Articles | Volume 12, issue 4
https://doi.org/10.5194/esd-12-1191-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-12-1191-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Nov 2021
Research article |
| 23 Nov 2021
| 23 Nov 2021
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century
Thomas Luke Smallman
CORRESPONDING AUTHOR
School of GeoSciences, University of Edinburgh, Edinburgh, UK
National Centre for Earth Observations, University of Edinburgh, Edinburgh, UK
David Thomas Milodowski
School of GeoSciences, University of Edinburgh, Edinburgh, UK
National Centre for Earth Observations, University of Edinburgh, Edinburgh, UK
Eráclito Sousa Neto
INPE, São José dos Campos, Brazil
Gerbrand Koren
Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands
Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
Jean Ometto
INPE, São José dos Campos, Brazil
Mathew Williams
School of GeoSciences, University of Edinburgh, Edinburgh, UK
National Centre for Earth Observations, University of Edinburgh, Edinburgh, UK
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Short summary
Short summary
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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
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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
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Preprint archived
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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.
Takashi Sekiya, Emanuele Emili, Kazuyuki Miyazaki, Antje Inness, Zhen Qu, R. Bradley Pierce, Dylan Jones, Helen Worden, William Y. Y. Cheng, Vincent Huijnen, and Gerbrand Koren
Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025, https://doi.org/10.5194/acp-25-2243-2025, 2025
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Five global chemical reanalysis datasets were used to assess the relative impacts of assimilating satellite ozone and its precursor measurements on tropospheric ozone analyses for 2010. The multiple reanalysis system comparison allows an evaluation of the dependency of the impacts on different reanalysis systems. The results suggested the importance of satellite ozone and its precursor measurements for improving ozone analysis in the whole troposphere, with varying magnitudes among the systems.
Tamara Emmerichs, Abdulla Al Mamun, Lisa Emberson, Huiting Mao, Leiming Zhang, Limei Ran, Clara Betancourt, Anthony Wong, Gerbrand Koren, Giacomo Gerosa, Min Huang, and Pierluigi Guaita
EGUsphere, https://doi.org/10.5194/egusphere-2025-429, https://doi.org/10.5194/egusphere-2025-429, 2025
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The risk of ozone pollution to plants is estimated based on the flux through the plant pores which still has uncertainties. In this study, we estimate this quantity with 9 models at different land types worldwide. The input data stems from a database. The models estimated mostly reasonable summertime ozone deposition. The different results of the models varied by land cover which were mostly related to the moisture deficit. This is an important step for assessing the ozone impact on vegetation.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 6, 171–195, https://doi.org/10.5194/wcd-6-171-2025, https://doi.org/10.5194/wcd-6-171-2025, 2025
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Variability in the extratropical stratosphere and troposphere is coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too weak; however downward coupling from the lower stratosphere to the near surface is too strong.
Dylan Jones, Lucas Prates, Zhen Qu, William Cheng, Kazuyuki Miyazaki, Takashi Sekiya, Antje Inness, Rajesh Kumar, Xiao Tang, Helen Worden, Gerbrand Koren, and Vincent Huijen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3759, https://doi.org/10.5194/egusphere-2024-3759, 2025
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We evaluate five chemical reanalysis products to assess their potential to provide useful information on tropospheric ozone variability. We find that the reanalyses produce consistent information on ozone variations in the free troposphere, but have large discrepancies at the surface. The results suggests that improvements in the reanalyses are needed to better exploit the assimilated observations to enhance the utility of the reanalysis products at the surface.
