Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
26 Mar 2020
Model evaluation paper |
| 26 Mar 2020
| 26 Mar 2020
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production
CREAF, Campus UAB, 08193 Bellaterra, Catalonia, Spain
Earth System Science, Stanford University, Stanford, CA 94305, USA
Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH, Universitätsstrasse 2, 8092 Zürich, Switzerland
Department of Earth System Science, Tsinghua University, Haidian, Beijing, 100084, China
Nicholas G. Smith
Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA
Sandy P. Harrison
Geography and Environmental Science, Reading University, Reading, RG6 6AH, UK
Trevor F. Keenan
Earth and Environmental Sciences Area, Lawrence Berkeley National Lab, Berkeley, CA 94709, USA
Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA 94720, USA
David Sandoval
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK
Tyler Davis
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK
Center for Geospatial Analysis, The College of William & Mary, Williamsburg, VA 23185, USA
I. Colin Prentice
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK
Department of Earth System Science, Tsinghua University, Haidian, Beijing, 100084, China
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
Related authors
Gabriela Sophia, Silvia Caldararu, Benjamin David Stocker, and Sönke Zaehle
Biogeosciences, 21, 4169–4193, https://doi.org/10.5194/bg-21-4169-2024, https://doi.org/10.5194/bg-21-4169-2024, 2024
Short summary
Short summary
Through an extensive global dataset of leaf nutrient resorption and a multifactorial analysis, we show that the majority of spatial variation in nutrient resorption may be driven by leaf habit and type, with thicker, longer-lived leaves having lower resorption efficiencies. Climate, soil fertility and soil-related factors emerge as strong drivers with an additional effect on its role. These results are essential for comprehending plant nutrient status, plant productivity and nutrient cycling.
Silvia Caldararu, Victor Rolo, Benjamin D. Stocker, Teresa E. Gimeno, and Richard Nair
Biogeosciences, 20, 3637–3649, https://doi.org/10.5194/bg-20-3637-2023, https://doi.org/10.5194/bg-20-3637-2023, 2023
Short summary
Short summary
Ecosystem manipulative experiments are large experiments in real ecosystems. They include processes such as species interactions and weather that would be omitted in more controlled settings. They offer a high level of realism but are underused in combination with vegetation models used to predict the response of ecosystems to global change. We propose a workflow using models and ecosystem experiments together, taking advantage of the benefits of both tools for Earth system understanding.
Piersilvio De Bartolomeis, Alexandru Meterez, Zixin Shu, and Benjamin David Stocker
EGUsphere, https://doi.org/10.5194/egusphere-2023-1826, https://doi.org/10.5194/egusphere-2023-1826, 2023
Preprint withdrawn
Short summary
Short summary
Our research highlights the effectiveness of a recurrent neural network, LSTM, in predicting plant carbon absorption using weather and satellite data. LSTM outperforms other models, even for new locations, suggesting its broad application. Yet, challenges remain in predicting diverse ecosystems globally due to varying plant and climate factors. Our work enhances understanding of Earth's complex ecosystems using advanced models.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
Short summary
Short summary
Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Fortunat Joos, Renato Spahni, Benjamin D. Stocker, Sebastian Lienert, Jurek Müller, Hubertus Fischer, Jochen Schmitt, I. Colin Prentice, Bette Otto-Bliesner, and Zhengyu Liu
Biogeosciences, 17, 3511–3543, https://doi.org/10.5194/bg-17-3511-2020, https://doi.org/10.5194/bg-17-3511-2020, 2020
Short summary
Short summary
Results of the first globally resolved simulations of terrestrial carbon and nitrogen (N) cycling and N2O emissions over the past 21 000 years are compared with reconstructed N2O emissions. Modelled and reconstructed emissions increased strongly during past abrupt warming events. This evidence appears consistent with a dynamic response of biological N fixation to increasing N demand by ecosystems, thereby reducing N limitation of plant productivity and supporting a land sink for atmospheric CO2.
Sandy P. Harrison, Marie-José Gaillard, Benjamin D. Stocker, Marc Vander Linden, Kees Klein Goldewijk, Oliver Boles, Pascale Braconnot, Andria Dawson, Etienne Fluet-Chouinard, Jed O. Kaplan, Thomas Kastner, Francesco S. R. Pausata, Erick Robinson, Nicki J. Whitehouse, Marco Madella, and Kathleen D. Morrison
Geosci. Model Dev., 13, 805–824, https://doi.org/10.5194/gmd-13-805-2020, https://doi.org/10.5194/gmd-13-805-2020, 2020
Short summary
Short summary
The Past Global Changes LandCover6k initiative will use archaeological records to refine scenarios of land use and land cover change through the Holocene to reduce the uncertainties about the impacts of human-induced changes before widespread industrialization. We describe how archaeological data are used to map land use change and how the maps can be evaluated using independent palaeoenvironmental data. We propose simulations to test land use and land cover change impacts on past climates.
Hubertus Fischer, Jochen Schmitt, Michael Bock, Barbara Seth, Fortunat Joos, Renato Spahni, Sebastian Lienert, Gianna Battaglia, Benjamin D. Stocker, Adrian Schilt, and Edward J. Brook
Biogeosciences, 16, 3997–4021, https://doi.org/10.5194/bg-16-3997-2019, https://doi.org/10.5194/bg-16-3997-2019, 2019
Short summary
Short summary
N2O concentrations were subject to strong variations accompanying glacial–interglacial but also rapid climate changes over the last 21 kyr. The sources of these N2O changes can be identified by measuring the isotopic composition of N2O in ice cores and using the distinct isotopic composition of terrestrial and marine N2O. We show that both marine and terrestrial sources increased from the last glacial to the Holocene but that only terrestrial emissions responded quickly to rapid climate changes.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
Short summary
Short summary
We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek K. Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Catherine E. Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antonio Padin, Anna Peregon, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, and Dan Zhu
Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, https://doi.org/10.5194/essd-10-405-2018, 2018
Short summary
Short summary
The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Tyler W. Davis, I. Colin Prentice, Benjamin D. Stocker, Rebecca T. Thomas, Rhys J. Whitley, Han Wang, Bradley J. Evans, Angela V. Gallego-Sala, Martin T. Sykes, and Wolfgang Cramer
Geosci. Model Dev., 10, 689–708, https://doi.org/10.5194/gmd-10-689-2017, https://doi.org/10.5194/gmd-10-689-2017, 2017
Short summary
Short summary
This research presents a comprehensive description for calculating necessary, but sparsely observed, factors related to Earth's surface energy and water budgets relevant in, but not limited to, the study of ecosystems. We present the equations, including their derivations and assumptions, as well as example indicators relevant to plant-available moisture. The robustness of these relatively simple equations provides a tool to be used across broad fields of scientific research.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
Short summary
Short summary
The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
D. Fowler, C. E. Steadman, D. Stevenson, M. Coyle, R. M. Rees, U. M. Skiba, M. A. Sutton, J. N. Cape, A. J. Dore, M. Vieno, D. Simpson, S. Zaehle, B. D. Stocker, M. Rinaldi, M. C. Facchini, C. R. Flechard, E. Nemitz, M. Twigg, J. W. Erisman, K. Butterbach-Bahl, and J. N. Galloway
Atmos. Chem. Phys., 15, 13849–13893, https://doi.org/10.5194/acp-15-13849-2015, https://doi.org/10.5194/acp-15-13849-2015, 2015
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
Short summary
Short summary
Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
B. D. Stocker and F. Joos
Earth Syst. Dynam., 6, 731–744, https://doi.org/10.5194/esd-6-731-2015, https://doi.org/10.5194/esd-6-731-2015, 2015
Short summary
Short summary
Estimates for land use change CO2 emissions (eLUC) rely on different approaches, implying conceptual differences of what eLUC represents. We use an Earth System Model and quantify differences between two commonly applied methods to be ~20% for historical eLUC but increasing under a future scenario. We decompose eLUC into component fluxes, quantify them, and discuss best practices for global carbon budget accountings and model-data intercomparisons relying on different methods to estimate eLUC.
A. Berchet, I. Pison, F. Chevallier, J.-D. Paris, P. Bousquet, J.-L. Bonne, M. Y. Arshinov, B. D. Belan, C. Cressot, D. K. Davydov, E. J. Dlugokencky, A. V. Fofonov, A. Galanin, J. Lavrič, T. Machida, R. Parker, M. Sasakawa, R. Spahni, B. D. Stocker, and J. Winderlich
Biogeosciences, 12, 5393–5414, https://doi.org/10.5194/bg-12-5393-2015, https://doi.org/10.5194/bg-12-5393-2015, 2015
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
Short summary
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
Short summary
Short summary
Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
A. P. Ballantyne, R. Andres, R. Houghton, B. D. Stocker, R. Wanninkhof, W. Anderegg, L. A. Cooper, M. DeGrandpre, P. P. Tans, J. B. Miller, C. Alden, and J. W. C. White
Biogeosciences, 12, 2565–2584, https://doi.org/10.5194/bg-12-2565-2015, https://doi.org/10.5194/bg-12-2565-2015, 2015
B. D. Stocker, R. Spahni, and F. Joos
Geosci. Model Dev., 7, 3089–3110, https://doi.org/10.5194/gmd-7-3089-2014, https://doi.org/10.5194/gmd-7-3089-2014, 2014
Short summary
Short summary
Simulating the spatio-temporal dynamics of inundation is key to understanding the role of wetlands under past and future climate change. Here, we describe and assess the DYPTOP model that predicts the extent of inundation and the global spatial distribution of peatlands. DYPTOP makes use of high-resolution topography information and uses ecosystem water balance and peatland soil C balance criteria to simulate peatland spatial dynamics and carbon accumulation.
C. Le Quéré, G. P. Peters, R. J. Andres, R. M. Andrew, T. A. Boden, P. Ciais, P. Friedlingstein, R. A. Houghton, G. Marland, R. Moriarty, S. Sitch, P. Tans, A. Arneth, A. Arvanitis, D. C. E. Bakker, L. Bopp, J. G. Canadell, L. P. Chini, S. C. Doney, A. Harper, I. Harris, J. I. House, A. K. Jain, S. D. Jones, E. Kato, R. F. Keeling, K. Klein Goldewijk, A. Körtzinger, C. Koven, N. Lefèvre, F. Maignan, A. Omar, T. Ono, G.-H. Park, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. Schwinger, J. Segschneider, B. D. Stocker, T. Takahashi, B. Tilbrook, S. van Heuven, N. Viovy, R. Wanninkhof, A. Wiltshire, and S. Zaehle
Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, https://doi.org/10.5194/essd-6-235-2014, 2014
R. Spahni, F. Joos, B. D. Stocker, M. Steinacher, and Z. C. Yu
Clim. Past, 9, 1287–1308, https://doi.org/10.5194/cp-9-1287-2013, https://doi.org/10.5194/cp-9-1287-2013, 2013
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
Amin Hassan, Iain Colin Prentice, and Xu Liang
EGUsphere, https://doi.org/10.5194/egusphere-2025-622, https://doi.org/10.5194/egusphere-2025-622, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Evapotranspiration (ET) is the evaporation occurring from plants, soil, and water bodies. Separating these components is challenging due to the lack of measurements and uncertainty of existing ET partitioning methods. We propose a method that utilizes hydrological measurements such as streamflow to determine the ratio of transpiration (evaporation from plants) to evapotranspiration. The results provide a better understanding of plant-water interactions and new perspective on a challenging topic.
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
Short summary
Short summary
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.
Jierong Zhao, Boya Zhou, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2024-3897, https://doi.org/10.5194/egusphere-2024-3897, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
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, 6,000 years ago). Low CO2 at the LGM was as important as climate in reducing tree cover and productivity, and 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.
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
Short summary
Short summary
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.
Gabriela Sophia, Silvia Caldararu, Benjamin David Stocker, and Sönke Zaehle
Biogeosciences, 21, 4169–4193, https://doi.org/10.5194/bg-21-4169-2024, https://doi.org/10.5194/bg-21-4169-2024, 2024
Short summary
Short summary
Through an extensive global dataset of leaf nutrient resorption and a multifactorial analysis, we show that the majority of spatial variation in nutrient resorption may be driven by leaf habit and type, with thicker, longer-lived leaves having lower resorption efficiencies. Climate, soil fertility and soil-related factors emerge as strong drivers with an additional effect on its role. These results are essential for comprehending plant nutrient status, plant productivity and nutrient cycling.
Luke Fionn Sweeney, Sandy P. Harrison, and Marc Vander Linden
EGUsphere, https://doi.org/10.5194/egusphere-2024-1523, https://doi.org/10.5194/egusphere-2024-1523, 2024
Short summary
Short summary
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 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.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Mengmeng Liu, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-12, https://doi.org/10.5194/cp-2024-12, 2024
Preprint under review for CP
Short summary
Short summary
Dansgaard-Oeschger events were large and rapid warming events that occurred multiple times during the last ice age. We show that changes in the northern extratropics and the southern extratropics were anti-phased, with warming over most of the north and cooling in the south. The reconstructions do not provide evidence for a change in seasonality in temperature. However, they do indicate that warming was generally accompanied by wetter conditions and cooling by drier conditions.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Silvia Caldararu, Victor Rolo, Benjamin D. Stocker, Teresa E. Gimeno, and Richard Nair
Biogeosciences, 20, 3637–3649, https://doi.org/10.5194/bg-20-3637-2023, https://doi.org/10.5194/bg-20-3637-2023, 2023
Short summary
Short summary
Ecosystem manipulative experiments are large experiments in real ecosystems. They include processes such as species interactions and weather that would be omitted in more controlled settings. They offer a high level of realism but are underused in combination with vegetation models used to predict the response of ecosystems to global change. We propose a workflow using models and ecosystem experiments together, taking advantage of the benefits of both tools for Earth system understanding.
