Articles | Volume 13, issue 4
https://doi.org/10.5194/esd-13-1667-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-13-1667-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
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24 Nov 2022
Research article | Highlight paper |
| 24 Nov 2022
| 24 Nov 2022
Evidence of localised Amazon rainforest dieback in CMIP6 models
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QE, UK
Paul D. L. Ritchie
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QE, UK
Peter M. Cox
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QE, UK
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Rebecca M. Varney, Pierre Friedlingstein, Sarah E. Chadburn, Eleanor J. Burke, and Peter M. Cox
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Rebecca M. Varney, Sarah E. Chadburn, Eleanor J. Burke, Simon Jones, Andy J. Wiltshire, and Peter M. Cox
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Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
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Morgan Sparey, Peter Cox, and Mark S. Williamson
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Accurate climate models are vital for mitigating climate change; however, projections often disagree. Using Köppen–Geiger bioclimate classifications we show that CMIP6 climate models agree well on the fraction of global land surface that will change classification per degree of global warming. We find that 13 % of land will change climate per degree of warming from 1 to 3 K; thus, stabilising warming at 1.5 rather than 2 K would save over 7.5 million square kilometres from bioclimatic change.
Rebecca M. Varney, Sarah E. Chadburn, Eleanor J. Burke, and Peter M. Cox
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Soil carbon is the Earth’s largest terrestrial carbon store, and the response to climate change represents one of the key uncertainties in obtaining accurate global carbon budgets required to successfully militate against climate change. The ability of climate models to simulate present-day soil carbon is therefore vital. This study assesses soil carbon simulation in the latest ensemble of models which allows key areas for future model development to be identified.
Garry D. Hayman, Edward Comyn-Platt, Chris Huntingford, Anna B. Harper, Tom Powell, Peter M. Cox, William Collins, Christopher Webber, Jason Lowe, Stephen Sitch, Joanna I. House, Jonathan C. Doelman, Detlef P. van Vuuren, Sarah E. Chadburn, Eleanor Burke, and Nicola Gedney
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We model greenhouse gas emission scenarios consistent with limiting global warming to either 1.5 or 2 °C above pre-industrial levels. We quantify the effectiveness of methane emission control and land-based mitigation options regionally. Our results highlight the importance of reducing methane emissions for realistic emission pathways that meet the global warming targets. For land-based mitigation, growing bioenergy crops on existing agricultural land is preferable to replacing forests.
Andrew J. Wiltshire, Eleanor J. Burke, Sarah E. Chadburn, Chris D. Jones, Peter M. Cox, Taraka Davies-Barnard, Pierre Friedlingstein, Anna B. Harper, Spencer Liddicoat, Stephen Sitch, and Sönke Zaehle
Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, https://doi.org/10.5194/gmd-14-2161-2021, 2021
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Limited nitrogen availbility can restrict the growth of plants and their ability to assimilate carbon. It is important to include the impact of this process on the global land carbon cycle. This paper presents a model of the coupled land carbon and nitrogen cycle, which is included within the UK Earth System model to improve projections of climate change and impacts on ecosystems.
Bettina K. Gier, Michael Buchwitz, Maximilian Reuter, Peter M. Cox, Pierre Friedlingstein, and Veronika Eyring
Biogeosciences, 17, 6115–6144, https://doi.org/10.5194/bg-17-6115-2020, https://doi.org/10.5194/bg-17-6115-2020, 2020
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Models from Coupled Model Intercomparison Project (CMIP) phases 5 and 6 are compared to a satellite data product of column-averaged CO2 mole fractions (XCO2). The previously believed discrepancy of the negative trend in seasonal cycle amplitude in the satellite product, which is not seen in in situ data nor in the models, is attributed to a sampling characteristic. Furthermore, CMIP6 models are shown to have made progress in reproducing the observed XCO2 time series compared to CMIP5.
