Articles | Volume 20, issue 12
https://doi.org/10.5194/bg-20-2265-2023
© Author(s) 2023. 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-20-2265-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model
Climate Processes Section, Climate Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada
Vivek K. Arora
Canadian Centre for Climate Modelling and Analysis, Climate Research
Division, Environment and Climate Change Canada, Victoria, BC, Canada
Paul Bartlett
Climate Processes Section, Climate Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada
Climate Processes Section, Climate Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada
Salvatore R. Curasi
Department of Geography and Environmental Studies, Carleton
University, Ottawa, ON, Canada
Climate Processes Section, Climate Research Division, Environment and
Climate Change Canada, Victoria, BC, Canada
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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
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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.
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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
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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
Short summary
Short summary
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
Short summary
Short summary
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
Arora, V.: Land surface modelling in general circulation models: a
hydrological perspective, PhD thesis, Department of Civil and Environmental
Engineering, University of Melbourne, 1997.
Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the
terrestrial ecosystem component of climate models, Glob. Chang. Biol., 11,
39–59, 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,
https://doi.org/10.1111/j.1365-2486.2010.02202.x, 2010.
Arora, V. K., Boer, G. J., Christian, J. R., Curry, C. L., Denman, K. L.,
Zahariev, K., Flato, G. M., Scinocca, J. F., Merryfield, W. J., and Lee, W.
G.: The Effect of Terrestrial Photosynthesis Down Regulation on the
Twentieth-Century Carbon Budget Simulated with the CCCma Earth System Model,
J. Climate, 22, 6066–6088, https://doi.org/10.1175/2009JCLI3037.1, 2009.
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.
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, 2005.
Bartlett, P. A. and Verseghy, D. L.: Modified treatment of intercepted snow
improves the simulated forest albedo in the Canadian land surface scheme,
Hydrol. Process., 29, 3208–3226, https://doi.org/10.1002/hyp.10431, 2015.
Bartlett, P. A., MacKay, M. D., and Verseghy, D. L.: Modified snow
algorithms in the Canadian land surface scheme: Model runs and sensitivity
analysis at three boreal forest stands, Atmos.-Ocean, 44, 207–222, 2006.
Beaudoin, A., Bernier, P. Y., Guindon, L., Villemaire, P., Guo, X. J.,
Stinson, G., Bergeron, T., Magnussen, S., and Hall, R. J.: Mapping attributes
of Canada's forests at moderate resolution through kNN and MODIS imagery,
Can. J. For. Res., 44, 521–532, https://doi.org/10.1139/cjfr-2013-0401, 2014.
Betts, R. A.: Biogeophysical impacts of land use on present-day climate:
near-surface temperature change and radiative forcing, Atmos. Sci. Lett., 2,
39–51, https://doi.org/10.1006/asle.2001.0037, 2001.
Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., et al.: Plant
functional trait change across a warming tundra biome, Nature, 562, 57–62,
https://doi.org/10.1038/s41586-018-0563-7, 2018.
Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as patches
of plant functional types: An integrating concept for climate and ecosystem
models, Global Biogeochem. Cy., 16, 1021,
https://doi.org/10.1029/2000GB001360, 2002.
Bonan, G. B., Levis, S., Sitch, S., Vertenstein, M., and Oleson, K. W.: A
dynamic global vegetation model for use with climate models: Concepts and
description of simulated vegetation dynamics, Global Change Biol., 9,
1543–1566, 2003.
Bontemps, S., Herold, M., Kooistra, L., van Groenestijn, A., Hartley, A., Arino, O., Moreau, I., and Defourny, P.: Revisiting land cover observation to address the needs of the climate modeling community, Biogeosciences, 9, 2145–2157, https://doi.org/10.5194/bg-9-2145-2012, 2012.
Di Gregorio, A.: Land Cover Classification System – Classification concepts
and user manual for Software version 2, FAO Environment and Natural
Resources Service Series, No. 8, Rome, 208 pp., 2005.