Piersilvio De Bartolomeis, Alexandru Meterez, Zixin Shu, and Benjamin David Stocker
EGUsphere, https://doi.org/10.5194/egusphere-2023-1826, https://doi.org/10.5194/egusphere-2023-1826, 2023
Preprint withdrawn
Short summary
Short summary
Our research highlights the effectiveness of a recurrent neural network, LSTM, in predicting plant carbon absorption using weather and satellite data. LSTM outperforms other models, even for new locations, suggesting its broad application. Yet, challenges remain in predicting diverse ecosystems globally due to varying plant and climate factors. Our work enhances understanding of Earth's complex ecosystems using advanced models.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
Short summary
Short summary
Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Sarah E. Parker, Sandy P. Harrison, Laia Comas-Bru, Nikita Kaushal, Allegra N. LeGrande, and Martin Werner
Clim. Past, 17, 1119–1138, https://doi.org/10.5194/cp-17-1119-2021, https://doi.org/10.5194/cp-17-1119-2021, 2021
Short summary
Short summary
Regional trends in the oxygen isotope (δ18O) composition of stalagmites reflect several climate processes. We compare stalagmite δ18O records from monsoon regions and model simulations to identify the causes of δ18O variability over the last 12 000 years, and between glacial and interglacial states. Precipitation changes explain the glacial–interglacial δ18O changes in all monsoon regions; Holocene trends are due to a combination of precipitation, atmospheric circulation and temperature changes.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, https://doi.org/10.5194/cp-17-1065-2021, 2021
Short summary
Short summary
The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
Laia Comas-Bru, Kira Rehfeld, Carla Roesch, Sahar Amirnezhad-Mozhdehi, Sandy P. Harrison, Kamolphat Atsawawaranunt, Syed Masood Ahmad, Yassine Ait Brahim, Andy Baker, Matthew Bosomworth, Sebastian F. M. Breitenbach, Yuval Burstyn, Andrea Columbu, Michael Deininger, Attila Demény, Bronwyn Dixon, Jens Fohlmeister, István Gábor Hatvani, Jun Hu, Nikita Kaushal, Zoltán Kern, Inga Labuhn, Franziska A. Lechleitner, Andrew Lorrey, Belen Martrat, Valdir Felipe Novello, Jessica Oster, Carlos Pérez-Mejías, Denis Scholz, Nick Scroxton, Nitesh Sinha, Brittany Marie Ward, Sophie Warken, Haiwei Zhang, and SISAL Working Group members
Earth Syst. Sci. Data, 12, 2579–2606, https://doi.org/10.5194/essd-12-2579-2020, https://doi.org/10.5194/essd-12-2579-2020, 2020
Short summary
Short summary
This paper presents an updated version of the SISAL (Speleothem Isotope Synthesis and Analysis) database. This new version contains isotopic data from 691 speleothem records from 294 cave sites and new age–depth models, including their uncertainties, for 512 speleothems.
Chris M. Brierley, Anni Zhao, Sandy P. Harrison, Pascale Braconnot, Charles J. R. Williams, David J. R. Thornalley, Xiaoxu Shi, Jean-Yves Peterschmitt, Rumi Ohgaito, Darrell S. Kaufman, Masa Kageyama, Julia C. Hargreaves, Michael P. Erb, Julien Emile-Geay, Roberta D'Agostino, Deepak Chandan, Matthieu Carré, Partrick J. Bartlein, Weipeng Zheng, Zhongshi Zhang, Qiong Zhang, Hu Yang, Evgeny M. Volodin, Robert A. Tomas, Cody Routson, W. Richard Peltier, Bette Otto-Bliesner, Polina A. Morozova, Nicholas P. McKay, Gerrit Lohmann, Allegra N. Legrande, Chuncheng Guo, Jian Cao, Esther Brady, James D. Annan, and Ayako Abe-Ouchi
Clim. Past, 16, 1847–1872, https://doi.org/10.5194/cp-16-1847-2020, https://doi.org/10.5194/cp-16-1847-2020, 2020
Short summary
Short summary
This paper provides an initial exploration and comparison to climate reconstructions of the new climate model simulations of the mid-Holocene (6000 years ago). These use state-of-the-art models developed for CMIP6 and apply the same experimental set-up. The models capture several key aspects of the climate, but some persistent issues remain.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
Short summary
Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Fortunat Joos, Renato Spahni, Benjamin D. Stocker, Sebastian Lienert, Jurek Müller, Hubertus Fischer, Jochen Schmitt, I. Colin Prentice, Bette Otto-Bliesner, and Zhengyu Liu
Biogeosciences, 17, 3511–3543, https://doi.org/10.5194/bg-17-3511-2020, https://doi.org/10.5194/bg-17-3511-2020, 2020
Short summary
Short summary
Results of the first globally resolved simulations of terrestrial carbon and nitrogen (N) cycling and N2O emissions over the past 21 000 years are compared with reconstructed N2O emissions. Modelled and reconstructed emissions increased strongly during past abrupt warming events. This evidence appears consistent with a dynamic response of biological N fixation to increasing N demand by ecosystems, thereby reducing N limitation of plant productivity and supporting a land sink for atmospheric CO2.
Sean F. Cleator, Sandy P. Harrison, Nancy K. Nichols, I. Colin Prentice, and Ian Roulstone
Clim. Past, 16, 699–712, https://doi.org/10.5194/cp-16-699-2020, https://doi.org/10.5194/cp-16-699-2020, 2020
Short summary
Short summary
We present geographically explicit reconstructions of seasonal temperature and annual moisture variables at the Last Glacial Maximum (LGM), 21 000 years ago. The reconstructions use existing site-based estimates of climate, interpolated in space and time in a physically consistent way using climate model simulations. The reconstructions give a much better picture of the LGM climate and will provide a robust evaluation of how well state-of-the-art climate models simulate large climate changes.
Sandy P. Harrison, Marie-José Gaillard, Benjamin D. Stocker, Marc Vander Linden, Kees Klein Goldewijk, Oliver Boles, Pascale Braconnot, Andria Dawson, Etienne Fluet-Chouinard, Jed O. Kaplan, Thomas Kastner, Francesco S. R. Pausata, Erick Robinson, Nicki J. Whitehouse, Marco Madella, and Kathleen D. Morrison
Geosci. Model Dev., 13, 805–824, https://doi.org/10.5194/gmd-13-805-2020, https://doi.org/10.5194/gmd-13-805-2020, 2020
Short summary
Short summary
The Past Global Changes LandCover6k initiative will use archaeological records to refine scenarios of land use and land cover change through the Holocene to reduce the uncertainties about the impacts of human-induced changes before widespread industrialization. We describe how archaeological data are used to map land use change and how the maps can be evaluated using independent palaeoenvironmental data. We propose simulations to test land use and land cover change impacts on past climates.
Hubertus Fischer, Jochen Schmitt, Michael Bock, Barbara Seth, Fortunat Joos, Renato Spahni, Sebastian Lienert, Gianna Battaglia, Benjamin D. Stocker, Adrian Schilt, and Edward J. Brook
Biogeosciences, 16, 3997–4021, https://doi.org/10.5194/bg-16-3997-2019, https://doi.org/10.5194/bg-16-3997-2019, 2019
Short summary
Short summary
N2O concentrations were subject to strong variations accompanying glacial–interglacial but also rapid climate changes over the last 21 kyr. The sources of these N2O changes can be identified by measuring the isotopic composition of N2O in ice cores and using the distinct isotopic composition of terrestrial and marine N2O. We show that both marine and terrestrial sources increased from the last glacial to the Holocene but that only terrestrial emissions responded quickly to rapid climate changes.
Lina Teckentrup, Sandy P. Harrison, Stijn Hantson, Angelika Heil, Joe R. Melton, Matthew Forrest, Fang Li, Chao Yue, Almut Arneth, Thomas Hickler, Stephen Sitch, and Gitta Lasslop
Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, https://doi.org/10.5194/bg-16-3883-2019, 2019
Short summary
Short summary
This study compares simulated burned area of seven global vegetation models provided by the Fire Model Intercomparison Project (FireMIP) since 1900. We investigate the influence of five forcing factors: atmospheric CO2, population density, land–use change, lightning and climate.
We find that the anthropogenic factors lead to the largest spread between models. Trends due to climate are mostly not significant but climate strongly influences the inter-annual variability of burned area.
Laia Comas-Bru, Sandy P. Harrison, Martin Werner, Kira Rehfeld, Nick Scroxton, Cristina Veiga-Pires, and SISAL working group members
Clim. Past, 15, 1557–1579, https://doi.org/10.5194/cp-15-1557-2019, https://doi.org/10.5194/cp-15-1557-2019, 2019
Short summary
Short summary
We use an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5-wiso isotope-enabled climate model to provide a protocol for using speleothem isotopic data for model evaluation, including screening the observations and the optimum period for the modern observational baseline. We also illustrate techniques through which the absolute isotopic values during any time period could be used for model evaluation.
Guangqi Li, Sandy P. Harrison, and I. Colin Prentice
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-63, https://doi.org/10.5194/bg-2019-63, 2019
Publication in BG not foreseen
Short summary
Short summary
Current methods of removing age effect from tree-ring are influenced by sampling biases – older trees are more abundantly sampled for recent decades, when the strongest environmental change happens. New technique of extracting environment-driven signals from tree ring is specifically designed to overcome this bias, drawing on theoretical tree growth. It removes sampling-bias effectively and shows consistent relationships between growth and climates through time and across two conifer species.
Dongyang Wei, Penélope González-Sampériz, Graciela Gil-Romera, Sandy P. Harrison, and I. Colin Prentice
Clim. Past Discuss., https://doi.org/10.5194/cp-2019-16, https://doi.org/10.5194/cp-2019-16, 2019
Revised manuscript not accepted
Short summary
Short summary
El Cañizar de Villarquemado provides a pollen record from semi-arid Spain since before the last interglacial. We use modern pollen–climate relationships to reconstruct changes in seasonal temperature and moisture, accounting for CO2 effects on plants, and show coherent climate changes on glacial–interglacial and orbital timescales. The low glacial CO2 means moisture changes are less extreme than suggested by the vegetation shifts, and driven by evapotranspiration rather than rainfall changes.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, https://doi.org/10.5194/bg-16-57-2019, 2019
Short summary
Short summary
Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
Short summary
Short summary
We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Kamolphat Atsawawaranunt, Laia Comas-Bru, Sahar Amirnezhad Mozhdehi, Michael Deininger, Sandy P. Harrison, Andy Baker, Meighan Boyd, Nikita Kaushal, Syed Masood Ahmad, Yassine Ait Brahim, Monica Arienzo, Petra Bajo, Kerstin Braun, Yuval Burstyn, Sakonvan Chawchai, Wuhui Duan, István Gábor Hatvani, Jun Hu, Zoltán Kern, Inga Labuhn, Matthew Lachniet, Franziska A. Lechleitner, Andrew Lorrey, Carlos Pérez-Mejías, Robyn Pickering, Nick Scroxton, and SISAL Working Group Members
Earth Syst. Sci. Data, 10, 1687–1713, https://doi.org/10.5194/essd-10-1687-2018, https://doi.org/10.5194/essd-10-1687-2018, 2018
Short summary
Short summary
This paper is an overview of the contents of the SISAL database and its structure. The database contains oxygen and carbon isotope measurements from 371 individual speleothem records and 10 composite records from 174 cave systems from around the world. The SISAL database is created by a collective effort of the members of the Past Global Changes SISAL working group, which aims to provide a comprehensive compilation of speleothem isotope records for climate reconstruction and model evaluation.
Henrique Fürstenau Togashi, Iain Colin Prentice, Owen K. Atkin, Craig Macfarlane, Suzanne M. Prober, Keith J. Bloomfield, and Bradley John Evans
Biogeosciences, 15, 3461–3474, https://doi.org/10.5194/bg-15-3461-2018, https://doi.org/10.5194/bg-15-3461-2018, 2018
Short summary
Short summary
Ecosystem models commonly assume that photosynthetic traits, such as carboxylation capacity measured at a standard temperature, are constant in time and therefore do not acclimate. Optimality hypotheses suggest this assumption may be incorrect. We investigated acclimation by carrying out measurements on woody species during distinct seasons in Western Australia. Our study shows evidence that carboxylation capacity should acclimate so that it increases somewhat with growth temperature.
Sandy P. Harrison, Patrick J. Bartlein, Victor Brovkin, Sander Houweling, Silvia Kloster, and I. Colin Prentice
Earth Syst. Dynam., 9, 663–677, https://doi.org/10.5194/esd-9-663-2018, https://doi.org/10.5194/esd-9-663-2018, 2018
Short summary
Short summary
Temperature affects fire occurrence and severity. Warming will increase fire-related carbon emissions and thus atmospheric CO2. The size of this feedback is not known. We use charcoal records to estimate pre-industrial fire emissions and a simple land–biosphere model to quantify the feedback. We infer a feedback strength of 5.6 3.2 ppm CO2 per degree of warming and a gain of 0.09 ± 0.05 for a climate sensitivity of 2.8 K. Thus, fire feedback is a large part of the climate–carbon-cycle feedback.