Cited articles
Aragão, L., Anderson, L., Fonseca, M., Rosan, T. M., Vedovato, L., Wagner, F., Silva, C., Silva-Junior, C., Arai, E., Aguiar, A. P., Barlow, J., Berenguer, E., Deeter, M., Domingues, L., Gatti, L., Gloor, M., Malhi, Y., Marengo, J., Miller, J., and Saatchi, S.: 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions, Nat. Commun., 9, 536, https://doi.org/10.1038/s41467-017-02771-y, 2018. a
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., Rosie, A. F., 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. a
Bethke, I., Wang, Y., Counillon, F., Kimmritz, M., Fransner, F., Samuelsen, A., Langehaug, H. R., Chiu, P.-G., Bentsen, M., Guo, C., Kirkevåg, A., Oliviè, D. J. L., Seland, Ø., Fan, Y., Lawrence, P., Eldevik, T., and Keenlyside, N.: NCC NorCPM1 model output prepared for CMIP6 CMIP 1pctCO2,
Earth System Grid Federation [code],
https://doi.org/10.22033/ESGF/CMIP6.10861, 2019. a
Betts, R. A., Cox, P. M., Collins, M., Harris, P. P., Huntingford, C., and
Jones, C. D.: The role of ecosystem-atmosphere interactions in simulated
Amazonian precipitation decrease and forest dieback under global climate
warming, Theor. Appl. Climatol., 78, 157–175, https://doi.org/10.1007/s00704-004-0050-y, 2004. a
Booth, B., Jones, C., Collins, M., Totterdell, I., Cox, P., Sitch, S.,
Huntingford, C., Betts, R., Harris, G., and Lloyd, J.: High sensitivity of
future global warming to land carbon cycle processes, Environ. Res. Lett., 7, 024002, https://doi.org/10.1088/1748-9326/7/2/024002, 2012. a
Boulton, C., Good, P., and Lenton, T.: Early warning signals of simulated
Amazon rainforest dieback, Theor. Ecol., 6, 273–384, https://doi.org/10.13140/RG.2.2.20894.33601, 2013. a
Boulton, C. A., Lenton, T. M., and Boers, N.: Pronounced loss of Amazon
rainforest resilience since the early 2000s, Nat. Clim. Change, 12, 271–278, https://doi.org/10.1038/s41558-022-01287-8, 2022. a, b
Chai, Y., Martins, G., Nobre, C., von Randow, C., Chen, T., and Dolman, H.:
Constraining Amazonian land surface temperature sensitivity to precipitation
and the probability of forest dieback, npj Clim. Atmos. Sci., 4, 6, https://doi.org/10.1038/s41612-021-00162-1, 2021. a
Cox, P., Betts, R., Jones, C., Spall, S., and Totterdell, I.: Acceleration of
global warming due to carbon-cycle feedbacks in a coupled climate model,
Nature, 408, 184–187, https://doi.org/10.1038/35041539, 2000. a
Cox, P., Betts, R., Collins, M., Harris, P., Huntingford, C., and Jones, C.:
Amazonian forest dieback under climate-carbon cycle projections for the 21st century, Theor. Appl. Climatol., 78, 137–156, https://doi.org/10.1007/s00704-004-0049-4, 2004. a, b, c
Cox, P., Pearson, D., Booth, B., Friedlingstein, P., Huntingford, C., Jones,
C., and Luke, C.: Sensitivity of tropical carbon to climate change
constrained by carbon dioxide variability, Nature, 494, 341–344, https://doi.org/10.1038/nature11882, 2013. a
Dakos, V., Scheffer, M., van Nes, E. H., Brovkin, V., Petoukhov, V., and Held, H.: Slowing down as an early warning signal for abrupt climate change,
P. Natl. Acad. Sci. USA, 105, 14308–14312, https://doi.org/10.1073/pnas.0802430105, 2008. a, b
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a
Drijfhout, S., Bathiany, S., Beaulieu, C., Brovkin, V., Claussen, M.,
Huntingford, C., Scheffer, M., Sgubin, G., and Swingedouw, D.: Catalogue of
abrupt shifts in Intergovernmental Panel on Climate Change climate models, P. Natla. Acad. Sci. USA, 112, E5777–E5786, https://doi.org/10.1073/pnas.1511451112, 2015. a
EC-Earth Consortium: EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP 1pctCO2, Earth System Grid Federation [code], https://doi.org/10.22033/ESGF/CMIP6.4507, 2019. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Hirota, M., Holmgren, M., Van Nes, E. H., and Scheffer, M.: Global resilience
of tropical forest and savanna to critical transitions, Science, 334, 232–235, https://doi.org/10.1126/science.1210657, 2011. a
