Articles | Volume 19, issue 17
https://doi.org/10.5194/bg-19-4147-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-4147-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2022
Research article |
| 06 Sep 2022
| 06 Sep 2022
The carbon budget of the managed grasslands of Great Britain – informed by earth observations
School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UK
Thomas Luke Smallman
School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UK
Mathew Williams
School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UK
Related authors
Vasileios Myrgiotis, Mathew Williams, Robert M. Rees, and Cairistiona F. E. Topp
Biogeosciences, 16, 1641–1655, https://doi.org/10.5194/bg-16-1641-2019, https://doi.org/10.5194/bg-16-1641-2019, 2019
Short summary
Short summary
This study focuses on a northwestern European cropland region and shows that the type of crop growing on a soil has notable effects on the emission of nitrous oxide (N2O – a greenhouse gas) from that soil. It was found that N2O emissions from soils under oilseed cultivation are significantly higher than soils under cereal cultivation. This variation is mostly explained by the fact that oilseeds require more nitrogen (fertiliser) than cereals, especially at early crop growth stages.
Liang Feng, Paul Palmer, Luke Smallman, Jingfeng Xiao, Paulo Cristofanelli, Ove Hermansen, John Lee, Casper Labuschagne, Simonetta Montaguti, Steffen Noe, Stephen Platt, Xinrong Ren, Martin Steinbacher, and Irene Xueref-Remy
EGUsphere, https://doi.org/10.5194/egusphere-2025-1793, https://doi.org/10.5194/egusphere-2025-1793, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
2023 saw an unexpectedly high global atmospheric CO2 growth. Satellite data reveal a role for increased emissions over the tropics. Larger emissions over eastern Brazil can be explained by warmer temperatures, while changes in rainfall and soil moisture play more of a role in emission increases elsewhere in the tropics.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Mathew Williams, David T. Milodowski, T. Luke Smallman, Kyle G. Dexter, Gabi C. Hegerl, Iain M. McNicol, Michael O'Sullivan, Carla M. Roesch, Casey M. Ryan, Stephen Sitch, and Aude Valade
Biogeosciences, 22, 1597–1614, https://doi.org/10.5194/bg-22-1597-2025, https://doi.org/10.5194/bg-22-1597-2025, 2025
Short summary
Short summary
Southern African woodlands are important in both regional and global carbon cycles. A new carbon analysis created by combining satellite data with ecosystem modelling shows that the region has a neutral C balance overall but with important spatial variations. Patterns of biomass and C balance across the region are the outcome of climate controls on production and vegetation–fire interactions, which determine the mortality of vegetation and spatial variations in vegetation function.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Marcos B. Sanches, Manoel Cardoso, Celso von Randow, Chris Jones, and Mathew Williams
EGUsphere, https://doi.org/10.5194/egusphere-2025-942, https://doi.org/10.5194/egusphere-2025-942, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This study examines South America's role in the global carbon cycle using flux and stock analyses from CMIP6 Earth System Models. We discuss the continent’s relevance, model-observation agreement, and the impacts of dry and wet years on major biomes. Additionally, we assess model results indicating that parts of South America could shift from carbon sinks to emitters, significantly affecting the global carbon balance.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
Short summary
Short summary
The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Luana S. Basso, Chris Wilson, Martyn P. Chipperfield, Graciela Tejada, Henrique L. G. Cassol, Egídio Arai, Mathew Williams, T. Luke Smallman, Wouter Peters, Stijn Naus, John B. Miller, and Manuel Gloor
Atmos. Chem. Phys., 23, 9685–9723, https://doi.org/10.5194/acp-23-9685-2023, https://doi.org/10.5194/acp-23-9685-2023, 2023
Short summary
Short summary
The Amazon’s carbon balance may have changed due to forest degradation, deforestation and warmer climate. We used an atmospheric model and atmospheric CO2 observations to quantify Amazonian carbon emissions (2010–2018). The region was a small carbon source to the atmosphere, mostly due to fire emissions. Forest uptake compensated for ~ 50 % of the fire emissions, meaning that the remaining forest is still a small carbon sink. We found no clear evidence of weakening carbon uptake over the period.
David T. Milodowski, T. Luke Smallman, and Mathew Williams
Biogeosciences, 20, 3301–3327, https://doi.org/10.5194/bg-20-3301-2023, https://doi.org/10.5194/bg-20-3301-2023, 2023
Short summary
Short summary
Model–data fusion (MDF) allows us to combine ecosystem models with Earth observation data. Fragmented landscapes, with a mosaic of contrasting ecosystems, pose a challenge for MDF. We develop a novel MDF framework to estimate the carbon balance of fragmented landscapes and show the importance of accounting for ecosystem heterogeneity to prevent scale-dependent bias in estimated carbon fluxes, disturbance fluxes in particular, and to improve ecological fidelity of the calibrated models.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev., 15, 1789–1802, https://doi.org/10.5194/gmd-15-1789-2022, https://doi.org/10.5194/gmd-15-1789-2022, 2022
Short summary
Short summary
Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
Short summary
Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Thomas Luke Smallman, David Thomas Milodowski, Eráclito Sousa Neto, Gerbrand Koren, Jean Ometto, and Mathew Williams
Earth Syst. Dynam., 12, 1191–1237, https://doi.org/10.5194/esd-12-1191-2021, https://doi.org/10.5194/esd-12-1191-2021, 2021
Short summary
Short summary
Our study provides a novel assessment of model parameter, structure and climate change scenario uncertainty contribution to future predictions of the Brazilian terrestrial carbon stocks to 2100. We calibrated (2001–2017) five models of the terrestrial C cycle of varied structure. The calibrated models were then projected to 2100 under multiple climate change scenarios. Parameter uncertainty dominates overall uncertainty, being ~ 40 times that of either model structure or climate change scenario.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
Short summary
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, https://doi.org/10.5194/bg-17-6393-2020, 2020
Short summary
Short summary
We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
Sophie Flack-Prain, Patrick Meir, Yadvinder Malhi, Thomas Luke Smallman, and Mathew Williams
Biogeosciences, 16, 4463–4484, https://doi.org/10.5194/bg-16-4463-2019, https://doi.org/10.5194/bg-16-4463-2019, 2019
Short summary
Short summary
Across the Amazon rainforest, trees take in carbon through photosynthesis. However, photosynthesis across the basin is threatened by predicted shifts in rainfall patterns. To unpick how changes in rainfall affect photosynthesis, we use a model which combines climate data with our knowledge of photosynthesis and other plant processes. We find that stomatal constraints are less important, and instead shifts in leaf surface area and leaf properties drive changes in photosynthesis with rainfall.
Thomas Luke Smallman and Mathew Williams
Geosci. Model Dev., 12, 2227–2253, https://doi.org/10.5194/gmd-12-2227-2019, https://doi.org/10.5194/gmd-12-2227-2019, 2019
Short summary
Short summary
Photosynthesis and evapotranspiration are processes with global significance for climate, carbon and water cycling. Process-orientated simulation of these processes and their interactions have till now come at high computational cost. Here we present a new coupled model of intermediate complexity operating at orders of magnitude greater speed. Independent evaluation at FLUXNET sites for a single, global parameterization shows good agreement, with a typical R2 value of ~ 0.60.
Efrén López-Blanco, Jean-François Exbrayat, Magnus Lund, Torben R. Christensen, Mikkel P. Tamstorf, Darren Slevin, Gustaf Hugelius, Anthony A. Bloom, and Mathew Williams
Earth Syst. Dynam., 10, 233–255, https://doi.org/10.5194/esd-10-233-2019, https://doi.org/10.5194/esd-10-233-2019, 2019
Short summary
Short summary
The terrestrial CO2 exchange in Arctic ecosystems plays an important role in the global carbon cycle and is particularly sensitive to the ongoing warming experienced in recent years. To improve our understanding of the atmosphere–biosphere interplay, we evaluated the state of the terrestrial pan-Arctic carbon cycling using a promising data assimilation system in the first 15 years of the 21st century. This is crucial when it comes to making predictions about the future state of the carbon cycle.
Vasileios Myrgiotis, Mathew Williams, Robert M. Rees, and Cairistiona F. E. Topp
Biogeosciences, 16, 1641–1655, https://doi.org/10.5194/bg-16-1641-2019, https://doi.org/10.5194/bg-16-1641-2019, 2019
Short summary
Short summary
This study focuses on a northwestern European cropland region and shows that the type of crop growing on a soil has notable effects on the emission of nitrous oxide (N2O – a greenhouse gas) from that soil. It was found that N2O emissions from soils under oilseed cultivation are significantly higher than soils under cereal cultivation. This variation is mostly explained by the fact that oilseeds require more nitrogen (fertiliser) than cereals, especially at early crop growth stages.
Anne Sofie Lansø, Thomas Luke Smallman, Jesper Heile Christensen, Mathew Williams, Kim Pilegaard, Lise-Lotte Sørensen, and Camilla Geels
Biogeosciences, 16, 1505–1524, https://doi.org/10.5194/bg-16-1505-2019, https://doi.org/10.5194/bg-16-1505-2019, 2019
Short summary
Short summary
Although coastal regions only amount to 7 % of the global oceans, their contribution to the global oceanic surface exchange of CO2 is much greater. In this study, we gain detailed insight into how these coastal marine fluxes compare to CO2 exchange from coastal land regions. Annually, the coastal marine exchanges are smaller than the total uptake of CO2 from the land surfaces within the study area but comparable in size to terrestrial fluxes from individual land cover classes of the region.