Masa Kageyama, Pascale Braconnot, Sandy P. Harrison, Alan M. Haywood, Johann H. Jungclaus, Bette L. Otto-Bliesner, Jean-Yves Peterschmitt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Chris Brierley, Michel Crucifix, Aisling Dolan, Laura Fernandez-Donado, Hubertus Fischer, Peter O. Hopcroft, Ruza F. Ivanovic, Fabrice Lambert, Daniel J. Lunt, Natalie M. Mahowald, W. Richard Peltier, Steven J. Phipps, Didier M. Roche, Gavin A. Schmidt, Lev Tarasov, Paul J. Valdes, Qiong Zhang, and Tianjun Zhou
Geosci. Model Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, https://doi.org/10.5194/gmd-11-1033-2018, 2018
Short summary
Short summary
The Paleoclimate Modelling Intercomparison Project (PMIP) takes advantage of the existence of past climate states radically different from the recent past to test climate models used for climate projections and to better understand these climates. This paper describes the PMIP contribution to CMIP6 (Coupled Model Intercomparison Project, 6th phase) and possible analyses based on PMIP results, as well as on other CMIP6 projects.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek K. Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Catherine E. Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antonio Padin, Anna Peregon, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, and Dan Zhu
Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, https://doi.org/10.5194/essd-10-405-2018, 2018
Short summary
Short summary
The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Bette L. Otto-Bliesner, Pascale Braconnot, Sandy P. Harrison, Daniel J. Lunt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Emilie Capron, Anders E. Carlson, Andrea Dutton, Hubertus Fischer, Heiko Goelzer, Aline Govin, Alan Haywood, Fortunat Joos, Allegra N. LeGrande, William H. Lipscomb, Gerrit Lohmann, Natalie Mahowald, Christoph Nehrbass-Ahles, Francesco S. R. Pausata, Jean-Yves Peterschmitt, Steven J. Phipps, Hans Renssen, and Qiong Zhang
Geosci. Model Dev., 10, 3979–4003, https://doi.org/10.5194/gmd-10-3979-2017, https://doi.org/10.5194/gmd-10-3979-2017, 2017
Short summary
Short summary
The PMIP4 and CMIP6 mid-Holocene and Last Interglacial simulations provide an opportunity to examine the impact of two different changes in insolation forcing on climate at times when other forcings were relatively similar to present. This will allow exploration of the role of feedbacks relevant to future projections. Evaluating these simulations using paleoenvironmental data will provide direct out-of-sample tests of the reliability of state-of-the-art models to simulate climate changes.
Masa Kageyama, Samuel Albani, Pascale Braconnot, Sandy P. Harrison, Peter O. Hopcroft, Ruza F. Ivanovic, Fabrice Lambert, Olivier Marti, W. Richard Peltier, Jean-Yves Peterschmitt, Didier M. Roche, Lev Tarasov, Xu Zhang, Esther C. Brady, Alan M. Haywood, Allegra N. LeGrande, Daniel J. Lunt, Natalie M. Mahowald, Uwe Mikolajewicz, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Hans Renssen, Robert A. Tomas, Qiong Zhang, Ayako Abe-Ouchi, Patrick J. Bartlein, Jian Cao, Qiang Li, Gerrit Lohmann, Rumi Ohgaito, Xiaoxu Shi, Evgeny Volodin, Kohei Yoshida, Xiao Zhang, and Weipeng Zheng
Geosci. Model Dev., 10, 4035–4055, https://doi.org/10.5194/gmd-10-4035-2017, https://doi.org/10.5194/gmd-10-4035-2017, 2017
Short summary
Short summary
The Last Glacial Maximum (LGM, 21000 years ago) is an interval when global ice volume was at a maximum, eustatic sea level close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. This paper describes the implementation of the LGM numerical experiment for the PMIP4-CMIP6 modelling intercomparison projects and the associated sensitivity experiments.
María Fernanda Sánchez Goñi, Stéphanie Desprat, Anne-Laure Daniau, Frank C. Bassinot, Josué M. Polanco-Martínez, Sandy P. Harrison, Judy R. M. Allen, R. Scott Anderson, Hermann Behling, Raymonde Bonnefille, Francesc Burjachs, José S. Carrión, Rachid Cheddadi, James S. Clark, Nathalie Combourieu-Nebout, Colin. J. Courtney Mustaphi, Georg H. Debusk, Lydie M. Dupont, Jemma M. Finch, William J. Fletcher, Marco Giardini, Catalina González, William D. Gosling, Laurie D. Grigg, Eric C. Grimm, Ryoma Hayashi, Karin Helmens, Linda E. Heusser, Trevor Hill, Geoffrey Hope, Brian Huntley, Yaeko Igarashi, Tomohisa Irino, Bonnie Jacobs, Gonzalo Jiménez-Moreno, Sayuri Kawai, A. Peter Kershaw, Fujio Kumon, Ian T. Lawson, Marie-Pierre Ledru, Anne-Marie Lézine, Ping Mei Liew, Donatella Magri, Robert Marchant, Vasiliki Margari, Francis E. Mayle, G. Merna McKenzie, Patrick Moss, Stefanie Müller, Ulrich C. Müller, Filipa Naughton, Rewi M. Newnham, Tadamichi Oba, Ramón Pérez-Obiol, Roberta Pini, Cesare Ravazzi, Katy H. Roucoux, Stephen M. Rucina, Louis Scott, Hikaru Takahara, Polichronis C. Tzedakis, Dunia H. Urrego, Bas van Geel, B. Guido Valencia, Marcus J. Vandergoes, Annie Vincens, Cathy L. Whitlock, Debra A. Willard, and Masanobu Yamamoto
Earth Syst. Sci. Data, 9, 679–695, https://doi.org/10.5194/essd-9-679-2017, https://doi.org/10.5194/essd-9-679-2017, 2017
Short summary
Short summary
The ACER (Abrupt Climate Changes and Environmental Responses) global database includes 93 pollen records from the last glacial period (73–15 ka) plotted against a common chronology; 32 also provide charcoal records. The database allows for the reconstruction of the regional expression, vegetation and fire of past abrupt climate changes that are comparable to those expected in the 21st century. This work is a major contribution to understanding the processes behind rapid climate change.
Daniel S. Goll, Alexander J. Winkler, Thomas Raddatz, Ning Dong, Ian Colin Prentice, Philippe Ciais, and Victor Brovkin
Geosci. Model Dev., 10, 2009–2030, https://doi.org/10.5194/gmd-10-2009-2017, https://doi.org/10.5194/gmd-10-2009-2017, 2017
Short summary
Short summary
The response of soil organic carbon decomposition to warming and the interactions between nitrogen and carbon cycling affect the feedbacks between the land carbon cycle and the climate. In the model JSBACH carbon–nitrogen interactions have only a small effect on the feedbacks, whereas modifications of soil organic carbon decomposition have a large effect. The carbon cycle in the improved model is more resilient to climatic changes than in previous version of the model.
Tyler W. Davis, I. Colin Prentice, Benjamin D. Stocker, Rebecca T. Thomas, Rhys J. Whitley, Han Wang, Bradley J. Evans, Angela V. Gallego-Sala, Martin T. Sykes, and Wolfgang Cramer
Geosci. Model Dev., 10, 689–708, https://doi.org/10.5194/gmd-10-689-2017, https://doi.org/10.5194/gmd-10-689-2017, 2017
Short summary
Short summary
This research presents a comprehensive description for calculating necessary, but sparsely observed, factors related to Earth's surface energy and water budgets relevant in, but not limited to, the study of ecosystems. We present the equations, including their derivations and assumptions, as well as example indicators relevant to plant-available moisture. The robustness of these relatively simple equations provides a tool to be used across broad fields of scientific research.
Ning Dong, Iain Colin Prentice, Bradley J. Evans, Stefan Caddy-Retalic, Andrew J. Lowe, and Ian J. Wright
Biogeosciences, 14, 481–495, https://doi.org/10.5194/bg-14-481-2017, https://doi.org/10.5194/bg-14-481-2017, 2017
Short summary
Short summary
The nitrogen content of leaves is a key quantity for understanding ecosystem function. We analysed variations in nitrogen per unit leaf area among species at sites along a transect across Australia including many climates and ecosystem types. The data could be explained by the idea that leaf nitrogen comprises two parts, one proportional to leaf mass, the other (metabolic) part proportional to light intensity and declining with CO2 drawdown and temperature, as optimal allocation theory predicts.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
Short summary
Short summary
The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Bette L. Otto-Bliesner, Pascale Braconnot, Sandy P. Harrison, Daniel J. Lunt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Emilie Capron, Anders E. Carlson, Andrea Dutton, Hubertus Fischer, Heiko Goelzer, Aline Govin, Alan Haywood, Fortunat Joos, Allegra N. Legrande, William H. Lipscomb, Gerrit Lohmann, Natalie Mahowald, Christoph Nehrbass-Ahles, Jean-Yves Peterschmidt, Francesco S.-R. Pausata, Steven Phipps, and Hans Renssen
Clim. Past Discuss., https://doi.org/10.5194/cp-2016-106, https://doi.org/10.5194/cp-2016-106, 2016
Preprint retracted
Corinne Le Quéré, Erik T. Buitenhuis, Róisín Moriarty, Séverine Alvain, Olivier Aumont, Laurent Bopp, Sophie Chollet, Clare Enright, Daniel J. Franklin, Richard J. Geider, Sandy P. Harrison, Andrew G. Hirst, Stuart Larsen, Louis Legendre, Trevor Platt, I. Colin Prentice, Richard B. Rivkin, Sévrine Sailley, Shubha Sathyendranath, Nick Stephens, Meike Vogt, and Sergio M. Vallina
Biogeosciences, 13, 4111–4133, https://doi.org/10.5194/bg-13-4111-2016, https://doi.org/10.5194/bg-13-4111-2016, 2016
Short summary
Short summary
We present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types, and use the model to assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean. Our results suggest that observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton growth.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
Short summary
Short summary
Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
A. V. Gallego-Sala, D. J. Charman, S. P. Harrison, G. Li, and I. C. Prentice
Clim. Past, 12, 129–136, https://doi.org/10.5194/cp-12-129-2016, https://doi.org/10.5194/cp-12-129-2016, 2016
Short summary
Short summary
It has become a well-established paradigm that blanket bog landscapes in the British Isles are a result of forest clearance by early human populations. We provide a novel test of this hypothesis using results from bioclimatic modelling driven by cimate reconstructions compared with a database of peat initiation dates. Both results show similar patterns of peat initiation over time and space. This suggests that climate was the main driver of blanket bog inception and not human disturbance.
B. A. A. Hoogakker, R. S. Smith, J. S. Singarayer, R. Marchant, I. C. Prentice, J. R. M. Allen, R. S. Anderson, S. A. Bhagwat, H. Behling, O. Borisova, M. Bush, A. Correa-Metrio, A. de Vernal, J. M. Finch, B. Fréchette, S. Lozano-Garcia, W. D. Gosling, W. Granoszewski, E. C. Grimm, E. Grüger, J. Hanselman, S. P. Harrison, T. R. Hill, B. Huntley, G. Jiménez-Moreno, P. Kershaw, M.-P. Ledru, D. Magri, M. McKenzie, U. Müller, T. Nakagawa, E. Novenko, D. Penny, L. Sadori, L. Scott, J. Stevenson, P. J. Valdes, M. Vandergoes, A. Velichko, C. Whitlock, and C. Tzedakis
Clim. Past, 12, 51–73, https://doi.org/10.5194/cp-12-51-2016, https://doi.org/10.5194/cp-12-51-2016, 2016
Short summary
Short summary
In this paper we use two climate models to test how Earth’s vegetation responded to changes in climate over the last 120 000 years, looking at warm interglacial climates like today, cold ice-age glacial climates, and intermediate climates. The models agree well with observations from pollen, showing smaller forested areas and larger desert areas during cold periods. Forests store most terrestrial carbon; the terrestrial carbon lost during cold climates was most likely relocated to the oceans.
M. G. De Kauwe, S.-X. Zhou, B. E. Medlyn, A. J. Pitman, Y.-P. Wang, R. A. Duursma, and I. C. Prentice
Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, https://doi.org/10.5194/bg-12-7503-2015, 2015
Short summary
Short summary
Future climate change has the potential to increase drought in many regions of the globe. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art models currently assume the same drought sensitivity for all vegetation. Our results indicate that models will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.
D. Fowler, C. E. Steadman, D. Stevenson, M. Coyle, R. M. Rees, U. M. Skiba, M. A. Sutton, J. N. Cape, A. J. Dore, M. Vieno, D. Simpson, S. Zaehle, B. D. Stocker, M. Rinaldi, M. C. Facchini, C. R. Flechard, E. Nemitz, M. Twigg, J. W. Erisman, K. Butterbach-Bahl, and J. N. Galloway
Atmos. Chem. Phys., 15, 13849–13893, https://doi.org/10.5194/acp-15-13849-2015, https://doi.org/10.5194/acp-15-13849-2015, 2015
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
Short summary
Short summary
Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
B. D. Stocker and F. Joos
Earth Syst. Dynam., 6, 731–744, https://doi.org/10.5194/esd-6-731-2015, https://doi.org/10.5194/esd-6-731-2015, 2015
Short summary
Short summary
Estimates for land use change CO2 emissions (eLUC) rely on different approaches, implying conceptual differences of what eLUC represents. We use an Earth System Model and quantify differences between two commonly applied methods to be ~20% for historical eLUC but increasing under a future scenario. We decompose eLUC into component fluxes, quantify them, and discuss best practices for global carbon budget accountings and model-data intercomparisons relying on different methods to estimate eLUC.