ESA: Land Cover CCI Product User Guide Version 2. Tech. Rep.,
http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf, last access: August 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.
Global Change Biol., 24, 35–54, https://doi.org/10.1111/gcb.13910, 2018.
Fox, D. L., Pau, S., Taylor, L., Strömberg, C. A. E., Osborne, C. P.,
Bradshaw, C., Conn, S., Beerling, D. J., and Still, C. J.: Climatic Controls on
C4 Grassland Distributions During the Neogene: A Model-Data Comparison,
Front. Ecol. Evol., 6, 147,
https://doi.org/10.3389/fevo.2018.00147, 2018.
Fritz, S., See, L., McCallum, I., Schill, C., Obersteiner, M., van der
Velde, M., Boettcher, H., Havlík, P., and Achard, F.: Highlighting
continued uncertainty in global land cover maps for the user community,
Environ. Res. Lett., 6, 44005,
https://doi.org/10.1088/1748-9326/6/4/044005, 2011.
Georgievski, G. and Hagemann, S.: Characterizing uncertainties in the ESA-CCI
land cover map of the epoch 2010 and their impacts on MPI-ESM climate
simulations, Theor. Appl. Climatol., 137, 1587–1603,
https://doi.org/10.1007/s00704-018-2675-2, 2019.
Gillis, M. D., Omule, A. Y., and Brierley, T.: Monitoring Canada's forests:
The National Forest Inventory, The Forestry Chronicle, 81, 214–221,
https://doi.org/10.5558/tfc81214-2, 2005.
Harris, I. C.: CRU JRA v2.1: A forcings dataset of gridded land surface
blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data;
Jan. 1901–Dec. 2019, Centre for Environmental Data Analysis, University of
East Anglia Climatic Research Unit,
https://catalogue.ceda.ac.uk/uuid/10d2c73e5a7d46f4ada08b0a26302ef7 (last access: July 2022), 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.
Harder, P., Warren D. H., and Pomeroy, J. W.: Modeling the Snowpack Energy
Balance during Melt under Exposed Crop Stubble, J. Hydrometeorol., 19,
1191–1214, https://doi.org/10.1175/JHM-D-18-0039.1, 2018.
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Sohlberg, R., Dimiceli,
C., Carroll, M. L.: Towards an operational MODIS continuous field of percent
tree cover algorithm: Examples using AVHRR and MODIS data, Remote Sens.
Environ., 83, 303–319, 2002.
Hansen, M. C., Stehman, S. V., and Potapov, P. V.: Quantification of global
gross forest cover loss, P. Natl. Acad. Sci., 107, 8650–8655,
https://doi.org/10.1073/pnas.0912668107, 2010.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.: Highresolution global maps of 21st-century forest cover change,
Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
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, 1–40, https://doi.org/10.1371/journal.pone.0169748,
2017.
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, https://doi.org/10.1080/07038992.2018.1437719, 2018.
Herold, M., Mayaux, P., Woodcock, C. E., BaESACCIni, A., and Schmullius, C.:
Some Challenges in Global Land Cover Mapping: An Assessment of Agreement and
Accuracy in Existing 1 km Datasets, Remote Sens. Environ., 112, 2538–2556,
2008.
Hopkinson, C., Chasmer, L., Lim, K., Treitz, P., and Creed, I.: Towards a
universal lidar canopy height indicator, Can. J. Remote Sens.,
32, 139–152, https://doi.org/10.5589/m06-006, 2006.
Krinner, G., Viovy, N., de Noblet-Ducoudre, N., Ogee, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Latifovic, R., Pouliot, D., and Olthof, I.: Circa 2010 Land Cover of Canada:
Local Optimization Methodology and Product Development, Remote Sens., 9,
1098, https://doi.org/10.3390/rs9111098, 2017.