Emily D. White, Matthew Rigby, Mark F. Lunt, T. Luke Smallman, Edward Comyn-Platt, Alistair J. Manning, Anita L. Ganesan, Simon O'Doherty, Ann R. Stavert, Kieran Stanley, Mathew Williams, Peter Levy, Michel Ramonet, Grant L. Forster, Andrew C. Manning, and Paul I. Palmer
Atmos. Chem. Phys., 19, 4345–4365, https://doi.org/10.5194/acp-19-4345-2019, https://doi.org/10.5194/acp-19-4345-2019, 2019
Short summary
Short summary
Understanding carbon dioxide (CO2) fluxes from the terrestrial biosphere on a national scale is important for evaluating land use strategies to mitigate climate change. We estimate emissions of CO2 from the UK biosphere using atmospheric data in a top-down approach. Our findings show that bottom-up estimates from models of biospheric fluxes overestimate the amount of CO2 uptake in summer. This suggests these models wrongly estimate or omit key processes, e.g. land disturbance due to harvest.
Jean-François Exbrayat, A. Anthony Bloom, Pete Falloon, Akihiko Ito, T. Luke Smallman, and Mathew Williams
Earth Syst. Dynam., 9, 153–165, https://doi.org/10.5194/esd-9-153-2018, https://doi.org/10.5194/esd-9-153-2018, 2018
Short summary
Short summary
We use global observations of current terrestrial net primary productivity (NPP) to constrain the uncertainty in large ensemble 21st century projections of NPP under a "business as usual" scenario using a skill-based multi-model averaging technique. Our results show that this procedure helps greatly reduce the uncertainty in global projections of NPP. We also identify regions where uncertainties in models and observations remain too large to confidently conclude a sign of the change of NPP.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, https://doi.org/10.5194/bg-14-4711-2017, 2017
Short summary
Short summary
This paper attempts to review some of the current challenges faced by the modelling community in simulating the behaviour of savanna ecosystems. We provide a particular focus on three dynamic processes (phenology, root-water access, and fire) that are characteristic of savannas, which we believe are not adequately represented in current-generation terrestrial biosphere models. We highlight reasons for these misrepresentations, possible solutions and a future direction for research in this area.
Efrén López-Blanco, Magnus Lund, Mathew Williams, Mikkel P. Tamstorf, Andreas Westergaard-Nielsen, Jean-François Exbrayat, Birger U. Hansen, and Torben R. Christensen
Biogeosciences, 14, 4467–4483, https://doi.org/10.5194/bg-14-4467-2017, https://doi.org/10.5194/bg-14-4467-2017, 2017
Short summary
Short summary
An improvement in our process-based understanding of CO2 exchanges in the Arctic and their climate sensitivity is critical. With continued warming temperatures and longer growing seasons, tundra systems will likely increase rates of C cycling, although shifts in sink strength could take place, challenging the forecast of upcoming C states. In this context, we investigated the functional responses of C exchange to environmental characteristics across 8 consecutive years in West Greenland.
Darren Slevin, Simon F. B. Tett, Jean-François Exbrayat, A. Anthony Bloom, and Mathew Williams
Geosci. Model Dev., 10, 2651–2670, https://doi.org/10.5194/gmd-10-2651-2017, https://doi.org/10.5194/gmd-10-2651-2017, 2017
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, Vanessa Haverd, Longhui Li, Youngryel Ryu, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, https://doi.org/10.5194/bg-13-3245-2016, 2016
Short summary
Short summary
In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
C. Safta, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, and P. E. Thornton
Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, https://doi.org/10.5194/gmd-8-1899-2015, 2015
Short summary
Short summary
In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient
simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
L. Rowland, A. Harper, B. O. Christoffersen, D. R. Galbraith, H. M. A. Imbuzeiro, T. L. Powell, C. Doughty, N. M. Levine, Y. Malhi, S. R. Saleska, P. R. Moorcroft, P. Meir, and M. Williams
Geosci. Model Dev., 8, 1097–1110, https://doi.org/10.5194/gmd-8-1097-2015, https://doi.org/10.5194/gmd-8-1097-2015, 2015
Short summary
Short summary
This study evaluates the capability of five vegetation models to simulate the response of forest productivity to changes in temperature and drought, using data collected from an Amazonian forest. This study concludes that model consistencies in the responses of net canopy carbon production to temperature and precipitation change were the result of inconsistently modelled leaf-scale process responses and substantial variation in modelled leaf area responses.
A. A. Bloom and M. Williams
Biogeosciences, 12, 1299–1315, https://doi.org/10.5194/bg-12-1299-2015, https://doi.org/10.5194/bg-12-1299-2015, 2015
D. Slevin, S. F. B. Tett, and M. Williams
Geosci. Model Dev., 8, 295–316, https://doi.org/10.5194/gmd-8-295-2015, https://doi.org/10.5194/gmd-8-295-2015, 2015
G. B. Bonan, M. Williams, R. A. Fisher, and K. W. Oleson
Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, https://doi.org/10.5194/gmd-7-2193-2014, 2014
R. Q. Thomas and M. Williams
Geosci. Model Dev., 7, 2015–2037, https://doi.org/10.5194/gmd-7-2015-2014, https://doi.org/10.5194/gmd-7-2015-2014, 2014
G. Xenakis and M. Williams
Geosci. Model Dev., 7, 1519–1533, https://doi.org/10.5194/gmd-7-1519-2014, https://doi.org/10.5194/gmd-7-1519-2014, 2014
T. L. Smallman, M. Williams, and J. B. Moncrieff
Biogeosciences, 11, 735–747, https://doi.org/10.5194/bg-11-735-2014, https://doi.org/10.5194/bg-11-735-2014, 2014
T. L. Smallman, J. B. Moncrieff, and M. Williams
Geosci. Model Dev., 6, 1079–1093, https://doi.org/10.5194/gmd-6-1079-2013, https://doi.org/10.5194/gmd-6-1079-2013, 2013
Related subject area
Biogeochemistry: Modelling, Terrestrial
Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat model
Technical note: A modified formulation of dynamic energy budget theory for faster computation of biological growth
Estimates of critical loads and exceedances of acidity and nutrient nitrogen for mineral soils in Canada for 2014–2016 average annual sulfur and nitrogen atmospheric deposition
Development of the DO3SE-Crop model to assess ozone effects on crop phenology, biomass, and yield
Future methane fluxes of peatlands are controlled by management practices and fluctuations in hydrological conditions due to climatic variability
Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model
Representation of the terrestrial carbon cycle in CMIP6
Does dynamically modeled leaf area improve predictions of land surface water and carbon fluxes? Insights into dynamic vegetation modules
Observational benchmarks inform representation of soil organic carbon dynamics in land surface models
X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X
A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method
Future projections of Siberian wildfire and aerosol emissions
Mechanisms of soil organic carbon and nitrogen stabilization in mineral-associated organic matter – insights from modeling in phase space
Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework
Modeling integrated soil fertility management for maize production in Kenya using a Bayesian calibration of the DayCent model
When and why microbial-explicit soil organic carbon models can be unstable
The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback
Climate-based prediction of carbon fluxes from deadwood in Australia
Integration of tree hydraulic processes and functional impairment to capture the drought resilience of a semiarid pine forest
The effect of temperature on photosystem II efficiency across plant functional types and climate
Modeling microbial carbon fluxes and stocks in global soils from 1901 to 2016
Elevated atmospheric CO2 concentration and vegetation structural changes contributed to gross primary productivity increase more than climate and forest cover changes in subtropical forests of China
Non-steady-state stomatal conductance modeling and its implications: from leaf to ecosystem
Modelled forest ecosystem carbon–nitrogen dynamics with integrated mycorrhizal processes under elevated CO2
A chemical kinetics theory for interpreting the non-monotonic temperature dependence of enzymatic reactions
Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle
Multiscale assessment of North American terrestrial carbon balance
Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site
Spatial biases reduce the ability of Earth system models to simulate soil heterotrophic respiration fluxes
Tropical dry forest response to nutrient fertilization: a model validation and sensitivity analysis
Connecting competitor, stress-tolerator and ruderal (CSR) theory and Lund Potsdam Jena managed Land 5 (LPJmL 5) to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions
A global fuel characteristic model and dataset for wildfire prediction
Can models adequately reflect how long-term nitrogen enrichment alters the forest soil carbon cycle?
Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers
Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia
A modeling approach to investigate drivers, variability and uncertainties in O2 fluxes and O2 : CO2 exchange ratios in a temperate forest
Modeling coupled nitrification–denitrification in soil with an organic hotspot
A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
Enabling a process-oriented hydro-biogeochemical model to simulate soil erosion and nutrient losses
Potassium limitation of forest productivity – Part 1: A mechanistic model simulating the effects of potassium availability on canopy carbon and water fluxes in tropical eucalypt stands
Potassium limitation of forest productivity – Part 2: CASTANEA-MAESPA-K shows a reduction in photosynthesis rather than a stoichiometric limitation of tissue formation
Global evaluation of terrestrial biogeochemistry in the Energy Exascale Earth System Model (E3SM) and the role of the phosphorus cycle in the historical terrestrial carbon balance
Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration
Optimizing the carbonic anhydrase temperature response and stomatal conductance of carbonyl sulfide leaf uptake in the Simple Biosphere model (SiB4)
Exploring environmental and physiological drivers of the annual carbon budget of biocrusts from various climatic zones with a mechanistic data-driven model
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance
Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model
Exploring the impacts of unprecedented climate extremes on forest ecosystems: hypotheses to guide modeling and experimental studies
Effect of droughts and climate change on future soil weathering rates in Sweden
Information content in time series of litter decomposition studies and the transit time of litter in arid lands
Daniel Escobar, Stefano Manzoni, Jeimar Tapasco, Patrik Vestin, and Salim Belyazid
Biogeosciences, 22, 2023–2047, https://doi.org/10.5194/bg-22-2023-2025, https://doi.org/10.5194/bg-22-2023-2025, 2025
Short summary
Short summary
We studied carbon dynamics in afforested, drained peatlands using the ForSAFE-Peat model over two forest rotations. Our simulations showed that, while trees store carbon, significant soil carbon losses occur, particularly early on, indicating that forest growth may not fully offset these losses once carbon time dynamics are considered. This emphasises the need to consider both soil and harvested wood products when evaluating the climate impact of such systems.
Jinyun Tang and William J. Riley
Biogeosciences, 22, 1809–1819, https://doi.org/10.5194/bg-22-1809-2025, https://doi.org/10.5194/bg-22-1809-2025, 2025
Short summary
Short summary
A new mathematical formulation of the dynamic energy budget model is presented for the growth of biological organisms. This new formulation combines mass conservation law and chemical kinetics theory and is computationally faster than the standard formulation of dynamic energy budget models. In simulating the growth of Thalassiosira weissflogii in a nitrogen-limiting chemostat, the new model is as good as the standard dynamic energy budget model using almost the same parameter values.
Hazel Cathcart, Julian Aherne, Michael D. Moran, Verica Savic-Jovcic, Paul A. Makar, and Amanda Cole
Biogeosciences, 22, 535–554, https://doi.org/10.5194/bg-22-535-2025, https://doi.org/10.5194/bg-22-535-2025, 2025
Short summary
Short summary
Deposition from sulfur and nitrogen pollution can harm ecosystems, and recovery from this type of pollution can take decades or longer. To identify risk to Canadian soils, we created maps showing sensitivity to sulfur and nitrogen pollution. Results show that some ecosystems are at risk from acid and nutrient nitrogen deposition: 10 % of protected areas are receiving acid deposition beyond their damage threshold, and 70 % may be receiving nitrogen deposition that could cause biodiversity loss.
Pritha Pande, Sam Bland, Nathan Booth, Jo Cook, Zhaozhong Feng, and Lisa Emberson
Biogeosciences, 22, 181–212, https://doi.org/10.5194/bg-22-181-2025, https://doi.org/10.5194/bg-22-181-2025, 2025
Short summary
Short summary
The DO3SE-Crop model extends the DO3SE to simulate ozone's impact on crops with modules for ozone uptake, damage, and crop growth from JULES-crop. It's versatile, suits China's varied agriculture, and improves yield predictions under ozone stress. It is essential for policy, water management, and climate response, and it integrates into Earth system models for a comprehensive understanding of agriculture's interaction with global systems.
Vilna Tyystjärvi, Tiina Markkanen, Leif Backman, Maarit Raivonen, Antti Leppänen, Xuefei Li, Paavo Ojanen, Kari Minkkinen, Roosa Hautala, Mikko Peltoniemi, Jani Anttila, Raija Laiho, Annalea Lohila, Raisa Mäkipää, and Tuula Aalto
Biogeosciences, 21, 5745–5771, https://doi.org/10.5194/bg-21-5745-2024, https://doi.org/10.5194/bg-21-5745-2024, 2024
Short summary
Short summary
Drainage of boreal peatlands strongly influences soil methane fluxes, with important implications for climatic impacts. Here we simulate methane fluxes in forestry-drained and restored peatlands during the 21st century. We found that restoration turned peatlands into a source of methane, but the magnitude varied regionally. In forests, changes in the water table level influenced methane fluxes, and in general, the sink was weaker under rotational forestry compared to continuous cover forestry.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
Short summary
Short summary
Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Bettina K. Gier, Manuel Schlund, Pierre Friedlingstein, Chris D. Jones, Colin Jones, Sönke Zaehle, and Veronika Eyring
Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, https://doi.org/10.5194/bg-21-5321-2024, 2024
Short summary
Short summary
This study investigates present-day carbon cycle variables in CMIP5 and CMIP6 simulations. Overall, CMIP6 models perform better but also show many remaining biases. A significant improvement in the simulation of photosynthesis in models with a nitrogen cycle is found, with only small differences between emission- and concentration-based simulations. Thus, we recommend using emission-driven simulations in CMIP7 by default and including the nitrogen cycle in all future carbon cycle models.
Sven Armin Westermann, Anke Hildebrandt, Souhail Bousetta, and Stephan Thober
Biogeosciences, 21, 5277–5303, https://doi.org/10.5194/bg-21-5277-2024, https://doi.org/10.5194/bg-21-5277-2024, 2024
Short summary
Short summary
Plants at the land surface mediate between soil and the atmosphere regarding water and carbon transport. Since plant growth is a dynamic process, models need to consider these dynamics. Two models that predict water and carbon fluxes by considering plant temporal evolution were tested against observational data. Currently, dynamizing plants in these models did not enhance their representativeness, which is caused by a mismatch between implemented physical relations and observable connections.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
Short summary
Short summary
The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Xin Chen, Tiexi Chen, Xiaodong Li, Yuanfang Chai, Shengjie Zhou, Renjie Guo, and Jie Dai
Biogeosciences, 21, 4285–4300, https://doi.org/10.5194/bg-21-4285-2024, https://doi.org/10.5194/bg-21-4285-2024, 2024
Short summary
Short summary
We provide an ensemble-model-based GPP dataset (ERF_GPP) that explains 85.1 % of the monthly variation in GPP across 170 sites, which is higher than other GPP estimate models. In addition, ERF_GPP improves the phenomenon of “high-value underestimation and low-value overestimation” in GPP estimation to some extent. Overall, ERF_GPP provides a more reliable estimate of global GPP and will facilitate further development of carbon cycle research.
Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata
Biogeosciences, 21, 4195–4227, https://doi.org/10.5194/bg-21-4195-2024, https://doi.org/10.5194/bg-21-4195-2024, 2024
Short summary
Short summary
SPITFIRE (SPread and InTensity of FIRE) was integrated into a spatially explicit individual-based dynamic global vegetation model to improve the accuracy of depicting Siberian forest fire frequency, intensity, and extent. Fires showed increased greenhouse gas and aerosol emissions in 2006–2100 for Representative Concentration Pathways. This study contributes to understanding fire dynamics, land ecosystem–climate interactions, and global material cycles under the threat of escalating fires.
Stefano Manzoni and M. Francesca Cotrufo
Biogeosciences, 21, 4077–4098, https://doi.org/10.5194/bg-21-4077-2024, https://doi.org/10.5194/bg-21-4077-2024, 2024
Short summary
Short summary
Organic carbon and nitrogen are stabilized in soils via microbial assimilation and stabilization of necromass (in vivo pathway) or via adsorption of the products of extracellular decomposition (ex vivo pathway). Here we use a diagnostic model to quantify which stabilization pathway is prevalent using data on residue-derived carbon and nitrogen incorporation in mineral-associated organic matter. We find that the in vivo pathway is dominant in fine-textured soils with low organic matter content.
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024, https://doi.org/10.5194/bg-21-3735-2024, 2024
Short summary
Short summary
Ecosystem carbonyl sulfide (COS) fluxes were employed to optimize GPP estimation across ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was developed for simulating the canopy COS uptake under its state-of-the-art two-leaf modeling framework. Our results showcased the efficacy of COS in improving model prediction and reducing prediction uncertainty of GPP and enhanced insights into the sensitivity, identifiability, and interactions of parameters related to COS.
Moritz Laub, Magdalena Necpalova, Marijn Van de Broek, Marc Corbeels, Samuel Mathu Ndungu, Monicah Wanjiku Mucheru-Muna, Daniel Mugendi, Rebecca Yegon, Wycliffe Waswa, Bernard Vanlauwe, and Johan Six
Biogeosciences, 21, 3691–3716, https://doi.org/10.5194/bg-21-3691-2024, https://doi.org/10.5194/bg-21-3691-2024, 2024
Short summary
Short summary
We used the DayCent model to assess the potential impact of integrated soil fertility management (ISFM) on maize production, soil fertility, and greenhouse gas emission in Kenya. After adjustments, DayCent represented measured mean yields and soil carbon stock changes well and N2O emissions acceptably. Our results showed that soil fertility losses could be reduced but not completely eliminated with ISFM and that, while N2O emissions increased with ISFM, emissions per kilogram yield decreased.