A. Berchet, I. Pison, F. Chevallier, J.-D. Paris, P. Bousquet, J.-L. Bonne, M. Y. Arshinov, B. D. Belan, C. Cressot, D. K. Davydov, E. J. Dlugokencky, A. V. Fofonov, A. Galanin, J. Lavrič, T. Machida, R. Parker, M. Sasakawa, R. Spahni, B. D. Stocker, and J. Winderlich
Biogeosciences, 12, 5393–5414, https://doi.org/10.5194/bg-12-5393-2015, https://doi.org/10.5194/bg-12-5393-2015, 2015
T.-T. Meng, H. Wang, S. P. Harrison, I. C. Prentice, J. Ni, and G. Wang
Biogeosciences, 12, 5339–5352, https://doi.org/10.5194/bg-12-5339-2015, https://doi.org/10.5194/bg-12-5339-2015, 2015
Short summary
Short summary
By analysing the quantitative leaf-traits along extensive temperature and moisture gradients with generalized linear models, we found that metabolism-related traits are universally acclimated to environmental conditions, rather than being fixed within plant functional types. The results strongly support a move towards Dynamic Global Vegetation Models in which continuous, adaptive trait variation provides the fundamental mechanism for changes in ecosystem properties along environmental gradients.
X. Yue, N. Unger, T. F. Keenan, X. Zhang, and C. S. Vogel
Biogeosciences, 12, 4693–4709, https://doi.org/10.5194/bg-12-4693-2015, https://doi.org/10.5194/bg-12-4693-2015, 2015
Short summary
Short summary
We performed model inter-comparison and selected the best model capturing the spatial and temporal variations of observations to predict trends of forest phenology over the past 3 decades. Our results show that phenological trends, which are dominantly driven by temperature changes, are not uniform over the contiguous USA, with a significant spring advance in the east, an autumn delay in the northeast and west, but no evidence of change elsewhere.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
Short summary
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
I. C. Prentice, X. Liang, B. E. Medlyn, and Y.-P. Wang
Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, https://doi.org/10.5194/acp-15-5987-2015, 2015
Short summary
Short summary
Land surface models (LSMs) describe how carbon and water fluxes react to environmental change. They are key component of climate models, yet they differ enormously. Many perform poorly, despite having many parameters. We outline a development strategy emphasizing robustness, reliability and realism, none of which is guaranteed by complexity alone. We propose multiple constraints, benchmarking and data assimilation, and representing unresolved processes stochastically, as tools in this endeavour.
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
Short summary
Short summary
Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
A. P. Ballantyne, R. Andres, R. Houghton, B. D. Stocker, R. Wanninkhof, W. Anderegg, L. A. Cooper, M. DeGrandpre, P. P. Tans, J. B. Miller, C. Alden, and J. W. C. White
Biogeosciences, 12, 2565–2584, https://doi.org/10.5194/bg-12-2565-2015, https://doi.org/10.5194/bg-12-2565-2015, 2015
G. Li, S. P. Harrison, and I. C. Prentice
Biogeosciences Discuss., https://doi.org/10.5194/bgd-12-4769-2015, https://doi.org/10.5194/bgd-12-4769-2015, 2015
Revised manuscript has not been submitted
B. D. Stocker, R. Spahni, and F. Joos
Geosci. Model Dev., 7, 3089–3110, https://doi.org/10.5194/gmd-7-3089-2014, https://doi.org/10.5194/gmd-7-3089-2014, 2014
Short summary
Short summary
Simulating the spatio-temporal dynamics of inundation is key to understanding the role of wetlands under past and future climate change. Here, we describe and assess the DYPTOP model that predicts the extent of inundation and the global spatial distribution of peatlands. DYPTOP makes use of high-resolution topography information and uses ecosystem water balance and peatland soil C balance criteria to simulate peatland spatial dynamics and carbon accumulation.
G. Li, S. P. Harrison, I. C. Prentice, and D. Falster
Biogeosciences, 11, 6711–6724, https://doi.org/10.5194/bg-11-6711-2014, https://doi.org/10.5194/bg-11-6711-2014, 2014
M. Martin Calvo, I. C. Prentice, and S. P. Harrison
Biogeosciences, 11, 6017–6027, https://doi.org/10.5194/bg-11-6017-2014, https://doi.org/10.5194/bg-11-6017-2014, 2014
H. Wang, I. C. Prentice, and T. W. Davis
Biogeosciences, 11, 5987–6001, https://doi.org/10.5194/bg-11-5987-2014, https://doi.org/10.5194/bg-11-5987-2014, 2014
D. I. Kelley, S. P. Harrison, and I. C. Prentice
Geosci. Model Dev., 7, 2411–2433, https://doi.org/10.5194/gmd-7-2411-2014, https://doi.org/10.5194/gmd-7-2411-2014, 2014
I. Bistinas, S. P. Harrison, I. C. Prentice, and J. M. C. Pereira
Biogeosciences, 11, 5087–5101, https://doi.org/10.5194/bg-11-5087-2014, https://doi.org/10.5194/bg-11-5087-2014, 2014
P. N. Foster, I. C. Prentice, C. Morfopoulos, M. Siddall, and M. van Weele
Biogeosciences, 11, 3437–3451, https://doi.org/10.5194/bg-11-3437-2014, https://doi.org/10.5194/bg-11-3437-2014, 2014
C. Le Quéré, G. P. Peters, R. J. Andres, R. M. Andrew, T. A. Boden, P. Ciais, P. Friedlingstein, R. A. Houghton, G. Marland, R. Moriarty, S. Sitch, P. Tans, A. Arneth, A. Arvanitis, D. C. E. Bakker, L. Bopp, J. G. Canadell, L. P. Chini, S. C. Doney, A. Harper, I. Harris, J. I. House, A. K. Jain, S. D. Jones, E. Kato, R. F. Keeling, K. Klein Goldewijk, A. Körtzinger, C. Koven, N. Lefèvre, F. Maignan, A. Omar, T. Ono, G.-H. Park, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. Schwinger, J. Segschneider, B. D. Stocker, T. Takahashi, B. Tilbrook, S. van Heuven, N. Viovy, R. Wanninkhof, A. Wiltshire, and S. Zaehle
Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, https://doi.org/10.5194/essd-6-235-2014, 2014
A. M. Foley, D. Dalmonech, A. D. Friend, F. Aires, A. T. Archibald, P. Bartlein, L. Bopp, J. Chappellaz, P. Cox, N. R. Edwards, G. Feulner, P. Friedlingstein, S. P. Harrison, P. O. Hopcroft, C. D. Jones, J. Kolassa, J. G. Levine, I. C. Prentice, J. Pyle, N. Vázquez Riveiros, E. W. Wolff, and S. Zaehle
Biogeosciences, 10, 8305–8328, https://doi.org/10.5194/bg-10-8305-2013, https://doi.org/10.5194/bg-10-8305-2013, 2013
A. M. Ukkola and I. C. Prentice
Hydrol. Earth Syst. Sci., 17, 4177–4187, https://doi.org/10.5194/hess-17-4177-2013, https://doi.org/10.5194/hess-17-4177-2013, 2013
H. Wang, I. C. Prentice, and J. Ni
Biogeosciences, 10, 5817–5830, https://doi.org/10.5194/bg-10-5817-2013, https://doi.org/10.5194/bg-10-5817-2013, 2013
R. Spahni, F. Joos, B. D. Stocker, M. Steinacher, and Z. C. Yu
Clim. Past, 9, 1287–1308, https://doi.org/10.5194/cp-9-1287-2013, https://doi.org/10.5194/cp-9-1287-2013, 2013
D. I. Kelley, I. C. Prentice, S. P. Harrison, H. Wang, M. Simard, J. B. Fisher, and K. O. Willis
Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, https://doi.org/10.5194/bg-10-3313-2013, 2013
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
F. J. Bragg, I. C. Prentice, S. P. Harrison, G. Eglinton, P. N. Foster, F. Rommerskirchen, and J. Rullkötter
Biogeosciences, 10, 2001–2010, https://doi.org/10.5194/bg-10-2001-2013, https://doi.org/10.5194/bg-10-2001-2013, 2013
D. J. Charman, D. W. Beilman, M. Blaauw, R. K. Booth, S. Brewer, F. M. Chambers, J. A. Christen, A. Gallego-Sala, S. P. Harrison, P. D. M. Hughes, S. T. Jackson, A. Korhola, D. Mauquoy, F. J. G. Mitchell, I. C. Prentice, M. van der Linden, F. De Vleeschouwer, Z. C. Yu, J. Alm, I. E. Bauer, Y. M. C. Corish, M. Garneau, V. Hohl, Y. Huang, E. Karofeld, G. Le Roux, J. Loisel, R. Moschen, J. E. Nichols, T. M. Nieminen, G. M. MacDonald, N. R. Phadtare, N. Rausch, Ü. Sillasoo, G. T. Swindles, E.-S. Tuittila, L. Ukonmaanaho, M. Väliranta, S. van Bellen, B. van Geel, D. H. Vitt, and Y. Zhao
Biogeosciences, 10, 929–944, https://doi.org/10.5194/bg-10-929-2013, https://doi.org/10.5194/bg-10-929-2013, 2013
Related subject area
Biogeosciences
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
H2MV (v1.0): Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
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
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Process-based Modeling of Solar-induced Chlorophyll Fluorescence with VISIT-SIF version 1.0
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
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.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have 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.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
Short summary
Short summary
Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
Short summary
Short summary
We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
Short summary
Short summary
When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us see what changes on our lands.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
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
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Cited articles
Acosta, M., Pavelka, M., Montagnani, L., Kutsch, W., Lindroth, A., Juszczak,
R., and Janouš, D.: Soil surface CO2 efflux measurements in Norway
spruce forests: Comparison between four different sites across Europe
– from boreal to alpine forest, Geoderma, 192, 295–303,
https://doi.org/10.1016/j.geoderma.2012.08.027, 2013. a
Adams, W. W., Zarter, C. R., Ebbert, V., and Demmig-Adams, B.: Photoprotective Strategies of Overwintering Evergreens, Biosci., 54, 41–49, 2004. a
Ammann, C., Spirig, C., Leifeld, J., and Neftel, A.: Assessment of the nitrogen
and carbon budget of two managed temperate grassland fields, Agr.
Ecosyst. Environ., 133, 150–162, https://doi.org/10.1016/j.agee.2009.05.006, 2009. a
Archibald, S. A., Kirton, A., van der Merwe, M. R., Scholes, R. J., Williams, C. A., and Hanan, N.: Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa, Biogeosci., 6, 251–266, https://doi.org/10.5194/bg-6-251-2009, 2009. a
Ardo, J., Molder, M., El-Tahir, B. A., and Elkhidir, H. A. M.: Seasonal
variation of carbon fluxes in a sparse savanna in semi arid Sudan, Carb.