Latifovic, R. and Olthof, I.: Accuracy assessment using sub-pixel
fractional error matrices of global land cover products derived from
satellite data, Remote Sens. Environ., 90, 153–165, 2004.
Macander, M. J., Frost, G. V., Nelson, P. R., and Swingley, C. S.: ABoVE:
Tundra Plant Functional Type Continuous-Cover, North Slope, Alaska,
2010–2015, ORNL DAAC, Oak Ridge, Tennessee, USA,
https://doi.org/10.3334/ORNLDAAC/1830, 2020.
Mayaux, P., Eva, H., Gallego, J., Strahler, A. H., Herold, M., Agrawal, S.,
Naumov, S., De Miranda, E. E., Di Bella, C. M., Ordoyne, C., Kopin, Y., and Roy, P. S.:
Validation of the Global Land Cover 2000 Map, IEEE T. Geosci. Remote
Sens., 44, 1728–1739, 2006.
Melton, J.: Model code for the Canadian Land Surface Scheme Including
Biogeochemical Cycles (CLASSIC), [code],
https://cccma.gitlab.io/classic_pages/, last access: July
2022.
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., 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.
Moody, E. G., King, M. D., Schaaf, C. B., Hall, D. K., and Platnick, S.:
Northern Hemisphere five-year average (2000–2004) spectral albedos of
surfaces in the presence of snow: statistics computed from Terra MODIS land
products, Remote Sens. Environ., 111, 337–345, https://doi.org/10.1016/j.rse.2007.03.026, 2007.
Ottlé, C., Lescure, J., Maignan, F., Poulter, B., Wang, T., and Delbart, N.: Use of various remote sensing land cover products for plant functional type mapping over Siberia, Earth Syst. Sci. Data, 5, 331–348, https://doi.org/10.5194/essd-5-331-2013, 2013.
Pielke, R. A., Avissar, R., Raupach, M., Dolman, A. J., Zeng, X., and Denning,
S.: Interactions between the atmosphere and terrestrial ecosystem: influence
on weather and climate, Global Change Biol., 4, 461–475, 1998.
Poulter, B., Ciais, P., Hodson, E., Lischke, H., Maignan, F., Plummer, S., and Zimmermann, N. E.: Plant functional type mapping for earth system models, Geosci. Model Dev., 4, 993–1010, https://doi.org/10.5194/gmd-4-993-2011, 2011.
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R.,
Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S.,
Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters,
M., and Peylin, P.: Plant functional type classification for earth system
models: results from the European Space Agency's Land Cover Climate Change
Initiative, Geosci. Model Dev., 8, 2315–2328,
https://doi.org/10.5194/gmd-8-2315-2015, 2015.
Pomeroy, J. W., Gray, D. M., and Landine, P. G.: The Prairie Blowing Snow
Model: Characteristics, validation, operation, J. Hydrol., 144, 165–192,
https://doi.org/10.1016/0022-1694(93)90171-5, 1993.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J. P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d’Entremont, R. P., Hu, B., Liang, S., Privette, J. F., and Roy, D.: First operational BRDF, albedo nadir reflectance
products from MODIS, Remote Sens. Environ., 83, 135–148,
https://doi.org/10.1016/S0034-4257(02)00091-3, 2002.
Scheiter, S., Langan, L., and Higgins, S. I.: Next-generation dynamic global
vegetation models: learning from community ecology, New Phytol., 198,
957–969, https://doi.org/10.1111/nph.12210, 2013.
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 Syst., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017.
Smith, T. M., Shugart, H. H., and Woodward, F. I.: Plant functional types:
their relevance to ecosystem properties and global change, Cambridge
University Press, New York, 369 pp., 1997.
Smith, B., Prentice, I. C., and Sykes, M. T. : Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: Comparing two
contrasting approaches within European climate space, Global Ecol. Biogeo., 10, 621–637, 2001.
