Articles | Volume 21, issue 14
https://doi.org/10.5194/bg-21-3339-2024
© 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/bg-21-3339-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jul 2024
Research article |
| 24 Jul 2024
| 24 Jul 2024
The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback
Sian Kou-Giesbrecht
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, Dalhousie University, Halifax, NS, Canada
Vivek K. Arora
Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada, Victoria, BC, Canada
Christian Seiler
School of Environmental Studies, Queen's University, Kingston, ON, Canada
Libo Wang
Climate Processes Section, Climate Research Division, Environment and Climate Change Canada, Toronto, ON, Canada
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
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This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, Joe R. Melton, and Vivek K. Arora
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Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
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Ali Asaadi, Jörg Schwinger, Hanna Lee, Jerry Tjiputra, Vivek Arora, Roland Séférian, Spencer Liddicoat, Tomohiro Hajima, Yeray Santana-Falcón, and Chris D. Jones
Biogeosciences, 21, 411–435, https://doi.org/10.5194/bg-21-411-2024, https://doi.org/10.5194/bg-21-411-2024, 2024
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Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
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We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
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Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
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Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
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Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Vivek K. Arora, Christian Seiler, Libo Wang, and Sian Kou-Giesbrecht
Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, https://doi.org/10.5194/bg-20-1313-2023, 2023
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Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
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We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
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Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Sian Kou-Giesbrecht, Sergey Malyshev, Isabel Martínez Cano, Stephen W. Pacala, Elena Shevliakova, Thomas A. Bytnerowicz, and Duncan N. L. Menge
Biogeosciences, 18, 4143–4183, https://doi.org/10.5194/bg-18-4143-2021, https://doi.org/10.5194/bg-18-4143-2021, 2021
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Representing biological nitrogen fixation (BNF) is an important challenge for land models. We present a novel representation of BNF and updated nitrogen cycling in a land model. It includes a representation of asymbiotic BNF by soil microbes and the competitive dynamics between nitrogen-fixing and non-fixing plants. It improves estimations of major carbon and nitrogen pools and fluxes and their temporal dynamics in comparison to previous representations of BNF in land 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.
Ali Asaadi and Vivek K. Arora
Biogeosciences, 18, 669–706, https://doi.org/10.5194/bg-18-669-2021, https://doi.org/10.5194/bg-18-669-2021, 2021
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More than a quarter of the current anthropogenic CO2 emissions are taken up by land, reducing the atmospheric CO2 growth rate. This is because of the CO2 fertilization effect which benefits 80 % of global vegetation. However, if nitrogen and phosphorus nutrients cannot keep up with increasing atmospheric CO2, the magnitude of this terrestrial ecosystem service may reduce in future. This paper implements nitrogen constraints on photosynthesis in a model to understand the mechanisms involved.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 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.
Lena R. Boysen, Victor Brovkin, Julia Pongratz, David M. Lawrence, Peter Lawrence, Nicolas Vuichard, Philippe Peylin, Spencer Liddicoat, Tomohiro Hajima, Yanwu Zhang, Matthias Rocher, Christine Delire, Roland Séférian, Vivek K. Arora, Lars Nieradzik, Peter Anthoni, Wim Thiery, Marysa M. Laguë, Deborah Lawrence, and Min-Hui Lo
Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, https://doi.org/10.5194/bg-17-5615-2020, 2020
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We find a biogeophysically induced global cooling with strong carbon losses in a 20 million square kilometre idealized deforestation experiment performed by nine CMIP6 Earth system models. It takes many decades for the temperature signal to emerge, with non-local effects playing an important role. Despite a consistent experimental setup, models diverge substantially in their climate responses. This study offers unprecedented insights for understanding land use change effects in CMIP6 models.
Cited articles
Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, 2019.
Aleixo, I., Norris, D., Hemerik, L., Barbosa, A., Prata, E., Costa, F., and Poorter, L.: Amazonian rainforest tree mortality driven by climate and functional traits, Nat. Clim. Change, 9, 384–388, https://doi.org/10.1038/s41558-019-0458-0, 2019.