Bal. Manage., 3, 7, https://doi.org/10.1186/1750-0680-3-7, 2008. a
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, U.,
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. a
Aubinet, M., Chermanne, B., Vandenhaute, M., Longdoz, B., Yernaux, M., and
Laitat, E.: Long term carbon dioxide exchange above a mixed forest in the
Belgian Ardennes, Agr. Forest Meteorol., 108, 293–315,
https://doi.org/10.1016/s0168-1923(01)00244-1, 2001. a
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance
and terrestrial photosynthesis, Sci. Adv., 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. a
Baldocchi, D., Chen, Q., Chen, X., Ma, S., Miller, G., Ryu, Y., Xiao, J., Wenk,
R., and Battles, J.: The Dynamics of Energy, Water, and Carbon Fluxes in a
Blue Oak (Quercus douglasii) Savanna in California, in: Ecosystem
Function in Savannas, 135–151, CRC Press, https://doi.org/10.1201/b10275-10, 2010. a
Ball, J. T., Timothy Ball, J., Woodrow, I. E., and Berry, J. A.: A Model
Predicting Stomatal Conductance and its Contribution to the Control of
Photosynthesis under Different Environmental Conditions, in: Progress in
Photosynthesis Research, 221–224, 1987. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and
Wood, E. F.: Present and future Köppen-Geiger climate classification maps
at 1-km resolution, Sci. Data, 5, 180214,
https://doi.org/10.1038/sdata.2018.214, 2018. a
Beer, C., Ciais, P., Reichstein, M., Baldocchi, D., Law, B. E., Papale, D.,
Soussana, J.-F., Ammann, C., Buchmann, N., Frank, D., Gianelle, D., Janssens,
I. A., Knohl, A., Köstner, B., Moors, E., Roupsard, O., Verbeeck, H.,
Vesala, T., Williams, C. A., and Wohlfahrt, G.: Temporal and among-site
variability of inherent water use efficiency at the ecosystem level, Global
Biogeochem. Cy., 23, GB2018, https://doi.org/10.1029/2008GB003233,
2009. a
Belelli Marchesini, L., Papale, D., Reichstein, M., Vuichard, N., Tchebakova, N., and Valentini, R.: Carbon balance assessment of a natural steppe of southern Siberia by multiple constraint approach, Biogeosciences, 4, 581–595, https://doi.org/10.5194/bg-4-581-2007, 2007. a
Berberan-Santos, M. N., Bodunov, E. N., and Pogliani, L.: On the barometric
formula, Am. J. Phys., 65, 404–412, 1997. a
Berbigier, P., Bonnefond, J.-M., and Mellmann, P.: CO2 and water vapour
fluxes for 2 years above Euroflux forest site, Agr. Forest
Meteorol., 108, 183–197, https://doi.org/10.1016/s0168-1923(01)00240-4, 2001. a
Bergeron, O., Margolis, H. A., Black, T. A., Coursolle, C., Dunn, A. L., Barr,
A. G., and Wofsy, S. C.: Comparison of carbon dioxide fluxes over three
boreal black spruce forests in Canada, Global Change Biol., 13, 89–107,
https://doi.org/10.1111/j.1365-2486.2006.01281.x, 2007. a
Beringer, J., Hacker, J., Hutley, L. B., Leuning, R., Arndt, S. K., Amiri, R.,
Bannehr, L., Cernusak, L. A., Grover, S., Hensley, C., Hocking, D., Isaac,
P., Jamali, H., Kanniah, K., Livesley, S., Neininger, B., U, K. T. P., Sea,
W., Straten, D., Tapper, N., Weinmann, R., Wood, S., and Zegelin, S.:
SPECIAL–Savanna Patterns of Energy and Carbon Integrated across
the Landscape, B. Am. Meteorol. Soc., 92,
1467–1485, https://doi.org/10.1175/2011bams2948.1, 2011a. a
Beringer, J., Hutley, L. B., Hacker, J. M., Neininger, B., and U, K. T. P.:
Patterns and processes of carbon, water and energy cycles across northern
Australian landscapes: From point to region, Agr. Forest
Meteorol., 151, 1409–1416, https://doi.org/10.1016/j.agrformet.2011.05.003,
2011b. a
Beringer, J., Hutley, L. B., Hacker, J. M., Neininger, B., and U, K. T. P.:
Patterns and processes of carbon, water and energy cycles across northern
Australian landscapes: From point to region, Agr. Forest
Meteorol., 151, 1409–1416, https://doi.org/10.1016/j.agrformet.2011.05.003,
2011c. a
Beringer, J., Hutley, L. B., McHugh, I., Arndt, S. K., Campbell, D., Cleugh, H. A., Cleverly, J., Resco de Dios, V., Eamus, D., Evans, B., Ewenz, C., Grace, P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., Liddell, M. J., Macfarlane, C., Meyer, W., Moore, C., Pendall, E., Phillips, A., Phillips, R. L., Prober, S. M., Restrepo-Coupe, N., Rutledge, S., Schroder, I., Silberstein, R., Southall, P., Yee, M. S., Tapper, N. J., van Gorsel, E., Vote, C., Walker, J., and Wardlaw, T.: An introduction to the Australian and New Zealand flux tower network – OzFlux, Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, 2016. a, b, c, d
Biederman, J. A., Scott, R. L., Goulden, M. L., Vargas, R., Litvak, M. E.,
Kolb, T. E., Yepez, E. A., Oechel, W. C., Blanken, P. D., Bell, T. W.,
Garatuza-Payan, J., Maurer, G. E., Dore, S., and Burns, S. P.: Terrestrial
carbon balance in a drier world: the effects of water availability in
southwestern North America, Global Change Biol., 22, 1867–1879,
https://doi.org/10.1111/gcb.13222,
2016. a
Bonal, D., Bosc, A., Ponton, S., Goret, J.-Y., Burban, B., Gross, P.,
Bonnefond, J.-M., Elbers, J., Longdoz, B., Epron, D., Guehl, J.-M., and
Granier, A.: Impact of severe dry season on net ecosystem exchange in the
Neotropical rainforest of French Guiana, Global Change Biol., 14,
1917–1933, https://doi.org/10.1111/j.1365-2486.2008.01610.x, 2008. a
Bowling, D. R., Bethers-Marchetti, S., Lunch, C. K., Grote, E. E., and Belnap,
J.: Carbon, water, and energy fluxes in a semiarid cold desert grassland
during and following multiyear drought, J. Geophys. Res., 115, G4,
https://doi.org/10.1029/2010jg001322, 2010. a
Bristow, M., Hutley, L. B., Beringer, J., Livesley, S. J., Edwards, A. C., and Arndt, S. K.: Quantifying the relative importance of greenhouse gas emissions from current and future savanna land use change across northern Australia, Biogeosciences, 13, 6285–6303, https://doi.org/10.5194/bg-13-6285-2016, 2016. a
Cernusak, L. A., Hutley, L. B., Beringer, J., Holtum, J. A., and Turner, B. L.:
Photosynthetic physiology of eucalypts along a sub-continental rainfall
gradient in northern Australia, Agr. Forest Meteorol., 151,
1462–1470, https://doi.org/10.1016/j.agrformet.2011.01.006, 2011. a
Chen, B., Liu, J., Chen, J. M., Croft, H., Gonsamo, A., He, L., and Luo, X.:
Assessment of foliage clumping effects on evapotranspiration estimates in
forested ecosystems, Agr. Forest Meteorol., 216, 82–92,
https://doi.org/10.1016/j.agrformet.2015.09.017,
2016. a, b, c
Chen, S., Chen, J., Lin, G., Zhang, W., Miao, H., Wei, L., Huang, J., and Han, X.: Energy balance and partition in Inner Mongolia steppe ecosystems with
different land use types, Agr. Forest Meteorol., 149,
1800–1809, https://doi.org/10.1016/j.agrformet.2009.06.009, 2009. a
Chiesi, M., Maselli, F., Bindi, M., Fibbi, L., Cherubini, P., Arlotta, E.,
Tirone, G., Matteucci, G., and Seufert, G.: Modelling carbon budget of
Mediterranean forests using ground and remote sensing measurements,
Agr. Forest Meteorol., 135, 22–34,
https://doi.org/10.1016/j.agrformet.2005.09.011, 2005. a
Cleverly, J., Boulain, N., Villalobos-Vega, R., Grant, N., Faux, R., Wood, C.,
Cook, P. G., Yu, Q., Leigh, A., and Eamus, D.: Dynamics of component carbon
fluxes in a semi-arid Acacia woodland, central Australia, J.
Geophys. Res.-Biogeosci., 118, 1168–1185,
https://doi.org/10.1002/jgrg.20101, 2013. a
Cleverly, J., Eamus, D., Van Gorsel, E., Chen, C., Rumman, R., Luo, Q.,
Coupe, N. R., Li, L., Kljun, N., Faux, R., Yu, Q., and Huete, A.:
Productivity and evapotranspiration of two contrasting semiarid ecosystems
following the 2011 global carbon land sink anomaly, Agr. Forest
Meteorol., 220, 151–159, https://doi.org/10.1016/j.agrformet.2016.01.086,
2016. a
Cook, B. D., Davis, K. J., Wang, W., Desai, A., Berger, B. W., Teclaw, R. M.,
Martin, J. G., Bolstad, P. V., Bakwin, P. S., Yi, C., and Heilman, W.: Carbon
exchange and venting anomalies in an upland deciduous forest in northern
Wisconsin, USA, Agr. Forest Meteorol., 126, 271–295,
https://doi.org/10.1016/j.agrformet.2004.06.008, 2004. a
Cowan, I. R. and Farquhar, G. D.: Stomatal function in relation to leaf
metabolism and environment, Symp. Soc. Exp. Biol., 31, 471–505, 1977. a
Davis, T. W., Prentice, I. C., Stocker, B. D., Thomas, R. T., Whitley, R. J., Wang, H., Evans, B. J., Gallego-Sala, A. V., Sykes, M. T., and Cramer, W.: Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture, Geosci. Model Dev., 10, 689–708, https://doi.org/10.5194/gmd-10-689-2017, 2017. a, b
Delpierre, N., Berveiller, D., Granda, E., and Dufrêne, E.: Wood
phenology, not carbon input, controls the interannual variability of wood
growth in a temperate oak forest, New Phytol., 210, 459–470,
https://doi.org/10.1111/nph.13771,
2015. a
Desai, A. R., Bolstad, P. V., Cook, B. D., Davis, K. J., and Carey, E. V.:
Comparing net ecosystem exchange of carbon dioxide between an old-growth and
mature forest in the upper Midwest, USA, Agr. Forest
Meteorol., 128, 33–55, https://doi.org/10.1016/j.agrformet.2004.09.005, 2005. a
Desai, A. R., Xu, K., Tian, H., Weishampel, P., Thom, J., Baumann, D., Andrews,
A. E., Cook, B. D., King, J. Y., and Kolka, R.: Landscape-level terrestrial
methane flux observed from a very tall tower, Agr. Forest
Meteorol., 201, 61–75, https://doi.org/10.1016/j.agrformet.2014.10.017, 2015. a
Didan, K.: MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006, https://doi.org/10.5067/MODIS/MOD13Q1.006, 2015. a
Dragoni, D., Schmid, H. P., Wayson, C. A., Potter, H., Grimmond, C. S. B., and Randolph, J. C.: Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA, Global Change Biol., 17, 886–897,
https://doi.org/10.1111/j.1365-2486.2010.02281.x, 2011. a
Dunn, A. L., Barford, C. C., Wofsy, S. C., Goulden, M. L., and Daube, B. C.: A
long-term record of carbon exchange in a boreal black spruce forest: means,
responses to interannual variability, and decadal trends, Global Change
Biol., 13, 577–590, https://doi.org/10.1111/j.1365-2486.2006.01221.x, 2007. a
Egea, G., Verhoef, A., and Vidale, P. L.: Towards an improved and more flexible
representation of water stress in coupled photosynthesis–stomatal
conductance models, Agr. Forest Meteorol., 151, 1370–1384, 2011. a
Ensminger, I., Sveshnikov, D., Campbell, D. A., Funk, C., Jansson, S., Lloyd,
J., Shibistova, O., and Öquist, G.: Intermittent low temperatures constrain
spring recovery of photosynthesis in boreal Scots pine forests, Global Change
Biol., 10, 995–1008, https://doi.org/10.1111/j.1365-2486.2004.00781.x,
2004. a
Etzold, S., Ruehr, N. K., Zweifel, R., Dobbertin, M., Zingg, A., Pluess, P.,
Häsler, R., Eugster, W., and Buchmann, N.: The Carbon Balance of Two
Contrasting Mountain Forest Ecosystems in Switzerland: Similar Annual
Trends, but Seasonal Differences, Ecosystems, 14, 1289–1309,
https://doi.org/10.1007/s10021-011-9481-3, 2011. a
Falge, E., Aubinet, M., Bakwin, P. S., Baldocchi, D., Berbigier, P., Bernhofer,
C., Black, T. A., Ceulemans, R., Davis, K. J., Dolman, A. J., Goldstein, A.,
Goulden, M. L., Granier, A., Hollinger, D. Y., Jarvis, P. G., Jensen, N.,
Pilegaard, K., Katul, G., Kyaw Tha Paw, P., Law, B. E., Lindroth, A.,
Loustau, D., Mahli, Y., Monson, R., Moncrieff, P., Moors, E., Munger, J. W.,
Meyers, T., Oechel, W., Schulze, E. d., Thorgeirsson, H., Tenhunen, J.,
Valentini, R., Verma, S. B., Vesala, T., and Wofsy, S. C.: FLUXNET Research
Network Site Characteristics, Investigators, and Bibliography, 2016,
https://doi.org/10.3334/ornldaac/1530, 2017. a, b
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table
Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013. a
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and Otero-Casal,
C.: Hydrologic regulation of plant rooting depth, P. Natl.
Acad. Sci. USA, 114, 10572–10577, https://doi.org/10.1073/pnas.1712381114, 2017. a
Fares, S., Savi, F., Muller, J., Matteucci, G., and Paoletti, E.: Simultaneous
measurements of above and below canopy ozone fluxes help partitioning ozone
deposition between its various sinks in a Mediterranean Oak Forest,
Agr. Forest Meteorol., 198-199, 181–191,
https://doi.org/10.1016/j.agrformet.2014.08.014, 2014. a
Farquhar, G. D. and Wong, S. C.: An Empirical Model of Stomatal Conductance,
Funct. Plant Biol., 11, 191–210,
https://doi.org/10.1071/PP9840191, 1984. a
Ferréa, C., Zenone, T., Comolli, R., and Seufert, G.: Estimating
heterotrophic and autotrophic soil respiration in a semi-natural forest of
Lombardy, Italy, Pedobiologia, 55, 285–294,
https://doi.org/10.1016/j.pedobi.2012.05.001, 2012. a
Fick, A.: Ueber Diffusion, Ann. Phys., 170, 59–86,
https://doi.org/10.1002/andp.18551700105,
1855. a
Field, C. B., Randerson, J. T., and Malmström, C. M.: Global net primary
production: Combining ecology and remote sensing, Remote Sens.
Environ., 51, 74–88,
https://doi.org/10.1016/0034-4257(94)00066-V,
1995. a
Frank, J. M., Massman, W. J., Ewers, B. E., Huckaby, L. S., and Negrón,
J. F.: Ecosystem CO2∕H2O fluxes are explained by hydraulically limited
gas exchange during tree mortality from spruce bark beetles, J.
Geophys. Res.-Biogeosci., 119, 1195–1215,
https://doi.org/10.1002/2013jg002597, 2014. a
Frankenberg, C., Köhler, P., Magney, T. S., Geier, S., Lawson, P.,
Schwochert, M., McDuffie, J., Drewry, D. T., Pavlick, R., and Kuhnert, A.:
The Chlorophyll Fluorescence Imaging Spectrometer (CFIS), mapping far red
fluorescence from aircraft, Remote Sens. Environ., 217, 523–536, 2018. a
Galvagno, M., Wohlfahrt, G., Cremonese, E., Rossini, M., Colombo, R., Filippa,
G., Julitta, T., Manca, G., Siniscalco, C., di Cella, U. M., and Migliavacca,
M.: Phenology and carbon dioxide source/sink strength of a subalpine
grassland in response to an exceptionally short snow season, Environ.
Res. Lett., 8, 025008, https://doi.org/10.1088/1748-9326/8/2/025008, 2013. a
Gamon, J., Peñuelas, J., and Field, C.: A narrow-waveband spectral index
that tracks diurnal changes in photosynthetic efficiency, Remote Sens.
