Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2683-2024
© Author(s) 2024. 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-17-2683-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Apr 2024
Development and technical paper |
| 12 Apr 2024
| 12 Apr 2024
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Salvatore R. Curasi
CORRESPONDING AUTHOR
Climate Research Division, Environment, and Climate Change Canada, Victoria, BC, V8N 1V8, Canada
Department of Geography & Environmental Studies, Carleton University, Ottawa, ON, K1S 5B6, Canada
Joe R. Melton
Climate Research Division, Environment, and Climate Change Canada, Victoria, BC, V8N 1V8, Canada
Elyn R. Humphreys
Department of Geography & Environmental Studies, Carleton University, Ottawa, ON, K1S 5B6, Canada
Txomin Hermosilla
Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, Victoria, BC, V8Z 1M5, Canada
Michael A. Wulder
Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, Victoria, BC, V8Z 1M5, Canada
Related authors
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
Short summary
Short summary
Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Hanyu Liu, Felix R. Vogel, Misa Ishizawa, Zhen Zhang, Benjamin Poulter, Doug E. J. Worthy, Leyang Feng, Anna L. Gagné-Landmann, Ao Chen, Ziting Huang, Dylan C. Gaeta, Joe R. Melton, Douglas Chan, Vineet Yadav, Deborah Huntzinger, and Scot M. Miller
EGUsphere, https://doi.org/10.5194/egusphere-2025-2150, https://doi.org/10.5194/egusphere-2025-2150, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
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.
Min Feng, Joseph O. Sexton, Panshi Wang, Paul M. Montesano, Leonardo Calle, Nuno Carvalhais, Benjamin Poulter, Matthew J. Macander, Michael A. Wulder, Margaret Wooten, William Wagner, Akiko Elders, Saurabh Channan, and Christopher S. R. Neigh
EGUsphere, https://doi.org/10.5194/egusphere-2025-2268, https://doi.org/10.5194/egusphere-2025-2268, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
The boreal forest, warming fastest among forested biomes, shows a northward shift in tree cover. Using the longest, highest-resolution satellite maps, we found an 0.844 million km² increase in tree cover and a 0.45° northward shift from 1985–2020, especially in northern latitudes. Stable disturbance rates suggest climate-driven growth. Young forests' biomass may help reduce global CO2, despite uncertainties in carbon balance, disturbance, and respiration.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
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
Short summary
Short summary
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.
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
EGUsphere, https://doi.org/10.5194/egusphere-2025-1264, https://doi.org/10.5194/egusphere-2025-1264, 2025
Short summary
Short summary
This study shows that an alternate snow cover fraction (SCF) parameterization significantly improves SCF simulated in the CLASSIC model 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 SCF observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Leeza Speranskaya, David I. Campbell, Peter M. Lafleur, and Elyn R. Humphreys
Biogeosciences, 21, 1173–1190, https://doi.org/10.5194/bg-21-1173-2024, https://doi.org/10.5194/bg-21-1173-2024, 2024
Short summary
Short summary
Higher evaporation has been predicted in peatlands due to climatic drying. We determined whether the water-conservative vegetation at a Southern Hemisphere bog could cause a different response to dryness compared to a "typical" Northern Hemisphere bog, using decades-long evaporation datasets from each site. At the southern bog, evaporation increased at a much lower rate with increasing dryness, suggesting that this peatland type may be more resilient to climate warming than northern bogs.
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
Short summary
Short summary
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
Short summary
Short summary
Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Hongxing He, Tim Moore, Elyn R. Humphreys, Peter M. Lafleur, and Nigel T. Roulet
Hydrol. Earth Syst. Sci., 27, 213–227, https://doi.org/10.5194/hess-27-213-2023, https://doi.org/10.5194/hess-27-213-2023, 2023
Short summary
Short summary
We applied CoupModel to quantify the impacts of natural and human disturbances to adjacent water bodies in regulating net CO2 uptake of northern peatlands. We found that 1 m drops of the water level at the beaver pond lower the peatland water table depth 250 m away by 0.15 m and reduce the peatland net CO2 uptake by 120 g C m-2 yr-1. Therefore, although bogs are ombrotrophic rainfed systems, the boundary hydrological conditions play an important role in regulating water storage and CO2 uptake.
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
Short summary
Short summary
We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot
Geosci. Model Dev., 15, 4709–4738, https://doi.org/10.5194/gmd-15-4709-2022, https://doi.org/10.5194/gmd-15-4709-2022, 2022
Short summary
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.
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
Short summary
Short summary
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.