Erik Schwarz, Samia Ghersheen, Salim Belyazid, and Stefano Manzoni
Biogeosciences, 21, 3441–3461, https://doi.org/10.5194/bg-21-3441-2024, https://doi.org/10.5194/bg-21-3441-2024, 2024
Short summary
Short summary
The occurrence of unstable equilibrium points (EPs) could impede the applicability of microbial-explicit soil organic carbon models. For archetypal model versions we identify when instability can occur and describe mathematical conditions to avoid such unstable EPs. We discuss implications for further model development, highlighting the important role of considering basic ecological principles to ensure biologically meaningful models.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, and Libo Wang
Biogeosciences, 21, 3339–3371, https://doi.org/10.5194/bg-21-3339-2024, https://doi.org/10.5194/bg-21-3339-2024, 2024
Short summary
Short summary
Terrestrial biosphere models can either prescribe the geographical distribution of biomes or simulate them dynamically, capturing climate-change-driven biome shifts. We isolate and examine the differences between these different land cover implementations. We find that the simulated terrestrial carbon sink at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover due to important range shifts in the Arctic and Amazon.
Elizabeth S. Duan, Luciana Chavez Rodriguez, Nicole Hemming-Schroeder, Baptiste Wijas, Habacuc Flores-Moreno, Alexander W. Cheesman, Lucas A. Cernusak, Michael J. Liddell, Paul Eggleton, Amy E. Zanne, and Steven D. Allison
Biogeosciences, 21, 3321–3338, https://doi.org/10.5194/bg-21-3321-2024, https://doi.org/10.5194/bg-21-3321-2024, 2024
Short summary
Short summary
Understanding the link between climate and carbon fluxes is crucial for predicting how climate change will impact carbon sinks. We estimated carbon dioxide (CO2) fluxes from deadwood in tropical Australia using wood moisture content and temperature. Our model predicted that the majority of deadwood carbon is released as CO2, except when termite activity is detected. Future models should also incorporate wood traits, like species and chemical composition, to better predict fluxes.
Daniel Nadal-Sala, Rüdiger Grote, David Kraus, Uri Hochberg, Tamir Klein, Yael Wagner, Fedor Tatarinov, Dan Yakir, and Nadine K. Ruehr
Biogeosciences, 21, 2973–2994, https://doi.org/10.5194/bg-21-2973-2024, https://doi.org/10.5194/bg-21-2973-2024, 2024
Short summary
Short summary
A hydraulic model approach is presented that can be added to any physiologically based ecosystem model. Simulated plant water potential triggers stomatal closure, photosynthesis decline, root–soil resistance increases, and sapwood and foliage senescence. The model has been evaluated at an extremely dry site stocked with Aleppo pine and was able to represent gas exchange, soil water content, and plant water potential. The model also responded realistically regarding leaf senescence.
Patrick Neri, Lianhong Gu, and Yang Song
Biogeosciences, 21, 2731–2758, https://doi.org/10.5194/bg-21-2731-2024, https://doi.org/10.5194/bg-21-2731-2024, 2024
Short summary
Short summary
A first-of-its-kind global-scale model of temperature resilience and tolerance of photosystem II maximum quantum yield informs how plants maintain their efficiency of converting light energy to chemical energy for photosynthesis under temperature changes. Our finding explores this variation across plant functional types and habitat climatology, highlighting diverse temperature response strategies and a method to improve global-scale photosynthesis modeling under climate change.
Liyuan He, Jorge L. Mazza Rodrigues, Melanie A. Mayes, Chun-Ta Lai, David A. Lipson, and Xiaofeng Xu
Biogeosciences, 21, 2313–2333, https://doi.org/10.5194/bg-21-2313-2024, https://doi.org/10.5194/bg-21-2313-2024, 2024
Short summary
Short summary
Soil microbes are the driving engine for biogeochemical cycles of carbon and nutrients. This study applies a microbial-explicit model to quantify bacteria and fungal biomass carbon in soils from 1901 to 2016. Results showed substantial increases in bacterial and fungal biomass carbon over the past century, jointly influenced by vegetation growth and soil temperature and moisture. This pioneering century-long estimation offers crucial insights into soil microbial roles in global carbon cycling.
Tao Chen, Félicien Meunier, Marc Peaucelle, Guoping Tang, Ye Yuan, and Hans Verbeeck
Biogeosciences, 21, 2253–2272, https://doi.org/10.5194/bg-21-2253-2024, https://doi.org/10.5194/bg-21-2253-2024, 2024
Short summary
Short summary
Chinese subtropical forest ecosystems are an extremely important component of global forest ecosystems and hence crucial for the global carbon cycle and regional climate change. However, there is still great uncertainty in the relationship between subtropical forest carbon sequestration and its drivers. We provide first quantitative estimates of the individual and interactive effects of different drivers on the gross primary productivity changes of various subtropical forest types in China.
Ke Liu, Yujie Wang, Troy S. Magney, and Christian Frankenberg
Biogeosciences, 21, 1501–1516, https://doi.org/10.5194/bg-21-1501-2024, https://doi.org/10.5194/bg-21-1501-2024, 2024
Short summary
Short summary
Stomata are pores on leaves that regulate gas exchange between plants and the atmosphere. Existing land models unrealistically assume stomata can jump between steady states when the environment changes. We implemented dynamic modeling to predict gradual stomatal responses at different scales. Results suggested that considering this effect on plant behavior patterns in diurnal cycles was important. Our framework also simplified simulations and can contribute to further efficiency improvements.
Melanie A. Thurner, Silvia Caldararu, Jan Engel, Anja Rammig, and Sönke Zaehle
Biogeosciences, 21, 1391–1410, https://doi.org/10.5194/bg-21-1391-2024, https://doi.org/10.5194/bg-21-1391-2024, 2024
Short summary
Short summary
Due to their crucial role in terrestrial ecosystems, we implemented mycorrhizal fungi into the QUINCY terrestrial biosphere model. Fungi interact with mineral and organic soil to support plant N uptake and, thus, plant growth. Our results suggest that the effect of mycorrhizal interactions on simulated ecosystem dynamics is minor under constant environmental conditions but necessary to reproduce and understand observed patterns under changing conditions, such as rising atmospheric CO2.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
Short summary
Short summary
A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024, https://doi.org/10.5194/bg-21-1017-2024, 2024
Short summary
Short summary
Observations are used to reduce uncertainty in land surface models (LSMs) by optimising poorly constraining parameters. However, optimising against current conditions does not necessarily ensure that the parameters treated as invariant will be robust in a changing climate. Manipulation experiments offer us a unique chance to optimise our models under different (here atmospheric CO2) conditions. By using these data in optimisations, we gain confidence in the future projections of LSMs.
Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
Short summary
Short summary
Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
Short summary
Short summary
We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Bertrand Guenet, Jérémie Orliac, Lauric Cécillon, Olivier Torres, Laura Sereni, Philip A. Martin, Pierre Barré, and Laurent Bopp
Biogeosciences, 21, 657–669, https://doi.org/10.5194/bg-21-657-2024, https://doi.org/10.5194/bg-21-657-2024, 2024
Short summary
Short summary
Heterotrophic respiration fluxes are a major flux between surfaces and the atmosphere, but Earth system models do not yet represent them correctly. Here we benchmarked Earth system models against observation-based products, and we identified the important mechanisms that need to be improved in the next-generation Earth system models.
Shuyue Li, Bonnie Waring, Jennifer Powers, and David Medvigy
Biogeosciences, 21, 455–471, https://doi.org/10.5194/bg-21-455-2024, https://doi.org/10.5194/bg-21-455-2024, 2024
Short summary
Short summary
We used an ecosystem model to simulate primary production of a tropical forest subjected to 3 years of nutrient fertilization. Simulations parameterized such that relative allocation to fine roots increased with increasing soil phosphorus had leaf, wood, and fine root production consistent with observations. However, these simulations seemed to over-allocate to fine roots on multidecadal timescales, affecting aboveground biomass. Additional observations across timescales would benefit models.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Joe R. McNorton and Francesca Di Giuseppe
Biogeosciences, 21, 279–300, https://doi.org/10.5194/bg-21-279-2024, https://doi.org/10.5194/bg-21-279-2024, 2024
Short summary
Short summary
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
Brooke A. Eastman, William R. Wieder, Melannie D. Hartman, Edward R. Brzostek, and William T. Peterjohn
Biogeosciences, 21, 201–221, https://doi.org/10.5194/bg-21-201-2024, https://doi.org/10.5194/bg-21-201-2024, 2024
Short summary
Short summary
We compared soil model performance to data from a long-term nitrogen addition experiment in a forested ecosystem. We found that in order for soil carbon models to accurately predict future forest carbon sequestration, two key processes must respond dynamically to nitrogen availability: (1) plant allocation of carbon to wood versus roots and (2) rates of soil organic matter decomposition. Long-term experiments can help improve our predictions of the land carbon sink and its climate impact.
Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo
Biogeosciences, 20, 4795–4818, https://doi.org/10.5194/bg-20-4795-2023, https://doi.org/10.5194/bg-20-4795-2023, 2023
Short summary
Short summary
The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.