Environ., 41, 35–44,
https://doi.org/10.1016/0034-4257(92)90059-S, 1992. a
Gamon, J. A., Huemmrich, K. F., Wong, C. Y. S., Ensminger, I., Garrity, S.,
Hollinger, D. Y., Noormets, A., and Peñuelas, J.: A remotely sensed
pigment index reveals photosynthetic phenology in evergreen conifers,
P. Natl. Acad. Sci. USA, 113, 13087–13092,
https://doi.org/10.1073/pnas.1606162113, 2016. a
Goldstein, A., Hultman, N., Fracheboud, J., Bauer, M., Panek, J., Xu, M., Qi,
Y., Guenther, A., and Baugh, W.: Effects of climate variability on the carbon
dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in
the Sierra Nevada (CA), Agr. Forest Meteorol., 101,
113–129, https://doi.org/10.1016/s0168-1923(99)00168-9, 2000. a
Gough, C. M., Hardiman, B. S., Nave, L. E., Bohrer, G., Maurer, K. D., Vogel,
C. S., Nadelhoffer, K. J., and Curtis, P. S.: Sustained carbon uptake and
storage following moderate disturbance in a Great Lakes forest,
Ecol. Appl., 23, 1202–1215, https://doi.org/10.1890/12-1554.1, 2013. a, b
Greve, P., Orlowsky, B., Mueller, B., Sheffield, J., Reichstein, M., and
Seneviratne, S. I.: Global assessment of trends in wetting and drying over
land, Nat. Geosci, 7, 716–721, https://doi.org/10.1038/ngeo2247, 2014. a
Grünwald, T. and Bernhofer, C.: A decade of carbon, water and energy flux
measurements of an old spruce forest at the Anchor Station Tharandt,
Tellus B, 59, 387–396, https://doi.org/10.3402/tellusb.v59i3.17000, 2007. a
Guan, D.-X., Wu, J.-B., Zhao, X.-S., Han, S.-J., Yu, G.-R., Sun, X.-M., and
Jin, C.-J.: CO2 fluxes over an old, temperate mixed forest in northeastern
China, Agr. Forest Meteorol., 137, 138–149,
https://doi.org/10.1016/j.agrformet.2006.02.003, 2006. a
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J.-E., Susan Moran, M.,
Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D.,
Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111, E1327–E1333, 2014. a
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution
grids of monthly climatic observations – the CRU TS3.10 Dataset,
Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014. a
He, L., Chen, J. M., Gonsamo, A., Luo, X., Wang, R., Liu, Y., and Liu, R.:
Changes in the Shadow: The Shifting Role of Shaded Leaves in Global Carbon
and Water Cycles Under Climate Change, Geophys. Res. Lett., 45,
5052–5061, https://doi.org/10.1029/2018GL077560,
2018. a, b, c, d
Heinsch, F. A., , Running, S. W., Kimball, J. S., Nemani, R. R.,
Davis, K. J., Bolstad, P. V., Cook, B. D., Desai, A. R., Ricciuto,
D. M., Law, B. E., Oechel, W. C., Wofsy, S. C., Dunn, A. L.,
Munger, J. W., Baldocchi, D. D., Hollinger, D. Y., Richardson,
A. D., Stoy, P. C., Siqueira, M. B. S., Monson, R. K., Burns, S. P., and Flanagan, L. B.: Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations, IEEE Trans. Geosci. Remote Sens., 44, 1908–1925,
https://doi.org/10.1109/TGRS.2005.853936, 2006. a
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G.
B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km–global soil information based on
automated mapping, PLoS One, 9, e105992, https://doi.org/10.1371/journal.pone.0105992, 2014. a, b
Heskel, M., O'Sullivan, O., Reich, P., Tjoelker, M., Weerasinghe, L.,
Penillard, A., Egerton, J., Creek, D., Bloomfield, K., Xiang, J., Sinca, F.,
Stangl, Z., Martinez-De La Torre, A., Griffin, K., Huntingford, C., Hurry,
V., Meir, P., Turnbull, M., and Atkin, O.: 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. a
Hinko-Najera, N., Isaac, P., Beringer, J., van Gorsel, E., Ewenz, C., McHugh, I., Exbrayat, J.-F., Livesley, S. J., and Arndt, S. K.: Net ecosystem carbon exchange of a dry temperate eucalypt forest, Biogeosciences, 14, 3781–3800, https://doi.org/10.5194/bg-14-3781-2017, 2017. a
Hoshika, Y., Fares, S., Savi, F., Gruening, C., Goded, I., De Marco, A.,
Sicard, P., and Paoletti, E.: Stomatal conductance models for ozone risk
assessment at canopy level in two Mediterranean evergreen forests,
Agr. Forest Meteorol., 234-235, 212–221,
https://doi.org/10.1016/j.agrformet.2017.01.005, 2017. a
Hufkens, K.: khufkens/gee_subset: Google Earth Engine subset
script and library, https://doi.org/10.5281/zenodo.833789, 2017. a
Huner, N. P., Oquist, G., Hurry, V. M., Krol, M., Falk, S., and Griffith, M.:
Photosynthesis, photoinhibition and low temperature acclimation in cold
tolerant plants, Photosynth. Res., 37, 19–39, 1993. a
Hutley, L. B., Beringer, J., Isaac, P. R., Hacker, J. M., and Cernusak, L. A.:
A sub-continental scale living laboratory: Spatial patterns of savanna
vegetation over a rainfall gradient in northern Australia, Agr.
Forest Meteorol., 151, 1417–1428, https://doi.org/10.1016/j.agrformet.2011.03.002, 2011. a
Imer, D., Merbold, L., Eugster, W., and Buchmann, N.: Temporal and spatial variations of soil CO2, CH4 and N2O fluxes at three differently managed grasslands, Biogeosciences, 10, 5931–5945, https://doi.org/10.5194/bg-10-5931-2013, 2013. a
Irvine, J., Law, B. E., and Hibbard, K. A.: Postfire carbon pools and fluxes in semiarid ponderosa pine in Central Oregon, Global Change Biol., 13,
1748–1760, https://doi.org/10.1111/j.1365-2486.2007.01368.x, 2007. a
Irvine, J., Law, B. E., Martin, J. G., and Vickers, D.: Interannual variation
in soil CO2 efflux and the response of root respiration to climate and
canopy gas exchange in mature ponderosa pine, Global Change Biol., 14,
2848–2859, https://doi.org/10.1111/j.1365-2486.2008.01682.x, 2008. a
Jacobs, C. M. J., Jacobs, A. F. G., Bosveld, F. C., Hendriks, D. M. D., Hensen, A., Kroon, P. S., Moors, E. J., Nol, L., Schrier-Uijl, A., and Veenendaal, E. M.: Variability of annual CO2 exchange from Dutch grasslands, Biogeosciences, 4, 803–816, https://doi.org/10.5194/bg-4-803-2007, 2007. a
Joiner, J., Guanter, L., Lindstrot, R., Voigt, M., Vasilkov, A. P., Middleton, E. M., Huemmrich, K. F., Yoshida, Y., and Frankenberg, C.: Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803–2823, https://doi.org/10.5194/amt-6-2803-2013, 2013. a
Joiner, J., Yoshida, Y., Guanter, L., and Middleton, E. M.: New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY, Atmos. Meas. Tech., 9, 3939–3967, https://doi.org/10.5194/amt-9-3939-2016, 2016. a
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D.,
Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D.,
Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A.,
Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M.,
Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance,
satellite, and meteorological observations, J. Geophys. Res.-Biogeosci., 116, g00J07, https://doi.org/10.1029/2010JG001566, 2011. a, b, c, d
Kato, T., Tang, Y., Gu, S., Hirota, M., Du, M., Li, Y., and Zhao, X.:
Temperature and biomass influences on interannual changes in CO2 exchange
in an alpine meadow on the Qinghai-Tibetan Plateau, Global Change
Biol., 12, 1285–1298, https://doi.org/10.1111/j.1365-2486.2006.01153.x, 2006. a
Keenan, T., Sabate, S., and Gracia, C.: Soil water stress and coupled
photosynthesis–conductance models: Bridging the gap between conflicting
reports on the relative roles of stomatal, mesophyll conductance and
biochemical limitations to photosynthesis, Agr. Forest Meteorol., 150,
443–453, 2010. a
Keenan, T., Baker, I., Barr, A., Ciais, P., Davis, K., Dietze, M., Dragoni, D.,
Gough, C. M., Grant, R., Hollinger, D., Hufkens, K., Poulter, B., McCaughey,
H., Raczka, B., Ryu, Y., Schaefer, K., Tian, H., Verbeeck, H., Zhao, M., and
Richardson, A. D.: Terrestrial biosphere model performance for inter-annual
variability of land-atmosphere CO2 exchange, Global Change Biol., 18,
1971–1987, https://doi.org/10.1111/j.1365-2486.2012.02678.x, 2012. a, b
Keenan, T. F., Prentice, I. C., Canadell, J. G., Williams, C. A., Wang, H., Raupach, M., and Collatz, G. J.: Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake, Nat. Commun., 7, 13428, https://doi.org/10.1038/ncomms13428, 2016. a, b, c
Keenan, T. F., Migliavacca, M., Papale, D., Baldocchi, D., Reichstein, M.,
Torn, M., and Wutzler, T.: Widespread inhibition of daytime ecosystem
respiration, Nat. Ecol. Evolut., 3, 407–415, https://doi.org/10.1038/s41559-019-0809-2, 2019. a
Kilinc, M., Beringer, J., Hutley, L. B., Tapper, N. J., and McGuire, D. A.:
Carbon and water exchange of the world's tallest angiosperm forest,
Agr. Forest Meteorol., 182–183, 215–224,
https://doi.org/10.1016/j.agrformet.2013.07.003, 2013. a
Knohl, A., Schulze, E.-D., Kolle, O., and Buchmann, N.: Large carbon uptake by
an unmanaged 250-year-old deciduous forest in Central Germany, Agr. Forest Meteorol., 118, 151–167, https://doi.org/10.1016/s0168-1923(03)00115-1, 2003. a
Kok, B.: On the interrelation of respiration and photosynthesis in green
plants, Biochim. Biophys. Acta, 3, 625–631,
https://doi.org/10.1016/0006-3002(49)90136-5,
1949. a
Kurbatova, J., Li, C., Varlagin, A., Xiao, X., and Vygodskaya, N.: Modeling carbon dynamics in two adjacent spruce forests with different soil conditions in Russia, Biogeosciences, 5, 969–980, https://doi.org/10.5194/bg-5-969-2008, 2008. a
Landsberg, J. J. and Waring, R. H.: A generalised model of forest productivity
using simplified concepts of radiation-use efficiency, carbon balance and
partitioning, For. Ecol. Manage., 95, 209–228, 1997. a
Lasslop, G., Reichstein, M., Papale, D., Richardson, A., Arneth, A., Barr, A.,
Stoy, P., and Wohlfahrt, G.: Separation of net ecosystem exchange into
assimilation and respiration using a light response curve approach: critical
issues and global evaluation, Global Change Biol., 16, 187–208,
https://doi.org/10.1111/j.1365-2486.2009.02041.x, 2010. a, b
Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis model
for C3 plants, Plant Cell Environ., 18, 339–355, 1995. a
Leuning, R., Cleugh, H. A., Zegelin, S. J., and Hughes, D.: Carbon and water
fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in
Australia: measurements and comparison with MODIS remote sensing
estimates, Agr. Forest Meteorol., 129, 151–173,
https://doi.org/10.1016/j.agrformet.2004.12.004, 2005. a
Li, X., Xiao, J., He, B., Altaf Arain, M., Beringer, J., Desai, A. R., Emmel,
C., Hollinger, D. Y., Krasnova, A., Mammarella, I., Noe, S. M., Ortiz, P. S.,
Rey-Sanchez, A. C., Rocha, A. V., and Varlagin, A.: Solar-induced chlorophyll
fluorescence is strongly correlated with terrestrial photosynthesis for a
wide variety of biomes: First global analysis based on OCO-2 and flux tower
observations, Glob. Chang. Biol., 24, 3990–4008, https://doi.org/10.1111/gcb.14297, 2018. a
Lindauer, M., Schmid, H., Grote, R., Mauder, M., Steinbrecher, R., and Wolpert,
B.: Net ecosystem exchange over a non-cleared wind-throw-disturbed upland
spruce forest–Measurements and simulations, Agr.
