Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4709-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4709-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
20 Jun 2022
Model description paper |
| 20 Jun 2022
| 20 Jun 2022
A map of global peatland extent created using machine learning (Peat-ML)
Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
Climate Research Division, Environment and Climate Change Canada, Toronto, ON, Canada
Koreen Millard
Geography and Environmental Studies, Carleton University, Ottawa, ON, Canada
Matthew Fortier
Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
R. Scott Winton
Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
Department of Surface Waters, Eawag, Swiss Federal Institution of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
Javier M. Martín-López
Agroecosystems and Sustainable Landscapes Program, Alliance Bioversity-CIAT, Cali, Colombia
Hinsby Cadillo-Quiroz
School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
Darren Kidd
Natural Values Science Services, Department of Natural Resources and Environment, Hobart, Tasmania, Australia
Louis V. Verchot
Agroecosystems and Sustainable Landscapes Program, Alliance Bioversity-CIAT, Cali, Colombia
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Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
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Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
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Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Joe R. Melton, Vivek K. Arora, Eduard Wisernig-Cojoc, Christian Seiler, Matthew Fortier, Ed Chan, and Lina Teckentrup
Geosci. Model Dev., 13, 2825–2850, https://doi.org/10.5194/gmd-13-2825-2020, https://doi.org/10.5194/gmd-13-2825-2020, 2020
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We transitioned the CLASS-CTEM land surface model to an open-source community model format by modernizing the code base to make the model easier to use and understand, providing a complete software environment to run the model within, developing a benchmarking suite for model evaluation, and creating an infrastructure to support community involvement. The new model, the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC), is now available for the community to use and develop.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
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The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Joe R. Melton, Diana L. Verseghy, Reinel Sospedra-Alfonso, and Stephan Gruber
Geosci. Model Dev., 12, 4443–4467, https://doi.org/10.5194/gmd-12-4443-2019, https://doi.org/10.5194/gmd-12-4443-2019, 2019
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Soils in cold regions store large amounts of carbon that could be released to the atmosphere if the soils thaw. To best simulate these soils, we explored different configurations and parameterizations of the CLASS-CTEM model and compared to observations. The revised model with a deeper soil column, new soil depth dataset, and inclusion of moss simulated greatly improved annual thaw depths and ground temperatures. We estimate subgrid-scale features limit further improvements against observations.
Fang Li, Maria Val Martin, Meinrat O. Andreae, Almut Arneth, Stijn Hantson, Johannes W. Kaiser, Gitta Lasslop, Chao Yue, Dominique Bachelet, Matthew Forrest, Erik Kluzek, Xiaohong Liu, Stephane Mangeon, Joe R. Melton, Daniel S. Ward, Anton Darmenov, Thomas Hickler, Charles Ichoku, Brian I. Magi, Stephen Sitch, Guido R. van der Werf, Christine Wiedinmyer, and Sam S. Rabin
Atmos. Chem. Phys., 19, 12545–12567, https://doi.org/10.5194/acp-19-12545-2019, https://doi.org/10.5194/acp-19-12545-2019, 2019
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Fire emissions are critical for atmospheric composition, climate, carbon cycle, and air quality. We provide the first global multi-model fire emission reconstructions for 1700–2012, including carbon and 33 species of trace gases and aerosols, based on the nine state-of-the-art global fire models that participated in FireMIP. We also provide information on the recent status and limitations of the model-based reconstructions and identify the main uncertainty sources in their long-term changes.
Lina Teckentrup, Sandy P. Harrison, Stijn Hantson, Angelika Heil, Joe R. Melton, Matthew Forrest, Fang Li, Chao Yue, Almut Arneth, Thomas Hickler, Stephen Sitch, and Gitta Lasslop
Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, https://doi.org/10.5194/bg-16-3883-2019, 2019
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This study compares simulated burned area of seven global vegetation models provided by the Fire Model Intercomparison Project (FireMIP) since 1900. We investigate the influence of five forcing factors: atmospheric CO2, population density, land–use change, lightning and climate.