Sterling, S. M., Ducharne, A., and Polcher, J.: The impact of global land-cover
change on the terrestrial water cycle, Nat. Clim. Chang., 3, 385–390,
https://doi.org/10.1038/nclimate1690, 2013.
Still, C. J. and Berry, J. A.: Global distribution of C3 and C4 vegetation:
Carbon cycle implications, Global Biogeochem. Cycles, 17, 1006,
https://doi.org/10.1029/2001GB001807, 2003.
Sun, W., Liang, S., Xu, G., Fang, H., and Dickinson, R. E.: Mapping plant
functional types from MODIS data using multisource evidential reasoning,
Remote Sens. Environ., 112, 1010–1024,
https://doi.org/10.1016/j.rse.2007.07.022, 2008.
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.
Tsendbazar, N. E., de Bruin, S., Mora, B., Schouten, L., and Herold, M.:
Comparative assessment of thematic accuracy of GLC maps for specific
applications using existing reference data, Int. J. Appl. Earth Obs.
Geoinf., 44, 124–135, https://doi.org/10.1016/j.jag.2015.08.009, 2016.
Wang, A., Price, D. T., and Arora, V. K.: 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, L., MacKay, M., Brown, R., Bartlett, P., Harvey, R., and Langlois, A.:
Application of satellite data for evaluating the cold climate performance of
the Canadian Regional Climate model over Québec, Canada, J.
Hydrometeorol., 15, 614–630, https://doi.org/10.1175/JHM-D-13-086.1, 2014.
Wang, L., Cole, J. N. S., Bartlett, P., Verseghy, D., Derksen, C., Brown, R.,
and von Salzen, K.: Investigating the spread in surface albedo for snow-covered
forests in CMIP5 models, J. Geophys. Res.-Atmos., 121, 1104–1119,
https://doi.org/10.1002/2015JD023824, 2016.
Wang, L., Bartlett, P., Chan, E., and Xiao, M.: Mapping of Plant Functional
Type from Satellite-Derived Land Cover Datasets for Climate Models, In
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing
Symposium, Valencia, Spain, 22–27, https://doi.org/10.1109/IGARSS.2018.8518046, 2018.
Wang, L., Bartlett, P., Pouliot, D., Chan, E., Lamarche, C., Wulder, M. A.,
Defourny, P., and Brady, M.: Comparison and Assessment of Regional and Global
Land Cover Datasets for Use in CLASS over Canada, Remote Sens., 11,
2286, https://doi.org/10.3390/rs11192286, 2019.
Wulder, M. A. and Nelson, T.: EOSD land cover classification legend report:
Version 2, Natural Resources Canada, Canadian Forest Service, Pacific
Forestry Centre, Victoria, British Columbia, Canada, 13 January 2003,
83 pp., http://www.pfc.forestry.ca/eosd/cover/EOSD_Legend_Report-v2.pdf (last access: June 2021), 2003.
Wulder, M. A., Bater, C. W., Coops, N. C., Hilker, T., and White, J. C.: The
role of LiDAR in sustainable forest management, The Forestry Chronicle, 84
807–826, 2008.
Wulder, M. A., White, J. C., Bater, C. W., Coops, N. C., Hopkinson, C., and Chen,
G.: Lidar plots – a new large-area data collection option: context,
concepts, and case study, Can. J. Remote. Sens., 38, 600–618,
https://doi.org/10.5589/m12-049, 2012.
Verseghy, D. L.: CLASS – A Canadian Land Surface Scheme for GCMs, I. Soil
model, Int. J. Climatol., 11, 111–133,
https://doi.org/10.1002/joc.3370110202, 1991.
Verseghy, D., McFarlane, N., 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.
Zakharova, L., Meyer, K. M., and Seifan, M.: Trait-based modelling in ecology: A
review of two decades of research, Ecol. Model., 407, 108703,
https://doi.org/10.1016/j.ecolmodel.2019.05.008, 2019.
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.
Plant functional types (PFTs) are groups of plant species used to represent vegetation...
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