Alo, C. A. and Wang, G.: Potential future changes of the terrestrial ecosystem based on climate projections by eight general circulation models, J. Geophys. Res.-Biogeo., 113, G01004, https://doi.org/10.1029/2007JG000528, 2008.
Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Glob. Change Biol., 11, 39–59, https://doi.org/10.1111/j.1365-2486.2004.00890.x, 2005a.
Arora, V. K. and Boer, G. J.: Fire as an interactive component of dynamic vegetation models, J. Geophys. Res.-Biogeo., 110, G02008, https://doi.org/10.1029/2005jg000042, 2005b.
Arora, V. K. and Boer, G. J.: Simulating Competition and Coexistence between Plant Functional Types in a Dynamic Vegetation Model, Earth Interact., 10, 1–30, https://doi.org/10.1175/EI170.1, 2006.
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, 1–10, https://doi.org/10.1038/s41467-018-03838-0, 2018.
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020.
Arora, V. K., Seiler, C., Wang, L., and Kou-Giesbrecht, S.: Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations, Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, 2023.
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.
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical biomass map using multiple reference datasets, Glob. Change Biol., 22, 1406–1420, https://doi.org/10.1111/gcb.13139, 2016.
Baltzer, J. L., Day, N. J., Walker, X. J., Greene, D., Mack, M. C., Alexander, H. D., Arseneault, D., Barnes, J., Bergeron, Y., Boucher, Y., Bourgeau-Chavez, L., Brown, C. D., Carrière, S., Howard, B. K., Gauthier, S., Parisien, M.-A., Reid, K. A., Rogers, B. M., Roland, C., Sirois, L., Stehn, S., Thompson, D. K., Turetsky, M. R., Veraverbeke, S., Whitman, E., Yang, J., and Johnstone, J. F.: Increasing fire and the decline of fire adapted black spruce in the boreal forest, P. Natl. Acad. Sci. USA, 118, e2024872118, https://doi.org/10.1073/pnas.2024872118, 2021.
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.
Box, E. O.: Plant functional types and climate at the global scale, J. Veg. Sci., 7, 309–320, 1996.
Braghiere, R. K., Fisher, J. B., Miner, K. R., Miller, C. E., Worden, J. R., Schimel, D. S., and Frankenberg, C.: Tipping point in North American Arctic-Boreal carbon sink persists in new generation Earth system models despite reduced uncertainty, Environ. Res. Lett., 18, 025008, https://doi.org/10.1088/1748-9326/acb226, 2023.
Brandt, M., Mbow, C., Diouf, A. A., Verger, A., Samimi, C., and Fensholt, R.: Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel, Glob. Change Biol., 21, 1610–1620, https://doi.org/10.1111/gcb.12807, 2015.
Chapin, F. S., Sturm, M., Serreze, M. C., McFadden, J. P., Key, J. R., Lloyd, A. H., McGuire, A. D., Rupp, T. S., Lynch, A. H., Schimel, J. P., Beringer, J., Chapman, W. L., Epstein, H. E., Euskirchen, E. S., Hinzman, L. D., Jia, G., Ping, C.-L., Tape, K. D., Thompson, C. D. C., Walker, D. A., and Welker, J. M.: Role of Land-Surface Changes in Arctic Summer Warming, Science, 310, 657–660, https://doi.org/10.1126/science.1117368, 2005.
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B., and Thomas, C. D.: Rapid Range Shifts of Species Associated with High Levels of Climate Warming, Science, 333, 1024–1026, https://doi.org/10.1126/science.1206432, 2011.
Chini, L., Hurtt, G., Sahajpal, R., Frolking, S., Goldewijk, K. K., Sitch, S., Ganzenmüller, R., Ma, L., Ott, L., Pongratz, J., and Poulter, B.: Land-use harmonization datasets for annual global carbon budgets, Earth Sy. Sci. Data, 13, 4175–4189, https://doi.org/10.5194/essd-13-4175-2021, 2021.