We find that the anthropogenic factors lead to the largest spread between models. Trends due to climate are mostly not significant but climate strongly influences the inter-annual variability of burned area.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, https://doi.org/10.5194/bg-16-57-2019, 2019
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Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Ali Asaadi, Vivek K. Arora, Joe R. Melton, and Paul Bartlett
Biogeosciences, 15, 6885–6907, https://doi.org/10.5194/bg-15-6885-2018, https://doi.org/10.5194/bg-15-6885-2018, 2018
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Non-structural carbohydrates (NSCs), which play a central role in a plant's life processes and its response to environmental conditions, are typically not included in terrestrial biogeochemistry models used in Earth system models (ESMs). In this study, we include NSC pools in the framework of the land component of the Canadian ESM and show how they help address the long-standing problem of delayed leaf phenology.
Vivek K. Arora, Joe R. Melton, and David Plummer
Biogeosciences, 15, 4683–4709, https://doi.org/10.5194/bg-15-4683-2018, https://doi.org/10.5194/bg-15-4683-2018, 2018
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Earth system models (ESMs) project future changes in climate in response to changes in anthropogenic emissions of greenhouse gases (GHGs). However, before this can be achieved the natural fluxes of a given GHG must also be modelled. This paper evaluates the natural methane fluxes simulated by the CLASS-CTEM model (which is the land component of the Canadian ESM) against observations to show that the simulated methane emissions from wetlands and fires, and soil uptake of methane are realistic.
Bakr Badawy, Saroja Polavarapu, Dylan B. A. Jones, Feng Deng, Michael Neish, Joe R. Melton, Ray Nassar, and Vivek K. Arora
Geosci. Model Dev., 11, 631–663, https://doi.org/10.5194/gmd-11-631-2018, https://doi.org/10.5194/gmd-11-631-2018, 2018
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We assess the impact of using the meteorological fields from GEM-MACH-GHG to drive CLASS-CTEM. This coupling is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.
Rudra K. Shrestha, Vivek K. Arora, Joe R. Melton, and Laxmi Sushama
Biogeosciences, 14, 4733–4753, https://doi.org/10.5194/bg-14-4733-2017, https://doi.org/10.5194/bg-14-4733-2017, 2017
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Computer models of vegetation provide a tool to assess how future changes in climate may the affect geographical distribution of vegetation. However, such models must first be assessed for their ability to reproduce the present-day geographical distribution of vegetation. Here, we assess the ability of one such dynamic vegetation model. We find that while the model is broadly successful in reproducing the geographical distribution of trees and grasses in North America some limitations remain.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Joe R. Melton, Reinel Sospedra-Alfonso, and Kelly E. McCusker
Geosci. Model Dev., 10, 2761–2783, https://doi.org/10.5194/gmd-10-2761-2017, https://doi.org/10.5194/gmd-10-2761-2017, 2017
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Climate models have large grid cells due to the computational cost of running these complex models. Within grid cells like these, the land surface can vary dramatically impacting the exchange of water, carbon, and energy between the atmosphere and land. We use a technique to determine natural clusters of high-resolution soil texture within large grid cells and use them as inputs to our model. We find relatively low sensitivity to soil texture changes except in very dry regions and peatlands.
Yuanqiao Wu, Ed Chan, Joe R. Melton, and Diana L. Verseghy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-152, https://doi.org/10.5194/gmd-2017-152, 2017
Preprint withdrawn
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Peatlands are an important component of the carbon cycle that is expected to change under climate change, but accurate information on the global distribution of peatlands is presently unavailable. We use a machine-learning method to create a map of global peatland extent suitable for use in an Earth system model. For areas where data exists we find excellent agreement with observations and our method has greater skill than solely using soil datasets to estimate peatland coverage.