Hirota, M., Flores, B., Betts, R., Borma, L., Esquivel Muelbert, A., Jakovac,
C., Lapola, D., Montoya, E., Oliveira, R., and Sakschewski, B.: Amazon
Assessment Report 2021, in: chap. 24 Resilience of the Amazon forest to global changes: Assessing the risk of tipping points,United Nations
Sustainable Development Solutions Network, 1–32, https://doi.org/10.55161/QPYS9758, 2021. a
Huntingford, C., Zelazowski, P., Galbraith, D., Mercado, L., Sitch, S., Fisher, R., Lomas, M., Walker, A., Jones, C., Booth, B., Malhi, Y., Hemming, D., Kay, G., Good, P., Lewis, S., Phillips, O., Atkin, O., Lloyd, J., Gloor, M., and Cox, P.: Simulated resilience of tropical rainforests to CO2-induced climate change, Nat. Geosci., 6, 268–273, https://doi.org/10.1038/ngeo1741, 2013. a
Jørgensen, S. V., Hauschild, M. Z., and Nielsen, P. H.: Assessment of urgent impacts of greenhouse gas emissions – the climate tipping potential (CTP), Int. J. Life Cy. Assess., 19, 919–930, https://doi.org/10.1007/s11367-013-0693-y, 2014. a
Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A., Gauthier, P. P. G., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Silvers, L., Wyman, B., Zeng, Y., Adcroft, A., Dunne, J. P., Dussin, R., Guo, H., He, J., Held, I. M., Horowitz, L. W., Lin, P., Milly, P. C. D., Shevliakova, E., Stock, C., Winton, M., Wittenberg, A. T., Xie, Y., and Zhao, M.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP 1pctCO2, Earth System Grid Federation [code], https://doi.org/10.22033/ESGF/CMIP6.8473, 2018. a
Lee, W.-L. and Liang, H.-C.: AS-RCEC TaiESM1.0 model output prepared for CMIP6 CMIP 1pctCO2, Earth System Grid Federation [code], https://doi.org/10.22033/ESGF/CMIP6.9702, 2020. a
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, https://doi.org/10.1073/pnas.0705414105, 2008. a
Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson, K.,
Steffen, W., and Schellnhuber, H. J.: Environmental Tipping Points, Annu.
Rev. Environ. Resour., 38, 1–29, https://doi.org/10.1038/d41586-019-03595-0, 2013. a, b
Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson, K.,
Steffen, W., and Schellnhuber, H. J.: Climate tipping points – too risky to
bet against, Nature, 575, 592–595, https://doi.org/10.1038/d41586-019-03595-0, 2019. a
Luo, X. and Keenan, T.: Tropical extreme droughts drive long-term increase in
atmospheric CO2 growth rate variability, Nat. Commun., 13, 1193, https://doi.org/10.1038/s41467-022-28824-5, 2022. a, b
Malhi, Y., Aragão, L. E. O. C., Galbraith, D., Huntingford, C., Fisher, R., Zelazowski, P., Sitch, S., McSweeney, C., and Meir, P.: Exploring the
likelihood and mechanism of a climate-change-induced dieback of the Amazon
rainforest, P. Natl. Acad. Sci. USA, 106, 20610–20615, https://doi.org/10.1073/pnas.0804619106, 2009. a, b, c, d
Meehl, G. A., Moss, R., Taylor, K. E., Eyring, V., Stouffer, R. J., Bony, S.,
and Stevens, B.: Climate Model Intercomparisons: Preparing for the Next Phase, EOS Trans. Am. Geophys. Union, 95, 77–78, https://doi.org/10.1002/2014eo090001, 2014. a
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS)
from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11,
737–750, https://doi.org/10.5194/esd-11-737-2020, 2020. a
Park, S. and Shin, J.: SNU SAM0-UNICON model output prepared for CMIP6 CMIP 1pctCO2, Earth System Grid Federation [code], https://doi.org/10.22033/ESGF/CMIP6.7782, 2019. a
Parry, I., Ritchie, P, and Cox, P.: Evidence-of-localised-Amazon-rainforest-dieback-in-CMIP6-models-code, Zenodo [code], https://doi.org/10.5281/zenodo.7038389, 2022. a
Parsons, L. A.: Implications of CMIP6 Projected Drying Trends for 21st Century Amazonian Drought Risk, Earth's Future, 8, e2020EF001608, https://doi.org/10.1029/2020ef001608, 2020. a
