total aerosols, clouds, and radiative effects.
Kieran M. R. Hunt and Sandy P. Harrison
Clim. Past, 21, 1–26, https://doi.org/10.5194/cp-21-1-2025, https://doi.org/10.5194/cp-21-1-2025, 2025
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In this study, we train machine learning models on tree rings, speleothems, and instrumental rainfall to estimate seasonal monsoon rainfall over India over the last 500 years. Our models highlight multidecadal droughts in the mid-17th and 19th centuries, and we link these to historical famines. Using techniques from explainable AI (artificial intelligence), we show that our models use known relationships between local hydroclimate and the monsoon circulation.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
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Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Nikita Kaushal, Franziska A. Lechleitner, Micah Wilhelm, Khalil Azennoud, Janica C. Bühler, Kerstin Braun, Yassine Ait Brahim, Andy Baker, Yuval Burstyn, Laia Comas-Bru, Jens Fohlmeister, Yonaton Goldsmith, Sandy P. Harrison, István G. Hatvani, Kira Rehfeld, Magdalena Ritzau, Vanessa Skiba, Heather M. Stoll, József G. Szűcs, Péter Tanos, Pauline C. Treble, Vitor Azevedo, Jonathan L. Baker, Andrea Borsato, Sakonvan Chawchai, Andrea Columbu, Laura Endres, Jun Hu, Zoltán Kern, Alena Kimbrough, Koray Koç, Monika Markowska, Belen Martrat, Syed Masood Ahmad, Carole Nehme, Valdir Felipe Novello, Carlos Pérez-Mejías, Jiaoyang Ruan, Natasha Sekhon, Nitesh Sinha, Carol V. Tadros, Benjamin H. Tiger, Sophie Warken, Annabel Wolf, Haiwei Zhang, and SISAL Working Group members
Earth Syst. Sci. Data, 16, 1933–1963, https://doi.org/10.5194/essd-16-1933-2024, https://doi.org/10.5194/essd-16-1933-2024, 2024
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Speleothems are a popular, multi-proxy climate archive that provide regional to global insights into past hydroclimate trends with precise chronologies. We present an update to the SISAL (Speleothem Isotopes
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Dana A. Lapides, W. Jesse Hahm, Matthew Forrest, Daniella M. Rempe, Thomas Hickler, and David N. Dralle
Biogeosciences, 21, 1801–1826, https://doi.org/10.5194/bg-21-1801-2024, https://doi.org/10.5194/bg-21-1801-2024, 2024
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Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models despite increasing recognition of its importance. Here, we add a weathered bedrock component to a widely used vegetation model. Using a case study of two sites in California and model runs across the United States, we show that more accurately representing subsurface water storage and hydrology increases summer plant water use so that it better matches patterns in distributed data products.
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.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Huiying Xu, Han Wang, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 4511–4525, https://doi.org/10.5194/bg-20-4511-2023, https://doi.org/10.5194/bg-20-4511-2023, 2023
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Leaf carbon (C) and nitrogen (N) are crucial elements in leaf construction and physiological processes. This study reconciled the roles of phylogeny, species identity, and climate in stoichiometric traits at individual and community levels. The variations in community-level leaf N and C : N ratio were captured by optimality-based models using climate data. Our results provide an approach to improve the representation of leaf stoichiometry in vegetation models to better couple N with C cycling.
Esmeralda Cruz-Silva, Sandy P. Harrison, I. Colin Prentice, Elena Marinova, Patrick J. Bartlein, Hans Renssen, and Yurui Zhang
Clim. Past, 19, 2093–2108, https://doi.org/10.5194/cp-19-2093-2023, https://doi.org/10.5194/cp-19-2093-2023, 2023
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We examined 71 pollen records (12.3 ka to present) in the eastern Mediterranean, reconstructing climate changes. Over 9000 years, winters gradually warmed due to orbital factors. Summer temperatures peaked at 4.5–5 ka, likely declining because of ice sheets. Moisture increased post-11 kyr, remaining high from 10–6 kyr before a slow decrease. Climate models face challenges in replicating moisture transport.
Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
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We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
Olivia Haas, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 3981–3995, https://doi.org/10.5194/bg-20-3981-2023, https://doi.org/10.5194/bg-20-3981-2023, 2023
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We quantify the impact of CO2 and climate on global patterns of burnt area, fire size, and intensity under Last Glacial Maximum (LGM) conditions using three climate scenarios. Climate change alone did not produce the observed LGM reduction in burnt area, but low CO2 did through reducing vegetation productivity. Fire intensity was sensitive to CO2 but strongly affected by changes in atmospheric dryness. Low CO2 caused smaller fires; climate had the opposite effect except in the driest scenario.