Sam S. Rabin, Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, Fang Li, Stéphane Mangeon, Daniel S. Ward, Chao Yue, Vivek K. Arora, Thomas Hickler, Silvia Kloster, Wolfgang Knorr, Lars Nieradzik, Allan Spessa, Gerd A. Folberth, Tim Sheehan, Apostolos Voulgarakis, Douglas I. Kelley, I. Colin Prentice, Stephen Sitch, Sandy Harrison, and Almut Arneth
Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, https://doi.org/10.5194/gmd-10-1175-2017, 2017
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Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
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The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Yuanqiao Wu, Diana L. Verseghy, and Joe R. Melton
Geosci. Model Dev., 9, 2639–2663, https://doi.org/10.5194/gmd-9-2639-2016, https://doi.org/10.5194/gmd-9-2639-2016, 2016
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About 20 % of the carbon stored in global soils occurs in peatlands. Warmer and drier conditions will both tend to stimulate the decomposition of peat and increase CO2 and methane emissions, thus potentially enhancing the warming trend. It is important that this feedback mechanism be captured in climate models. This work integrated peatlands into the Canadian Earth system model (CanESM) for global climate predictions and represent a valuable enhancement to the family of Earth system models.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
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Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
Scot M. Miller, Roisin Commane, Joe R. Melton, Arlyn E. Andrews, Joshua Benmergui, Edward J. Dlugokencky, Greet Janssens-Maenhout, Anna M. Michalak, Colm Sweeney, and Doug E. J. Worthy
Biogeosciences, 13, 1329–1339, https://doi.org/10.5194/bg-13-1329-2016, https://doi.org/10.5194/bg-13-1329-2016, 2016
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We use atmospheric data from the US and Canada to examine seven wetland methane flux estimates. Relative to existing estimates, we find a methane source that is smaller in magnitude with a broader seasonal cycle. Furthermore, we estimate the largest fluxes over the Hudson Bay Lowlands, a spatial distribution that differs from commonly used remote sensing estimates of wetland location.
J. R. Melton and V. K. Arora
Geosci. Model Dev., 9, 323–361, https://doi.org/10.5194/gmd-9-323-2016, https://doi.org/10.5194/gmd-9-323-2016, 2016
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We use a modified form of the Lotka–Volterra (L–V) equations to simulate competition between plant functional types (PFTs) on a global scale with the Canadian Terrestrial Ecosystem Model (CTEM) version 2.0. Our modified L–V simulations compare well against observation-based records of PFT distributions, while simulations with unmodified L–V equations show significant biases. We include an appendix detailing all aspects of CTEM v. 2.0.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
J. R. Melton, R. K. Shrestha, and V. K. Arora
Biogeosciences, 12, 1151–1168, https://doi.org/10.5194/bg-12-1151-2015, https://doi.org/10.5194/bg-12-1151-2015, 2015
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Net ecosystem productivity (NEP) in seasonally dry Amazon forests varies
greatly between sites with similar precipitation patterns. We ran CLASS-CTEM at two LBA Amazon sites (Tapajós 83km & Jarú Reserve) that exhibit opposite seasonal NEP cycles despite reasonably similar meteorological conditions. We find the influence of soil texture and depth, through soil moisture, on seasonal patterns of GPP and, especially, heterotrophic respiration is important for correctly simulating NEP seasonality.
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
J. R. Melton and V. K. Arora
Biogeosciences, 11, 1021–1036, https://doi.org/10.5194/bg-11-1021-2014, https://doi.org/10.5194/bg-11-1021-2014, 2014
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
J. R. Melton, R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan
Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, https://doi.org/10.5194/bg-10-753-2013, 2013
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Xi Yi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1584, https://doi.org/10.5194/egusphere-2024-1584, 2024
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This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 per year in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Joseph G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihito Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joel Thanwerdas, Hanquin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido van der Werf, Doug E. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-115, https://doi.org/10.5194/essd-2024-115, 2024
Preprint under review for ESSD
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Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesize and update the budget of the sources and sinks of CH4. This edition benefits from important progresses in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, Joe R. Melton, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2023-2550, https://doi.org/10.5194/egusphere-2023-2550, 2023
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Methane (CH4) emissions in Canada for 2007–2017 were estimated using Canada’s surface Greenhouse Gas measurements. The estimated decadal emissions show no significant trend, but the uncertainty is reduced over time as more measurements became available. We find notable features in the CH4 emissions, such as a positive correlation with surface air temperature in summer. The measurement network across Canada could monitor future CH4 emission changes and compliance for climate mitigation goals.