Forest Meteorol., 197, 219–234, https://doi.org/10.1016/j.agrformet.2014.07.005, 2014. a
Lloyd, J. and Farquhar, G. D.: 13C discrimination during CO2 assimilation by
the terrestrial biosphere, Oecologia, 99, 201–215, https://doi.org/10.1007/BF00627732, 1994. a
Long, S. P., Postl, W. F., and Bolhár-Nordenkampf, H. R.: Quantum yields
for uptake of carbon dioxide in C3 vascular plants of contrasting habitats
and taxonomic groupings, Planta, 189, 226–234, 1993. a
Luo, X., Keenan, T. F., Fisher, J. B., Jiménez-Muñoz, J.-C., Chen, J. M.,
Jiang, C., Ju, W., Perakalapudi, N.-V., Ryu, Y., and Tadić, J. M.: The impact of the 2015/2016 El Niño on global photosynthesis using satellite remote sensing, Philos. Trans. Roy. Soc. B, 373, 20170409, https://doi.org/10.1098/rstb.2017.0409,
2018. a, b
Ma, S., Baldocchi, D. D., Xu, L., and Hehn, T.: Inter-annual variability in
carbon dioxide exchange of an oak/grass savanna and open grassland in
California, Agr. Forest Meteorol., 147, 157–171,
https://doi.org/10.1016/j.agrformet.2007.07.008, 2007. a
MacFarling Meure, C., Etheridge, D., Trudinger, C., Steele, P., Langenfelds, R., van Ommen, T., Smith, A., and Elkins, J.: Law Dome CO2, CH4 and N2O ice core records extended to 2000 years BP, Geophys. Res. Lett., 33, L14810, https://doi.org/10.1029/2006GL026152, 2006. a
Mäkelä, A., Hari, P., Berninger, F., Hänninen, H., and Nikinmaa,
E.: Acclimation of photosynthetic capacity in Scots pine to the annual cycle of temperature, Tree Physiol., 24, 369–376, 2004. a
Marcolla, B., Pitacco, A., and Cescatti, A.: Canopy Architecture and Turbulence
Structure in a Coniferous Forest, Bound.-Layer Meteorol., 108, 39–59,
https://doi.org/10.1023/a:1023027709805,
2003a. a
Marcolla, B., Pitacco, A., and Cescatti, A.: Canopy Architecture and Turbulence
Structure in a Coniferous Forest, Bound.-Layer Meteorol., 108, 39–59,
https://doi.org/10.1023/a:1023027709805,
2003b. a
Marcolla, B., Cescatti, A., Manca, G., Zorer, R., Cavagna, M., Fiora, A.,
Gianelle, D., Rodeghiero, M., Sottocornola, M., and Zampedri, R.: Climatic
controls and ecosystem responses drive the inter-annual variability of the
net ecosystem exchange of an alpine meadow, Agr. Forest
Meteorol., 151, 1233–1243, https://doi.org/10.1016/j.agrformet.2011.04.015, 2011. a
Matsumoto, K., Ohta, T., Nakai, T., Kuwada, T., Daikoku, K., Iida, S., Yabuki, H., Kononov, A. V., van der Molen, M. K., Kodama, Y., Maximov, T. C., Dolman, A. J., and Hattori, S.: Energy consumption and evapotranspiration at several boreal and temperate forests in the Far East, Agr. Forest
Meteorol., 148, 1978–1989, https://doi.org/10.1016/j.agrformet.2008.09.008, 2008. a, b
McHugh, I. D., Beringer, J., Cunningham, S. C., Baker, P. J., Cavagnaro, T. R., Mac Nally, R., and Thompson, R. M.: Interactions between nocturnal turbulent flux, storage and advection at an “deal” eucalypt woodland site, Biogeosciences, 14, 3027–3050, https://doi.org/10.5194/bg-14-3027-2017, 2017. a
McNevin, D., von Caemmerer, S., and Farquhar, G.: Determining RuBisCO
activation kinetics and other rate and equilibrium constants by simultaneous
multiple non-linear regression of a kinetic model, J. Exp. Bot., 57,
3883–3900, 2006. a
Medlyn, B. E.: Physiological basis of the light use efficiency model, Tree
Physiol., 18, 167–176, 1998. a
Meek, D. W., Hatfield, J. L., Howell, T. A., Idso, S. B., and Reginato, R. J.:
A Generalized Relationship between Photosynthetically Active Radiation and
Solar Radiation1, Agron. J., 76, 939–945, 1984. a
Merbold, L., Ardö, J., Arneth, A., Scholes, R. J., Nouvellon, Y., de Grandcourt, A., Archibald, S., Bonnefond, J. M., Boulain, N., Brueggemann, N., Bruemmer, C., Cappelaere, B., Ceschia, E., El-Khidir, H. A. M., El-Tahir, B. A., Falk, U., Lloyd, J., Kergoat, L., Le Dantec, V., Mougin, E., Muchinda, M., Mukelabai, M. M., Ramier, D., Roupsard, O., Timouk, F., Veenendaal, E. M., and Kutsch, W. L.: Precipitation as driver of carbon fluxes in 11 African ecosystems, Biogeosciences, 6, 1027–1041, https://doi.org/10.5194/bg-6-1027-2009, 2009. a
Merbold, L., Eugster, W., Stieger, J., Zahniser, M., Nelson, D., and Buchmann,
N.: Greenhouse gas budget (CO2, CH4 and N2O) of intensively managed
grassland following restoration, Global Change Biol., 20, 1913–1928,
https://doi.org/10.1111/gcb.12518,
2014. a
Meyer, W. S., Kondrlovà, E., and Koerber, G. R.: Evaporation of perennial
semi-arid woodland in southeastern Australia is adapted for irregular but
common dry periods, Hydrol. Process., 29, 3714–3726,
https://doi.org/10.1002/hyp.10467,
2015. a
Michaletz, S. T., Weiser, M. D., Zhou, J., Kaspari, M., Helliker, B. R., and
Enquist, B. J.: Plant Thermoregulation: Energetics, Trait-Environment
Interactions, and Carbon Economics, Trends Ecol. Evol., 30, 714–724, 2015. a
Migliavacca, M., Meroni, M., Busetto, L., Colombo, R., Zenone, T., Matteucci,
G., Manca, G., and Seufert, G.: Modeling Gross Primary Production of
Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in
a Process-Based Model, Sensors, 9, 922–942, https://doi.org/10.3390/s90200922, 2009. a
Monson, R. K., Turnipseed, A. A., Sparks, J. P., Harley, P. C., Scott-Denton,
L. E., Sparks, K., and Huxman, T. E.: Carbon sequestration in a
high-elevation, subalpine forest, Global Change Biol., 8, 459–478,
https://doi.org/10.1046/j.1365-2486.2002.00480.x, 2002. a
Montagnani, L., Manca, G., Canepa, E., Georgieva, E., Acosta, M., Feigenwinter,
C., Janous, D., Kerschbaumer, G., Lindroth, A., Minach, L., Minerbi, S.,
Mölder, M., Pavelka, M., Seufert, G., Zeri, M., and Ziegler, W.: A new mass
conservation approach to the study of CO2 advection in an alpine forest,
J. Geophys. Res., 114, D07306, https://doi.org/10.1029/2008jd010650, 2009. a
Monteith, J. L.: Solar Radiation and Productivity in Tropical Ecosystems,
J. Appl. Ecol., 9, 747–766, 1972. a
Moors, E.: Water Use of Forests in The Netherlands, Ph.D. thesis, Vrije
Universiteit Amsterdam, 2012. a
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A3H MODIS/Combined Terra+Aqua
Leaf Area Index/FPAR Daily L4 Global 500 m SIN Grid V006, Data set, NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MODIS/MCD15A3H.006, 2015. a
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid V006, Data set, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006, 2020. a
Nakai, T., Kim, Y., Busey, R. C., Suzuki, R., Nagai, S., Kobayashi, H., Park,
H., Sugiura, K., and Ito, A.: Characteristics of evapotranspiration from a
permafrost black spruce forest in interior Alaska, Polar Sci., 7,
136–148, https://doi.org/10.1016/j.polar.2013.03.003, 2013. a
Oquist, G. and Huner, N. P. A.: Photosynthesis of overwintering evergreen
plants, Annu. Rev. Plant Biol., 54, 329–355, 2003. a
Papale, D., Migliavacca, M., Cremonese, E., Cescatti, A., Alberti, G.,
Balzarolo, M., Marchesini, L. B., Canfora, E., Casa, R., Duce, P., Facini,
O., Galvagno, M., Genesio, L., Gianelle, D., Magliulo, V., Matteucci, G.,
Montagnani, L., Petrella, F., Pitacco, A., Seufert, G., Spano, D., Stefani,
P., Vaccari, F. P., and Valentini, R.: Carbon, Water and Energy Fluxes of
Terrestrial Ecosystems in Italy, in: The Greenhouse Gas Balance of Italy,
11–45, Springer Berlin Heidelberg, https://doi.org/10.1007/978-3-642-32424-6_2, 2014. a
Pelkonen, P. and Hari, P.: The Dependence of the Springtime Recovery of CO2
Uptake in Scots Pine on Temperature and Internal Factors, Flora, 169,
398–404, 1980. a
Pilegaard, K., Ibrom, A., Courtney, M. S., Hummelshøj, P., and Jensen,
N. O.: Increasing net CO2 uptake by a Danish beech forest during the
period from 1996 to 2009, Agr. Forest Meteorol., 151, 934–946,
https://doi.org/10.1016/j.agrformet.2011.02.013, 2011. a
Porcar-Castell, A., Tyystjärvi, E., Atherton, J., van der Tol, C., Flexas,
J., Pfündel, E. E., Moreno, J., Frankenberg, C., and Berry, J. A.:
Linking chlorophyll a fluorescence to photosynthesis for remote sensing
applications: mechanisms and challenges, J. Exp. Bot., 65, 4065–4095, 2014. a
Posse, G., Lewczuk, N., Richter, K., and Cristiano, P.: Carbon and water vapor
balance in a subtropical pine plantation, iForest – Biogeosci.
Forest., 9, 736–742, https://doi.org/10.3832/ifor1815-009, 2016. a
Post, H., Hendricks Franssen, H. J., Graf, A., Schmidt, M., and Vereecken, H.: Uncertainty analysis of eddy covariance CO2 flux measurements for different EC tower distances using an extended two-tower approach, Biogeosciences, 12, 1205–1221, https://doi.org/10.5194/bg-12-1205-2015, 2015. a
Powell, T. L., Bracho, R., Li, J., Dore, S., Hinkle, C. R., and Drake, B. G.:
Environmental controls over net ecosystem carbon exchange of scrub oak in
central Florida, Agr. Forest Meteorol., 141, 19–34,
https://doi.org/10.1016/j.agrformet.2006.09.002, 2006. a
Prentice, I. C., Liang, X., Medlyn, B. E., and Wang, Y.-P.: Reliable, robust and realistic: the three R's of next-generation land-surface modelling, Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, 2015. a
Prescher, A.-K., Grünwald, T., and Bernhofer, C.: Land use regulates carbon
budgets in eastern Germany: From NEE to NBP, Agr. Forest
Meteorol., 150, 1016–1025, https://doi.org/10.1016/j.agrformet.2010.03.008, 2010. a
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux
and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100,
81–92, 1972. a
Prober, S. M., Thiele, K. R., Rundel, P. W., Yates, C. J., Berry, S. L., Byrne,
M., Christidis, L., Gosper, C. R., Grierson, P. F., Lemson, K., Lyons, T.,
Macfarlane, C., O'Connor, M. H., Scott, J. K., Standish, R. J., Stock, W. D.,
van Etten, E. J., Wardell-Johnson, G. W., and Watson, A.: Facilitating
adaptation of biodiversity to climate change: A conceptual framework applied
to the world's largest Mediterranean-climate woodland, Clim. Change, 110,
227–248, https://doi.org/10.1007/s10584-011-0092-y, 2012. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at: https://www.R-project.org/ (last access: 6 March 2020), 2016. a
Rambal, S., Joffre, R., Ourcival, J. M., Cavender-Bares, J., and Rocheteau, A.:
The growth respiration component in eddy CO2 flux from a Quercus ilex
mediterranean forest, Global Change Biol., 10, 1460–1469,
https://doi.org/10.1111/j.1365-2486.2004.00819.x, 2004. a
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier,
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T.,
Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila,
A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M.,
Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G.,
Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of
net ecosystem exchange into assimilation and ecosystem respiration: review
and improved algorithm, Global Chang. Biol., 11, 1424–1439, 2005. a, b, c
Reverter, B. R., Sánchez-Cañete, E. P., Resco, V., Serrano-Ortiz, P., Oyonarte, C., and Kowalski, A. S.: Analyzing the major drivers of NEE in a Mediterranean alpine shrubland, Biogeosciences, 7, 2601–2611, https://doi.org/10.5194/bg-7-2601-2010, 2010. a
Richardson, A. D., Hollinger, D. Y., Aber, J. D., Ollinger, S. V., and
Braswell, B. H.: Environmental variation is directly responsible for short-
but not long-term variation in forest-atmosphere carbon exchange, Global
Change Biol., 13, 788–803, https://doi.org/10.1111/j.1365-2486.2007.01330.x,
2007. a, b
Rogers, A.: The use and misuse of Vc,max in Earth System Models, Photosynt.