Chuvieco, E., Lizundia-Loiola, J., Pettinari, M. L., Ramo, R., Padilla, M., Tansey, K., Mouillot, F., Laurent, P., Storm, T., Heil, A., and Plummer, S.: Generation and analysis of a new global burned area product based on MODIS 250m reflectance bands and thermal anomalies, Earth Sy. Sci. Data, 10, 2015–2031, https://doi.org/10.5194/essd-10-2015-2018, 2018.
Claverie, M., Matthews, J. L., Vermote, E. F., and Justice, C. O.: A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation, Remote Sens., 8, 263, https://doi.org/10.3390/rs8030263, 2016.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C. D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation, J. Adv. Model. Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018MS001354, 2018.
Corlett, R. T. and Westcott, D. A.: Will plant movements keep up with climate change?, Trend. Ecol. Evol., 28, 482–488, https://doi.org/10.1016/j.tree.2013.04.003, 2013.
Dai, A. and Trenberth, K. E.: Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations, J. Hydrometeorol., 3, 660–687, https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2, 2002.
Davidson, E. A., de Araújo, A. C., Artaxo, P., Balch, J. K., Brown, I. F., C. Bustamante, M. M., Coe, M. T., DeFries, R. S., Keller, M., Longo, M., Munger, J. W., Schroeder, W., Soares-Filho, B. S., Souza, C. M., and Wofsy, S. C.: The Amazon basin in transition, Nature, 481, 321–328, https://doi.org/10.1038/nature10717, 2012.
Defourny, P., Lamarche, C., Brockmann, C., Boettcher, M., Bontemps, S., De Maet, T., Duveiller, G. L. Harper, K., Hartley A., Kirches, G., Moreau, I., Peylin, P., Ottlé, C., Radoux J., Van Bogaert, E., Ramoino, F., Albergel, C., and Arino, O.: Observed annual global land-use change from 1992 to 2020 three times more dynamic than reported by inventory-based statistics, in preparation, http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Last access: 2017), 2023.
Dusenge, M. E., Duarte, A. G., and Way, D. A.: Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration, New Phytol., 221, 32–49, 2019.
Elmendorf, S. C., Henry, G. H. R., Hollister, R. D., Björk, R. G., Boulanger-Lapointe, N., Cooper, E. J., Cornelissen, J. H. C., Day, T. A., Dorrepaal, E., Elumeeva, T. G., Gill, M., Gould, W. A., Harte, J., Hik, D. S., Hofgaard, A., Johnson, D. R., Johnstone, J. F., Jónsdóttir, I. S., Jorgenson, J. C., Klanderud, K., Klein, J. A., Koh, S., Kudo, G., Lara, M., Lévesque, E., Magnússon, B., May, J. L., Mercado-Díaz, J. A., Michelsen, A., Molau, U., Myers-Smith, I. H., Oberbauer, S. F., Onipchenko, V. G., Rixen, C., Martin Schmidt, N., Shaver, G. R., Spasojevic, M. J., Þórhallsdóttir, Þ. E., Tolvanen, A., Troxler, T., Tweedie, C. E., Villareal, S., Wahren, C.-H., Walker, X., Webber, P. J., Welker, J. M., and Wipf, S.: Plot-scale evidence of tundra vegetation change and links to recent summer warming, Nat. Clim. Change, 2, 453–457, https://doi.org/10.1038/nclimate1465, 2012.
Fisher, R. A. and Koven, C. D.: Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Garrigues, S., Lacaze, R., Baret, F., Morisette, J. T., Weiss, M., Nickeson, J. E., Fernandes, R., Plummer, S., Shabanov, N. V., Myneni, R. B., Knyazikhin, Y., and Yang, W.: Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res.-Biogeo., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 1–18, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hartley, A. J., MacBean, N., Georgievski, G., and Bontemps, S.: Uncertainty in plant functional type distributions and its impact on land surface models, Remote Sens. Environ., 203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037, 2017.