Chad A. Burton, Luigi J. Renzullo, Sami W. Rifai, and Albert I. J. M. Van Dijk
Biogeosciences, 20, 4109–4134, https://doi.org/10.5194/bg-20-4109-2023, https://doi.org/10.5194/bg-20-4109-2023, 2023
Short summary
Short summary
Australia's land-based ecosystems play a critical role in controlling the variability in the global land carbon sink. However, uncertainties in the methods used for quantifying carbon fluxes limit our understanding. We develop high-resolution estimates of Australia's land carbon fluxes using machine learning methods and find that Australia is, on average, a stronger carbon sink than previously thought and that the seasonal dynamics of the fluxes differ from those described by other methods.
Yuan Yan, Anne Klosterhalfen, Fernando Moyano, Matthias Cuntz, Andrew C. Manning, and Alexander Knohl
Biogeosciences, 20, 4087–4107, https://doi.org/10.5194/bg-20-4087-2023, https://doi.org/10.5194/bg-20-4087-2023, 2023
Short summary
Short summary
A better understanding of O2 fluxes, their exchange ratios with CO2 and their interrelations with environmental conditions would provide further insights into biogeochemical ecosystem processes. We, therefore, used the multilayer canopy model CANVEG to simulate and analyze the flux exchange for our forest study site for 2012–2016. Based on these simulations, we further successfully tested the application of various micrometeorological methods and the prospects of real O2 flux measurements.
Jie Zhang, Elisabeth Larsen Kolstad, Wenxin Zhang, Iris Vogeler, and Søren O. Petersen
Biogeosciences, 20, 3895–3917, https://doi.org/10.5194/bg-20-3895-2023, https://doi.org/10.5194/bg-20-3895-2023, 2023
Short summary
Short summary
Manure application to agricultural land often results in large and variable N2O emissions. We propose a model with a parsimonious structure to investigate N transformations around such N2O hotspots. The model allows for new detailed insights into the interactions between transport and microbial activities regarding N2O emissions in heterogeneous soil environments. It highlights the importance of solute diffusion to N2O emissions from such hotspots which are often ignored by process-based models.
Jukka Alm, Antti Wall, Jukka-Pekka Myllykangas, Paavo Ojanen, Juha Heikkinen, Helena M. Henttonen, Raija Laiho, Kari Minkkinen, Tarja Tuomainen, and Juha Mikola
Biogeosciences, 20, 3827–3855, https://doi.org/10.5194/bg-20-3827-2023, https://doi.org/10.5194/bg-20-3827-2023, 2023
Short summary
Short summary
In Finland peatlands cover one-third of land area. For half of those, with 4.3 Mha being drained for forestry, Finland reports sinks and sources of greenhouse gases in forest lands on organic soils following its UNFCCC commitment. We describe a new method for compiling soil CO2 balance that follows changes in tree volume, tree harvests and temperature. An increasing trend of emissions from 1.4 to 7.9 Mt CO2 was calculated for drained peatland forest soils in Finland for 1990–2021.
Siqi Li, Bo Zhu, Xunhua Zheng, Pengcheng Hu, Shenghui Han, Jihui Fan, Tao Wang, Rui Wang, Kai Wang, Zhisheng Yao, Chunyan Liu, Wei Zhang, and Yong Li
Biogeosciences, 20, 3555–3572, https://doi.org/10.5194/bg-20-3555-2023, https://doi.org/10.5194/bg-20-3555-2023, 2023
Short summary
Short summary
Physical soil erosion and particulate carbon, nitrogen and phosphorus loss modules were incorporated into the process-oriented hydro-biogeochemical model CNMM-DNDC to realize the accurate simulation of water-induced erosion and subsequent particulate nutrient losses at high spatiotemporal resolution.
Ivan Cornut, Nicolas Delpierre, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, Otavio Campoe, Jose Luiz Stape, Vitoria Fernanda Santos, and Guerric le Maire
Biogeosciences, 20, 3093–3117, https://doi.org/10.5194/bg-20-3093-2023, https://doi.org/10.5194/bg-20-3093-2023, 2023
Short summary
Short summary
Potassium is an essential element for living organisms. Trees are dependent upon this element for certain functions that allow them to build their trunks using carbon dioxide. Using data from experiments in eucalypt plantations in Brazil and a simplified computer model of the plantations, we were able to investigate the effect that a lack of potassium can have on the production of wood. Understanding nutrient cycles is useful to understand the response of forests to environmental change.
Ivan Cornut, Guerric le Maire, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, and Nicolas Delpierre
Biogeosciences, 20, 3119–3135, https://doi.org/10.5194/bg-20-3119-2023, https://doi.org/10.5194/bg-20-3119-2023, 2023
Short summary
Short summary
After simulating the effects of low levels of potassium on the canopy of trees and the uptake of carbon dioxide from the atmosphere by leaves in Part 1, here we tried to simulate the way the trees use the carbon they have acquired and the interaction with the potassium cycle in the tree. We show that the effect of low potassium on the efficiency of the trees in acquiring carbon is enough to explain why they produce less wood when they are in soils with low levels of potassium.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
Short summary
Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Ara Cho, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Richard Wehr, and Maarten C. Krol
Biogeosciences, 20, 2573–2594, https://doi.org/10.5194/bg-20-2573-2023, https://doi.org/10.5194/bg-20-2573-2023, 2023
Short summary
Short summary
Carbonyl sulfide (COS) is a useful constraint for estimating photosynthesis. To simulate COS leaf flux better in the SiB4 model, we propose a novel temperature function for enzyme carbonic anhydrase (CA) activity and optimize conductances using observations. The optimal activity of CA occurs below 40 °C, and Ball–Woodrow–Berry parameters are slightly changed. These reduce/increase uptakes in the tropics/higher latitudes and contribute to resolving discrepancies in the COS global budget.
Yunyao Ma, Bettina Weber, Alexandra Kratz, José Raggio, Claudia Colesie, Maik Veste, Maaike Y. Bader, and Philipp Porada
Biogeosciences, 20, 2553–2572, https://doi.org/10.5194/bg-20-2553-2023, https://doi.org/10.5194/bg-20-2553-2023, 2023
Short summary
Short summary
We found that the modelled annual carbon balance of biocrusts is strongly affected by both the environment (mostly air temperature and CO2 concentration) and physiology, such as temperature response of respiration. However, the relative impacts of these drivers vary across regions with different climates. Uncertainty in driving factors may lead to unrealistic carbon balance estimates, particularly in temperate climates, and may be explained by seasonal variation of physiology due to acclimation.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
Short summary
Short summary
Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
Short summary
Short summary
Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Veronika Kronnäs, Klas Lucander, Giuliana Zanchi, Nadja Stadlinger, Salim Belyazid, and Cecilia Akselsson
Biogeosciences, 20, 1879–1899, https://doi.org/10.5194/bg-20-1879-2023, https://doi.org/10.5194/bg-20-1879-2023, 2023
Short summary
Short summary
In a future climate, extreme droughts might become more common. Climate change and droughts can have negative effects on soil weathering and plant health.
In this study, climate change effects on weathering were studied on sites in Sweden using the model ForSAFE, a climate change scenario and an extreme drought scenario. The modelling shows that weathering is higher during summer and increases with global warming but that weathering during drought summers can become as low as winter weathering.
Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
Short summary
Short summary
Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Cited articles
Abdalla, M., Hastings, A., Chadwick, D. R., Jones, D. L., Evans, C. D., Jones,
M. B., Rees, R. M., and Smith, P.: Critical review of the impacts of grazing
intensity on soil organic carbon storage and other soil quality indicators in
extensively managed grasslands, Agr. Ecosyst. Environ.,
253, 62–81, https://doi.org/10.1016/j.agee.2017.10.023, 2018. a, b, c
EDINA: AgCensus, http://agcensus.edina.ac.uk, last access:
1 March 2019. a
Ammann, C., Neftel, A., Jocher, M., Fuhrer, J., and Leifeld, J.: Effect of
management and weather variations on the greenhouse gas budget of two
grasslands during a 10-year experiment, Agr. Ecosyst.