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
Short summary
Short summary
The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
June Skeeter, Andreas Christen, Andrée-Anne Laforce, Elyn Humphreys, and Greg Henry
Biogeosciences, 17, 4421–4441, https://doi.org/10.5194/bg-17-4421-2020, https://doi.org/10.5194/bg-17-4421-2020, 2020
Short summary
Short summary
This study investigates carbon fluxes at Illisarvik, an artificial drained thermokarst lake basin (DTLB) in Canada's Northwest Territories. This is the first carbon balance study in a DTLB outside of Alaska. We used neural networks to identify the factors controlling fluxes and to model the effects of the controlling factors. We discuss the role of vegetation heterogeneity in fluxes, especially of methane, and we show how the carbon fluxes differ from Alaskan DTLBs.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
Short summary
Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
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
Short summary
Short summary
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
Short summary
Short summary
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.
M. Graham Clark, Elyn R. Humphreys, and Sean K. Carey
Biogeosciences, 17, 667–682, https://doi.org/10.5194/bg-17-667-2020, https://doi.org/10.5194/bg-17-667-2020, 2020
Short summary
Short summary
Natural and restored wetlands typically emit methane to the atmosphere. However, we found that a wetland constructed after oil sand mining in boreal Canada using organic soils from local peatlands had negligible emissions of methane in its first 3 years. Methane production was likely suppressed due to an abundance of alternate inorganic electron acceptors. Methane emissions may increase in the future if the alternate electron acceptors continue to decrease.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This study compares simulated burned area of seven global vegetation models provided by the Fire Model Intercomparison Project (FireMIP) since 1900. We investigate the influence of five forcing factors: atmospheric CO2, population density, land–use change, lightning and climate.
We find that the anthropogenic factors lead to the largest spread between models. Trends due to climate are mostly not significant but climate strongly influences the inter-annual variability of burned area.
Olli Peltola, Timo Vesala, Yao Gao, Olle Räty, Pavel Alekseychik, Mika Aurela, Bogdan Chojnicki, Ankur R. Desai, Albertus J. Dolman, Eugenie S. Euskirchen, Thomas Friborg, Mathias Göckede, Manuel Helbig, Elyn Humphreys, Robert B. Jackson, Georg Jocher, Fortunat Joos, Janina Klatt, Sara H. Knox, Natalia Kowalska, Lars Kutzbach, Sebastian Lienert, Annalea Lohila, Ivan Mammarella, Daniel F. Nadeau, Mats B. Nilsson, Walter C. Oechel, Matthias Peichl, Thomas Pypker, William Quinton, Janne Rinne, Torsten Sachs, Mateusz Samson, Hans Peter Schmid, Oliver Sonnentag, Christian Wille, Donatella Zona, and Tuula Aalto
Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, https://doi.org/10.5194/essd-11-1263-2019, 2019
Short summary
Short summary
Here we develop a monthly gridded dataset of northern (> 45 N) wetland methane (CH4) emissions. The data product is derived using a random forest machine-learning technique and eddy covariance CH4 fluxes from 25 wetland sites. Annual CH4 emissions from these wetlands calculated from the derived data product are comparable to prior studies focusing on these areas. This product is an independent estimate of northern wetland CH4 emissions and hence could be used, e.g. for process model evaluation.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, https://doi.org/10.5194/bg-16-57-2019, 2019
Short summary
Short summary
Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
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
Short summary
Short summary
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.
Gustaf Granath, Håkan Rydin, Jennifer L. Baltzer, Fia Bengtsson, Nicholas Boncek, Luca Bragazza, Zhao-Jun Bu, Simon J. M. Caporn, Ellen Dorrepaal, Olga Galanina, Mariusz Gałka, Anna Ganeva, David P. Gillikin, Irina Goia, Nadezhda Goncharova, Michal Hájek, Akira Haraguchi, Lorna I. Harris, Elyn Humphreys, Martin Jiroušek, Katarzyna Kajukało, Edgar Karofeld, Natalia G. Koronatova, Natalia P. Kosykh, Mariusz Lamentowicz, Elena Lapshina, Juul Limpens, Maiju Linkosalmi, Jin-Ze Ma, Marguerite Mauritz, Tariq M. Munir, Susan M. Natali, Rayna Natcheva, Maria Noskova, Richard J. Payne, Kyle Pilkington, Sean Robinson, Bjorn J. M. Robroek, Line Rochefort, David Singer, Hans K. Stenøien, Eeva-Stiina Tuittila, Kai Vellak, Anouk Verheyden, James Michael Waddington, and Steven K. Rice
Biogeosciences, 15, 5189–5202, https://doi.org/10.5194/bg-15-5189-2018, https://doi.org/10.5194/bg-15-5189-2018, 2018
Short summary
Short summary