Hengl, T., De Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Herrmann, S. M., Anyamba, A., and Tucker, C. J.: Recent trends in vegetation dynamics in the African Sahel and their relationship to climate, Glob. Environ. Change, 15, 394–404, https://doi.org/10.1016/j.gloenvcha.2005.08.004, 2005.
Hisano, M., Ryo, M., Chen, X., and Chen, H. Y. H.: Rapid functional shifts across high latitude forests over the last 65 years, Glob. Change Biol., 27, 3846–3858, https://doi.org/10.1111/gcb.15710, 2021.
Hobeichi, S., Abramowitz, G., and Evans, J.: Conserving Land–Atmosphere Synthesis Suite (CLASS), J. Clim., 33, 1821–1844, https://doi.org/10.1175/JCLI-D-19-0036.1, 2020.
Houghton, R. A., House, J. I., Pongratz, J., van der Werf, G. R., DeFries, R. S., Hansen, M. C., Le Quéré, C., and Ramankutty, N.: Carbon emissions from land use and land-cover change, Biogeosciences, 9, 5125–5142, https://doi.org/10.5194/bg-9-5125-2012, 2012.
Huang, K., Xia, J., Wang, Y., Ahlström, A., Chen, J., Cook, R. B., Cui, E., Fang, Y., Fisher, J. B., Huntzinger, D. N., Li, Z., Michalak, A. M., Qiao, Y., Schaefer, K., Schwalm, C., Wang, J., Wei, Y., Xu, X., Yan, L., Bian, C., and Luo, Y.: Enhanced peak growth of global vegetation and its key mechanisms, Nat. Ecol. Evol., 2, 1897–1905, https://doi.org/10.1038/s41559-018-0714-0, 2018.
Huang, Y., Ciais, P., Santoro, M., Makowski, D., Chave, J., Schepaschenko, D., Abramoff, R. Z., Goll, D. S., Yang, H., Chen, Y., Wei, W., and Piao, S.: A global map of root biomass across the world's forests, Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, 2021.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Jacobson, A. R., Schuldt, K. N., Miller, J. B., Oda, T., Tans, P., Andrews, A., Mund, J., Ott, L., Collatz, G. J., and Aalto, T.: Carbontracker ct2019, NOAA Earth system research laboratory, global monitoring division, https://doi.org/10.25925/39M3-6069, 2020.
Johnstone, J. F. and Chapin, F. S.: Effects of Soil Burn Severity on Post-Fire Tree Recruitment in Boreal Forest, Ecosystems, 9, 14–31, https://doi.org/10.1007/s10021-004-0042-x, 2006.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.
Kato, S., Loeb, N. G., Rose, F. G., Doelling, D. R., Rutan, D. A., Caldwell, T. E., Yu, L., and Weller, R. A.: Surface Irradiances Consistent with CERES-Derived Top-of-Atmosphere Shortwave and Longwave Irradiances, J. Clim., 26, 2719–2740, https://doi.org/10.1175/JCLI-D-12-00436.1, 2013.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Kou-Giesbrecht, S.: The impacts of modelling prescribed vs. dynamic land cover in a high CO2 future scenario – greening of the Arctic and Amazonian dieback, Zenodo [data set], https://doi.org/10.5281/zenodo.11520769, 2024.
Kou-Giesbrecht, S. and Arora, V. K.: Representing the Dynamic Response of Vegetation to Nitrogen Limitation via Biological Nitrogen Fixation in the CLASSIC Land Model, Global Biogeochem. Cy., 36, e2022GB007341, https://doi.org/10.1029/2022GB007341, 2022.
Koven, C. D., Arora, V. K., Cadule, P., Fisher, R. A., Jones, C. D., Lawrence, D. M., Lewis, J., Lindsay, K., Mathesius, S., Meinshausen, M., Mills, M., Nicholls, Z., Sanderson, B. M., Séférian, R., Swart, N. C., Wieder, W. R., and Zickfeld, K.: Multi-century dynamics of the climate and carbon cycle under both high and net negative emissions scenarios, Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, 2022.