Environ., 292, 106814, https://doi.org/10.1016/j.agee.2019.106814, 2020. a
Bahn, M., Rodeghiero, M., Anderson-Dunn, M., Dore, S., Gimeno, C., Drösler,
M., Williams, M., Ammann, C., Berninger, F., Flechard, C., Jones, S.,
Balzarolo, M., Kumar, S., Newesely, C., Priwitzer, T., Raschi, A., Siegwolf,
R., Susiluoto, S., Tenhunen, J., Wohlfahrt, G., and Cernusca, A.: Soil
Respiration in European Grasslands in Relation to Climate and Assimilate
Supply, Ecosystems, 11, 1352–1367, https://doi.org/10.1007/s10021-008-9198-0, 2008. a
Bell, M. J., Cloy, J. M., Topp, C. F., Ball, B. C., Bagnall, A., Rees, R. M.,
and Chadwick, D. R.: Quantifying N2O emissions from intensive grassland
production: The role of synthetic fertilizer type, application rate, timing
and nitrification inhibitors, J. Agr. Sci., 154,
812–827, https://doi.org/10.1017/S0021859615000945, 2016. a
Blanke, J., Boke-Olén, N., Olin, S., Chang, J., Sahlin, U., Lindeskog,
M., and Lehsten, V.: Implications of accounting for management intensity on
carbon and nitrogen balances of European grasslands, Plos One, 13,
e0201058, https://doi.org/10.1371/journal.pone.0201058, 2018. a, b
Bloom, A. A. and Williams, M.: Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological “common sense” in a model–data fusion framework, Biogeosciences, 12, 1299–1315, https://doi.org/10.5194/bg-12-1299-2015, 2015. a
Bloom, A. A., Exbrayat, J.-F., van der Velde, I. R., Feng, L., and Williams, M.:
The decadal state of the terrestrial carbon cycle: Global retrievals of
terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113, 1285–1290,
https://doi.org/10.1073/pnas.1515160113, 2016. a
Chang, J., Ciais, P., Viovy, N., Vuichard, N., Sultan, B., and Soussana, J. F.:
The greenhouse gas balance of European grasslands, Glob. Change Biol.,
21, 3748–3761, https://doi.org/10.1111/gcb.12998, 2015a. a
Chang, J., Viovy, N., Vuichard, N., Ciais, P., Campioli, M., Klumpp, K.,
Martin, R., Leip, A., and Soussana, J.-F.: Modeled Changes in Potential
Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe
over 1961–2010, PLOS ONE, 10, e0127554,
https://doi.org/10.1371/journal.pone.0127554, 2015b. a
Chang, J., Ciais, P., Viovy, N., Soussana, J. F., Klumpp, K., and Sultan, B.:
Future productivity and phenology changes in European grasslands for
different warming levels: Implications for grassland management and carbon
balance, Carbon Balance and Management, 12, 11, https://doi.org/10.1186/s13021-017-0079-8,
2017. a
Chang, J., Ciais, P., Gasser, T., Smith, P., Herrero, M., Havlík, P.,
Obersteiner, M., Guenet, B., Goll, D. S., Li, W., Naipal, V., Peng, S., Qiu,
C., Tian, H., Viovy, N., Yue, C., and Zhu, D.: Climate warming from managed
grasslands cancels the cooling effect of carbon sinks in sparsely grazed and
natural grasslands, Nat. Commun., 12, 118,
https://doi.org/10.1038/s41467-020-20406-7, 2021. a, b
Chang, J. F., Viovy, N., Vuichard, N., Ciais, P., Wang, T., Cozic, A., Lardy, R., Graux, A.-I., Klumpp, K., Martin, R., and Soussana, J.-F.: Incorporating grassland management in ORCHIDEE: model description and evaluation at 11 eddy-covariance sites in Europe, Geosci. Model Dev., 6, 2165–2181, https://doi.org/10.5194/gmd-6-2165-2013, 2013. a
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V.,
Aubinet, M., Buchmann, N., Bernhofer, C., Carrara, A., Chevallier, F.,
Noblet, N. D., Friend, A. D., Friedlingstein, P., Grünwald, T., Heinesch,
B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci,
G., Miglietta, F., Ourcival, J. M., Papale, D., Pilegaard, K., Rambal, S.,
Seufert, G., Soussana, J. F., Sanz, M. J., Schulze, E. D., Vesala, T., and
Valentini, R.: Europe-wide reduction in primary productivity caused by the
heat and drought in 2003, Nature, 437, 529–533, https://doi.org/10.1038/nature03972,
2005. a
Committee on Climate Change: Net Zero: The UK's contribution to stopping
global warming, Tech. Rep. May,
https://www.theccc.org.uk/publication/net-zero-the-uks-contribution-to-stopping-global-warming/ (last access: 1 October 2021),
2019. a
Conant, R. T., Cerri, C. E., Osborne, B. B., and Paustian, K.: Grassland
management impacts on soil carbon stocks: A new synthesis, Ecol. Appl., 27, 662–668, https://doi.org/10.1002/eap.1473, 2017. a
Dangal, S. R. S., Tian, H., Pan, S., Zhang, L., and Xu, R.: Greenhouse gas
balance in global pasturelands and rangelands, Environ. Res.
Lett., 15, 104006, https://doi.org/10.1088/1748-9326/abaa79, 2020. a
Dietze, M. C.: Ecological Forecasting, Princeton University Press,
JSTOR, https://doi.org/10.2307/j.ctvc7796h.3, 2017. a
Dusseux, P., Gong, X., Hubert-Moy, L., and Corpetti, T.: Identification of
grassland management practices from leaf area index time series, J.
Appl. Remote Sens., 8, 083559, https://doi.org/10.1117/1.jrs.8.083559,
2014. a
Felber, R., Bretscher, D., Münger, A., Neftel, A., and Ammann, C.: Determination of the carbon budget of a pasture: effect of system boundaries and flux uncertainties, Biogeosciences, 13, 2959–2969, https://doi.org/10.5194/bg-13-2959-2016, 2016. a
Fetzel, T., Havlik, P., Herrero, M., Kaplan, J. O., Kastner, T., Kroisleitner,
C., Rolinski, S., Searchinger, T., Van BODEGOM, P. M., Wirsenius, S., and
Erb, K. H.: Quantification of uncertainties in global grazing systems
assessment, Global Biogeochem. Cy., 31, 1089–1102,
https://doi.org/10.1002/2016GB005601, 2017. a, b, c
Gastal, F. and Lemaire, G.: Defoliation, Shoot Plasticity, Sward Structure and
Herbage Utilization in Pasture: Review of the Underlying Ecophysiological
Processes, Agriculture, 5, 1146–1171, https://doi.org/10.3390/agriculture5041146,
2015. a
Gilmanov, T. G., Soussana, J. F., Aires, L., Allard, V., Ammann, C., Balzarolo,
M., Barcza, Z., Bernhofer, C., Campbell, C. L., Cernusca, A., Cescatti, A.,
Clifton-Brown, J., Dirks, B. O., Dore, S., Eugster, W., Fuhrer, J., Gimeno,
C., Gruenwald, T., Haszpra, L., Hensen, A., Ibrom, A., Jacobs, A. F., Jones,
M. B., Lanigan, G., Laurila, T., Lohila, A., Manca, G., Marcolla, B., Nagy, Z.,
Pilegaard, K., Pinter, K., Pio, C., Raschi, A., Rogiers, N., Sanz, M. J.,
Stefani, P., Sutton, M., Tuba, Z., Valentini, R., Williams, M. L., and
Wohlfahrt, G.: Partitioning European grassland net ecosystem CO2 exchange
into gross primary productivity and ecosystem respiration using light
response function analysis, Agr. Ecosyst. Environ., 121,
93–120, https://doi.org/10.1016/j.agee.2006.12.008, 2007. a, b, c
Giménez, M. G., de Jong, R., Peruta, R. D., Keller, A., and Schaepman,
M. E.: Determination of grassland use intensity based on multi-temporal
remote sensing data and ecological indicators, Remote Sens.
