https://www.nature.com/articles/s41586-023-06391-z Skip to main content Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Advertisement Nature * View all journals * Search * Log in * Explore content * About the journal * Publish with us * Subscribe * Sign up for alerts * RSS feed 1. nature 2. articles 3. article * Article * Published: 23 August 2023 Tropical forests are approaching critical temperature thresholds * Christopher E. Doughty ORCID: orcid.org/0000-0003-3985-7960^1, * Jenna M. Keany^1, * Benjamin C. Wiebe ORCID: orcid.org/0000-0002-9325-1540^1, * Camilo Rey-Sanchez^2, * Kelsey R. Carter^3,4, * Kali B. Middleby ORCID: orcid.org/0000-0002-9323-0870^5, * Alexander W. Cheesman^5, * Michael L. Goulden ORCID: orcid.org/0000-0002-9379-3948^6, * Humberto R. da Rocha^7, * Scott D. Miller^8, * Yadvinder Malhi ORCID: orcid.org/0000-0002-3503-4783^9, * Sophie Fauset ORCID: orcid.org/0000-0003-4246-1828^10, * Emanuel Gloor^11, * Martijn Slot^12, * Imma Oliveras Menor ORCID: orcid.org/0000-0001-5345-2236^9,13, * Kristine Y. Crous ORCID: orcid.org/0000-0001-9478-7593^14, * Gregory R. Goldsmith^15 & * ... * Joshua B. Fisher ORCID: orcid.org/0000-0003-4734-9085^15 Show authors Nature (2023)Cite this article * 622 Accesses * 788 Altmetric * Metrics details Subjects * Climate-change ecology * Environmental impact * Environmental sciences * Photosystem I Abstract The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages approximately 46.7 degC (T [crit])^1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. Here we found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers and remote sensing (ECOSTRESS) have midday peak temperatures of approximately 34 degC during dry periods, with a long high-temperature tail that can exceed 40 degC. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper canopy leaves exceed T[crit] 0.01% of the time. Furthermore, upper canopy leaf warming experiments (+2, 3 and 4 degC in Brazil, Puerto Rico and Australia, respectively) increased leaf temperatures non-linearly, with peak leaf temperatures exceeding T[crit] 1.3% of the time (11% for more than 43.5 degC, and 0.3% for more than 49.9 degC). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 +- 0.5 degC increase in air temperatures before a potential tipping point in metabolic function, but remaining uncertainty in the plasticity and range of T[crit] in tropical trees and the effect of leaf death on tree death could drastically change this prediction. The 4.0 degC estimate is within the 'worst-case scenario' (representative concentration pathway (RCP) 8.5) of climate change predictions^2 for tropical forests and therefore it is still within our power to decide (for example, by not taking the RCP 6.0 or 8.5 route) the fate of these critical realms of carbon, water and biodiversity^3,4. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution Access options Access through your institution Access through your institution Change institution Buy or subscribe Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $29.99 / 30 days cancel any time Learn more Subscribe to this journal Receive 51 print issues and online access $199.00 per year only $3.90 per issue Learn more Rent or buy this article Prices vary by article type from$1.95 to$39.95 Learn more Prices may be subject to local taxes which are calculated during checkout Additional access options: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support Fig. 1: In situ and warming experiment leaf temperatures compared with canopy temperatures. [41586_2023_6391_Fig1_HTML] Fig. 2: Remotely sensed peak canopy temperature across the tropics. [41586_2023_6391_Fig2_HTML] Fig. 3: Modelled effect of future warming on tropical forests. [41586_2023_6391_Fig3_HTML] Data availability We provide key data in the supplementary information. Data and code to produce all figures are available at https://doi.org/10.5061/ dryad.fqz612jx1. Source data are provided with this paper. Code availability Data and code to produce all figures are available at https://doi.org /10.5061/dryad.fqz612jx1. References 1. Slot, M. et al. Leaf heat tolerance of 147 tropical forest species varies with elevation and leaf functional traits, but not with phylogeny. Plant. Cell Environ. 