Lan, X., Tans, P., and Thoning, K. W.: Trends in globally-averaged CO2 determined from NOAA Global Monitoring Laboratory measurements, Version 2024-07, https://doi.org/10.15138/9N0H-ZH07, 2024.
Lange, S. and Büchner, M.: ISIMIP3b bias-adjusted atmospheric climate input data (v1.1), ISIMIP Repository [data set], https://doi.org/10.48364/ISIMIP.842396.1, 2021.
Lenoir, J. and Svenning, J.: Climate-related range shifts – a global multidimensional synthesis and new research directions, Ecography, 38, 15–28, 2015.
Li, X. and Xiao, J.: Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2, Remote Sens., 11, 2563, https://doi.org/10.3390/rs11212563, 2019.
Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436, https://doi.org/10.5194/hess-15-425-2011, 2011.
Luo, X., Zhou, H., Satriawan, T. W., Tian, J., Zhao, R., Keenan, T. F., Griffith, D. M., Sitch, S., Smith, N. G., and Still, C. J.: Mapping the global distribution of C4 vegetation using observations and optimality theory, Nat. Commun., 15, 1219, https://doi.org/10.1038/s41467-024-45606-3, 2024.
Macander, M. J., Nelson, P. R., Nawrocki, T. W., Frost, G. V., Orndahl, K. M., Palm, E. C., Wells, A. F., and Goetz, S. J.: Time-series maps reveal widespread change in plant functional type cover across Arctic and boreal Alaska and Yukon, Environ. Res. Lett., 17, 054042, https://doi.org/10.1088/1748-9326/ac6965, 2022.
Mack, M. C., Walker, X. J., Johnstone, J. F., Alexander, H. D., Melvin, A. M., Jean, M., and Miller, S. N.: Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees, Science, 372, 280–283, https://doi.org/10.1126/science.abf3903, 2021.
Malhi, Y., Roberts, J. T., Betts, R. A., Killeen, T. J., Li, W., and Nobre, C. A.: Climate Change, Deforestation, and the Fate of the Amazon, Science, 319, 169–172, https://doi.org/10.1126/science.1146961, 2008.
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., Shrestha, R. K., and Arora, V. K.: The influence of soils on heterotrophic respiration exerts a strong control on net ecosystem productivity in seasonally dry Amazonian forests, Biogeosciences, 12, 1151–1168, https://doi.org/10.5194/bg-12-1151-2015, 2015.
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.
Melton, J. R., Arora, V., Wisernig-Cojoc, E., Seiler, C., Fortier, M., Chan, E., and Teckentrup, L.: The Canadian Land Surface Scheme including Biogeochemical Cycles (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.3522407, 2019.
Monerie, P.-A., Wainwright, C. M., Sidibe, M., and Akinsanola, A. A.: Model uncertainties in climate change impacts on Sahel precipitation in ensembles of CMIP5 and CMIP6 simulations, Clim. Dynam., 55, 1385–1401, https://doi.org/10.1007/s00382-020-05332-0, 2020.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Myers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H. E., Assmann, J. J., John, C., Andreu-Hayles, L., Angers-Blondin, S., Beck, P. S. A., Berner, L. T., Bhatt, U. S., Bjorkman, A. D., Blok, D., Bryn, A., Christiansen, C. T., Cornelissen, J. H. C., Cunliffe, A. M., Elmendorf, S. C., Forbes, B. C., Goetz, S. J., Hollister, R. D., de Jong, R., Loranty, M. M., Macias-Fauria, M., Maseyk, K., Normand, S., Olofsson, J., Parker, T. C., Parmentier, F.-J. W., Post, E., Schaepman-Strub, G., Stordal, F., Sullivan, P. F., Thomas, H. J. D., Tømmervik, H., Treharne, R., Tweedie, C. E., Walker, D. A., Wilmking, M., and Wipf, S.: Complexity revealed in the greening of the Arctic, Nat. Clim. Change, 10, 106–117, https://doi.org/10.1038/s41558-019-0688-1, 2020.
Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R., and Running, S. W.: Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data, Remote Sens. Environ., 83, 214–231, https://doi.org/10.1016/S0034-4257(02)00074-3, 2002.
National Research Council: Earth observations from space: The first 50 years of scientific achievements, National Academies Press, https://doi.org/10.17226/11991, 2008.
Olsson, L., Eklundh, L., and Ardö, J.: A recent greening of the Sahel – trends, patterns and potential causes, J. Arid Environ., 63, 556–566, https://doi.org/10.1016/j.jaridenv.2005.03.008, 2005.
Parry, I. M., Ritchie, P. D. L., and Cox, P. M.: Evidence of localised Amazon rainforest dieback in CMIP6 models, Earth Syst. Dynam., 13, 1667–1675, https://doi.org/10.5194/esd-13-1667-2022, 2022.
Pastorello, G., Trotta, C., Canfora, E., et al.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Pearson, R. G., Phillips, S. J., Loranty, M. M., Beck, P. S. A., Damoulas, T., Knight, S. J., and Goetz, S. J.: Shifts in Arctic vegetation and associated feedbacks under climate change, Nat. Clim. Change, 3, 673–677, https://doi.org/10.1038/nclimate1858, 2013.
Pecl, G. T., Araújo, M. B., Bell, J. D., Blanchard, J., Bonebrake, T. C., Chen, I.-C., Clark, T. D., Colwell, R. K., Danielsen, F., and Evengård, B.: Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being, Science, 355, eaai9214, https://doi.org/10.1126/science.aai9214, 2017.
Phillips, O. L., Aragão, L. E. O. C., Lewis, S. L., Fisher, J. B., Lloyd, J., López-González, G., Malhi, Y., Monteagudo, A., Peacock, J., Quesada, C. A., van der Heijden, G., Almeida, S., Amaral, I., Arroyo, L., Aymard, G., Baker, T. R., Bánki, O., Blanc, L., Bonal, D., Brando, P., Chave, J., de Oliveira, Á. C. A., Cardozo, N. D., Czimczik, C. I., Feldpausch, T. R., Freitas, M. A., Gloor, E., Higuchi, N., Jiménez, E., Lloyd, G., Meir, P., Mendoza, C., Morel, A., Neill, D. A., Nepstad, D., Patiño, S., Peñuela, M. C., Prieto, A., Ramírez, F., Schwarz, M., Silva, J., Silveira, M., Thomas, A. S., Steege, H. ter, Stropp, J., Vásquez, R., Zelazowski, P., Dávila, E. A., Andelman, S., Andrade, A., Chao, K.-J., Erwin, T., Di Fiore, A., C., E. H., Keeling, H., Killeen, T. J., Laurance, W. F., Cruz, A. P., Pitman, N. C. A., Vargas, P. N., Ramírez-Angulo, H., Rudas, A., Salamão, R., Silva, N., Terborgh, J., and Torres-Lezama, A.: Drought Sensitivity of the Amazon Rainforest, Science, 323, 1344–1347, https://doi.org/10.1126/science.1164033, 2009.
Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W., Chen, A., Ciais, P., Tømmervik, H., Nemani, R. R., and Myneni, R. B.: Characteristics, drivers and feedbacks of global greening, Nat. Rev. Earth Environ., 1, 14–27, https://doi.org/10.1038/s43017-019-0001-x, 2020.
Port, U., Brovkin, V., and Claussen, M.: The influence of vegetation dynamics on anthropogenic climate change, Earth Syst. Dynam., 3, 233–243, https://doi.org/10.5194/esd-3-233-2012, 2012.
Reich, P. B., Wright, I. J., Cavender-Bares, J., Craine, J. M., Oleksyn, J., Westoby, M., and Walters, M. B.: The Evolution of Plant Functional Variation: Traits, Spectra, and Strategies, Int. J. Plant Sci., 164, S143–S164, https://doi.org/10.1086/374368, 2003.
