Articles | Volume 15, issue 4
https://doi.org/10.5194/esd-15-913-2024
© 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/esd-15-913-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
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24 Jul 2024
Research article | Highlight paper |
| 24 Jul 2024
| 24 Jul 2024
Observation-inferred resilience loss of the Amazon rainforest possibly due to internal climate variability
Raphael Grodofzig
CORRESPONDING AUTHOR
Department of Meteorology, Stockholm University, Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Barcelona Supercomputing Center, Barcelona, Spain
Martin Renoult
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Department of Geological Sciences, Stockholm University, Stockholm, Sweden
Thorsten Mauritsen
Department of Meteorology, Stockholm University, Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
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Martin Renoult, Navjit Sagoo, Johannes Hörner, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2981, https://doi.org/10.5194/egusphere-2024-2981, 2024
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Geological evidence indicate persistent tropical sea-ice cover in the deep past, often called Snowball Earth. Using a climate model, we show here that clouds substantially cool down the tropics and facilitate the advance of sea-ice into lower latitudes. We identify a critical threshold temperature of 0 °C from where cooling down the Earth is accelerated. This value can be used as a constraint on Earth's sensitivity to CO2, as recent cold paleoclimates never entered Snowball Earth.
Antoine Hermant, Linnea Huusko, and Thorsten Mauritsen
Atmos. Chem. Phys., 24, 10707–10715, https://doi.org/10.5194/acp-24-10707-2024, https://doi.org/10.5194/acp-24-10707-2024, 2024
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Aerosol particles, from natural and human sources, have a cooling effect on the climate, partially offsetting global warming. They do this through direct (sunlight reflection) and indirect (cloud property alteration) mechanisms. Using a global climate model, we found that, despite declining emissions, the direct effect of human aerosols has increased while the indirect effect has decreased, which is attributed to the shift in emissions from North America and Europe to Southeast Asia.
James D. Annan, Julia C. Hargreaves, Thorsten Mauritsen, Erin McClymont, and Sze Ling Ho
Clim. Past, 20, 1989–1999, https://doi.org/10.5194/cp-20-1989-2024, https://doi.org/10.5194/cp-20-1989-2024, 2024
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We have created a new global surface temperature reconstruction of the climate of the mid-Pliocene Warm Period, representing the period roughly 3.2 million years before the present day. We estimate that the globally averaged mean temperature was around 3.9 °C warmer than it was in pre-industrial times, but there is significant uncertainty in this value.
Alejandro Uribe, Frida Bender, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1559, https://doi.org/10.5194/egusphere-2024-1559, 2024
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Our study explores climate feedbacks, vital for understanding global warming. It links them to shifts in Earth's energy balance at the atmosphere's top due to natural temperature variations. It takes roughly 50-years to establish this connection. Combined satellite observations and reanalysis suggest that Earth cools more than expected under carbon dioxide influence. However, continuous satellite data until at least the mid-2030s are crucial for refining our understanding of climate feedbacks.
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
EGUsphere, https://doi.org/10.5194/egusphere-2024-356, https://doi.org/10.5194/egusphere-2024-356, 2024
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The imbalance between the energy the Earth absorbs from the Sun and the energy the Earth emits back to space gives rise to climate change, but measuring the small imbalance is challenging. We simulate satellites in various orbits to investigate how well they sample the imbalance, and find that the best option is to combine at least two satellites that see complementary parts of the Earth and cover the daily and annual cycles. This information is useful when planning future satellite missions.
Andrea Mosso, Thomas Hocking, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-618, https://doi.org/10.5194/egusphere-2024-618, 2024
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Clouds play a crucial role in the energy balance of the earth, as they can either warm up or cool down the area they cover depending on their height and depth. It is expected that they will alter their behaviour under climate change, which will affect the warming generated by greenhouse gases. This paper proposes a new method to estimate their overall effect by simulating a climate where clouds are transparent. Results show that, with the model used, clouds have a stabilising effect on climate.
Clare Marie Flynn, Linnea Huusko, Angshuman Modak, and Thorsten Mauritsen
Atmos. Chem. Phys., 23, 15121–15133, https://doi.org/10.5194/acp-23-15121-2023, https://doi.org/10.5194/acp-23-15121-2023, 2023
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The latest-generation climate models show surprisingly cold mid-20th century global-mean temperatures, often despite exhibiting more realistic late 20th/early 21st century temperatures. A too-strong aerosol forcing in many models was thought to the be primary cause of these too-cold mid-century temperatures, but this was found to only be a partial explanation. This also partly undermines the hope to construct a strong relationship between the mid-century temperatures and aerosol forcing.