Giulia Mengoli, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2023-1261, https://doi.org/10.5194/egusphere-2023-1261, 2023
Preprint archived
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Soil water availability affects plant carbon uptake by reducing leaf area and/or by closing stomata, which reduces its efficiency. We present a new formulation of how climatic dryness reduces both maximum carbon uptake and the soil-moisture threshold below which it declines further. This formulation illustrates how plants adapt their water conservation strategy to thrive in dry climates, and is step towards a better representation of soil-moisture effects in climate models.
Mengmeng Liu, Yicheng Shen, Penelope González-Sampériz, Graciela Gil-Romera, Cajo J. F. ter Braak, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past, 19, 803–834, https://doi.org/10.5194/cp-19-803-2023, https://doi.org/10.5194/cp-19-803-2023, 2023
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We reconstructed the Holocene climates in the Iberian Peninsula using a large pollen data set and found that the west–east moisture gradient was much flatter than today. We also found that the winter was much colder, which can be expected from the low winter insolation during the Holocene. However, summer temperature did not follow the trend of summer insolation, instead, it was strongly correlated with moisture.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
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Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Yicheng Shen, Luke Sweeney, Mengmeng Liu, Jose Antonio Lopez Saez, Sebastián Pérez-Díaz, Reyes Luelmo-Lautenschlaeger, Graciela Gil-Romera, Dana Hoefer, Gonzalo Jiménez-Moreno, Heike Schneider, I. Colin Prentice, and Sandy P. Harrison
Clim. Past, 18, 1189–1201, https://doi.org/10.5194/cp-18-1189-2022, https://doi.org/10.5194/cp-18-1189-2022, 2022
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We present a method to reconstruct burnt area using a relationship between pollen and charcoal abundances and the calibration of charcoal abundance using modern observations of burnt area. We use this method to reconstruct changes in burnt area over the past 12 000 years from sites in Iberia. We show that regional changes in burnt area reflect known changes in climate, with a high burnt area during warming intervals and low burnt area when the climate was cooler and/or wetter than today.
Sandy P. Harrison, Roberto Villegas-Diaz, Esmeralda Cruz-Silva, Daniel Gallagher, David Kesner, Paul Lincoln, Yicheng Shen, Luke Sweeney, Daniele Colombaroli, Adam Ali, Chéïma Barhoumi, Yves Bergeron, Tatiana Blyakharchuk, Přemysl Bobek, Richard Bradshaw, Jennifer L. Clear, Sambor Czerwiński, Anne-Laure Daniau, John Dodson, Kevin J. Edwards, Mary E. Edwards, Angelica Feurdean, David Foster, Konrad Gajewski, Mariusz Gałka, Michelle Garneau, Thomas Giesecke, Graciela Gil Romera, Martin P. Girardin, Dana Hoefer, Kangyou Huang, Jun Inoue, Eva Jamrichová, Nauris Jasiunas, Wenying Jiang, Gonzalo Jiménez-Moreno, Monika Karpińska-Kołaczek, Piotr Kołaczek, Niina Kuosmanen, Mariusz Lamentowicz, Martin Lavoie, Fang Li, Jianyong Li, Olga Lisitsyna, José Antonio López-Sáez, Reyes Luelmo-Lautenschlaeger, Gabriel Magnan, Eniko Katalin Magyari, Alekss Maksims, Katarzyna Marcisz, Elena Marinova, Jenn Marlon, Scott Mensing, Joanna Miroslaw-Grabowska, Wyatt Oswald, Sebastián Pérez-Díaz, Ramón Pérez-Obiol, Sanna Piilo, Anneli Poska, Xiaoguang Qin, Cécile C. Remy, Pierre J. H. Richard, Sakari Salonen, Naoko Sasaki, Hieke Schneider, William Shotyk, Migle Stancikaite, Dace Šteinberga, Normunds Stivrins, Hikaru Takahara, Zhihai Tan, Liva Trasune, Charles E. Umbanhowar, Minna Väliranta, Jüri Vassiljev, Xiayun Xiao, Qinghai Xu, Xin Xu, Edyta Zawisza, Yan Zhao, Zheng Zhou, and Jordan Paillard
Earth Syst. Sci. Data, 14, 1109–1124, https://doi.org/10.5194/essd-14-1109-2022, https://doi.org/10.5194/essd-14-1109-2022, 2022
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We provide a new global data set of charcoal preserved in sediments that can be used to examine how fire regimes have changed during past millennia and to investigate what caused these changes. The individual records have been standardised, and new age models have been constructed to allow better comparison across sites. The data set contains 1681 records from 1477 sites worldwide.
Cited articles
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.: Spatiotemporal patterns of terrestrial gross primary production: A review, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015RG000483, 2015.
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020.
Atkin, O. K., Evans, J. R., and Siebke, K.: Relationship between the inhibition of leaf respiration by light and enhancement of leaf dark respiration following light treatment, Aust. J. Plant Physiol., 25, 437–443, https://doi.org/10.1071/PP97159, 1998.