Bo Qu, Alexandre Roy, Joe R. Melton, Jennifer L. Baltzer, Youngryel Ryu, Matteo Detto, and Oliver Sonnentag
EGUsphere, https://doi.org/10.5194/egusphere-2023-1167, https://doi.org/10.5194/egusphere-2023-1167, 2023
Preprint archived
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Accurately simulating photosynthesis and evapotranspiration challenges terrestrial biosphere models across North America’s boreal biome, in part due to uncertain representation of the maximum rate of photosynthetic carboxylation (Vcmax). This study used forest stand scale observations in an optimization framework to improve Vcmax values for representative vegetation types. Several stand characteristics well explained spatial Vcmax variability and were useful to improve boreal forest modelling.
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
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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.
R. Scott Winton, Silvia López-Casas, Daniel Valencia-Rodríguez, Camilo Bernal-Forero, Juliana Delgado, Bernhard Wehrli, and Luz Jiménez-Segura
Hydrol. Earth Syst. Sci., 27, 1493–1505, https://doi.org/10.5194/hess-27-1493-2023, https://doi.org/10.5194/hess-27-1493-2023, 2023
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Dams are an important and rapidly growing means of energy generation in the Tropical Andes of South America. To assess the impacts of dams in the region, we assessed differences in the upstream and downstream water quality of all hydropower dams in Colombia. We found evidence of substantial dam-induced changes in water temperature, dissolved oxygen concentration and suspended sediments. Dam-induced changes in Colombian waters violate regulations and are likely impacting aquatic life.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
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The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Claude-Michel Nzotungicimpaye, Kirsten Zickfeld, Andrew H. MacDougall, Joe R. Melton, Claire C. Treat, Michael Eby, and Lance F. W. Lesack
Geosci. Model Dev., 14, 6215–6240, https://doi.org/10.5194/gmd-14-6215-2021, https://doi.org/10.5194/gmd-14-6215-2021, 2021
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In this paper, we describe a new wetland methane model (WETMETH) developed for use in Earth system models. WETMETH consists of simple formulations to represent methane production and oxidation in wetlands. We also present an evaluation of the model performance as embedded in the University of Victoria Earth System Climate Model (UVic ESCM). WETMETH is capable of reproducing mean annual methane emissions consistent with present-day estimates from the regional to the global scale.
Gesa Meyer, Elyn R. Humphreys, Joe R. Melton, Alex J. Cannon, and Peter M. Lafleur
Biogeosciences, 18, 3263–3283, https://doi.org/10.5194/bg-18-3263-2021, https://doi.org/10.5194/bg-18-3263-2021, 2021
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Shrub and sedge plant functional types (PFTs) were incorporated in the land surface component of the Canadian Earth System Model to improve representation of Arctic tundra ecosystems. Evaluated against 14 years of non-winter measurements, the magnitude and seasonality of carbon dioxide and energy fluxes at a Canadian dwarf-shrub tundra site were better captured by the shrub PFTs than by previously used grass and tree PFTs. Model simulations showed the tundra site to be an annual net CO2 source.
Wolfgang A. Obermeier, Julia E. M. S. Nabel, Tammas Loughran, Kerstin Hartung, Ana Bastos, Felix Havermann, Peter Anthoni, Almut Arneth, Daniel S. Goll, Sebastian Lienert, Danica Lombardozzi, Sebastiaan Luyssaert, Patrick C. McGuire, Joe R. Melton, Benjamin Poulter, Stephen Sitch, Michael O. Sullivan, Hanqin Tian, Anthony P. Walker, Andrew J. Wiltshire, Soenke Zaehle, and Julia Pongratz
Earth Syst. Dynam., 12, 635–670, https://doi.org/10.5194/esd-12-635-2021, https://doi.org/10.5194/esd-12-635-2021, 2021
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We provide the first spatio-temporally explicit comparison of different model-derived fluxes from land use and land cover changes (fLULCCs) by using the TRENDY v8 dynamic global vegetation models used in the 2019 global carbon budget. We find huge regional fLULCC differences resulting from environmental assumptions, simulated periods, and the timing of land use and land cover changes, and we argue for a method consistent across time and space and for carefully choosing the accounting period.
