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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We find that the state-of-the-art process-based methane flux models have both lower flux magnitude and reduced inter-model uncertainty compared to a previous model inter-comparison from over a decade ago. Despite these improvements, methane flux estimates from process-based models are still likely too high compared to atmospheric observations. We also find that models with simpler parameterizations often result in better agreement with atmospheric observations in high-latitude North America.
Jade Skye, Joe R. Melton, Colin Goldblatt, Louis Saumier, Angela Gallego-Sala, Michelle Garneau, R. Scott Winton, Erick B. Bahati, Juan C. Benavides, Lee Fedorchuk, Gérard Imani, Carol Kagaba Kairumba, Frank Kansiime, Mariusz Lamentowicz, Michel Mbasi, Daria Wochal, Sambor Czerwiński, Jacek Landowski, Joanna Landowska, Vincent Maire, Minna M. Väliranta, Matthew Warren, Lydia E. S. Cole, Marissa A. Davies, Erik A. Lilleskov, Jingjing Sun, and Yuwan Wang
Earth Syst. Sci. Data, 17, 7313–7330, https://doi.org/10.5194/essd-17-7313-2025, https://doi.org/10.5194/essd-17-7313-2025, 2025
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Peatlands are large stores of carbon but are vulnerable to human activities and climate change. Comprehensive peatland data are vital to understand these ecosystems, but existing datasets are fragmented and contain errors. To address this, we created Peat-DBase – a standardized global database of peat depth measurements with > 200 000 measurements worldwide, showing median depths of 130 cm. Peat-DBase avoids overlapping data compilation efforts while identifying critical observational gaps.
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
Geosci. Model Dev., 18, 6597–6621, https://doi.org/10.5194/gmd-18-6597-2025, https://doi.org/10.5194/gmd-18-6597-2025, 2025
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This study shows that an alternate snow cover fraction parameterization significantly improves snow simulation by CLASSIC in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and snow cover fraction.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep 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, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. 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, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
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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 synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress 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.
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 H. 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, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
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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 yr-1 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.
Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, Joe R. Melton, and Vivek K. Arora
Atmos. Chem. Phys., 24, 10013–10038, https://doi.org/10.5194/acp-24-10013-2024, https://doi.org/10.5194/acp-24-10013-2024, 2024
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Methane (CH4) emissions in Canada for 2007–2017 were estimated using Canada’s surface greenhouse gas measurements. The estimated emissions show no significant trend, but emission uncertainty was reduced as more measurement sites became available. Notably for climate change, we find the wetland CH4 emissions show a positive correlation with surface air temperature in summer. Canada’s measurement network could monitor future CH4 emission changes and compliance with climate change mitigation goals.
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.
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.
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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...