Atkin, O. K., Bloomfield, K. J., Reich, P. B., Tjoelker, M. G., Asner, G. P., Bonal, D., Bönisch, G., Bradford, M. G., Cernusak, L. A., Cosio, E. G., Creek, D., Crous, K. Y., Domingues, T. F., Dukes, J. S., Egerton, J. J. G., Evans, J. R., Farquhar, G. D., Fyllas, N. M., Gauthier, P. P. G., Gloor, E., Gimeno, T. E., Griffin, K. L., Guerrieri, R., Heskel, M. A., Huntingford, C., Ishida, F. Y., Kattge, J., Lambers, H., Liddell, M. J., Lloyd, J., Lusk, C. H., Martin, R. E., Maksimov, A. P., Maximov, T. C., Malhi, Y., Medlyn, B. E., Meir, P., Mercado, L. M., Mirotchnick, N., Ng, D., Niinemets, Ü., O'Sullivan, O. S., Phillips, O. L., Poorter, L., Poot, P., Prentice, I. C., Salinas, N., Rowland, L. M., Ryan, M. G., Sitch, S., Slot, M., Smith, N. G., Turnbull, M. H., VanderWel, M. C., Valladares, F., Veneklaas, E. J., Weerasinghe, L. K., Wirth, C., Wright, I. J., Wythers, K. R., Xiang, J., Xiang, S., and Zaragoza-Castells, J.: Global variability in leaf respiration in relation to climate, plant functional types and leaf traits, New Phytol., 206, 614–636, https://doi.org/10.1111/nph.13253, 2015.
Atkin, O. K., Bahar, N. H. A., Bloomfield, K. J., Griffin, K. L., Heskel, M. A., Huntingford, C., de la Torre, A. M., and Turnbull, M. H.: Leaf Respiration in Terrestrial Biosphere Models, in: Plant Respiration: Metabolic Fluxes and Carbon Balance, edited by: Tcherkez, G. and Ghashghaie, J., Springer International Publishing, Cham, 107–142, https://doi.org/10.1007/978-3-319-68703-2_6, 2017.
Bagley, J., Rosenthal, D. M., Ruiz-Vera, U. M., Siebers, M. H., Kumar, P., Ort, D. R., and Bernacchi, C. J.: The influence of photosynthetic acclimation to rising CO2 and warmer temperatures on leaf and canopy photosynthesis models, Global Biogeochem. Cy., 29, 194–206, https://doi.org/10.1002/2014GB004848, 2015.
Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis Jr., A. R., and Long, S. P.: Improved temperature response functions for models of Rubisco-limited photosynthesis, Plant Cell Environ., 24, 253–259, https://doi.org/10.1111/j.1365-3040.2001.00668.x, 2001.
Bernacchi, C. J., Pimentel, C., and Long, S. P.: In vivo temperature response functions of parameters required to model RuBP-limited photosynthesis, Plant Cell Environ., 26, 1419–1430, https://doi.org/10.1046/j.0016-8025.2003.01050.x, 2003.
Bloomfield, K. J., Stocker, B. D., Keenan, T. F., and Prentice, I. C.: Environmental controls on the light use efficiency of terrestrial gross primary production, Global Change Biol., 29, 1037–1053, https://doi.org/10.1111/gcb.16511, 2023.
Butler, E. E., Wythers, K. R., Flores-Moreno, H., Chen, M., Datta, A., Ricciuto, D. M., Atkin, O. K., Kattge, J., Thornton, P. E., Banerjee, A., and Reich, P. B.: Updated respiration routines alter spatio-temporal patterns of carbon cycling in a global land surface model, Environ. Res. Lett., 16, 104015, https://doi.org/10.1088/1748-9326/ac2528, 2021.
Cai, W. and Prentice, I. C.: Recent trends in gross primary production and their drivers: analysis and modelling at flux-site and global scales, Environ. Res. Lett., 15, 124050, https://doi.org/10.1088/1748-9326/abc64e, 2020.
Chen, J.-L., Reynolds, J. F., Harley, P. C., and Tenhunen, J. D.: Coordination theory of leaf nitrogen distribution in a canopy, Oecologia, 93, 63–69, https://doi.org/10.1007/BF00321192, 1993.
Chen, R., Liu, L., Liu, X., and Rascher, U.: CMLR: A Mechanistic Global GPP Dataset Derived from TROPOMIS SIF Observations, J. Remote Sens., 4, 0127, https://doi.org/10.34133/remotesensing.0127, 2024.