Environ., 198, 126–139, https://doi.org/10.1016/j.rse.2017.06.003, 2017. a
Grossiord, C., Buckley, T. N., Cernusak, L. A., Novick, K. A., Poulter, B.,
Siegwolf, R. T. W., Sperry, J. S., and McDowell, N. G.: Plant responses to
rising vapor pressure deficit, New Phytol., 226, 1550–1566,
https://doi.org/10.1111/nph.16485, 2020. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, 1–40, https://doi.org/10.1371/journal.pone.0169748, 2017. a, b
Herrero, M., Henderson, B., Havlík, P., Thornton, P. K., Conant, R. T.,
Smith, P., Wirsenius, S., Hristov, A. N., Gerber, P., Gill, M.,
Butterbach-Bahl, K., Valin, H., Garnett, T., and Stehfest, E.: Greenhouse
gas mitigation potentials in the livestock sector, Nat. Clim. Change, 6,
452–461, https://doi.org/10.1038/nclimate2925, 2016. a
Hersbach, H., Bell, B., Berrisford, P.,
Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on
single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate
Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018. a
Jansen‐Willems, A. B., Lanigan, G. J., Grünhage, L., and Müller, C.:
Carbon cycling in temperate grassland under elevated temperature, Ecol. Evol., 6, 7856–7868, https://doi.org/10.1002/ece3.2210, 2016. a
Kan, G., Liang, K., Li, J., Ding, L., He, X., Hu, Y., and Amo-Boateng, M.:
Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU
and Many-Core GPU, Adv. Meteorol., 2016, 8483728,
https://doi.org/10.1155/2016/8483728, 2016. a
Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., and Legg, T.: State of
the UK climate 2017, Int. J. Climatol., 38, 1–35,
https://doi.org/10.1002/joc.5798, 2018. a
Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., and Legg, T.: State of
the UK climate 2018, Int. J. Climatol., 39, 1–55,
https://doi.org/10.1002/joc.6213, 2019. a
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models,
J. R. Stat. Soc. B,
63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001. a
Klumpp, K., Tallec, T., Guix, N., and Soussana, J.: Long‐term impacts of
agricultural practices and climatic variability on carbon storage in a
permanent pasture, Glob. Change Biol., 17, 3534–3545,
https://doi.org/10.1111/j.1365-2486.2011.02490.x, 2011. a
Koncz, P., Pintér, K., Balogh, J., Papp, M., Hidy, D., Csintalan, Z., Molnár,
E., Szaniszló, A., Kampfl, G., Horváth, L., and Nagy, Z.: Extensive
grazing in contrast to mowing is climate-friendly based on the farm-scale
greenhouse gas balance, Agr. Ecosyst. Environ., 240,
121–134, https://doi.org/10.1016/j.agee.2017.02.022, 2017. a, b
Lee, M. A., Todd, A., Sutton, M. A., Chagunda, M. G., Roberts, D. J., and Rees,
R. M.: A time-series of methane and carbon dioxide production from dairy
cows during a period of dietary transition, Cogent Environmental Science, 3,
1385693, https://doi.org/10.1080/23311843.2017.1385693, 2017. a
Ma, S., Lardy, R., Graux, A. I., Ben Touhami, H., Klumpp, K., Martin, R., and
Bellocchi, G.: Regional-scale analysis of carbon and water cycles on managed
grassland systems, Environ. Modell. Softw., 72, 356–371,
https://doi.org/10.1016/j.envsoft.2015.03.007, 2015. a
Maselli, F., Argenti, G., Chiesi, M., Angeli, L., and Papale, D.: Simulation
of grassland productivity by the combination of ground and satellite data, Agr. Ecosyst. Environ., 165, 163–172,
https://doi.org/10.1016/j.agee.2012.11.006, 2013. a
Massmann, A., Gentine, P., and Lin, C.: When Does Vapor Pressure Deficit Drive
or Reduce Evapotranspiration?, J. Adv. Model. Earth
Sy., 11, 3305–3320, https://doi.org/10.1029/2019ms001790, 2019. a
McSherry, M. E. and Ritchie, M. E.: Effects of grazing on grassland soil
carbon: A global review, Glob. Change Biol., 19, 1347–1357,
https://doi.org/10.1111/gcb.12144, 2013. a
Munier, S., Carrer, D., Planque, C., Camacho, F., Albergel, C., and Calvet,
J.-C.: Satellite Leaf Area Index: Global Scale Analysis of the Tendencies
Per Vegetation Type Over the Last 17 Years, Remote Sens., 10, 424,
https://doi.org/10.3390/rs10030424, 2018. a
Myrgiotis, V. and Williams, M.: The carbon budget of the managed grasslands of Great Britain – informed by earth observations, 2017–2018, University of Edinburgh, School of GeoSciences, Global Change Ecology Lab, Datashare Edinburgh [data set], https://doi.org/10.7488/ds/3510, 2022. a
Myrgiotis, V., Blei, E., Clement, R., Jones, S. K., Keane, B., Lee, M. A.,
Levy, P. E., Rees, R. M., Skiba, U. M., Smallman, T. L., Toet, S., and
Williams, M.: A model-data fusion approach to analyse carbon dynamics in
managed grasslands, Agr. Syst., 184, 102907,
https://doi.org/10.1016/j.agsy.2020.102907, 2020. a, b, c, d, e
Myrgiotis, V., Harris, P., Revill, A., Sint, H., and Williams, M.: Inferring
management and predicting sub-field scale C dynamics in UK grasslands using
biogeochemical modelling and satellite-derived leaf area data, Agr. Forest Meteorol., 307, 108466,
https://doi.org/10.1016/j.agrformet.2021.108466, 2021. a, b, c, d, e, f
Oijen, M. v., Cameron, D., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson,
P.-E., Kiese, R., Rahn, K.-H., Werner, C., and Yeluripati, J.: A Bayesian
framework for model calibration, comparison and analysis: Application to four
models for the biogeochemistry of a Norway spruce forest, Agr. Forest Meteorol., 151, 1609–1621, https://doi.org/10.1016/j.agrformet.2011.06.017,
2011. a
Ostle, N. J., Smith, P., Fisher, R., Woodward, F. I., Fisher, J. B., Smith,
J. U., Galbraith, D., Levy, P., Meir, P., McNamara, N. P., and Bardgett,
R. D.: Integrating plant–soil interactions into global carbon cycle
models, J. Ecol., 97, 851–863,
https://doi.org/10.1111/j.1365-2745.2009.01547.x, 2009. a
Parsons, A. J., Rowarth, J. S., and Newton, P. C. D.: Managing pasture for
animals and soil carbon, Pr. N. Z. Grassl. Assoc., 71, 77–84, https://doi.org/10.33584/jnzg.2009.71.2775, 2009. a
Patenaude, G., Milne, R., Oijen, M. V., Rowland, C. S., and Hill, R. A.:
Integrating remote sensing datasets into ecological modelling: a Bayesian
approach, Int. J. Remote Sens., 29, 1295–1315,
https://doi.org/10.1080/01431160701736414, 2008. a
Pawlok, D., Benjamin, Z. H., Yingping, W., and David, W.: Grasslands may be
more reliable carbon sinks than forests in California, Environ.
Res. Lett., 13, 74027, https://doi.org/10.1088/1748-9326/aacb39, 2018. a
Peters, W., Bastos, A., Ciais, P., and Vermeulen, A.: A historical,
geographical and ecological perspective on the 2018 European summer drought,
Philos. T. R. Soc. B, 375, 20190505,
https://doi.org/10.1098/rstb.2019.0505, 2020. a
Pique, G., Fieuzal, R., Bitar, A. A., Veloso, A., Tallec, T., Brut, A.,
Ferlicoq, M., Zawilski, B., Dejoux, J.-F., Gibrin, H., and Ceschia, E.:
Estimation of daily CO2 fluxes and of the components of the carbon budget
for winter wheat by the assimilation of Sentinel 2-like remote sensing data
into a crop model, Geoderma, 376, 114428,
https://doi.org/10.1016/j.geoderma.2020.114428, 2020a. a
Pique, G., Fieuzal, R., Debaeke, P., Bitar, A. A., Tallec, T., and Ceschia, E.:
Combining High-Resolution Remote Sensing Products with a Crop Model to
Estimate Carbon and Water Budget Components: Application to Sunflower, Remote Sens., 12, 2967, https://doi.org/10.3390/rs12182967, 2020b. a
Pope, A.: GB SRTM Digital Elevation Model (DEM) 90m,
EDINA [data set], https://doi.org/10.7488/ds/1928, 2017. a
Puche, N., Senapati, N., Flechard, C. R., Klumpp, K., Kirschbaum, M. U., and
Chabbi, A.: Modeling carbon and water fluxes of managed grasslands:
Comparing flux variability and net carbon budgets between grazed and mowed
systems, Agronomy, 9, 10–12, https://doi.org/10.3390/agronomy9040183, 2019. a
Qi, A., Holland, R. A., Taylor, G., and Richter, G. M.: Grassland futures in
Great Britain – Productivity assessment and scenarios for land use change
opportunities, Sci. Total Environ., 634, 1108–1118,
https://doi.org/10.1016/j.scitotenv.2018.03.395, 2018. a, b
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding
for data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Reinermann, S., Asam, S., and Kuenzer, C.: Remote Sensing of Grassland Production and
Management – A Review, Remote Sens., 12, 1949, https://doi.org/10.3390/rs12121949, 2020. a
Revill, A., Myrgiotis, V., Florence, A., Hoad, S., Rees, R., MacArthur, A., and
Williams, M.: Combining Process Modelling and LAI Observations to Diagnose
Winter Wheat Nitrogen Status and Forecast Yield, Agronomy, 11, 314,
https://doi.org/10.3390/agronomy11020314, 2021. a
Riederer, M., Serafimovich, A., and Foken, T.: Net ecosystem CO2 exchange measurements by the closed chamber method and the eddy covariance technique and their dependence on atmospheric conditions, Atmos. Meas. Tech., 7, 1057–1064, https://doi.org/10.5194/amt-7-1057-2014, 2014. a
Riederer, M., Pausch, J., Kuzyakov, Y., and Foken, T.: Partitioning NEE for
absolute C input into various ecosystem pools by combining results from
eddy-covariance, atmospheric flux partitioning and 13CO2 pulse labeling,
Plant Soil, 390, 61–76, https://doi.org/10.1007/s11104-014-2371-7, 2015. a
Rodríguez-Pérez, R. and Bajorath, J.: Interpretation of machine
learning models using shapley values: application to compound potency and
multi-target activity predictions, J. Comput. Aid Mol. Des., 34, 1013–1026, https://doi.org/10.1007/s10822-020-00314-0, 2020. a
Rolinski, S., Müller, C., Heinke, J., Weindl, I., Biewald, A., Bodirsky, B. L., Bondeau, A., Boons-Prins, E. R., Bouwman, A. F., Leffelaar, P. A., te Roller, J. A., Schaphoff, S., and Thonicke, K.: Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6, Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, 2018. a
Sándor, R., Ehrhardt, F., Brilli, L., Carozzi, M., Recous, S., Smith, P.,
Snow, V., Soussana, J. F., Dorich, C. D., Fuchs, K., Fitton, N., Gongadze,
K., Klumpp, K., Liebig, M., Martin, R., Merbold, L., Newton, P. C., Rees,
R. M., Rolinski, S., and Bellocchi, G.: The use of biogeochemical models to
evaluate mitigation of greenhouse gas emissions from managed grasslands,
Sci. Total Environ., 642, 292–306,
https://doi.org/10.1016/j.scitotenv.2018.06.020, 2018. a
Senapati, N., Chabbi, A., Gastal, F., Smith, P., Mascher, N., Loubet, B.,
Cellier, P., and Naisse, C.: Net carbon storage measured in a mowed and
grazed temperate sown grassland shows potential for carbon sequestration
under grazed system, Carbon Manag., 5, 131–144,
https://doi.org/10.1080/17583004.2014.912863, 2014. a
Sibley, A. M.: Wildfire outbreaks across the United Kingdom during summer
2018, Weather, 74, 397–402, https://doi.org/10.1002/wea.3614, 2019. a
Skinner, R. H.: High Biomass Removal Limits Carbon Sequestration Potential of
Mature Temperate Pastures, J. Environ. Qual., 37, 1319–1326,
https://doi.org/10.2134/jeq2007.0263, 2008. a, b, c
Skinner, R. H. and Goslee, S. C.: Defoliation Effects on Pasture
Photosynthesis and Respiration, Crop Sci., 56, 2045–2053,
https://doi.org/10.2135/cropsci2015.12.0733, 2016. a
Smallman, T. L. and Williams, M.: Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1, Geosci. Model Dev., 12, 2227–2253, https://doi.org/10.5194/gmd-12-2227-2019, 2019. a
Smallman, T. L., Exbrayat, J., Mencuccini, M., Bloom, A. A., and Williams, M.:
Assimilation of repeated woody biomass observations constrains decadal
ecosystem carbon cycle uncertainty in aggrading forests, J.