Renwick, K. M. and Rocca, M. E.: Temporal context affects the observed rate of climate-driven range shifts in tree species, Global Ecol. Biogeogr., 24, 44–51, https://doi.org/10.1111/geb.12240, 2015.
Riahi, K., Vuuren, D. P. van, Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Silva, L. A. D., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Glob. Environ. Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Rödenbeck, C., Zaehle, S., Keeling, R., and Heimann, M.: How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data, Biogeosciences, 15, 2481–2498, https://doi.org/10.5194/bg-15-2481-2018, 2018.
Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J. E. S., Hall, R. J., Pathe, C., Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner, M., and Wegmüller, U.: Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR, Remote Sens. Environ., 168, 316–334, https://doi.org/10.1016/j.rse.2015.07.005, 2015.
Schaaf, C. and Wang, Z.: MCD43A1 MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global – 500m V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A1.006, 2015.
Schepaschenko, D., Chave, J., Phillips, O. L., et al.: The Forest Observation System, building a global reference dataset for remote sensing of forest biomass, Sci. Data, 6, 198, https://doi.org/10.1038/s41597-019-0196-1, 2019.
Scholze, M., Knorr, W., Arnell, N. W., and Prentice, I. C.: A climate-change risk analysis for world ecosystems, P. Natl. Acad. Sci. USA, 103, 13116–13120, https://doi.org/10.1073/pnas.0601816103, 2006.
Seiler, C.: Automated Model Benchmarking (AMBER), Zenodo [code], https://doi.org/10.5281/zenodo.7799562, 2024.
Seiler, C., Melton, J., Arora, V., 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-2020-294, 2021.
Seiler, C., Melton, J. R., Arora, V. K., Sitch, S., Friedlingstein, P., Anthoni, P., Goll, D., Jain, A. K., Joetzjer, E., Lienert, S., Lombardozzi, D., Luyssaert, S., Nabel, J. E. M. S., Tian, H., Vuichard, N., Walker, A. P., Yuan, W., and Zaehle, S.: Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?, J. Adv. Model. Earth Sy., 14, e2021MS002946, https://doi.org/10.1029/2021MS002946, 2022.
Seiler, C., Kou-Giesbrecht, S., Arora, V. K., and Melton, J. R.: The impact of climate forcing biases and the nitrogen cycle on land carbon balance projections, J. Adv. Model. Earth Sy., 16, e2023MS003749, https://doi.org/10.1029/2023MS003749, 2024.
Sitch, S., Huntingford, C., Gedney, N., Levy, P., Lomas, M., Piao, S., Betts, R., Ciais, P., Cox, P., and Friedlingstein, P.: Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs), Glob. Change Biol., 14, 2015–2039, 2008.
Stackhouse, P. W. J., Gupta, S. K., Cox, S. J., Zhang, T., Mikovitz, C., and Hinkelman, L. M.: The NASA/GEWEX surface radiation budget release 3.0: 24.5-year dataset, Gewex news, 21, 10–12, 2011.
Still, C. J., Berry, J. A., Collatz, G. J., and DeFries, R. S.: Global distribution of C3 and C4 vegetation: Carbon cycle implications, Global Biogeochem. Cy., 17, 6–1, https://doi.org/10.1029/2001GB001807, 2003.
Swann, A. L., Fung, I. Y., Levis, S., Bonan, G. B., and Doney, S. C.: Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect, P. Natl. Acad. Sci. USA, 107, 1295–1300, https://doi.org/10.1073/pnas.0913846107, 2010.
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., and Von, K.: 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.
Tape, K., Sturm, M., and Racine, C.: The evidence for shrub expansion in Northern Alaska and the Pan-Arctic, Glob. Change Biol., 12, 686–702, 2006.
Thomas, C. D.: Climate, climate change and range boundaries, Divers. Distrib., 16, 488–495, 2010.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Verger, A., Baret, F., and Weiss, M.: Near Real-Time Vegetation Monitoring at Global Scale, IEEE J. Sel. Top. Appl., 7, 3473–3481, https://doi.org/10.1109/JSTARS.2014.2328632, 2014.