Collalti, A., Ibrom, A., Stockmarr, A., Cescatti, A., Alkama, R., Fernández-Martínez, M., Matteucci, G., Sitch, S., Friedlingstein, P., Ciais, P., Goll, D. S., Nabel, J. E. M. S., Pongratz, J., Arneth, A., Haverd, V., and Prentice, I. C.: Forest production efficiency increases with growth temperature, Nat. Commun., 11, 5322, https://doi.org/10.1038/s41467-020-19187-w, 2020.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer, Agr. Forest Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
Collatz, G. J., Ribas-Carbo, M., and Berry, J. A.: Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Funct. Plant Biol., 19, 519–538, https://doi.org/10.1071/pp9920519, 1992.
Crous, K. Y., Uddling, J., and De Kauwe, M. G.: Temperature responses of photosynthesis and respiration in evergreen trees from boreal to tropical latitudes, New Phytol., 234, 353–374, https://doi.org/10.1111/nph.17951, 2022.
Dantas de Paula, M.: LPJ-GUESS-CNP driver files, Zenodo [data set], https://doi.org/10.5281/zenodo.13472421, 2024.
Dantas de Paula, M., Forrest, M., Warlind, D., Darela Filho, J. P., Fleischer, K., Rammig, A., and Hickler, T.: 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, Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, 2025.
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.
Dong, N., Prentice, I. C., Wright, I. J., Wang, H., Atkin, O. K., Bloomfield, K. J., Domingues, T. F., Gleason, S. M., Maire, V., Onoda, Y., Poorter, H., and Smith, N. G.: Leaf nitrogen from the perspective of optimal plant function, J. Ecol., 110, 2585–2602, https://doi.org/10.1111/1365-2745.13967, 2022.
Dong, N., Dechant, B., Wang, H., Wright, I. J., and Prentice, I. C.: Global leaf-trait mapping based on optimality theory, Global Ecol. Biogeogr., 32, 1152–1162, https://doi.org/10.1111/geb.13680, 2023.
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Forrest, M.: Data and scripts for LPJ-GUESS-EEO model description paper. (final publication), Zenodo [code and data set], https://doi.org/10.5281/zenodo.23538243, 2026.
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.
Frieler, K., Volkholz, J., Lange, S., Schewe, J., Mengel, M., del Rocío Rivas López, M., Otto, C., Reyer, C. P. O., Karger, D. N., Malle, J. T., Treu, S., Menz, C., Blanchard, J. L., Harrison, C. S., Petrik, C. M., Eddy, T. D., Ortega-Cisneros, K., Novaglio, C., Rousseau, Y., Watson, R. A., Stock, C., Liu, X., Heneghan, R., Tittensor, D., Maury, O., Büchner, M., Vogt, T., Wang, T., Sun, F., Sauer, I. J., Koch, J., Vanderkelen, I., Jägermeyr, J., Müller, C., Rabin, S., Klar, J., Vega del Valle, I. D., Lasslop, G., Chadburn, S., Burke, E., Gallego-Sala, A., Smith, N., Chang, J., Hantson, S., Burton, C., Gädeke, A., Li, F., Gosling, S. N., Müller Schmied, H., Hattermann, F., Wang, J., Yao, F., Hickler, T., Marcé, R., Pierson, D., Thiery, W., Mercado-Bettín, D., Ladwig, R., Ayala-Zamora, A. I., Forrest, M., and Bechtold, M.: Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, 2024.
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.
Ghannoum, O., von Caemmerer, S., Barlow, E. W. R., and Conroy, J. P.: The Effect of CO2 Enrichment and Irradiance on the Growth, Morphology and Gas Exchange of a C3 (Panicum laxum) and a C4 (Panicum antidotale) Grass, Aust. J. Plant Physiol., 24, 227–237, https://doi.org/10.1071/PP96077, 1997.
Gomes de Almeida, F.: CRU-JRA v2.4 (1901–2022) climate data bias corrected with ICOS/FLUXNET sites data, Zenodo [data set], https://doi.org/10.5281/zenodo.20588381, 2026.
Gu, L., Baldocchi, D. D., Wofsy, S. C., Munger, J. W., Michalsky, J. J., Urbanski, S. P., and Boden, T. A.: Response of a Deciduous Forest to the Mount Pinatubo Eruption: Enhanced Photosynthesis, Science, 299, 2035–2038, https://doi.org/10.1126/science.1078366, 2003.
Harrison, S. P., Cramer, W., Franklin, O., Prentice, I. C., Wang, H., Brännström, Å., de Boer, H., Dieckmann, U., Joshi, J., Keenan, T. F., Lavergne, A., Manzoni, S., Mengoli, G., Morfopoulos, C., Peñuelas, J., Pietsch, S., Rebel, K. T., Ryu, Y., Smith, N. G., Stocker, B. D., and Wright, I. J.: Eco-evolutionary optimality as a means to improve vegetation and land-surface models, New Phytol., 231, 2125–2141, https://doi.org/10.1111/nph.17558, 2021.
Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger, C. M., Canadell, J. G., and Cuntz, M.: A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis, Geosci. Model Dev., 11, 2995–3026, https://doi.org/10.5194/gmd-11-2995-2018, 2018.
Haxeltine, A. and Prentice, I. C.: A General Model for the Light-Use Efficiency of Primary Production, Funct. Ecol., 10, 551–561, https://doi.org/10.2307/2390165, 1996a.
Haxeltine, A. and Prentice, I. C.: BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochem. Cy., 10, 693–709, https://doi.org/10.1029/96GB02344, 1996b.
Hengl, T., Walsh, M. G., Sanderman, J., Wheeler, I., Harrison, S. P., and Prentice, I. C.: Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential, Peer J., 6, e5457, https://doi.org/10.7717/peerj.5457, 2018.
Heskel, M. A., O'Sullivan, O. S., Reich, P. B., Tjoelker, M. G., Weerasinghe, L. K., Penillard, A., Egerton, J. J. G., Creek, D., Bloomfield, K. J., Xiang, J., Sinca, F., Stangl, Z. R., Martinez-de la Torre, A., Griffin, K. L., Huntingford, C., Hurry, V., Meir, P., Turnbull, M. H., and Atkin, O. K.: Convergence in the temperature response of leaf respiration across biomes and plant functional types, P. Natl. Acad. Sci. USA, 113, 3832–3837, https://doi.org/10.1073/pnas.1520282113, 2016.
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.
Huber, M. L., Perkins, R. A., Laesecke, A., Friend, D. G., Sengers, J. V., Assael, M. J., Metaxa, I. N., Vogel, E., Mareš, R., and Miyagawa, K.: New International Formulation for the Viscosity of H2O, J. Phys. Chem. Ref. Data, 38, 101–125, https://doi.org/10.1063/1.3088050, 2009.
Huntingford, C., Atkin, O. K., Martinez-de la Torre, A., Mercado, L. M., Heskel, M. A., Harper, A. B., Bloomfield, K. J., O'Sullivan, O. S., Reich, P. B., Wythers, K. R., Butler, E. E., Chen, M., Griffin, K. L., Meir, P., Tjoelker, M. G., Turnbull, M. H., Sitch, S., Wiltshire, A., and Malhi, Y.: Implications of improved representations of plant respiration in a changing climate, Nat. Commun., 8, 1602, https://doi.org/10.1038/s41467-017-01774-z, 2017.
IPCC – Intergovernmental Panel on Climate Change (Ed.): Global Carbon and Other Biogeochemical Cycles and Feedbacks, in: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 673–816, https://doi.org/10.1017/9781009157896.007, 2023.
Jeong, S., Ryu, Y., Gentine, P., Lian, X., Fang, J., Li, X., Dechant, B., Kong, J., Choi, W., Jiang, C., Keenan, T. F., Harrison, S. P., and Prentice, I. C.: Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency, Remote Sens. Environ., 311, 114282, https://doi.org/10.1016/j.rse.2024.114282, 2024.
Kaplan, J. O., Bigelow, N. H., Prentice, I. C., Harrison, S. P., Bartlein, P. J., Christensen, T. R., Cramer, W., Matveyeva, N. V., McGuire, A. D., Murray, D. F., Razzhivin, V. Y., Smith, B., Walker, D. A., Anderson, P. M., Andreev, A. A., Brubaker, L. B., Edwards, M. E., and Lozhkin, A. V.: Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD002559, 2003.
Kattge, J. and Knorr, W.: Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species, Plant Cell Environ., 30, 1176–1190, https://doi.org/10.1111/j.1365-3040.2007.01690.x, 2007.
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013.
Klein Goldewijk, K., Beusen, A., and Janssen, P.: Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1, Holocene, 20, 565–573, https://doi.org/10.1177/0959683609356587, 2010.
Knorr, W., Kaminski, T., Arneth, A., and Weber, U.: Impact of human population density on fire frequency at the global scale, Biogeosciences, 11, 1085–1102, https://doi.org/10.5194/bg-11-1085-2014, 2014.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, https://doi.org/10.1029/2003GB002199, 2005.
Kumarathunge, D. P., Medlyn, B. E., Drake, J. E., Tjoelker, M. G., Aspinwall, M. J., Battaglia, M., Cano, F. J., Carter, K. R., Cavaleri, M. A., Cernusak, L. A., Chambers, J. Q., Crous, K. Y., De Kauwe, M. G., Dillaway, D. N., Dreyer, E., Ellsworth, D. S., Ghannoum, O., Han, Q., Hikosaka, K., Jensen, A. M., Kelly, J. W. G., Kruger, E. L., Mercado, L. M., Onoda, Y., Reich, P. B., Rogers, A., Slot, M., Smith, N. G., Tarvainen, L., Tissue, D. T., Togashi, H. F., Tribuzy, E. S., Uddling, J., Vårhammar, A., Wallin, G., Warren, J. M., and Way, D. A.: Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale, New Phytol., 222, 768–784, https://doi.org/10.1111/nph.15668, 2019.