Peat constitutes a long-term archive for climate reconstruction by using the isotopic composition of carbon and oxygen. We analysed isotopes in two peat moss species across North America and Eurasia. Peat (moss tissue) isotope composition was predicted by soil moisture and isotopic composition of the rainwater but differed between species. Our results suggest that isotope composition can be used on a large scale for climatic reconstructions but that such models should be species-specific.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Chunjing Qiu, Dan Zhu, Philippe Ciais, Bertrand Guenet, Gerhard Krinner, Shushi Peng, Mika Aurela, Christian Bernhofer, Christian Brümmer, Syndonia Bret-Harte, Housen Chu, Jiquan Chen, Ankur R. Desai, Jiří Dušek, Eugénie S. Euskirchen, Krzysztof Fortuniak, Lawrence B. Flanagan, Thomas Friborg, Mateusz Grygoruk, Sébastien Gogo, Thomas Grünwald, Birger U. Hansen, David Holl, Elyn Humphreys, Miriam Hurkuck, Gerard Kiely, Janina Klatt, Lars Kutzbach, Chloé Largeron, Fatima Laggoun-Défarge, Magnus Lund, Peter M. Lafleur, Xuefei Li, Ivan Mammarella, Lutz Merbold, Mats B. Nilsson, Janusz Olejnik, Mikaell Ottosson-Löfvenius, Walter Oechel, Frans-Jan W. Parmentier, Matthias Peichl, Norbert Pirk, Olli Peltola, Włodzimierz Pawlak, Daniel Rasse, Janne Rinne, Gaius Shaver, Hans Peter Schmid, Matteo Sottocornola, Rainer Steinbrecher, Torsten Sachs, Marek Urbaniak, Donatella Zona, and Klaudia Ziemblinska
Geosci. Model Dev., 11, 497–519, https://doi.org/10.5194/gmd-11-497-2018, https://doi.org/10.5194/gmd-11-497-2018, 2018
Short summary
Short summary
Northern peatlands store large amount of soil carbon and are vulnerable to climate change. We implemented peatland hydrological and carbon accumulation processes into the ORCHIDEE land surface model. The model was evaluated against EC measurements from 30 northern peatland sites. The model generally well reproduced the spatial gradient and temporal variations in GPP and NEE at these sites. Water table depth was not well predicted but had only small influence on simulated NEE.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
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
Short summary
Short summary
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
Short summary
Short summary
Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
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
Short summary
Short summary
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.
H. N. Mbufong, M. Lund, M. Aurela, T. R. Christensen, W. Eugster, T. Friborg, B. U. Hansen, E. R. Humphreys, M. Jackowicz-Korczynski, L. Kutzbach, P. M. Lafleur, W. C. Oechel, F. J. W. Parmentier, D. P. Rasse, A. V. Rocha, T. Sachs, M. K. van der Molen, and M. P. Tamstorf
Biogeosciences, 11, 4897–4912, https://doi.org/10.5194/bg-11-4897-2014, https://doi.org/10.5194/bg-11-4897-2014, 2014
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
C. A. Emmerton, V. L. St. Louis, I. Lehnherr, E. R. Humphreys, E. Rydz, and H. R. Kosolofski
Biogeosciences, 11, 3095–3106, https://doi.org/10.5194/bg-11-3095-2014, https://doi.org/10.5194/bg-11-3095-2014, 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
B. J. Kopp, J. H. Fleckenstein, N. T. Roulet, E. Humphreys, J. Talbot, and C. Blodau
Hydrol. Earth Syst. Sci., 17, 3485–3498, https://doi.org/10.5194/hess-17-3485-2013, https://doi.org/10.5194/hess-17-3485-2013, 2013
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
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
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)
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025, https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Short summary
Physical–biogeochemical ocean global models are required to analyze difficult oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Cited articles
Arora, V. K.: Simulating energy and carbon fluxes over winter wheat using coupled land surface and terrestrial ecosystem models, Agr. Forest Meteorol., 118, 21–47, 2003.
Arora, V. K. and Boer, G. J.: Fire as an interactive component of dynamic vegetation models, J. Geophys. Res., 110, https://doi.org/10.1029/2005jg000042, 2005.
Arora, V. K. and Boer, G. J.: Uncertainties in the 20th century carbon budget associated with land use change, Glob. Change Biol., 16, 3327–3348, 2010.
Arora, V. K. and Melton, J. R.: Reduction in global area burned and wildfire emissions since 1930s enhances carbon uptake by land, Nat. Commun., 9, 1326, 2018.
Asaadi, A. and Arora, V. K.: Implementation of nitrogen cycle in the CLASSIC land model, Biogeosciences, 18, 669–706, https://doi.org/10.5194/bg-18-669-2021, 2021.
Asaadi, A., Arora, V. K., Melton, J. R., and Bartlett, P.: An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework, Biogeosciences, 15, 6885–6907, https://doi.org/10.5194/bg-15-6885-2018, 2018.
Babst, F., Bouriaud, O., Poulter, B., Trouet, V., Girardin, M. P., and Frank, D. C.: Twentieth century redistribution in climatic drivers of global tree growth, Sci. Adv., 5, eaat4313, https://doi.org/10.1126/sciadv.aat4313, 2019.
Beaudoin, A., Bernier, P. Y., Villemaire, P., Guindon, L., and Guo, X. J.: Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbors mapping approach applied to MODIS imagery, Can. J. Forest Res., 48, 85–93, 2018.