Verseghy, D. L.: CLASS – A Canadian land surface scheme for GCMS, I. Soil model, Int. J. Climatol., 11, 111–133, 1991.
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, https://doi.org/10.1002/joc.3370130402, 1993.
Vialet-Chabrand, S. R. M., Matthews, J. S. A., McAusland, L., Blatt, M. R., Griffiths, H., and Lawson, T.: Temporal Dynamics of Stomatal Behavior: Modeling and Implications for Photosynthesis and Water Use, Plant Physiol., 174, 603–613, https://doi.org/10.1104/pp.17.00125, 2017.
Walther, G.-R.: Community and ecosystem responses to recent climate change, Philos. T. R. Soc. B, 365, 2019–2024, https://doi.org/10.1098/rstb.2010.0021, 2010.
Wang, A., Price, D. T., and Arora, V.: Estimating changes in global vegetation cover (1850–2100) for use in climate models, Global Biogeochem. Cy., 20, GB3028, https://doi.org/10.1029/2005GB002514, 2006.
Wang, J., Taylor, A. R., and D'Orangeville, L.: Warming-induced tree growth may help offset increasing disturbance across the Canadian boreal forest, P. Natl. Acad. Sci. USA, 120, e2212780120, https://doi.org/10.1073/pnas.2212780120, 2023.
Wang, J. A., Sulla-Menashe, D., Woodcock, C. E., Sonnentag, O., Keeling, R. F., and Friedl, M. A.: Extensive land cover change across Arctic–Boreal Northwestern North America from disturbance and climate forcing, Global Change Biology, 26, 807–822, https://doi.org/10.1111/gcb.14804, 2020.
Wang, J. A., Baccini, A., Farina, M., Randerson, J. T., and Friedl, M. A.: Disturbance suppresses the aboveground carbon sink in North American boreal forests, Nat. Clim. Change, 11, 435–441, https://doi.org/10.1038/s41558-021-01027-4, 2021.
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.
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The inter-sectoral impact model intercomparison project (ISI–MIP): project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, 2014.
Wieder, W.: Regridded Harmonized World Soil Database v1.2, https://doi.org/10.3334/ORNLDAAC/1247, 2014.
Wu, D., Zhao, X., Liang, S., Zhou, T., Huang, K., Tang, B., and Zhao, W.: Time-lag effects of global vegetation responses to climate change, Glob. Change Biol., 21, 3520–3531, 2015.
Wu, Z., Dijkstra, P., Koch, G. W., Peñuelas, J., and Hungate, B. A.: Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation, Glob. Change Biol., 17, 927–942, 2011.
Xue, B.-L., Guo, Q., Hu, T., Wang, G., Wang, Y., Tao, S., Su, Y., Liu, J., and Zhao, X.: Evaluation of modeled global vegetation carbon dynamics: Analysis based on global carbon flux and above-ground biomass data, Ecol. Model., 355, 84–96, https://doi.org/10.1016/j.ecolmodel.2017.04.012, 2017.
Zemp, D. C., Schleussner, C.-F., Barbosa, H. M. J., Hirota, M., Montade, V., Sampaio, G., Staal, A., Wang-Erlandsson, L., and Rammig, A.: Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks, Nat. Commun., 8, 14681, https://doi.org/10.1038/ncomms14681, 2017.
Zhang, Y. and Liang, S.: Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map, Remote Sens., 12, 2559, https://doi.org/10.3390/rs12162559, 2020.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, Sci. Data, 4, 1–13, https://doi.org/10.1038/sdata.2017.165, 2017.
Short summary
Terrestrial biosphere models can either prescribe the geographical distribution of biomes or simulate them dynamically, capturing climate-change-driven biome shifts. We isolate and examine the differences between these different land cover implementations. We find that the simulated terrestrial carbon sink at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover due to important range shifts in the Arctic and Amazon.
Terrestrial biosphere models can either prescribe the geographical distribution of biomes or...
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