Sushant Das, Frida Bender, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1605, https://doi.org/10.5194/egusphere-2023-1605, 2023
Preprint archived
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Quantifying global and Indian precipitation responses to anthropogenic aerosol and CO2 forcings using multiple models is needed for reducing climate uncertainty. The response to global warming from CO2 increases precipitation both globally and over India, whereas the cooling response to sulfate aerosol leads to a reduction in precipitation in both cases. An opposite response to black carbon is noted i.e., a global decrease but an increase of precipitation over India implying changes in dynamics.
Angshuman Modak and Thorsten Mauritsen
Atmos. Chem. Phys., 23, 7535–7549, https://doi.org/10.5194/acp-23-7535-2023, https://doi.org/10.5194/acp-23-7535-2023, 2023
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We provide an improved estimate of equilibrium climate sensitivity (ECS) constrained based on the instrumental temperature record including the corrections for the pattern effect. The improved estimate factors in the uncertainty caused by the underlying sea-surface temperature datasets used in the estimates of pattern effect. This together with the inter-model spread lifts the corresponding IPCC AR6 estimate to 3.2 K [1.8 to 11.0], which is lower and better constrained than in past studies.
Martin Renoult, Navjit Sagoo, Jiang Zhu, and Thorsten Mauritsen
Clim. Past, 19, 323–356, https://doi.org/10.5194/cp-19-323-2023, https://doi.org/10.5194/cp-19-323-2023, 2023
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The relationship between the Last Glacial Maximum and the sensitivity of climate models to a doubling of CO2 can be used to estimate the true sensitivity of the Earth. However, this relationship has varied in successive model generations. In this study, we assess multiple processes at the Last Glacial Maximum which weaken this relationship. For example, how models respond to the presence of ice sheets is a large contributor of uncertainty.
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, Joachim Biercamp, Hendryk Bockelmann, Renate Brokopf, Nils Brüggemann, Lucas Casaroli, Fatemeh Chegini, George Datseris, Monika Esch, Geet George, Marco Giorgetta, Oliver Gutjahr, Helmuth Haak, Moritz Hanke, Tatiana Ilyina, Thomas Jahns, Johann Jungclaus, Marcel Kern, Daniel Klocke, Lukas Kluft, Tobias Kölling, Luis Kornblueh, Sergey Kosukhin, Clarissa Kroll, Junhong Lee, Thorsten Mauritsen, Carolin Mehlmann, Theresa Mieslinger, Ann Kristin Naumann, Laura Paccini, Angel Peinado, Divya Sri Praturi, Dian Putrasahan, Sebastian Rast, Thomas Riddick, Niklas Roeber, Hauke Schmidt, Uwe Schulzweida, Florian Schütte, Hans Segura, Radomyra Shevchenko, Vikram Singh, Mia Specht, Claudia Christine Stephan, Jin-Song von Storch, Raphaela Vogel, Christian Wengel, Marius Winkler, Florian Ziemen, Jochem Marotzke, and Bjorn Stevens
Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
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Models of the Earth system used to understand climate and predict its change typically employ a grid spacing of about 100 km. Yet, many atmospheric and oceanic processes occur on much smaller scales. In this study, we present a new model configuration designed for the simulation of the components of the Earth system and their interactions at kilometer and smaller scales, allowing an explicit representation of the main drivers of the flow of energy and matter by solving the underlying equations.
James D. Annan, Julia C. Hargreaves, and Thorsten Mauritsen
Clim. Past, 18, 1883–1896, https://doi.org/10.5194/cp-18-1883-2022, https://doi.org/10.5194/cp-18-1883-2022, 2022
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We have created a new global surface temperature reconstruction of the climate of the Last Glacial Maximum, representing the period 19–23 000 years before the present day. We find that the globally averaged mean temperature was roughly 4.5 °C colder than it was in pre-industrial times, albeit there is significant uncertainty on this value.