Bellassen, V., Le Maire, G., Dhôte, J. F., Ciais, P., and Viovy, N.: Modelling forest management within a global vegetation model – Part 1: Model structure and general behaviour, Ecol. Modell., 221, 2458–2474, 2010.
Bond-Lamberty, B. and Gower, S. T.: Decomposition and Fragmentation of Coarse Woody Debris: Re-visiting a Boreal Black Spruce Chronosequence, Ecosystems, 11, 831–840, 2008.
Böttcher, H., Kurz, W. A., and Freibauer, A.: Accounting of forest carbon sinks and sources under a future climate protocol—factoring out past disturbance and management effects on age–class structure, Environ. Sci. Policy, 11, 669–686, 2008.
Bright, R. M., Astrup, R., and Strømman, A. H.: Empirical models of monthly and annual albedo in managed boreal forests of interior Norway, Climatic Change, 120, 183–196, 2013.
Chaste, E., Girardin, M. P., Kaplan, J. O., Portier, J., Bergeron, Y., and Hély, C.: The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model, Biogeosciences, 15, 1273–1292, https://doi.org/10.5194/bg-15-1273-2018, 2018.
Chen, J., Chen, W., Liu, J., Cihlar, J., and Gray, S.: Annual carbon balance of Canada's forests during 1895–1996, Global Biogeochem. Cy., 14, 839–849, 2000.
Chen, J. M., Ju, W., Cihlar, J., Price, D., Liu, J., Chen, W., Pan, J., Black, A., and Barr, A.: Spatial distribution of carbon sources and sinks in Canada's forests, Tellus B, 55, 622–641, 2003.
Curasi, S. R., Melton, J. R., Humphreys, E. R., Hermosilla, T., and Wulder, M. A.: Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in a tile-based land surface model, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8302974, 2023a.
Czimczik, C. I., Trumbore, S. E., Carbone, M. S., and Winston, G. C.: Changing sources of soil respiration with time since fire in a boreal forest, Glob. Change Biol., 12, 957–971, 2006.
D'Orangeville, L., Houle, D., Duchesne, L., Phillips, R. P., Bergeron, Y., and Kneeshaw, D.: Beneficial effects of climate warming on boreal tree growth may be transitory, Nat. Commun., 9, 3213, 2018.
Dore, S., Kolb, T. E., Montes-Helu, M., Eckert, S. E., Sullivan, B. W., Hungate, B. A., Kaye, J. P., Hart, S. C., Koch, G. W., and Finkral, A.: Carbon and water fluxes from ponderosa pine forests disturbed by wildfire and thinning, Ecol. Appl., 20, 663–683, 2010.
Earth System Research Laboratory (NOAA/ESRL): Trends in atmospheric carbon dioxide, National Oceanic & Atmospheric Administration, http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html, (last access: 11 March 2022).
ECMWF: ERA5 reanalysis (0.25 degree latitude-longitude grid), ECMWF [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 (last access: 11 March 2022), 2019.
Ellner, S. P. and Guckenheimer, J.: Dynamic Models in Biology, Princeton University Press, ISBN 9780691125893, 2011.
Erb, K.-H., Luyssaert, S., Meyfroidt, P., Pongratz, J., Don, A., Kloster, S., Kuemmerle, T., Fetzel, T., Fuchs, R., Herold, M., Haberl, H., Jones, C. D., Marín-Spiotta, E., McCallum, I., Robertson, E., Seufert, V., Fritz, S., Valade, A., Wiltshire, A., and Dolman, A. J.: Land management: data availability and process understanding for global change studies, Glob. Change Biol., 23, 512–533, 2017.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D., Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K., Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu, X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System Models: A review of progress and priorities, Glob. Change Biol., 24, 35–54, 2018.
Gelman, A. and Hill, J.: Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 651 pp., 2006.
Gillis, M. D., Omule, A. Y., and Brierley, T.: Monitoring Canada's forests: The National Forest Inventory, Forest. Chron., 81, 214–221, 2005.
Girardin, M. P., Bouriaud, O., Hogg, E. H., Kurz, W., Zimmermann, N. E., Metsaranta, J. M., de Jong, R., Frank, D. C., Esper, J., Büntgen, U., Guo, X. J., and Bhatti, J.: No growth stimulation of Canada's boreal forest under half-century of combined warming and CO2 fertilization, P. Natl. Acad. Sci. USA, 113, E8406–E8414, 2016.
Goetz, S. J., Bunn, A. G., Fiske, G. J., and Houghton, R. A.: Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance, P. Natl. Acad. Sci. USA, 102, 13521–13525, 2005.
Haverd, V., Smith, B., Nieradzik, L. P., and Briggs, P. R.: A stand-alone tree demography and landscape structure module for Earth system models: integration with inventory data from temperate and boreal forests, Biogeosciences, 11, 4039–4055, https://doi.org/10.5194/bg-11-4039-2014, 2014.
