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
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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
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Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
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Hongxing He, Tim Moore, Elyn R. Humphreys, Peter M. Lafleur, and Nigel T. Roulet
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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
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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
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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
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
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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
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The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Claude-Michel Nzotungicimpaye, Kirsten Zickfeld, Andrew H. MacDougall, Joe R. Melton, Claire C. Treat, Michael Eby, and Lance F. W. Lesack
Geosci. Model Dev., 14, 6215–6240, https://doi.org/10.5194/gmd-14-6215-2021, https://doi.org/10.5194/gmd-14-6215-2021, 2021
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In this paper, we describe a new wetland methane model (WETMETH) developed for use in Earth system models. WETMETH consists of simple formulations to represent methane production and oxidation in wetlands. We also present an evaluation of the model performance as embedded in the University of Victoria Earth System Climate Model (UVic ESCM). WETMETH is capable of reproducing mean annual methane emissions consistent with present-day estimates from the regional to the global scale.
Gesa Meyer, Elyn R. Humphreys, Joe R. Melton, Alex J. Cannon, and Peter M. Lafleur
Biogeosciences, 18, 3263–3283, https://doi.org/10.5194/bg-18-3263-2021, https://doi.org/10.5194/bg-18-3263-2021, 2021
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Shrub and sedge plant functional types (PFTs) were incorporated in the land surface component of the Canadian Earth System Model to improve representation of Arctic tundra ecosystems. Evaluated against 14 years of non-winter measurements, the magnitude and seasonality of carbon dioxide and energy fluxes at a Canadian dwarf-shrub tundra site were better captured by the shrub PFTs than by previously used grass and tree PFTs. Model simulations showed the tundra site to be an annual net CO2 source.
Wolfgang A. Obermeier, Julia E. M. S. Nabel, Tammas Loughran, Kerstin Hartung, Ana Bastos, Felix Havermann, Peter Anthoni, Almut Arneth, Daniel S. Goll, Sebastian Lienert, Danica Lombardozzi, Sebastiaan Luyssaert, Patrick C. McGuire, Joe R. Melton, Benjamin Poulter, Stephen Sitch, Michael O. Sullivan, Hanqin Tian, Anthony P. Walker, Andrew J. Wiltshire, Soenke Zaehle, and Julia Pongratz
Earth Syst. Dynam., 12, 635–670, https://doi.org/10.5194/esd-12-635-2021, https://doi.org/10.5194/esd-12-635-2021, 2021
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We provide the first spatio-temporally explicit comparison of different model-derived fluxes from land use and land cover changes (fLULCCs) by using the TRENDY v8 dynamic global vegetation models used in the 2019 global carbon budget. We find huge regional fLULCC differences resulting from environmental assumptions, simulated periods, and the timing of land use and land cover changes, and we argue for a method consistent across time and space and for carefully choosing the accounting period.
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021, https://doi.org/10.5194/acp-21-6663-2021, 2021
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NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite observes atmospheric CO2 globally. We use a multiple regression and inverse model to quantify the relationships between OCO-2 and environmental drivers within individual years for 2015–2018 and within seven global biomes. Our results point to limitations of current space-based observations for inferring environmental relationships but also indicate the potential to inform key relationships that are very uncertain in process-based models.
Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang
Geosci. Model Dev., 14, 2371–2417, https://doi.org/10.5194/gmd-14-2371-2021, https://doi.org/10.5194/gmd-14-2371-2021, 2021
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This study evaluates how well the CLASSIC land surface model reproduces the energy, water, and carbon cycle when compared against a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models and that failing to do so may potentially misguide future model development.
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
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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.
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...