Jule Radtke, Thorsten Mauritsen, and Cathy Hohenegger
Atmos. Chem. Phys., 21, 3275–3288, https://doi.org/10.5194/acp-21-3275-2021, https://doi.org/10.5194/acp-21-3275-2021, 2021
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Shallow trade wind clouds are a key source of uncertainty to projections of the Earth's changing climate. We perform high-resolution simulations of trade cumulus and investigate how the representation and climate feedback of these clouds depend on the specific grid spacing. We find that the cloud feedback is positive when simulated with kilometre but near zero when simulated with hectometre grid spacing. These findings suggest that storm-resolving models may exaggerate the trade cloud feedback.
Martin Renoult, James Douglas Annan, Julia Catherine Hargreaves, Navjit Sagoo, Clare Flynn, Marie-Luise Kapsch, Qiang Li, Gerrit Lohmann, Uwe Mikolajewicz, Rumi Ohgaito, Xiaoxu Shi, Qiong Zhang, and Thorsten Mauritsen
Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, https://doi.org/10.5194/cp-16-1715-2020, 2020
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Interest in past climates as sources of information for the climate system has grown in recent years. In particular, studies of the warm mid-Pliocene and cold Last Glacial Maximum showed relationships between the tropical surface temperature of the Earth and its sensitivity to an abrupt doubling of atmospheric CO2. In this study, we develop a new and promising statistical method and obtain similar results as previously observed, wherein the sensitivity does not seem to exceed extreme values.
James D. Annan, Julia C. Hargreaves, Thorsten Mauritsen, and Bjorn Stevens
Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, https://doi.org/10.5194/esd-11-709-2020, 2020
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In this paper we explore the potential of variability for constraining the equilibrium response of the climate system to external forcing. We show that the constraint is inherently skewed, with a long tail to high sensitivity, and that while the variability may contain some useful information, it is unlikely to generate a tight constraint.
Clare Marie Flynn and Thorsten Mauritsen
Atmos. Chem. Phys., 20, 7829–7842, https://doi.org/10.5194/acp-20-7829-2020, https://doi.org/10.5194/acp-20-7829-2020, 2020
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The range of climate sensitivity of models participating in CMIP6 has increased relative to models participating in CMIP5 due to decreases in the total feedback parameter. This is caused by increases in the shortwave all-sky and clear-sky feedbacks, particularly over the Southern Ocean. These shifts between CMIP6 and CMIP5 did not arise by chance. Both CMIP5 and CMIP6 models are found to exhibit aerosol forcing that is too strong, causing too much cooling relative to observations.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
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The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Andrew E. Dessler, Thorsten Mauritsen, and Bjorn Stevens
Atmos. Chem. Phys., 18, 5147–5155, https://doi.org/10.5194/acp-18-5147-2018, https://doi.org/10.5194/acp-18-5147-2018, 2018
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One of the most important parameters in climate science is the equilibrium climate sensitivity (ECS). Estimates of this quantity based on 20th-century observations suggest low values of ECS (below 2 °C). We show that these calculations may be significantly in error. Together with other recent work on this problem, it seems probable that the ECS is larger than suggested by the 20th-century observations.
Bjorn Stevens, Stephanie Fiedler, Stefan Kinne, Karsten Peters, Sebastian Rast, Jobst Müsse, Steven J. Smith, and Thorsten Mauritsen
Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, https://doi.org/10.5194/gmd-10-433-2017, 2017
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A simple analytic description of aerosol optical properties and their main effects on clouds is developed and described. The analytic description is easy to use and easy to modify and should aid experimentation to help understand how aerosol radiative and cloud interactions effect climate and circulation. The climatology is recommended for adoption by models participating in the sixth phase of the Coupled Model Intercomparison Project.