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021, https://doi.org/10.5194/acp-21-6663-2021, 2021
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NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite observes atmospheric CO2 globally. We use a multiple regression and inverse model to quantify the relationships between OCO-2 and environmental drivers within individual years for 2015–2018 and within seven global biomes. Our results point to limitations of current space-based observations for inferring environmental relationships but also indicate the potential to inform key relationships that are very uncertain in process-based models.
Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang
Geosci. Model Dev., 14, 2371–2417, https://doi.org/10.5194/gmd-14-2371-2021, https://doi.org/10.5194/gmd-14-2371-2021, 2021
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This study evaluates how well the CLASSIC land surface model reproduces the energy, water, and carbon cycle when compared against a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models and that failing to do so may potentially misguide future model development.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
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Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Joe R. Melton, Vivek K. Arora, Eduard Wisernig-Cojoc, Christian Seiler, Matthew Fortier, Ed Chan, and Lina Teckentrup
Geosci. Model Dev., 13, 2825–2850, https://doi.org/10.5194/gmd-13-2825-2020, https://doi.org/10.5194/gmd-13-2825-2020, 2020
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We transitioned the CLASS-CTEM land surface model to an open-source community model format by modernizing the code base to make the model easier to use and understand, providing a complete software environment to run the model within, developing a benchmarking suite for model evaluation, and creating an infrastructure to support community involvement. The new model, the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC), is now available for the community to use and develop.
Steffen Buessecker, Kaitlyn Tylor, Joshua Nye, Keith E. Holbert, Jose D. Urquiza Muñoz, Jennifer B. Glass, Hilairy E. Hartnett, and Hinsby Cadillo-Quiroz
Biogeosciences, 16, 4601–4612, https://doi.org/10.5194/bg-16-4601-2019, https://doi.org/10.5194/bg-16-4601-2019, 2019
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We investigated the potential for chemical reduction of nitrite into nitrous oxide (N2O) in soils from tropical peat. Among treatments, irradiation resulted in the lowest biological interference and least change of native soil chemistry (iron and organic matter). Nitrite depletion was as high in live or irradiated soils, and N2O production was significant in all tests. Thus, nonbiological production of N2O may be widely underestimated in wetlands and tropical peatlands.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
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The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Joe R. Melton, Diana L. Verseghy, Reinel Sospedra-Alfonso, and Stephan Gruber
Geosci. Model Dev., 12, 4443–4467, https://doi.org/10.5194/gmd-12-4443-2019, https://doi.org/10.5194/gmd-12-4443-2019, 2019
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Soils in cold regions store large amounts of carbon that could be released to the atmosphere if the soils thaw. To best simulate these soils, we explored different configurations and parameterizations of the CLASS-CTEM model and compared to observations. The revised model with a deeper soil column, new soil depth dataset, and inclusion of moss simulated greatly improved annual thaw depths and ground temperatures. We estimate subgrid-scale features limit further improvements against observations.
Fang Li, Maria Val Martin, Meinrat O. Andreae, Almut Arneth, Stijn Hantson, Johannes W. Kaiser, Gitta Lasslop, Chao Yue, Dominique Bachelet, Matthew Forrest, Erik Kluzek, Xiaohong Liu, Stephane Mangeon, Joe R. Melton, Daniel S. Ward, Anton Darmenov, Thomas Hickler, Charles Ichoku, Brian I. Magi, Stephen Sitch, Guido R. van der Werf, Christine Wiedinmyer, and Sam S. Rabin
Atmos. Chem. Phys., 19, 12545–12567, https://doi.org/10.5194/acp-19-12545-2019, https://doi.org/10.5194/acp-19-12545-2019, 2019
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Fire emissions are critical for atmospheric composition, climate, carbon cycle, and air quality. We provide the first global multi-model fire emission reconstructions for 1700–2012, including carbon and 33 species of trace gases and aerosols, based on the nine state-of-the-art global fire models that participated in FireMIP. We also provide information on the recent status and limitations of the model-based reconstructions and identify the main uncertainty sources in their long-term changes.