Sebastian H. M. Hickman, Makoto Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alexander T. Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3739, https://doi.org/10.5194/egusphere-2024-3739, 2025
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Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Marco M. Lehmann, Josie Geris, Ilja van Meerveld, Daniele Penna, Youri Rothfuss, Matteo Verdone, Pertti Ala-Aho, Matyas Arvai, Alise Babre, Philippe Balandier, Fabian Bernhard, Lukrecija Butorac, Simon Damien Carrière, Natalie C. Ceperley, Zuosinan Chen, Alicia Correa, Haoyu Diao, David Dubbert, Maren Dubbert, Fabio Ercoli, Marius G. Floriancic, Teresa E. Gimeno, Damien Gounelle, Frank Hagedorn, Christophe Hissler, Frédéric Huneau, Alberto Iraheta, Tamara Jakovljević, Nerantzis Kazakis, Zoltan Kern, Karl Knaebel, Johannes Kobler, Jiří Kocum, Charlotte Koeber, Gerbrand Koren, Angelika Kübert, Dawid Kupka, Samuel Le Gall, Aleksi Lehtonen, Thomas Leydier, Philippe Malagoli, Francesca Sofia Manca di Villahermosa, Chiara Marchina, Núria Martínez-Carreras, Nicolas Martin-StPaul, Hannu Marttila, Aline Meyer Oliveira, Gaël Monvoisin, Natalie Orlowski, Kadi Palmik-Das, Aurel Persoiu, Andrei Popa, Egor Prikaziuk, Cécile Quantin, Katja T. Rinne-Garmston, Clara Rohde, Martin Sanda, Matthias Saurer, Daniel Schulz, Michael Paul Stockinger, Christine Stumpp, Jean-Stéphane Venisse, Lukas Vlcek, Stylianos Voudouris, Björn Weeser, Mark E. Wilkinson, Giulia Zuecco, and Katrin Meusburger
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-409, https://doi.org/10.5194/essd-2024-409, 2024
Revised manuscript under review for ESSD
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This study describes a unique large-scale isotope dataset to study water dynamics in European forests. Researchers collected data from 40 beech and spruce forest sites in spring and summer 2023, using a standardized method to ensure consistency. The results show that water sources for trees change between seasons and vary by tree species. This large dataset offers valuable information for understanding plant water use, improving ecohydrological models, and mapping water cycles across Europe.
Pharahilda M. Steur, Hubertus A. Scheeren, Gerbrand Koren, Getachew A. Adnew, Wouter Peters, and Harro A. J. Meijer
Atmos. Chem. Phys., 24, 11005–11027, https://doi.org/10.5194/acp-24-11005-2024, https://doi.org/10.5194/acp-24-11005-2024, 2024
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We present records of the triple oxygen isotope signature (Δ(17O)) of atmospheric CO2 obtained with laser absorption spectroscopy from two mid-latitude stations. Significant interannual variability is observed in both records. A model sensitivity study suggests that stratosphere–troposphere exchange, which carries high-Δ(17O) CO2 from the stratosphere into the troposphere, causes most of the variability. This makes Δ(17O) a potential tracer for stratospheric intrusions into the troposphere.
Pierluigi Renan Guaita, Riccardo Marzuoli, Leiming Zhang, Steven Turnock, Gerbrand Koren, Oliver Wild, Paola Crippa, and Giacomo Alessandro Gerosa
EGUsphere, https://doi.org/10.5194/egusphere-2024-2573, https://doi.org/10.5194/egusphere-2024-2573, 2024
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This study assesses the global impact of tropospheric ozone on wheat crops in the 21st century under various climate scenarios. The research highlights that ozone damage to wheat varies by region and depends on both ozone levels and climate. Vulnerable regions include East Asia, Northern Europe, and the Southern and Eastern edges of the Tibetan Plateau. Our results emphasize the need of policies to reduce ozone levels and mitigate climate change to protect global food security.
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
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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.
Juliëtte C. S. Anema, Klaas Folkert Boersma, Piet Stammes, Gerbrand Koren, William Woodgate, Philipp Köhler, Christian Frankenberg, and Jacqui Stol
Biogeosciences, 21, 2297–2311, https://doi.org/10.5194/bg-21-2297-2024, https://doi.org/10.5194/bg-21-2297-2024, 2024
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To keep the Paris agreement goals within reach, negative emissions are necessary. They can be achieved with mitigation techniques, such as reforestation, which remove CO2 from the atmosphere. While governments have pinned their hopes on them, there is not yet a good set of tools to objectively determine whether negative emissions do what they promise. Here we show how satellite measurements of plant fluorescence are useful in detecting carbon uptake due to reforestation and vegetation regrowth.