M. Tjernström, C. Leck, C. E. Birch, J. W. Bottenheim, B. J. Brooks, I. M. Brooks, L. Bäcklin, R. Y.-W. Chang, G. de Leeuw, L. Di Liberto, S. de la Rosa, E. Granath, M. Graus, A. Hansel, J. Heintzenberg, A. Held, A. Hind, P. Johnston, J. Knulst, M. Martin, P. A. Matrai, T. Mauritsen, M. Müller, S. J. Norris, M. V. Orellana, D. A. Orsini, J. Paatero, P. O. G. Persson, Q. Gao, C. Rauschenberg, Z. Ristovski, J. Sedlar, M. D. Shupe, B. Sierau, A. Sirevaag, S. Sjogren, O. Stetzer, E. Swietlicki, M. Szczodrak, P. Vaattovaara, N. Wahlberg, M. Westberg, and C. R. Wheeler
Atmos. Chem. Phys., 14, 2823–2869, https://doi.org/10.5194/acp-14-2823-2014, https://doi.org/10.5194/acp-14-2823-2014, 2014
M. D. Shupe, P. O. G. Persson, I. M. Brooks, M. Tjernström, J. Sedlar, T. Mauritsen, S. Sjogren, and C. Leck
Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, https://doi.org/10.5194/acp-13-9379-2013, 2013
Cited articles
Berger, V. W. and Zhou, Y.: Kolmogorov–Smirnov Test: Overview, in: Wiley StatsRef: Statistics Reference Online, John Wiley & Sons, Ltd., ISBN 978-1-118-44511-2, https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat06558 (last access: 6 August 2023), 2014. a
Boers, N. and Rypdal, M.: Critical slowing down suggests that the western Greenland Ice Sheet is close to a tipping point, P. Natl. Acad. Sci. USA, 118, e2024192118, https://doi.org/10.1073/pnas.2024192118, 2021. a
Boers, N., Marwan, N., Barbosa, H. M. J., and Kurths, J.: A deforestation-induced tipping point for the South American monsoon system, Sci. Rep., 7, 41489, https://doi.org/10.1038/srep41489, 2017. a, b
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Boulton, C. A., Good, P., and Lenton, T. M.: Early warning signals of simulated Amazon rainforest dieback, Theor. Ecol., 6, 373–384, https://doi.org/10.1007/s12080-013-0191-7, 2013. a
Boulton, C. A., Booth, B. B. B., and Good, P.: Exploring uncertainty of Amazon dieback in a perturbed parameter Earth system ensemble, Glob. Change Biol., 23, 5032–5044, https://doi.org/10.1111/gcb.13733, 2017. a
Brando, P. M., Balch, J. K., Nepstad, D. C., Morton, D. C., Putz, F. E., Coe, M. T., Silvério, D., Macedo, M. N., Davidson, E. A., Nóbrega, C. C., Alencar, A., and Soares-Filho, B. S.: Abrupt increases in Amazonian tree mortality due to drought–fire interactions, P. Natl. Acad. Sci. USA, 111, 6347–6352, https://doi.org/10.1073/pnas.1305499111, 2014. 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, 1–7, https://doi.org/10.1038/s41612-021-00162-1, 2021. a
Cleveland, W. S., McRae, J. E., and Terpenning, I.: STL: A seasonal-trend decomposition, J. Off. Stat., 6, 3–73, 1990. a
CMIP6_database: The Climate Model Intercomparison Project version 6 data ensemble, Earth System Grid Federation portal [data set], https://esgf-node.llnl.gov/projects/cmip6/ (last access: 15 May 2023), 2021.
Cox, P. M., Harris, P. P., Huntingford, C., Betts, R. A., Collins, M., Jones, C. D., Jupp, T. E., Marengo, J. A., and Nobre, C. A.: Increasing risk of Amazonian drought due to decreasing aerosol pollution, Nature, 453, 212–215, https://doi.org/10.1038/nature06960, 2008. 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
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Doughty, C. E., Keany, J. M., Wiebe, B. C., Rey-Sanchez, C., Carter, K. R., Middleby, K. B., Cheesman, A. W., Goulden, M. L., da Rocha, H. R., Miller, S. D., Malhi, Y., Fauset, S., Gloor, E., Slot, M., Oliveras Menor, I., Crous, K. Y., Goldsmith, G. R., and Fisher, J. B.: Tropical forests are approaching critical temperature thresholds, Nature, 621, 105–111, https://doi.org/10.1038/s41586-023-06391-z, 2023. a
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