Lina Teckentrup, Sandy P. Harrison, Stijn Hantson, Angelika Heil, Joe R. Melton, Matthew Forrest, Fang Li, Chao Yue, Almut Arneth, Thomas Hickler, Stephen Sitch, and Gitta Lasslop
Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, https://doi.org/10.5194/bg-16-3883-2019, 2019
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This study compares simulated burned area of seven global vegetation models provided by the Fire Model Intercomparison Project (FireMIP) since 1900. We investigate the influence of five forcing factors: atmospheric CO2, population density, land–use change, lightning and climate.
We find that the anthropogenic factors lead to the largest spread between models. Trends due to climate are mostly not significant but climate strongly influences the inter-annual variability of burned area.
Robert Scott Winton, Elisa Calamita, and Bernhard Wehrli
Biogeosciences, 16, 1657–1671, https://doi.org/10.5194/bg-16-1657-2019, https://doi.org/10.5194/bg-16-1657-2019, 2019
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A global boom in dam construction throughout the world’s tropics motivated us to review and synthesize information on the water quality impacts of dams with a focus on low-latitude contexts and scope for mitigation. Sediment trapping and reservoir stratification are key process driving chemical and ecological impacts on tropical rivers. We analyze the 54 most-voluminous low-latitude reservoirs and find that stratification seems to be a ubiquitous phenomenon.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, https://doi.org/10.5194/bg-16-57-2019, 2019
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Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Ali Asaadi, Vivek K. Arora, Joe R. Melton, and Paul Bartlett
Biogeosciences, 15, 6885–6907, https://doi.org/10.5194/bg-15-6885-2018, https://doi.org/10.5194/bg-15-6885-2018, 2018
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Non-structural carbohydrates (NSCs), which play a central role in a plant's life processes and its response to environmental conditions, are typically not included in terrestrial biogeochemistry models used in Earth system models (ESMs). In this study, we include NSC pools in the framework of the land component of the Canadian ESM and show how they help address the long-standing problem of delayed leaf phenology.
Vivek K. Arora, Joe R. Melton, and David Plummer
Biogeosciences, 15, 4683–4709, https://doi.org/10.5194/bg-15-4683-2018, https://doi.org/10.5194/bg-15-4683-2018, 2018
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Earth system models (ESMs) project future changes in climate in response to changes in anthropogenic emissions of greenhouse gases (GHGs). However, before this can be achieved the natural fluxes of a given GHG must also be modelled. This paper evaluates the natural methane fluxes simulated by the CLASS-CTEM model (which is the land component of the Canadian ESM) against observations to show that the simulated methane emissions from wetlands and fires, and soil uptake of methane are realistic.
Bakr Badawy, Saroja Polavarapu, Dylan B. A. Jones, Feng Deng, Michael Neish, Joe R. Melton, Ray Nassar, and Vivek K. Arora
Geosci. Model Dev., 11, 631–663, https://doi.org/10.5194/gmd-11-631-2018, https://doi.org/10.5194/gmd-11-631-2018, 2018
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We assess the impact of using the meteorological fields from GEM-MACH-GHG to drive CLASS-CTEM. This coupling is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.
Rudra K. Shrestha, Vivek K. Arora, Joe R. Melton, and Laxmi Sushama
Biogeosciences, 14, 4733–4753, https://doi.org/10.5194/bg-14-4733-2017, https://doi.org/10.5194/bg-14-4733-2017, 2017
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Computer models of vegetation provide a tool to assess how future changes in climate may the affect geographical distribution of vegetation. However, such models must first be assessed for their ability to reproduce the present-day geographical distribution of vegetation. Here, we assess the ability of one such dynamic vegetation model. We find that while the model is broadly successful in reproducing the geographical distribution of trees and grasses in North America some limitations remain.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Joe R. Melton, Reinel Sospedra-Alfonso, and Kelly E. McCusker
Geosci. Model Dev., 10, 2761–2783, https://doi.org/10.5194/gmd-10-2761-2017, https://doi.org/10.5194/gmd-10-2761-2017, 2017
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Climate models have large grid cells due to the computational cost of running these complex models. Within grid cells like these, the land surface can vary dramatically impacting the exchange of water, carbon, and energy between the atmosphere and land. We use a technique to determine natural clusters of high-resolution soil texture within large grid cells and use them as inputs to our model. We find relatively low sensitivity to soil texture changes except in very dry regions and peatlands.