Hayes, D. J., Turner, D. P., Stinson, G., McGuire, A. D., Wei, Y., West, T. O., Heath, L. S., Jong, B., McConkey, B. G., Birdsey, R. A., Kurz, W. A., Jacobson, A. R., Huntzinger, D. N., Pan, Y., Post, W. M., and Cook, R. B.: Reconciling estimates of the contemporary North American carbon balance among terrestrial biosphere models, atmospheric inversions, and a new approach for estimating net ecosystem exchange from inventory-based data, Glob. Change Biol., 18, 1282–1299, 2012.
Hember, R. A., Kurz, W. A., Metsaranta, J. M., Black, T. A., Guy, R. D., and Coops, N. C.: Accelerating regrowth of temperate-maritime forests due to environmental change, Glob. Change Biol., 18, 2026–2040, 2012.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., and Hobart, G. W.: An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites, Remote Sens. Environ., 158, 220–234, 2015a.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., and Hobart, G. W.: Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics, Remote Sens. Environ., 170, 121–132, 2015b.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., and Campbell, L. B.: Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring, Int. J. Digit. Earth, 9, 1035–1054, 2016.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., and Hobart, G. W.: Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series, Can. J. Remote Sens., 44, 67–87, 2018.
Hermosilla, T., Wulder, M. A., White, J. C., and Coops, N. C.: Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985–2015), Remote Sens. Environ., 233, 111403, https://doi.org/10.1016/j.rse.2019.111403, 2019.
Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A., Lamigueiro, O. P., Bevan, A., Racine, E. B., Shortridge, A., and Hijmans, M. R. J.: Package “raster,” R package, 734, https://cran.r-project.org/web/packages/raster/index.html (last access: 11 March 2022), 2015.
Hirano, T., Suzuki, K., and Hirata, R.: Energy balance and evapotranspiration changes in a larch forest caused by severe disturbance during an early secondary succession, Agr. Forest Meteorol., 232, 457–468, 2017.
Ju, J. and Masek, J. G.: The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data, Remote Sens. Environ., 176, 1–16, 2016.
Ju, W. and Chen, J. M.: Simulating the effects of past changes in climate, atmospheric composition, and fire disturbance on soil carbon in Canada's forests and wetlands, Global Biogeochem. Cy., 22, https://doi.org/10.1029/2007GB002935, 2008.
Keenan, T. F. and Williams, C. A.: The Terrestrial Carbon Sink, Annu. Rev. Env. Resour., 43, 219–243, 2018.
Kim, H.: Global soil wetness project phase 3 atmospheric boundary conditions (Experiment 1), Data Integration and Analysis System (DIAS), Institute of Industrial Science, The University of Tokyo, Tokyo Japan [data set], 2017.
Körner, C.: Plant CO2 responses: an issue of definition, time and resource supply, New Phytol., 172, 393–411, 2006.
Kucharik, C. J., Foley, J. A., Delire, C., Fisher, V. A., Coe, M. T., Lenters, J. D., Young-Molling, C., Ramankutty, N., Norman, J. M., and Gower, S. T.: Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure, Global Biogeochem. Cy., 14, 795–825, 2000.
Kurz, W. A. and Apps, M. J.: Conntribution of northern forests to the global C cycle: Canada as a case study, Water Air Soil Pollut., 70, 163–176, 1993.
Kurz, W. A. and Apps, M. J.: A 70-year retrospective analysis of carbon fluxes in the Canadian forest sector, Ecol. Appl., 9, 526–547, 1999.
Kurz, W. A., Apps, M. J., Beukema, S. J., and Lekstrum, T.: 20th century carbon budget of Canadian forests, Tellus B, 47, 170–177, 1995.
Kurz, W. A., Beukema, S. J., and Apps, M. J.: Carbon budget implications of the transition from natural to manged disturbance regimes in forest landscapes, Mitig. Adapt. Strat. Gl., 2, 405–421, 1997.
Kurz, W. A., Stinson, G., Rampley, G. J., Dymond, C. C., and Neilson, E. T.: Risk of natural disturbances makes future contribution of Canada's forests to the global carbon cycle highly uncertain, P. Natl. Acad. Sci. USA, 105, 1551–1555, 2008.
Kurz, W. A., Dymond, C. C., White, T. M., Stinson, G., Shaw, C. H., Rampley, G. J., Smyth, C., Simpson, B. N., Neilson, E. T., Trofymow, J. A., Metsaranta, J., and Apps, M. J.: CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards, Ecol. Modell., 220, 480–504, 2009.
Kuuluvainen, T. and Gauthier, S.: Young and old forest in the boreal: critical stages of ecosystem dynamics and management under global change, Forest Ecosystems, 5, 26, https://doi.org/10.1186/s40663-018-0142-2, 2018.
Lange, S.: WFDE5 over land merged with ERA5 over the ocean (W5E5), V.1.0, GFZ Data Services, Potsdam, Germany, 2019.
Lange, S.: ISIMIP3BASD (Version 2.3), Zenodo [data set], https://doi.org/10.5281/zenodo.3648654, 2020a.