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.
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019.
Lapides, D. A., Hahm, W. J., Forrest, M., Rempe, D. M., Hickler, T., and Dralle, D. N.: Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and function, Biogeosciences, 21, 1801–1826, https://doi.org/10.5194/bg-21-1801-2024, 2024.
Li, X. and Xiao, J.: A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data, Remote Sens., 11, 517, https://doi.org/10.3390/rs11050517, 2019.
Li, Y., Zhou, L., Wang, S., Chi, Y., and Chen, J.: Leaf Temperature and Vapour Pressure Deficit (VPD) Driving Stomatal Conductance and Biochemical Processes of Leaf Photosynthetic Rate in a Subtropical Evergreen Coniferous Plantation, Sustainability, 10, 4063, https://doi.org/10.3390/su10114063, 2018.
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.
Liu, B., Wang, X., Liu, Q., Xu, Y., Arslan, A. M., Zheng, D., Li, L., and Gong, X.: Response of leaf day respiration in C4 plants to irradiance and vapour pressure deficit, Crop Environ., 3, 101–111, https://doi.org/10.1016/j.crope.2023.12.001, 2024.
Lombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S., and Fisher, R. A.: Temperature acclimation of photosynthesis and respiration: A key uncertainty in the carbon cycle-climate feedback, Geophys. Res. Lett., 42, 8624–8631, https://doi.org/10.1002/2015GL065934, 2015.
Maire, V., Martre, P., Kattge, J., Gastal, F., Esser, G., Fontaine, S., and Soussana, J.-F.: The Coordination of Leaf Photosynthesis Links C and N Fluxes in C3 Plant Species, PLOS ONE, 7, e38345, https://doi.org/10.1371/journal.pone.0038345, 2012.
Martín Belda, D., Anthoni, P., Wårlind, D., Olin, S., Schurgers, G., Tang, J., Smith, B., and Arneth, A.: LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism, Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, 2022.
Mengoli, G., Agustí-Panareda, A., Boussetta, S., Harrison, S. P., Trotta, C., and Prentice, I. C.: Ecosystem Photosynthesis in Land-Surface Models: A First-Principles Approach Incorporating Acclimation, J. Adv. Model. Earth Syst., 14, e2021MS002767, https://doi.org/10.1029/2021MS002767, 2022.
Mercado, L. M., Medlyn, B. E., Huntingford, C., Oliver, R. J., Clark, D. B., Sitch, S., 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, https://doi.org/10.1111/nph.15100, 2018.
Middleby, K. B., Cheesman, A. W., and Cernusak, L. A.: Impacts of elevated temperature and vapour pressure deficit on leaf gas exchange and plant growth across six tropical rainforest tree species, New Phytol., 243, 648–661, https://doi.org/10.1111/nph.19822, 2024.
Miller, P. A. and Smith, B.: Modelling Tundra Vegetation Response to Recent Arctic Warming, AMBIO, 41, 281–291, https://doi.org/10.1007/s13280-012-0306-1, 2012.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD015139, 2011.
Oliver, R. J., Mercado, L. M., Medlyn, B. E., Harris, P. P., and Clark, D. B.: Contrasting Impacts of Acclimation and Adaptation of Photosynthetic Capacity to Temperature and CO2 Across Biomes, Global Biogeochem. Cy., 39, e2024GB008398, https://doi.org/10.1029/2024GB008398, 2025.
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes, R. J., Schimel, D. S., Kirchner, T., Menaut, J.-C., Seastedt, T., Garcia Moya, E., Kamnalrut, A., and Kinyamario, J. I.: Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide, Global Biogeochem. Cy., 7, 785–809, https://doi.org/10.1029/93GB02042, 1993.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., Grandcourt, A. de, Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., Tommasi, P. di, Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., et al.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Peng, Y., Bloomfield, K. J., and Prentice, I. C.: A theory of plant function helps to explain leaf-trait and productivity responses to elevation, New Phytol., 226, 1274–1284, https://doi.org/10.1111/nph.16447, 2020.
Peng, Y., Bloomfield, K. J., Cernusak, L. A., Domingues, T. F., and Colin Prentice, I.: Global climate and nutrient controls of photosynthetic capacity, Commun. Biol., 4, 462, https://doi.org/10.1038/s42003-021-01985-7, 2021.
Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X., Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Huntingford, C., Jung, M., Levis, S., Levy, P. E., Li, J., Lin, X., Lomas, M. R., Lu, M., Luo, Y., Ma, Y., Myneni, R. B., Poulter, B., Sun, Z., Wang, T., Viovy, N., Zaehle, S., and Zeng, N.: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends, Global Change Biol., 19, 2117–2132, https://doi.org/10.1111/gcb.12187, 2013.