44, 2414-2427 (2021). Article CAS PubMed Google Scholar 2. IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013). 3. Wilson, E. & Raven, P. in Biodiversity (ed. Wilson, E. O.) Ch. 3 (National Academy Press, 1988). 4. Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. 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S.F. and E.G. acknowledge Natural Environmental Research Council grant NE/V008366/1. K.C. acknowledges the Australian Research Council grant DE160101484. Author information Authors and Affiliations 1. School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA Christopher E. Doughty, Jenna M. Keany & Benjamin C. Wiebe 2. Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA Camilo Rey-Sanchez 3. College of Forest Resources and Environmental Sciences, Michigan Technological University, Houghton, MI, USA Kelsey R. Carter 4. Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA Kelsey R. Carter 5. Centre for Tropical Environmental and Sustainability Science, James Cook University, Cairns, Queensland, Australia Kali B. Middleby & Alexander W. Cheesman 6. Department of Earth System Science, University of California, Irvine, CA, USA Michael L. Goulden 7. Departamento de Ciencias Atmosfericas, Universidade de Sao Paulo, Sao Paulo, Brazil Humberto R. da Rocha 8. Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY, USA Scott D. Miller 9. Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK Yadvinder Malhi & Imma Oliveras Menor 10. School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK Sophie Fauset 11. University of Leeds, Leeds, UK Emanuel Gloor 12. Smithsonian Tropical Research Institute, Balboa, Ancon, Republic of Panama Martijn Slot 13. AMAP (Botanique et Modelisation de l'Architecture des Plantes et des Vegetations), CIRAD, CNRS, INRA, IRD, Universite de Montpellier, Montpellier, France Imma Oliveras Menor 14. Western Sydney University, Hawkesbury Institute for the Environment, Penrith, New South Wales, Australia Kristine Y. Crous 15. Schmid College of Science and Technology, Chapman University, Orange, CA, USA Gregory R. Goldsmith & Joshua B. Fisher Authors 1. Christopher E. Doughty View author publications You can also search for this author in PubMed Google Scholar 2. Jenna M. Keany View author publications You can also search for this author in PubMed Google Scholar 3. Benjamin C. Wiebe View author publications You can also search for this author in PubMed Google Scholar 4. Camilo Rey-Sanchez View author publications You can also search for this author in PubMed Google Scholar 5. Kelsey R. Carter View author publications You can also search for this author in PubMed Google Scholar 6. Kali B. Middleby View author publications You can also search for this author in PubMed Google Scholar 7. Alexander W. Cheesman View author publications You can also search for this author in PubMed Google Scholar 8. Michael L. Goulden View author publications You can also search for this author in PubMed Google Scholar 9. Humberto R. da Rocha View author publications You can also search for this author in PubMed Google Scholar 10. Scott D. Miller View author publications You can also search for this author in PubMed Google Scholar 11. Yadvinder Malhi View author publications You can also search for this author in PubMed Google Scholar 12. Sophie Fauset View author publications You can also search for this author in PubMed Google Scholar 13. Emanuel Gloor View author publications You can also search for this author in PubMed Google Scholar 14. Martijn Slot View author publications You can also search for this author in PubMed Google Scholar 15. Imma Oliveras Menor View author publications You can also search for this author in PubMed Google Scholar 16. Kristine Y. Crous View author publications You can also search for this author in PubMed Google Scholar 17. Gregory R. Goldsmith View author publications You can also search for this author in PubMed Google Scholar 18. Joshua B. Fisher View author publications You can also search for this author in PubMed Google Scholar Contributions C.E.D., G.R.G., I.O.M., Y.M. and J.B.F. designed the study. C.E.D. and J.M.K. analysed the remote sensing data. C.E.D., M.L.G., H.R.d.R., S.D.M., S.F., E.G., C.R.