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
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We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
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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
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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
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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.
Auke M. van der Woude, Remco de Kok, Naomi Smith, Ingrid T. Luijkx, Santiago Botía, Ute Karstens, Linda M. J. Kooijmans, Gerbrand Koren, Harro A. J. Meijer, Gert-Jan Steeneveld, Ida Storm, Ingrid Super, Hubertus A. Scheeren, Alex Vermeulen, and Wouter Peters
Earth Syst. Sci. Data, 15, 579–605, https://doi.org/10.5194/essd-15-579-2023, https://doi.org/10.5194/essd-15-579-2023, 2023
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To monitor the progress towards the CO2 emission goals set out in the Paris Agreement, the European Union requires an independent validation of emitted CO2. For this validation, atmospheric measurements of CO2 can be used, together with first-guess estimates of CO2 emissions and uptake. To quickly inform end users, it is imperative that this happens in near real-time. To aid these efforts, we create estimates of European CO2 exchange at high resolution in near real time.
Stijn Naus, Lucas G. Domingues, Maarten Krol, Ingrid T. Luijkx, Luciana V. Gatti, John B. Miller, Emanuel Gloor, Sourish Basu, Caio Correia, Gerbrand Koren, Helen M. Worden, Johannes Flemming, Gabrielle Pétron, and Wouter Peters
Atmos. Chem. Phys., 22, 14735–14750, https://doi.org/10.5194/acp-22-14735-2022, https://doi.org/10.5194/acp-22-14735-2022, 2022
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We assimilate MOPITT CO satellite data in the TM5-4D-Var inverse modelling framework to estimate Amazon fire CO emissions for 2003–2018. We show that fire emissions have decreased over the analysis period, coincident with a decrease in deforestation rates. However, interannual variations in fire emissions are large, and they correlate strongly with soil moisture. Our results reveal an important role for robust, top-down fire CO emissions in quantifying and attributing Amazon fire intensity.
Vasileios Myrgiotis, Thomas Luke Smallman, and Mathew Williams
Biogeosciences, 19, 4147–4170, https://doi.org/10.5194/bg-19-4147-2022, https://doi.org/10.5194/bg-19-4147-2022, 2022
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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.
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
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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
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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.
Anteneh Getachew Mengistu, Gizaw Mengistu Tsidu, Gerbrand Koren, Maurits L. Kooreman, K. Folkert Boersma, Torbern Tagesson, Jonas Ardö, Yann Nouvellon, and Wouter Peters
Biogeosciences, 18, 2843–2857, https://doi.org/10.5194/bg-18-2843-2021, https://doi.org/10.5194/bg-18-2843-2021, 2021
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In this study, we assess the usefulness of Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval (SIFTER) data from the GOME-2A instrument and near-infrared reflectance of vegetation (NIRv) from MODIS to capture the seasonality and magnitudes of gross primary production (GPP) derived from six eddy-covariance flux towers in Africa in the overlap years between 2007–2014. We also test the robustness of sun-induced fluoresence and NIRv to compare the seasonality of GPP for the major biomes.
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
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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.
Joost Buitink, Anne M. Swank, Martine van der Ploeg, Naomi E. Smith, Harm-Jan F. Benninga, Frank van der Bolt, Coleen D. U. Carranza, Gerbrand Koren, Rogier van der Velde, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 6021–6031, https://doi.org/10.5194/hess-24-6021-2020, https://doi.org/10.5194/hess-24-6021-2020, 2020
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The amount of water stored in the soil is critical for the productivity of plants. Plant productivity is either limited by the available water or by the available energy. In this study, we infer this transition point by comparing local observations of water stored in the soil with satellite observations of vegetation productivity. We show that the transition point is not constant with soil depth, indicating that plants use water from deeper layers when the soil gets drier.
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
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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.
Cited articles
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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,
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Bonan, G. B., and Doney, S, C.:
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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.
Our study provides a novel assessment of model parameter, structure and climate change scenario...
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