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.: Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology, Ecol. Lett., 17, 82–91, https://doi.org/10.1111/ele.12211, 2014.
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.
Qian, L., Yu, X., Zhang, Z., Wu, L., Fan, J., Xiang, Y., Chen, J., and Liu, X.: Assessing and improving the high uncertainty of global gross primary productivity products based on deep learning under extreme climatic conditions, Sci. Total Environ., 957, 177344, https://doi.org/10.1016/j.scitotenv.2024.177344, 2024.
Qiao, S., Wang, H., Prentice, I. C., and Harrison, S. P.: Optimality-based modelling of climate impacts on global potential wheat yield, Environ. Res. Lett., 16, 114013, https://doi.org/10.1088/1748-9326/ac2e38, 2021.
Qiao, S., Harrison, S. P., Prentice, I. C., and Wang, H.: Optimality-based modelling of wheat sowing dates globally, Agricultural Systems, 206, 103608, https://doi.org/10.1016/j.agsy.2023.103608, 2023.
Qiao, S., Harrison, S. P., Prentice, I. C., Huang, X., Wang, H., and Yu, C.: Adaptive Sowing Helps Mitigate Future Wheat Losses Globally, Earth's Future, 14, e2025EF006554, https://doi.org/10.1029/2025EF006554, 2026.
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.
Reich, P. B., Sendall, K. M., Stefanski, A., Wei, X., Rich, R. L., and Montgomery, R. A.: Boreal and temperate trees show strong acclimation of respiration to warming, Nature, 531, 633–636, https://doi.org/10.1038/nature17142, 2016.
Reich, P. B., Stefanski, A., Rich, R. L., Sendall, K. M., Wei, X., Zhao, C., Hou, J., Montgomery, R. A., and Bermudez, R.: Assessing the relevant time frame for temperature acclimation of leaf dark respiration: A test with 10 boreal and temperate species, Global Change Biol., 27, 2945–2958, https://doi.org/10.1111/gcb.15609, 2021.
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, Global Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Ren, Y., Wang, H., Harrison, S. P., Prentice, I. C., Atkin, O. K., Smith, N. G., Mengoli, G., Stefanski, A., and Reich, P. B.: Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis, New Phytol., 241, 578–591, https://doi.org/10.1111/nph.19355, 2024.
Ren, Y., Wang, H., Harrison, S. P., Prentice, I. C., Mengoli, G., Zhao, L., Reich, P. B., and Yang, K.: Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah-MP Land Surface Model: Model Development and Evaluation, J. Adv. Model. Earth Syst., 17, e2024MS004599, https://doi.org/10.1029/2024MS004599, 2025.
Sage, R. F., Sharkey, T. D., and Seemann, J. R.: Acclimation of Photosynthesis to Elevated CO2 in Five C3 Species 1, Plant Physiol., 89, 590–596, https://doi.org/10.1104/pp.89.2.590, 1989.
Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., and Hall, F. G.: Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme, Remote Sens. Environ., 42, 187–216, https://doi.org/10.1016/0034-4257(92)90102-P, 1992.
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, Global 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., 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.
Smith, N. G. and Dukes, J. S.: Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2, Global Change Biol., 19, 45–63, https://doi.org/10.1111/j.1365-2486.2012.02797.x, 2013.
Smith, N. G., Keenan, T. F., Colin Prentice, I., Wang, H., Wright, I. J., Niinemets, Ü., Crous, K. Y., Domingues, T. F., Guerrieri, R., Yoko Ishida, F., Kattge, J., Kruger, E. L., Maire, V., Rogers, A., Serbin, S. P., Tarvainen, L., Togashi, H. F., Townsend, P. A., Wang, M., Weerasinghe, L. K., and Zhou, S.-X.: Global photosynthetic capacity is optimized to the environment, Ecol. Lett., 22, 506–517, https://doi.org/10.1111/ele.13210, 2019.
Spuler, F. R., Wessel, J. B., Comyn-Platt, E., Varndell, J., and Cagnazzo, C.: ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1), Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, 2024.
Stocker, B. D. and Prentice, I. C.: CN-model: A dynamic model for the coupled carbon and nitrogen cycles in terrestrial ecosystems, bioRxiv, https://doi.org/10.1101/2024.04.25.591063, 2024.
Stocker, B. D., Wang, H., Smith, N. G., Harrison, S. P., Keenan, T. F., Sandoval, D., Davis, T., and Prentice, I. C.: P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production, Geosci. Model Dev., 13, 1545–1581, https://doi.org/10.5194/gmd-13-1545-2020, 2020.