Yuanqiao Wu, Ed Chan, Joe R. Melton, and Diana L. Verseghy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-152, https://doi.org/10.5194/gmd-2017-152, 2017
Preprint withdrawn
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Peatlands are an important component of the carbon cycle that is expected to change under climate change, but accurate information on the global distribution of peatlands is presently unavailable. We use a machine-learning method to create a map of global peatland extent suitable for use in an Earth system model. For areas where data exists we find excellent agreement with observations and our method has greater skill than solely using soil datasets to estimate peatland coverage.
Sam S. Rabin, Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, Fang Li, Stéphane Mangeon, Daniel S. Ward, Chao Yue, Vivek K. Arora, Thomas Hickler, Silvia Kloster, Wolfgang Knorr, Lars Nieradzik, Allan Spessa, Gerd A. Folberth, Tim Sheehan, Apostolos Voulgarakis, Douglas I. Kelley, I. Colin Prentice, Stephen Sitch, Sandy Harrison, and Almut Arneth
Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, https://doi.org/10.5194/gmd-10-1175-2017, 2017
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Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
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The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Rosa Maria Roman-Cuesta, Martin Herold, Mariana C. Rufino, Todd S. Rosenstock, Richard A. Houghton, Simone Rossi, Klaus Butterbach-Bahl, Stephen Ogle, Benjamin Poulter, Louis Verchot, Christopher Martius, and Sytze de Bruin
Biogeosciences, 13, 5799–5819, https://doi.org/10.5194/bg-13-5799-2016, https://doi.org/10.5194/bg-13-5799-2016, 2016
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The land use sector (AFOLU) is a pivotal component of countries' mitigation commitments under the Paris Agreement. Global land use data are therefore important to complement and fill in countries' data gaps. But how different are the existing AFOLU datasets and why? Here we contrast six AFOLU datasets for the tropics at different levels of aggregation (spatial, gases, emission sources) and point out possible reasons for the observed differences and the next steps to improve land use emissions.
Yuanqiao Wu, Diana L. Verseghy, and Joe R. Melton
Geosci. Model Dev., 9, 2639–2663, https://doi.org/10.5194/gmd-9-2639-2016, https://doi.org/10.5194/gmd-9-2639-2016, 2016
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About 20 % of the carbon stored in global soils occurs in peatlands. Warmer and drier conditions will both tend to stimulate the decomposition of peat and increase CO2 and methane emissions, thus potentially enhancing the warming trend. It is important that this feedback mechanism be captured in climate models. This work integrated peatlands into the Canadian Earth system model (CanESM) for global climate predictions and represent a valuable enhancement to the family of Earth system models.
Rosa Maria Roman-Cuesta, Mariana C. Rufino, Martin Herold, Klaus Butterbach-Bahl, Todd S. Rosenstock, Mario Herrero, Stephen Ogle, Changsheng Li, Benjamin Poulter, Louis Verchot, Christopher Martius, John Stuiver, and Sytze de Bruin
Biogeosciences, 13, 4253–4269, https://doi.org/10.5194/bg-13-4253-2016, https://doi.org/10.5194/bg-13-4253-2016, 2016
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This research provides spatial data on gross emissions from the land use sector for the tropical region for the period 2000–2005. This sector contributes up to 24 % of the global emissions, but there is little understanding of where the hotspots of emissions are, how uncertain they are, and what the human activities behind these emissions are. Data provided here should assist countries to identify priority areas for mitigation action and contrast the effectiveness of their current measures.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
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Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
Scot M. Miller, Roisin Commane, Joe R. Melton, Arlyn E. Andrews, Joshua Benmergui, Edward J. Dlugokencky, Greet Janssens-Maenhout, Anna M. Michalak, Colm Sweeney, and Doug E. J. Worthy
Biogeosciences, 13, 1329–1339, https://doi.org/10.5194/bg-13-1329-2016, https://doi.org/10.5194/bg-13-1329-2016, 2016
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We use atmospheric data from the US and Canada to examine seven wetland methane flux estimates. Relative to existing estimates, we find a methane source that is smaller in magnitude with a broader seasonal cycle. Furthermore, we estimate the largest fluxes over the Hudson Bay Lowlands, a spatial distribution that differs from commonly used remote sensing estimates of wetland location.