Lange, S.: The Inter-Sectoral Impact Model Intercomparison Project Input data set: GSWP3-W5E5, The Inter-Sectoral Impact Model Intercomparison Project [data set], https://www.isimip.org/gettingstarted/input-data-bias-correction/details/80/ (last access: 11 March 2022), 2020b.
Latifovic, R., Pouliot, D., and Olthof, I.: Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development, Remote Sens.-Basel, 9, 1098, https://doi.org/10.3390/rs9111098, 2017.
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., and Schellnhuber, H. J.: Tipping elements in the Earth's climate system, P. Natl. Acad. Sci. USA, 105, 1786–1793, 2008.
Li, R. and Arora, V. K.: Effect of mosaic representation of vegetation in land surface schemes on simulated energy and carbon balances, Biogeosciences, 9, 593–605, https://doi.org/10.5194/bg-9-593-2012, 2012.
Liu, H.: Changes in the surface energy budget after fire in boreal ecosystems of interior Alaska: An annual perspective, J. Geophys. Res., 110, https://doi.org/10.1029/2004jd005158, 2005.
Luyssaert, S., Jammet, M., Stoy, P. C., Estel, S., Pongratz, J., Ceschia, E., Churkina, G., Don, A., Erb, K., Ferlicoq, M., Gielen, B., Grünwald, T., Houghton, R. A., Klumpp, K., Knohl, A., Kolb, T., Kuemmerle, T., Laurila, T., Lohila, A., Loustau, D., McGrath, M. J., Meyfroidt, P., Moors, E. J., Naudts, K., Novick, K., Otto, J., Pilegaard, K., Pio, C. A., Rambal, S., Rebmann, C., Ryder, J., Suyker, A. E., Varlagin, A., Wattenbach, M., and Dolman, A. J.: Land management and land-cover change have impacts of similar magnitude on surface temperature, Nat. Clim. Change, 4, 389–393, 2014.
Ma, Z., Peng, C., Zhu, Q., Chen, H., Yu, G., Li, W., Zhou, X., Wang, W., and Zhang, W.: Regional drought-induced reduction in the biomass carbon sink of Canada's boreal forests, P. Natl. Acad. Sci. USA, 109, 2423–2427, 2012.
MacKay, M. D., Meyer, G., and Melton, J. R.: On the Discretization of Richards Equation in Canadian Land Surface Models, Atmos. Ocean, 6, 1–11, 2022.
MacKenzie, W. H. and Meidinger, D. V.: The Biogeoclimatic Ecosystem Classification Approach: an ecological framework for vegetation classification, Phytocoenologia, 48, 203–213, 2018.
Maltman, J. C., Hermosilla, T., Wulder, M. A., Coops, N. C., and White, J. C.: Estimating and mapping forest age across Canada's forested ecosystems, Remote Sens. Environ., 290, 113529, https://doi.org/10.1016/j.rse.2023.113529, 2023.
Maness, H., Kushner, P. J., and Fung, I.: Summertime climate response to mountain pine beetle disturbance in British Columbia, Nat. Geosci., 6, 65–70, 2012.
Marchand, W., Girardin, M. P., Gauthier, S., Hartmann, H., Bouriaud, O., Babst, F., and Bergeron, Y.: Untangling methodological and scale considerations in growth and productivity trend estimates of Canada’s forests, Environ. Res. Lett., 13, 093001, https://doi.org/10.1088/1748-9326/aad82a, 2018.
Melton, J.: CLASSIC, GitLab [code], https://gitlab.com/cccma/classic (last access: 11 March 2022), 2021.
Melton, J. R. and Arora, V. K.: Sub-grid scale representation of vegetation in global land surface schemes: implications for estimation of the terrestrial carbon sink, Biogeosciences, 11, 1021–1036, https://doi.org/10.5194/bg-11-1021-2014, 2014.
Melton, J. R. and Arora, V. K.: Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0, Geosci. Model Dev., 9, 323–361, https://doi.org/10.5194/gmd-9-323-2016, 2016.
Melton, J. R., Sospedra-Alfonso, R., and McCusker, K. E.: Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1), Geosci. Model Dev., 10, 2761–2783, https://doi.org/10.5194/gmd-10-2761-2017, 2017.
Melton, J. R., Arora, V. K., Wisernig-Cojoc, E., Seiler, C., Fortier, M., Chan, E., and Teckentrup, L.: CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance, Geosci. Model Dev., 13, 2825–2850, https://doi.org/10.5194/gmd-13-2825-2020, 2020.
Meyer, G., Humphreys, E. R., Melton, J. R., Cannon, A. J., and Lafleur, P. M.: Simulating shrubs and their energy and carbon dioxide fluxes in Canada's Low Arctic with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC), Biogeosciences, 18, 3263–3283, https://doi.org/10.5194/bg-18-3263-2021, 2021.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A Method For Scaling Vegetation Dynamics: The Ecosystem Demography Model (Ed), Ecol. Monogr., 71, 557–586, 2001.