Res., 119, 15–29, https://doi.org/10.1007/s11120-013-9818-1, 2014. a
Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., von Caemmerer, S., Dietze,
M. C., Kattge, J., Leakey, A. D. B., Mercado, L. M., Niinemets, U., Prentice,
I. C., Serbin, S. P., Sitch, S., Way, D. A., and Zaehle, S.: A roadmap for
improving the representation of photosynthesis in Earth system models, New
Phytol., 213, 22–42 https://doi.org/10.1111/nph.14283, 2017. a, b, c, d, e
Ruehr, N. K., Martin, J. G., and Law, B. E.: Effects of water availability on
carbon and water exchange in a young ponderosa pine forest: Above- and
belowground responses, Agr. Forest Meteorol., 164, 136–148,
https://doi.org/10.1016/j.agrformet.2012.05.015, 2012. a
Running, S., Mu, Q., and Zhao, M.: MOD17A2H MODIS/Terra Gross Primary
Productivity 8-Day L4 Global 500 m SIN Grid V006, Data set, NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MODIS/MOD17A2H.006, 2015. a
Ryu, Y., Baldocchi, D. D., Kobayashi, H., van Ingen, C., Li, J., Black, T. A.,
Beringer, J., van Gorsel, E., Knohl, A., Law, B. E., and Roupsard, O.:
Integration of MODIS land and atmosphere products with a coupled-process
model to estimate gross primary productivity and evapotranspiration from 1 km
to global scales, Global Biogeochem. Cy., 25, 4, https://doi.org/10.1029/2011GB004053, 2011. a
Sabbatini, S., Arriga, N., Bertolini, T., Castaldi, S., Chiti, T., Consalvo, C., Njakou Djomo, S., Gioli, B., Matteucci, G., and Papale, D.: Greenhouse gas balance of cropland conversion to bioenergy poplar short-rotation coppice, Biogeosciences, 13, 95–113, https://doi.org/10.5194/bg-13-95-2016, 2016. a, b
Saxton, K. E. and Rawls, W. J.: Soil Water Characteristic Estimates by Texture
and Organic Matter for Hydrologic Solutions, Soil Sci. Soc. Am. J., 70,
1569–1578, 2006. a
Schroder, I., Kuske, T., and Zegelin, S.: Eddy Covariance Dataset for Arcturus
(2011–2013), Geoscience Australia, Canberra, Tech. rep.,
https://doi.org/102.100.100/14249, 2014. a
Scott, R. L., Jenerette, G. D., Potts, D. L., and Huxman, T. E.: Effects of
seasonal drought on net carbon dioxide exchange from a woody-plant-encroached
semiarid grassland, J. Geophys. Res., 114, G4,
https://doi.org/10.1029/2008jg000900, 2009. a
Scott, R. L., Hamerlynck, E. P., Jenerette, G. D., Moran, M. S., and
Barron-Gafford, G. A.: Carbon dioxide exchange in a semidesert grassland
through drought-induced vegetation change, J. Geophys. Res.,
115, G3, https://doi.org/10.1029/2010jg001348, 2010. a
Scott, R. L., Biederman, J. A., Hamerlynck, E. P., and Barron-Gafford, G. A.:
The carbon balance pivot point of southwestern U.S. semiarid ecosystems:
Insights from the 21st century drought, J. Geophys. Res.-Biogeosci., 120, 2612–2624, https://doi.org/10.1002/2015jg003181, 2015a. a
Scott, R. L., Biederman, J. A., Hamerlynck, E. P., and Barron-Gafford, G. A.:
The carbon balance pivot point of southwestern U.S. semiarid ecosystems:
Insights from the 21st century drought, J. Geophys. Res.-Biogeosci., 120, 2612–2624, https://doi.org/10.1002/2015jg003181, 2015b. a
Serrano-Ortiz, P., Marañón-Jiménez, S., Reverter, B. R.,
Sánchez-Cañete, E. P., Castro, J., Zamora, R., and Kowalski,
A. S.: Post-fire salvage logging reduces carbon sequestration in
Mediterranean coniferous forest, Forest Ecol. Manage., 262,
2287–2296, https://doi.org/10.1016/j.foreco.2011.08.023,
2011. a
Shao, C., Chen, J., Li, L., Dong, G., Han, J., Abraha, M., and John, R.:
Grazing effects on surface energy fluxes in a desert steppe on the Mongolian
Plateau:, Ecol. Appl., 27, 485–502, https://doi.org/10.1002/eap.1459,
2017. a
Shi, P., Sun, X., Xu, L., Zhang, X., He, Y., Zhang, D., and Yu, G.: Net
ecosystem CO2 exchange and controlling factors in a
steppe–Kobresia meadow on the Tibetan Plateau, Sci.
China Ser. D, 49, 207–218,
https://doi.org/10.1007/s11430-006-8207-4, 2006. a
Singsaas, E. L., Ort, D. R., and DeLucia, E. H.: Variation in measured values
of photosynthetic quantum yield in ecophysiological studies, Oecologia, 128, 15–23, https://doi.org/10.1007/s004420000624, 2001. a
Smith, E. L.: THE INFLUENCE OF LIGHT AND CARBON DIOXIDE ON
PHOTOSYNTHESIS, J. Gen. Physiol., 20, 807–830, 1937. a
Smith, N. G., Keenan, T. F., Colin Prentice, I., Wang, H., Wright, I. J.,
Niinemets, U., 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, 2019. a, b, c, d, e, f
Stevens, R. M., Ewenz, C. M., Grigson, G., and Conner, S. M.: Water use by an
irrigated almond orchard, Irrig. Sci., 30, 189–200,
https://doi.org/10.1007/s00271-011-0270-8, 2011. a
Stocker, B.: fLUE, https://doi.org/10.5281/zenodo.1158524, 2018. a
Stocker, B.: rpmodel: v1.0.4, https://doi.org/10.5281/zenodo.3560169, 2019a. a
Stocker, B.: sofun: v1.2.0, https://doi.org/10.5281/zenodo.3529466, 2019b. a
Stocker, B.: eval_pmodel, https://doi.org/10.5281/zenodo.3632308, 2020a. a
Stocker, B.: rsofun, https://doi.org/10.5281/zenodo.3632328, 2020b. a
Stocker, B. D.: GPP at FLUXNET Tier 1 sites from P-model,
https://doi.org/10.5281/zenodo.3559850, 2019c. a
Stocker, B. D., Zscheischler, J., Keenan, T. F., Prentice, I. C., Seneviratne,
S. I., and Peñuelas, J.: Drought impacts on terrestrial primary
production underestimated by satellite monitoring, Nat. Geosci., 12,
264–270, https://doi.org/10.1038/s41561-019-0318-6, 2019. a, b
Suni, T., Rinne, J., Reissel, A., Altimir, N., Keronen, P., Rannik, Ü.,
Maso, M., Kulmala, M., and Vesala, T.: Long-term measurements of surface
fluxes above a Scots pine forest in Hyytiälä, southern Finland,
Boreal Environ. Res., 4, 287–301, 2003. a
Suzuki, Y., Makino, A., and Mae, T.: Changes in the turnover of Rubisco and
levels of mRNAs of rbcL and rbcS in rice leaves from emergence to
senescence, Plant Cell Environ., 24, 1353–1360, 2001. a
Tagesson, T., Fensholt, R., Guiro, I., Rasmussen, M. O., Huber, S., Mbow, C.,
Garcia, M., Horion, S., Sandholt, I., Holm-Rasmussen, B., Göttsche,
F. M., Ridler, M.-E., Olén, N., Olsen, J. L., Ehammer, A., Madsen, M.,
Olesen, F. S., and Ardö, J.: Ecosystem properties of semiarid savanna
grassland in West Africa and its relationship with environmental
variability, Global Change Biol., 21, 250–264, https://doi.org/10.1111/gcb.12734, 2014. a
Tanja, S., Berninger, F., Vesala, T., Markkanen, T., Hari, P., Mäkelä, A.,
Ilvesniemi, H., Hänninen, H., Nikinmaa, E., Huttula, T., Laurila, T.,
Aurela, M., Grelle, A., Lindroth, A., Arneth, A., Shibistova, O., and Lloyd,
J.: Air temperature triggers the recovery of evergreen boreal forest
photosynthesis in spring, Global Change Biol., 9, 1410–1426,
https://doi.org/10.1046/j.1365-2486.2003.00597.x,
2003. a, b
Tedeschi, V., Rey, A., Manca, G., Valentini, R., Jarvis, P. G., and Borghetti,
M.: Soil respiration in a Mediterranean oak forest at different
developmental stages after coppicing, Global Change Biol., 12, 110–121,
https://doi.org/10.1111/j.1365-2486.2005.01081.x, 2006. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a, b, c
Ulke, A. G., Gattinoni, N. N., and Posse, G.: Analysis and modelling of
turbulent fluxes in two different ecosystems in Argentina, International
J. Environ. Pollut., 58, 52, https://doi.org/10.1504/ijep.2015.076583, 2015. a
Urbanski, S., Barford, C., Wofsy, S., Kucharik, C., Pyle, E., Budney, J.,
McKain, K., Fitzjarrald, D., Czikowsky, M., and Munger, J. W.: Factors
controlling CO2 exchange on timescales from hourly to decadal at Harvard
Forest, J. Geophys. Res., 112, https://doi.org/10.1029/2006jg000293, 2007a. a
Urbanski, S., Barford, C., Wofsy, S., Kucharik, C., Pyle, E., Budney, J.,
McKain, K., Fitzjarrald, D., Czikowsky, M., and Munger, J. W.: Factors
controlling CO2 exchange on timescales from hourly to decadal at Harvard
Forest, J. Geophys. Res.-Biogeosci., 112, G2,
https://doi.org/10.1029/2006JG000293,
2007b. a
Valentini, R., Angelis, P., Matteucci, G., Monaco, R., Dore, S., and Mucnozza,
G. E. S.: Seasonal net carbon dioxide exchange of a beech forest with the
atmosphere, Global Change Biol., 2, 199–207,
https://doi.org/10.1111/j.1365-2486.1996.tb00072.x, 1996. a
Veres, J. S. and Williams III, G. J.: Time course of photosynthetic
temperature acclimation in Carex eleocharis Bailey, Plant Cell Environ., 7,
545–547, 1984. a
Verhoeven, A.: Sustained energy dissipation in winter evergreens, New Phytol.,
201, 57–65, 2014. a
von Caemmerer, S. and Farquhar, G. D.: Some relationships between the
biochemistry of photosynthesis and the gas exchange of leaves, Planta, 153,
376–387, 1981. a
Wang, L., Zhu, H., Lin, A., Zou, L., Qin, W., and Du, Q.: Evaluation of the
Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance
Flux Data, Remote Sensing, 9, 5, https://doi.org/10.3390/rs9050418, 2017b. a, b
Way, D. A. and Yamori, W.: Thermal acclimation of photosynthesis: on the
importance of adjusting our definitions and accounting for thermal
acclimation of respiration, Photosynth. Res., 119, 89–100, 2014. a
Wehr, R., Munger, J. W., McManus, J. B., Nelson, D. D., Zahniser, M. S.,
Davidson, E. A., Wofsy, S. C., and Saleska, S. R.: Seasonality of temperate
forest photosynthesis and daytime respiration, Nature, 534, 680–683,
https://doi.org/10.1038/nature17966, 2016. a
Wen, X.-F., Wang, H.-M., Wang, J.-L., Yu, G.-R., and Sun, X.-M.: Ecosystem carbon exchanges of a subtropical evergreen coniferous plantation subjected to seasonal drought, 2003–2007, Biogeosciences, 7, 357–369, https://doi.org/10.5194/bg-7-357-2010, 2010. a
Wick, B., Veldkamp, E., de Mello, W. Z., Keller, M., and Crill, P.: Nitrous oxide fluxes and nitrogen cycling along a pasture chronosequence in Central Amazonia, Brazil, Biogeosciences, 2, 175–187, https://doi.org/10.5194/bg-2-175-2005, 2005. a
Wohlfahrt, G., Hammerle, A., Haslwanter, A., Bahn, M., Tappeiner, U., and
Cernusca, A.: Seasonal and inter-annual variability of the net ecosystem
CO2 exchange of a temperate mountain grassland: Effects of weather and
management, J. Geophys. Res., 113, D8, https://doi.org/10.1029/2007jd009286, 2008.
a
Xiang, Y., Gubian, S., Suomela, B., and Hoeng, J.: Generalized Simulated
Annealing for Efficient Global Optimization: the GenSA Package for R.,
The R Journal Volume 5/1, June 2013,
available at: https://journal.r-project.org/archive/2013/RJ-2013-002/index.html (last access: 6 March 2020), 2013. a
Yan, J., Zhang, Y., Yu, G., Zhou, G., Zhang, L., Li, K., Tan, Z., and Sha, L.:
Seasonal and inter-annual variations in net ecosystem exchange of two
old-growth forests in southern China, Agr. Forest Meteorol.,
182–183, 257–265, https://doi.org/10.1016/j.agrformet.2013.03.002,
2013. a
Yee, M. S., Pauwels, V. R., Daly, E., Beringer, J., Rüdiger, C., McCabe,
M. F., and Walker, J. P.: A comparison of optical and microwave
scintillometers with eddy covariance derived surface heat fluxes,
Agr. Forest Meteorol., 213, 226–239,
https://doi.org/10.1016/j.agrformet.2015.07.004, 2015. a
Zeller, K. and Nikolov, N.: Quantifying simultaneous fluxes of ozone, carbon
dioxide and water vapor above a subalpine forest ecosystem, Environ.
Pollut., 107, 1–20, https://doi.org/10.1016/s0269-7491(99)00156-6, 2000. a
Zeng, Y., Badgley, G., Dechant, B., Ryu, Y., Chen, M., and Berry, J.: A
practical approach for estimating the escape ratio of near-infrared
solar-induced chlorophyll fluorescence, Remote Sens. Environ., 232,
111209, https://doi.org/10.1016/j.rse.2019.05.028,
2019. a
Zhao, M., Heinsch, F. A., Nemani, R. R., and Running, S. W.: Improvements of
the MODIS terrestrial gross and net primary production global data set,
Remote Sens. Environ., 95, 164–176,
https://doi.org/10.1016/j.rse.2004.12.011,
2005. a, b, c
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S.,
Nemani, R. R., and Myneni, R. B.: Global Data Sets of Vegetation Leaf Area
Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g
Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized
Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011, Remote
Sens., 5, 927–948, https://doi.org/10.3390/rs5020927, 2013. a
Zielis, S., Etzold, S., Zweifel, R., Eugster, W., Haeni, M., and Buchmann, N.: NEP of a Swiss subalpine forest is significantly driven not only by current but also by previous year's weather, Biogeosciences, 11, 1627–1635, https://doi.org/10.5194/bg-11-1627-2014, 2014. a
Short summary
Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models simulating the efficiency by which light absorbed by vegetation is used for CO2 assimilation. This paper presents the P-model, a light use efficiency model derived from a carbon–water optimality principle, and evaluates its predictions of ecosystem-level photosynthesis against globally distributed observations. The model is implemented and openly accessible as an R package (rpmodel).
Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models...