Geophys. Res.-Biogeo., 122, 528–545,
https://doi.org/10.1002/2016jg003520, 2017. a
Smets, B., Jacobs, T., and Verger, A.: Gio Global Land Component – Lot I
“Operation of the Global Land Component”, Tech. rep.,
https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/GIOGL1_PUM_LAI300m-V1_I1.60.pdf (last access: 1 October 2021),
2018. a
Smith, K. A. and Williams, A. G.: Production and management of cattle manure
in the UK and implications for land application practice, Soil Use
Manage., 32, 73–82, https://doi.org/10.1111/sum.12247, 2016. a
Snow, V. O., Rotz, C. A., Moore, A. D., Martin-Clouaire, R., Johnson, I. R.,
Hutchings, N. J., and Eckard, R. J.: The challenges – and some solutions –
to process-based modelling of grazed agricultural systems, Environ.
Modell. Softw., 62, 420–436, https://doi.org/10.1016/j.envsoft.2014.03.009,
2014. a
Sollenberger, L. E., Kohmann, M. M., Dubeux, J. C., and Silveira, M. L.:
Grassland management affects delivery of regulating and supporting ecosystem
services, Crop Sci., 59, 441–459, https://doi.org/10.2135/cropsci2018.09.0594,
2019. a
Soussana, J. F., Allard, V., Pilegaard, K., Ambus, P., Amman, C., Campbell, C.,
Ceschia, E., Clifton-Brown, J., Czobel, S., Domingues, R., Flechard, C.,
Fuhrer, J., Hensen, A., Horvath, L., Jones, M., Kasper, G., Martin, C., Nagy,
Z., Neftel, A., Raschi, A., Baronti, S., Rees, R. M., Skiba, U., Stefani, P.,
Manca, G., Sutton, M., Tuba, Z., and Valentini, R.: Full accounting of the
greenhouse gas (CO2, N2O, CH4) budget of nine European grassland sites, Agr. Ecosyst. Environ., 121, 121–134,
https://doi.org/10.1016/j.agee.2006.12.022, 2007. a, b, c
Soussana, J. F., Tallec, T., and Blanfort, V.: Mitigating the greenhouse gas
balance of ruminant production systems through carbon sequestration in
grasslands, Animal, 4, 334–350, https://doi.org/10.1017/S1751731109990784, 2010. a
Thompson, R. L., Broquet, G., Gerbig, C., Koch, T., Lang, M., Monteil, G.,
Munassar, S., Nickless, A., Scholze, M., Ramonet, M., Karstens, U., Schaik,
E. v., Wu, Z., and Rödenbeck, C.: Changes in net ecosystem exchange over
Europe during the 2018 drought based on atmospheric observations,
Philos. T. R. Soc. B, 375, 20190512,
https://doi.org/10.1098/rstb.2019.0512, 2020. a
Ustin, S. L. and Middleton, E. M.: Current and near-term advances in Earth
observation for ecological applications, Ecol. Process., 10, 1,
https://doi.org/10.1186/s13717-020-00255-4, 2021. a
Vertès, F., Delaby, L., Klumpp, K., and Bloor, J.: C–N–P Uncoupling
in Grazed Grasslands and Environmental Implications of Management
Intensification, in: Agroecosystem Diversity, edited by: Lemaire, G., De Faccio Carvalho, P. C., Kronberg, S., and
Recous, S., Academic Press, 15–34,
https://doi.org/10.1016/b978-0-12-811050-8.00002-9, 2018. a, b
Vuichard, N., Ciais, P., Viovy, N., Calanca, P., and Soussana, J.-F.:
Estimating the greenhouse gas fluxes of European grasslands with a
process-based model: 2. Simulations at the continental level, Global
Biogeochem. Cy., 21, GB1005, https://doi.org/10.1029/2005GB002612, 2007. a
Ward, S. E., Smart, S. M., Quirk, H., Tallowin, J. R. B., Mortimer, S. R.,
Shiel, R. S., Wilby, A., and Bardgett, R. D.: Legacy effects of grassland
management on soil carbon to depth, Glob. Change Biol., 22, 2929–2938,
https://doi.org/10.1111/gcb.13246, 2016. a
Weiss, M. and Baret, F.: S2ToolBox Level products: LAI, FAPAR, FCOVER Version
1.1., Tech. rep., https://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf (last access: 10 August 2021), 2016. a
Williams, M., Rastetter, E. B., Fernandes, D. N., Goulden, M. L., Shaver,
G. R., and Johnson, L. C.: Predicing Gross Primary Productivity in
Terrestrial Ecosystems, Ecol. Appl., 7, 882–894, 1997. a
Worrall, F. and Clay, G. D.: The impact of sheep grazing on the carbon balance
of a peatland, Sci. Total Environ., 438, 426–434,
https://doi.org/10.1016/j.scitotenv.2012.08.084, 2012. a
Xia, J., Niu, S., Ciais, P., Janssens, I. A., Chen, J., Ammann, C., Arain, A.,
Blanken, P. D., Cescatti, A., Bonal, D., Buchmann, N., Curtis, P. S., Chen,
S., Dong, J., Flanagan, L. B., Frankenberg, C., Georgiadis, T., Gough, C. M.,
Hui, D., Kiely, G., Li, J., Lund, M., Magliulo, V., Marcolla, B., Merbold,
L., Montagnani, L., Moors, E. J., Olesen, J. E., Piao, S., Raschi, A.,
Roupsard, O., Suyker, A. E., Urbaniak, M., Vaccari, F. P., Varlagin, A.,
Vesala, T., Wilkinson, M., Weng, E., Wohlfahrt, G., Yan, L., and Luo, Y.:
Joint control of terrestrial gross primary productivity by plant phenology
and physiology, P. Natl. Acad. Sci. USA, 112,
2788–2793, https://doi.org/10.1073/pnas.1413090112, 2015. a, b
Yu, R., Evans, A. J., and Malleson, N.: Quantifying grazing patterns using a
new growth function based on MODIS Leaf Area Index, Remote Sens.
Environ., 209, 181–194, https://doi.org/10.1016/j.rse.2018.02.034, 2018. a
Zeeman, M. J., Hiller, R., Gilgen, A. K., Michna, P., Plüss, P., Buchmann, N.,
and Eugster, W.: Management and climate impacts on net CO2 fluxes and carbon
budgets of three grasslands along an elevational gradient in Switzerland,
Agr. Forest Meteorol., 150, 519–530,
https://doi.org/10.1016/j.agrformet.2010.01.011, 2010. a, b
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J.,
Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J., Asrar, G. R., and
Chen, M.: Optical vegetation indices for monitoring terrestrial ecosystems
globally, Nature Reviews Earth & Environment, 3, 477–493,
https://doi.org/10.1038/s43017-022-00298-5, 2022. a
Zhao, Y., Chen, X., Smallman, T. L., Flack-Prain, S., Milodowski, D. T., and
Williams, M.: Characterizing the Error and Bias of Remotely Sensed LAI
Products: An Example for Tropical and Subtropical Evergreen Forests in South
China, Remote Sens., 12, 3122, https://doi.org/10.3390/rs12193122, 2020. a, b
Short summary
This study shows that livestock grazing and grass cutting can determine whether a grassland is adding (source) or removing (sink) carbon (C) to/from the atmosphere. The annual C balance of 1855 managed grassland fields in Great Britain was quantified for 2017–2018 using process modelling and earth observation data. The examined fields were, on average, small C sinks, but the summer drought of 2018 led to a 9-fold increase in the number of fields that became C sources in 2018 compared to 2017.
This study shows that livestock grazing and grass cutting can determine whether a grassland is...
Altmetrics
Final-revised paper
Preprint