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., Átila Cristina Alves de Oliveira, 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., ter Steege, H., Stropp, J., Vásquez, R., Zelazowski, P., Dávila, E. A., Andelman, S., Andrade, A., Chao, K.-J., Erwin, T., Fiore, A. D., Eurídice Honorio, C., 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. a
Rammig, A., Jupp, T., Thonicke, K., Tietjen, B., Heinke, J., Ostberg, S.,
Lucht, W., Cramer, W., and Cox, P.: Estimating the risk of Amazonian forest
dieback, New Phytol., 187, 694–706, https://doi.org/10.1111/j.1469-8137.2010.03318.x, 2010. a, b
Ritchie, P. D., Clarke, J. J., Cox, P. M., and Huntingford, C.: Overshooting
tipping point thresholds in a changing climate, Nature, 592, 517–523,
https://doi.org/10.1038/s41586-021-03263-2, 2021. a
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R.,
Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., and Sugihara, G.: Early-warning signals for critical transitions, Nature, 461, 53–59,
https://doi.org/10.1038/nature08227, 2009. a, b, c
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP 1pctCO2, Earth System Grid Federation [code], https://doi.org/10.22033/ESGF/CMIP6.5792, 2019. a
Tokarska, K. B., Stolpe, M. B., Sippel, S., Fischer, E. M., Smith, C. J.,
Lehner, F., and Knutti, R.: Past warming trend constrains future warming in
CMIP6 models, Sci. Adv., 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549, 2020. a
UNFCCC: Adoption of the Paris Agreement, FCCC/CP/2015/L.9/Rev.1,
http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf (last access: 17 March 2022), 2015. a
Vogel, M., Hauser, M., and Seneviratne, S.: Projected changes in hot, dry and
wet extreme events' clusters in CMIP6 multi-model ensemble, Environ. Res. Lett., 15, 094021, https://doi.org/10.1088/1748-9326/ab90a7, 2020. a, b
Wang, J. A., Baccini, A., Farina, M., Randerson, J. T., and Friedl, M. A.:
Supplementary information: 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. a
Wang, X., Piao, S., Ciais, P., Friedlingstein, P., Myneni, R. B., Cox, P.,
Heimann, M., Miller, J., Peng, S., Wang, T., Yang, H., and Chen, A.: A two-fold increase of carbon cycle sensitivity to tropical temperature variations variability, Nature, 506, 212–215, https://doi.org/10.1038/nature12915, 2014. a
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent
constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system
models, J. Geophys. Res.-Biogeo., 119, 794–807, https://doi.org/10.1002/2013JG002591, 2014. a
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Projected land
photosynthesis constrained by changes in the seasonal cycle of atmospheric
CO2, Nature, 538, 499–501, https://doi.org/10.1038/nature19772, 2016. a
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP 1pctCO2, Earth System Grid Federation [code], https://doi.org/10.22033/ESGF/CMIP6.6435, 2019. a
Zemp, D., Schleussner, C.-F., Barbosa, H., and Rammig, A.: Deforestation
effects on Amazon forest resilience, Geophys. Res. Lett., 44, 6182–6190, https://doi.org/10.1002/2017GL072955, 2017. a
Chief editor
The health of the amazon ecosystem is a key indicator of the health of our planet. Hence, the editor feels that this paper will attract the attention of a broad audience and the media.
The health of the amazon ecosystem is a key indicator of the health of our planet. Hence, the...
Short summary
Despite little evidence of regional Amazon rainforest dieback, many localised abrupt dieback events are observed in the latest state-of-the-art global climate models under anthropogenic climate change. The detected dieback events would still cause severe consequences for local communities and ecosystems. This study suggests that 7 ± 5 % of the northern South America region would experience abrupt downward shifts in vegetation carbon for every degree of global warming past 1.5 °C.
Despite little evidence of regional Amazon rainforest dieback, many localised abrupt dieback...
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