Feldpausch, T. R., Phillips, O. L., Brienen, R. J. W., Gloor, E., Lloyd, J., Lopez-Gonzalez, G., Monteagudo-Mendoza, A., Malhi, Y., Alarcón, A., Álvarez Dávila, E., Alvarez-Loayza, P., Andrade, A., Aragao, L. E. O. C., Arroyo, L., Aymard C., G. A., Baker, T. R., Baraloto, C., Barroso, J., Bonal, D., Castro, W., Chama, V., Chave, J., Domingues, T. F., Fauset, S., Groot, N., Honorio Coronado, E., Laurance, S., Laurance, W. F., Lewis, S. L., Licona, J. C., Marimon, B. S., Marimon-Junior, B. H., Mendoza Bautista, C., Neill, D. A., Oliveira, E. A., Oliveira dos Santos, C., Pallqui Camacho, N. C., Pardo-Molina, G., Prieto, A., Quesada, C. A., Ramírez, F., Ramírez-Angulo, H., Réjou-Méchain, M., Rudas, A., Saiz, G., Salomão, R. P., Silva-Espejo, J. E., Silveira, M., ter Steege, H., Stropp, J., Terborgh, J., Thomas-Caesar, R., van der Heijden, G. M. F., Vásquez Martinez, R., Vilanova, E., and Vos, V. A.: Amazon forest response to repeated droughts, Global Biogeochem. Cy., 30, 964–982, https://doi.org/10.1002/2015GB005133, 2016. a
Forkel, M., Drüke, M., Thurner, M., Dorigo, W., Schaphoff, S., Thonicke, K., von Bloh, W., and Carvalhais, N.: Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations, Sci. Rep., 9, 18757, https://doi.org/10.1038/s41598-019-55187-7, 2019. a
Gatti, L. V., Basso, L. S., Miller, J. B., Gloor, M., Gatti Domingues, L., Cassol, H. L. G., Tejada, G., Aragão, L. E. O. C., Nobre, C., Peters, W., Marani, L., Arai, E., Sanches, A. H., Corrêa, S. M., Anderson, L., Von Randow, C., Correia, C. S. C., Crispim, S. P., and Neves, R. A. L.: Amazonia as a carbon source linked to deforestation and climate change, Nature, 595, 388–393, https://doi.org/10.1038/s41586-021-03629-6, 2021. a
Green, J. K., Konings, A. G., Alemohammad, S. H., Berry, J., Entekhabi, D., Kolassa, J., Lee, J.-E., and Gentine, P.: Regionally strong feedbacks between the atmosphere and terrestrial biosphere, Nat. Geosci., 10, 410–414, https://doi.org/10.1038/ngeo2957, 2017. a
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a
Hasselmann, K.: Multi-pattern fingerprint method for detection and attribution of climate change, Clim. Dynam., 13, 601–611, https://doi.org/10.1007/s003820050185, 1997. a
Hohenegger, C. and Stevens, B.: The role of the permanent wilting point in controlling the spatial distribution of precipitation, P. Natl. Acad. Sci. USA, 115, 5692–5697, https://doi.org/10.1073/pnas.1718842115, 2018. a
Huntingford, C., Zelazowski, P., Galbraith, D., Mercado, L. M., Sitch, S., Fisher, R., Lomas, M., Walker, A. P., Jones, C. D., Booth, B. B. B., Malhi, Y., Hemming, D., Kay, G., Good, P., Lewis, S. L., Phillips, O. L., Atkin, O. K., Lloyd, J., Gloor, E., Zaragoza-Castells, J., Meir, P., Betts, R., Harris, P. P., Nobre, C., Marengo, J., and Cox, P. M.: Simulated resilience of tropical rainforests to CO2-induced climate change, Nat. Geosci., 6, 268–273, https://doi.org/10.1038/ngeo1741, 2013. a
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. a, b, c, d
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability, Bull. Am. Meteorol. Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1, 2015. a
Kendall, M. G.: A New Measure of Rank Correlation, Biometrika, 30, 81–93, https://doi.org/10.1093/biomet/30.1-2.81, 1938. a
Kolby Smith, W., Reed, S. C., Cleveland, C. C., Ballantyne, A. P., Anderegg, W. R. L., Wieder, W. R., Liu, Y. Y., and Running, S. W.: Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization, Nat. Clim. Change, 6, 306–310, https://doi.org/10.1038/nclimate2879, 2016. a
Lenton, T. M., Livina, V. N., Dakos, V., van Nes, E. H., and Scheffer, M.: Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness, Philos. T. R. Soc. A, 370, 1185–1204, https://doi.org/10.1098/rsta.2011.0304, 2012. a
LUH2_database: The Land Use Harmonization 2 data set for CMIP6 [data set], https://luh.umd.edu/data.shtml (last access: 21 April 2023), 2016.