Nabel, J. E. M. S., Naudts, K., and Pongratz, J.: Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM, Geosci. Model Dev., 13, 185–200, https://doi.org/10.5194/gmd-13-185-2020, 2020.
Natural Resources Canada: Map of Forest Management in Canada, Natural Resources Canada [data set], https://open.canada.ca/data/en/dataset/d8fa9a38-c4df-442a-8319-9bbcbdc29060 (last access: 11 March 2022), 2019.
Naudts, K., Ryder, J., McGrath, M. J., Otto, J., Chen, Y., Valade, A., Bellasen, V., Berhongaray, G., Bönisch, G., Campioli, M., Ghattas, J., De Groote, T., Haverd, V., Kattge, J., MacBean, N., Maignan, F., Merilä, P., Penuelas, J., Peylin, P., Pinty, B., Pretzsch, H., Schulze, E. D., Solyga, D., Vuichard, N., Yan, Y., and Luyssaert, S.: A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes, Geosci. Model Dev., 8, 2035–2065, https://doi.org/10.5194/gmd-8-2035-2015, 2015.
NFIS: National Terrestrial Ecosystem Monitoring System for Canada, NFIS [data set], https://opendata.nfis.org/mapserver/nfis-change_eng.html, last access: 11 March 2022.
Nocedal, J., and Wright, S. J.: Numerical Optimization, edited by: Mikshch, T. V., Resnick, S. I., and Robinson, S. M., Springer Science+Business Media, 2006.
Pan, Y., Chen, J. M., Birdsey, R., McCullough, K., He, L., and Deng, F.: Age structure and disturbance legacy of North American forests, Biogeosciences, 8, 715–732, https://doi.org/10.5194/bg-8-715-2011, 2011.
Pan, Y., Birdsey, R. A., Phillips, O. L., and Jackson, R. B.: The Structure, Distribution, and Biomass of the World's Forests, Annu. Rev. Ecol. Evol. S., 44, 593–622, 2013.
Peng, Y., Arora, V. K., Kurz, W. A., Hember, R. A., Hawkins, B. J., Fyfe, J. C., and Werner, A. T.: Climate and atmospheric drivers of historical terrestrial carbon uptake in the province of British Columbia, Canada, Biogeosciences, 11, 635–649, https://doi.org/10.5194/bg-11-635-2014, 2014.
Pongratz, J., Dolman, H., Don, A., Erb, K.-H., Fuchs, R., Herold, M., Jones, C., Kuemmerle, T., Luyssaert, S., Meyfroidt, P., and Naudts, K.: Models meet data: Challenges and opportunities in implementing land management in Earth system models, Glob. Change Biol., 24, 1470–1487, 2018.
Potapov, P., Hansen, M. C., Stehman, S. V., Loveland, T. R., and Pittman, K.: Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss, Remote Sens. Environ., 112, 3708–3719, 2008.
Puettmann, K. J., Wilson, S. M., Baker, S. C., Donoso, P. J., Drössler, L., Amente, G., Harvey, B. D., Knoke, T., Lu, Y., Nocentini, S., Putz, F. E., Yoshida, T., and Bauhus, J.: Silvicultural alternatives to conventional even-aged forest management - what limits global adoption?, Forest Ecosystems, 2, 1–16, 2015.
R Core Team: R: A language and environment for statistical computing, The R Foundation for Statistical Computing, c/o Institute for Statistics and Mathematics, Wirtschaftsuniversität Wien, Welthandelsplatz 1, 1020 Vienna, Austria, 2013.
Reich, P. B., Sendall, K. M., Stefanski, A., Rich, R. L., Hobbie, S. E., and Montgomery, R. A.: Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture, Nature, 562, 263–267, 2018.
Salkfield, T., Walton, A., and Mackenzie, W.: Biogeoclimatic Ecosystem Classification Map, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, https://catalogue.data.gov.bc.ca/dataset/bec-map (last access: 7 August 2020), 2016.
Seiler, C., Melton, J. R., Arora, V. K., and Wang, L.: CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 2: Global benchmarking, Geosci. Model Dev., 14, 2371–2417, https://doi.org/10.5194/gmd-14-2371-2021, 2021.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Sy., 9, 65–88, 2017.
Shevliakova, E., Pacala, S. W., Malyshev, S., Hurtt, G. C., Milly, P. C. D., Caspersen, J. P., Sentman, L. T., Fisk, J. P., Wirth, C., and Crevoisier, C.: Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink, Global Biogeochem. Cy., 23, https://doi.org/10.1029/2007gb003176, 2009.
Shrestha, B. M. and Chen, H. Y. H.: Effects of stand age, wildfire and clearcut harvesting on forest floor in boreal mixedwood forests, Plant Soil, 336, 267–277, 2010.