J. R. Melton and V. K. Arora
Geosci. Model Dev., 9, 323–361, https://doi.org/10.5194/gmd-9-323-2016, https://doi.org/10.5194/gmd-9-323-2016, 2016
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We use a modified form of the Lotka–Volterra (L–V) equations to simulate competition between plant functional types (PFTs) on a global scale with the Canadian Terrestrial Ecosystem Model (CTEM) version 2.0. Our modified L–V simulations compare well against observation-based records of PFT distributions, while simulations with unmodified L–V equations show significant biases. We include an appendix detailing all aspects of CTEM v. 2.0.
J. van Lent, K. Hergoualc'h, and L. V. Verchot
Biogeosciences, 12, 7299–7313, https://doi.org/10.5194/bg-12-7299-2015, https://doi.org/10.5194/bg-12-7299-2015, 2015
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We summarized all available data on N-oxides and land use change, which could improve current IPCC tier 1 and 2 approaches for tropical countries. The meta-analysis showed that conversion to (non) fertilized agriculture had the largest effect. Further, we synthesize that the first years after conversion and land management practices are crucial for correctly accounting N2O and NO fluxes. Knowledge gaps remain for degraded forests, peat forests and dominant world crops such as oil palm and soy.
S. Carter, M. Herold, M. C. Rufino, K. Neumann, L. Kooistra, and L. Verchot
Biogeosciences, 12, 4809–4825, https://doi.org/10.5194/bg-12-4809-2015, https://doi.org/10.5194/bg-12-4809-2015, 2015
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Emission from agriculture-driven deforestation can be mitigated by reducing the expansion of agriculture into forests through intensification and utilizing non-forested land for agriculture. Climate-smart agriculture can reduce emissions from existing agricultural land. Tropical countries which are priorities for action can be identified by assessing the mitigation potential of these interventions, by assessing capacity for implementation and the risks associated with these approaches.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
J. R. Melton, R. K. Shrestha, and V. K. Arora
Biogeosciences, 12, 1151–1168, https://doi.org/10.5194/bg-12-1151-2015, https://doi.org/10.5194/bg-12-1151-2015, 2015
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Net ecosystem productivity (NEP) in seasonally dry Amazon forests varies
greatly between sites with similar precipitation patterns. We ran CLASS-CTEM at two LBA Amazon sites (Tapajós 83km & Jarú Reserve) that exhibit opposite seasonal NEP cycles despite reasonably similar meteorological conditions. We find the influence of soil texture and depth, through soil moisture, on seasonal patterns of GPP and, especially, heterotrophic respiration is important for correctly simulating NEP seasonality.
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
J. R. Melton and V. K. Arora
Biogeosciences, 11, 1021–1036, https://doi.org/10.5194/bg-11-1021-2014, https://doi.org/10.5194/bg-11-1021-2014, 2014
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
J. R. Melton, R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan
Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, https://doi.org/10.5194/bg-10-753-2013, 2013
Related subject area
Biogeosciences
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Exploring the Potential of History Matching for Land Surface Model Calibration
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Assimilation of Carbonyl Sulfide (COS) fluxes within the adjoint-based data assimilation system–Nanjing University Carbon Assimilation System (NUCAS v1.0)
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-6, https://doi.org/10.5194/gmd-2024-6, 2024
Revised manuscript accepted for GMD
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This study introduces a new scheme to simulate surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal response to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996, https://doi.org/10.5194/egusphere-2023-2996, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
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This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-242, https://doi.org/10.5194/gmd-2023-242, 2024
Revised manuscript accepted for GMD
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems as the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1955, https://doi.org/10.5194/egusphere-2023-1955, 2023
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In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
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Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
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Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
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Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
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Short summary
Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Peat-ML is a high-resolution global peatland extent map generated using machine learning...