Maher, N., Milinski, S., and Ludwig, R.: Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble, Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021, 2021. a
Malhi, Y., Phillips, O. L., Mayle, F. E., Beerling, D. J., Gosling, W. D., and Bush, M. B.: Responses of Amazonian ecosystems to climatic and atmospheric carbon dioxide changes since the last glacial maximum, Philos. T. R. Soc. Lond. Ser. B, 359, 499–514, https://doi.org/10.1098/rstb.2003.1434, 2004. a
Maslin, M., Malhi, Y., Phillips, O., and Cowling, S.: New views on an old forest: assessing the longevity, resilience and future of the Amazon rainforest, T. Brit. Geogr., 30, 477–499, https://doi.org/10.1111/j.1475-5661.2005.00181.x, 2005. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019. a, b
Moesinger, L., Dorigo, W., De Jeu, R., Van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.2575599, 2019. a
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. a, b, c
Otto, F. E.: Attribution of Extreme Events to Climate Change, Annu. Rev. Env. Resour., 48, 813–828, https://doi.org/10.1146/annurev-environ-112621-083538, 2023. a
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of satellite-derived global land surface moisture, J. Geophys. Res.-Earth, 113, F01002, https://doi.org/10.1029/2007JF000769, 2008. a
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. a, b
RAISG_data: RAISG, Amazon Network of Georeferenced Socio-Environmental Information [data set], https://www.raisg.org/en/maps (last access: 10 March 2023), 2023.
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., and Toomey, M.: Climate change, phenology, and phenological control of vegetation feedbacks to the climate system, Agr. Forest Meteorol., 169, 156–173, https://doi.org/10.1016/j.agrformet.2012.09.012, 2013. a
Salati, E., Dall'Olio, A., Matsui, E., and Gat, J. R.: Recycling of water in the Amazon Basin: An isotopic study, Water Resour. Res., 15, 1250–1258, https://doi.org/10.1029/WR015i005p01250, 1979. 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
Silvério, D. V., Brando, P. M., Macedo, M. N., Beck, P. S. A., Bustamante, M., and Coe, M. T.: Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing, Environ. Res. Lett., 10, 104015, https://doi.org/10.1088/1748-9326/10/10/104015, 2015. a
Simmons, C. S., Walker, R., Aldrich, S., Arima, E., Pereira, R., Castro, E. M. R. d., Michelotti, F., Waylen, M., and Antunes, A.: Discipline and Develop: Destruction of the Brazil Nut Forest in the Lower Amazon Basin, Ann. Am. Assoc. Geogr., 109, 242–265, https://doi.org/10.1080/24694452.2018.1489215, 2019. a
Song, X., Wang, D.-Y., Li, F., and Zeng, X.-D.: Evaluating the performance of CMIP6 Earth system models in simulating global vegetation structure and distribution, Adv. Clim. Change Res., 12, 584–595, https://doi.org/10.1016/j.accre.2021.06.008, 2021. a
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. a
Tao, S., Chave, J., Frison, P.-L., Le Toan, T., Ciais, P., Fang, J., Wigneron, J.-P., Santoro, M., Yang, H., Li, X., Labrière, N., and Saatchi, S.: Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts, P. Natl. Acad. Sci. USA, 119, e2116626119, https://doi.org/10.1073/pnas.2116626119, 2022. a
Verbesselt, J., Umlauf, N., Hirota, M., Holmgren, M., Van Nes, E. H., Herold, M., Zeileis, A., and Scheffer, M.: Remotely sensed resilience of tropical forests, Nat. Clim. Change, 6, 1028–1031, https://doi.org/10.1038/nclimate3108, 2016. a
Volodin, E. and Gritsun, A.: Simulation of observed climate changes in 1850–2014 with climate model INM-CM5, Earth Syst. Dynam., 9, 1235–1242, https://doi.org/10.5194/esd-9-1235-2018, 2018. a
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/nclimate3004, 2016. a
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., Srbinovsky, J., Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth System Model: ACCESS-ESM1.5, J. South. Hemis. Earth Syst. Sc., 70, 193–214, https://doi.org/10.1071/ES19035, 2020. a
Chief editor
The Amazon forest is frequently discussed as a tipping element in the Earth System. This study uses a multi-model multi-member ensemble of climate model simulations with interactive vegetation dynamics to evidence the importance of local land-management strategies for increasing Amazon resilience.
The Amazon forest is frequently discussed as a tipping element in the Earth System. This study...
Short summary
We investigate whether the Amazon rainforest has lost substantial resilience since 1990. This assertion is based on trends in the observational record of vegetation density. We calculate the same metrics in a large number of climate model simulations and find that several models behave indistinguishably from the observations, suggesting that the observed trend could be caused by internal variability and that the cause of the ongoing rapid loss of Amazon rainforest is not mainly global warming.
We investigate whether the Amazon rainforest has lost substantial resilience since 1990. This...
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