Shrestha, R. K., Arora, V. K., and Melton, J. R.: The sensitivity of simulated competition between different plant functional types to subgrid-scale representation of vegetation in a land surface model, J. Geophys. Res.-Biogeo., 121, 809–828, 2016.
Skakun, R., Whitman, E., Little, J. M., and Parisien, M.-A.: Area burned adjustments to historical wildland fires in Canada, Environ. Res. Lett., 16, 064014, https://doi.org/10.1088/1748-9326/abfb2c, 2021.
Stinson, G., Kurz, W. A., Smyth, C. E., Neilson, E. T., Dymond, C. C., Metsaranta, J. M., Boisvenue, C., Rampley, G. J., Li, Q., White, T. M., and Blain, D.: An inventory-based analysis of Canada's managed forest carbon dynamics, 1990 to 2008, Glob. Change Biol., 17, 2227–2244, 2011.
Stinson, G., Thandi, G., Aitkin, D., Bailey, C., Boyd, J., Colley, M., Fraser, C., Gelhorn, L., Groenewegen, K., Hogg, A., Kapron, J., Leboeuf, A., Makar, M., Montigny, M., Pittman, B., Price, K., Salkeld, T., Smith, L., Viveiros, A., and Wilson, D.: A new approach for mapping forest management areas in Canada, Forest. Chron., 95, 101–112, 2019.
Stocker, B. D., Spahni, R., and Joos, F.: DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands, Geosci. Model Dev., 7, 3089–3110, https://doi.org/10.5194/gmd-7-3089-2014, 2014.
Sulla-Menashe, D., Woodcock, C. E., and Friedl, M. A.: Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers, Environ. Res. Lett., 13, 014007, https://doi.org/10.1088/1748-9326/aa9b88, 2018.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019.
Torres-Rojas, L., Vergopolan, N., Herman, J. D. and Chaney, N. W.: Towards an Optimal Representation of Sub-Grid Heterogeneity in Land Surface Models, Water Resour. Res., 58, e2022WR032233, https://doi.org/10.1029/2022WR032233, 2022..
Van Wagner, C. E.: The historical pattern of annual burned area in Canada, Forest. Chron., 64, 182–185, 1988.
Verseghy, D.: CLASS–The Canadian land surface scheme (v.3.6.2), Climate Research Division, Science and Technology Branch, Environment Canada, Victoria BC, Canada, 35, 2017.
Verseghy, D. L.: The Canadian land surface scheme (CLASS): Its history and future, Atmos. Ocean, 38, 1–13, 2000.
Verseghy, D. L.: Class-A Canadian land surface scheme for GCMS. I. Soil model, Int. J. Climatol., 11, 111–133, 2007.
Verseghy, D. L., McFarlane, N. A., and Lazare, M.: Class—A Canadian land surface scheme for GCMS, II. Vegetation model and coupled runs, Int. J. Climatol., 13, 347–370, 1993.
Wang, L., Arora, V. K., Bartlett, P., Chan, E., and Curasi, S. R.: Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model, Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, 2023.
Weber, M. G. and Flannigan, M. D.: Canadian boreal forest ecosystem structure and function in a changing climate: impact on fire regimes, Environ. Rev., 5, 145–166, 1997.
White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C., and Hobart, G. W.: A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series, Remote Sens. Environ., 194, 303–321, 2017.
World Resources Institute: Canada's Forests at a Crossroads: An Assessment in the Year 2000: a Global Forest Watch Canada Report, World Resources Institute, Washington DC, USA, 114 pp., 2000.
Wulder, M. A., Hermosilla, T., White, J. C., and Coops, N. C.: Biomass status and dynamics over Canada's forests: Disentangling disturbed area from associated aboveground biomass consequences, Environ. Res. Lett., 15, 094093, https://doi.org/10.1088/1748-9326/ab8b11, 2020.
Yang, X., Richardson, T. K., and Jain, A. K.: Contributions of secondary forest and nitrogen dynamics to terrestrial carbon uptake, Biogeosciences, 7, 3041–3050, https://doi.org/10.5194/bg-7-3041-2010, 2010.
Yue, C., Ciais, P., Luyssaert, S., Li, W., McGrath, M. J., Chang, J., and Peng, S.: Representing anthropogenic gross land use change, wood harvest, and forest age dynamics in a global vegetation model ORCHIDEE-MICT v8.4.2, Geosci. Model Dev., 11, 409–428, https://doi.org/10.5194/gmd-11-409-2018, 2018a.
Yue, C., Ciais, P., and Li, W.: Smaller global and regional carbon emissions from gross land use change when considering sub-grid secondary land cohorts in a global dynamic vegetation model, Biogeosciences, 15, 1185–1201, https://doi.org/10.5194/bg-15-1185-2018, 2018b.
Zaehle, S., Sitch, S., Prentice, I. C., Liski, J., Cramer, W., Erhard, M., Hickler, T., and Smith, B.: The importance of age-related decline in forest NPP for modeling regional carbon balances, Ecol. Appl., 16, 1555–1574, 2006.
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify...