Stocker, B. D., Dong, N., Perkowski, E. A., Schneider, P. D., Xu, H., de Boer, H. J., Rebel, K. T., Smith, N. G., Van Sundert, K., Wang, H., Jones, S. E., Prentice, I. C., and Harrison, S. P.: Empirical evidence and theoretical understanding of ecosystem carbon and nitrogen cycle interactions, New Phytol., 245, 49–68, https://doi.org/10.1111/nph.20178, 2025.
Tissue, D. T., Thomas, R. B., and Strain, B. R.: Long-term effects of elevated CO2 and nutrients on photosynthesis and rubisco in loblolly pine seedlings, Plant Cell Environ., 16, 859–865, https://doi.org/10.1111/j.1365-3040.1993.tb00508.x, 1993.
University of East Anglia Climatic Research Unit: CRU JRA v2.4: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan. 1901–Dec. 2022, NERC EDS Centre for Environmental Data Analysis [data set], https://catalogue.ceda.ac.uk/uuid/aed8e269513f446fb1b5d2512bb387ad (last access: 12 July 2023), 2023.
Vuichard, N., Messina, P., Luyssaert, S., Guenet, B., Zaehle, S., Ghattas, J., Bastrikov, V., and Peylin, P.: Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): multi-scale evaluation of gross primary production, Geosci. Model Dev., 12, 4751–4779, https://doi.org/10.5194/gmd-12-4751-2019, 2019.
Walker, A. P., Beckerman, A. P., Gu, L., Kattge, J., Cernusak, L. A., Domingues, T. F., Scales, J. C., Wohlfahrt, G., Wullschleger, S. D., and Woodward, F. I.: The relationship of leaf photosynthetic traits – Vcmax and Jmax – to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta-analysis and modeling study, Ecol. Evol., 4, 3218–3235, https://doi.org/10.1002/ece3.1173, 2014.
Wang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J., Cornwell, W. K., Evans, B. J., and Peng, C.: Towards a universal model for carbon dioxide uptake by plants, Nat. Plants, 3, 734–741, https://doi.org/10.1038/s41477-017-0006-8, 2017.
Wang, H., Atkin, O. K., Keenan, T. F., Smith, N. G., Wright, I. J., Bloomfield, K. J., Kattge, J., Reich, P. B., and Prentice, I. C.: Acclimation of leaf respiration consistent with optimal photosynthetic capacity, Global Change Biol., 26, 2573–2583, https://doi.org/10.1111/gcb.14980, 2020.
Wang, H., Prentice, I. C., Wright, I. J., Warton, D. I., Qiao, S., Xu, X., Zhou, J., Kikuzawa, K., and Stenseth, N. C.: Leaf economics fundamentals explained by optimality principles, Sci. Adv., 9, eadd5667, https://doi.org/10.1126/sciadv.add5667, 2023.
Weiss, M., Miller, P. A., van den Hurk, B. J. J. M., van Noije, T., Štefănescu, S., Haarsma, R., van Ulft, L. H., Hazeleger, W., Le Sager, P., Smith, B., and Schurgers, G.: Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability, J. Climate, 27, 8563–8577, https://doi.org/10.1175/JCLI-D-13-00684.1, 2014.
Wolf, A., Callaghan, T. V., and Larson, K.: Future changes in vegetation and ecosystem function of the Barents Region, Climatic Change, 87, 51–73, https://doi.org/10.1007/s10584-007-9342-4, 2008.
Yang, J. C., Magney, T. S., Albert, L. P., Richardson, A. D., Frankenberg, C., Stutz, J., Grossmann, K., Burns, S. P., Seyednasrollah, B., Blanken, P. D., and Bowling, D. R.: Gross primary production (GPP) and red solar induced fluorescence (SIF) respond differently to light and seasonal environmental conditions in a subalpine conifer forest, Agr. Forest Meteorol., 317, 108904, https://doi.org/10.1016/j.agrformet.2022.108904, 2022.
Zhou, H., Tang, J., Olin, S., and Miller, P. A.: A comprehensive evaluation of hydrological processes in a second-generation dynamic vegetation model, Hydrol. Process., 38, e15152, https://doi.org/10.1002/hyp.15152, 2024.
Zhou, B., Cai, W., Zhu, Z., Wang, H., Harrison, S. P., and Prentice, I. C.: A General Model for the Seasonal to Decadal Dynamics of Leaf Area, Global Change Biol., 31, e70125, https://doi.org/10.1111/gcb.70125, 2025.
Short summary
Global photosynthesis on land is a large flow of carbon in the Earth system which underpins food production and ecosystems. It is particularly important to know how it responds to climate change, but current vegetation models do not agree on this. We implemented a new photosynthesis model based on recent theoretical advances into a vegetation model. We found that it improved the representation of photosynthesis when compared both to global data from satellites and detailed site measurements.
Global photosynthesis on land is a large flow of carbon in the Earth system which underpins food...
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