-S., M.S., K.R.C., K.Y.C., K.B.M. and A.W.C. collected and analysed the empirical data. C.E.D. created the model. C.E.D. and B.C.W. prepared the public data and code. C.E.D. wrote the paper with contributions from G.R.G., K.Y.C., J.B.F. and I.O.M. Corresponding author Correspondence to Christopher E. Doughty. Ethics declarations Competing interests The authors declare no competing interests. Peer review Peer review information Nature thanks Ben Bond-Lamberty, David Schimel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Additional information Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Extended data figures and tables Extended Data Fig. 1 Regions of interest. Tropical forest regions in A) Amazon, B) Central Africa and C) SE Asia used for the retrieval of ECOSTRESS LST and SMAP data. The red area was used to ground-truth ECOSTRESS LST with the pyrgeometer. Extended Data Fig. 2 Impacts on canopy temperature. (A) Linear regression of canopy temperature versus soil moisture (40 cm depth) at the km 83 eddy covariance tower (r^2 = 0.46, P = 7e-10, N = 62). (B) Linear regression of canopy temperature as a function of air temperature during sunny periods during the wet (green circles) and dry (red circles) season at the km 83 eddy covariance tower in the Tapajos region of Brazil. Red line shows a linear fit for the dry season (r^2 = 0.96, P = 3e-21, N = 29) and the lower line is a one-to-one line. (C) Linear regressions of canopy temperature as a function of latent heat flux for warm (>30 degC) periods (r^2 = 0.50, P = 0.009, N = 11) at the km 83 eddy covariance tower in the Tapajos region of Brazil. (D) Linear regression (r^2 = 0.75, P = 2e-5, N = 16) using data from Fig. 1a comparing ECOSTRESS dry season to pyrgeometer dry season data from the Tapajos (Km 83). Extended Data Fig. 3 Histograms of canopy temperature. Histograms of the canopy temperatures as (top) 30 min average periods and (bottom) two second instantaneous observations, where total shortwave energy load is >1000 W m^-2, as measured by a downward facing pyrgeometer in the Tapajos region of Brazil. Extended Data Fig. 4 Leaf thermocouple data from warming experiments. Canopy top tropical leaf thermocouple measurements for normal (blue) and warmed leaves (red) for Brazil (+2 degC), Puerto Rico (+3 degC), and Australia (+4 degC). Insets show the long tail distribution of temperatures and text records the highest leaf temperature. Extended Data Fig. 5 Leaf thermocouple data. Canopy top tropical leaf thermocouple measurements for (top) Brazil km 67, (middle) Panama and (bottom) the Atlantic Forest in Brazil. Insets show the long tail distribution of temperatures and text records the highest leaf temperature. The resampled assumes a similar number of samples (~N = 400) at 38 degC for both sites and fits a curve to extrapolate the long tail. The Atlantic forest is a cooler forest (at ~1000 m) and the median temperature of the Amazon is ~4 degC higher than the Atlantic forest. Extended Data Fig. 6 Duration of warming. Periods when the leaves were warmed by >8 min during the Tapajos warming experiment for individual leaves (thin lines) and averaged (thick red line). Text in figure indicates the percent of time leaves exceeded Tcrit for greater than 6 and 8 min. Extended Data Fig. 7 Finding African peak temperatures. Procedure for finding peak canopy temperatures using ECOSTRESS data for central Africa. (A) Log10 histogram of temperatures for (B) a region of Central Africa. A diurnal curve showing all ECOSTRESS LST data for central Africa versus (C) time of day and (D) time of year. (E) SMAP soil moisture (m^3 m^-3) data showing periods of dry weather. Extended Data Fig. 8 Finding SE Asian peak temperatures. Procedure for finding peak canopy temperatures using ECOSTRESS data for SE Asia. (A) Log10 histogram of temperatures for (B) a region of Central Africa. A diurnal curve showing all ECOSTRESS LST data for SE Asia versus (C) time of day and (D) time of year. (E) SMAP soil moisture data (m^3 m^-3) showing periods of dry weather. Extended Data Fig. 9 Comparison of LST temperature data. We show the spatial distribution of LST data for three sensors (VIIRS, MODIS, and ECOSTRESS) for similar time periods (Sept 18-28, 2019) for similar areas in the Amazon basin. The difference between the left, middle and right are different data quality flags for no flag (left), QF g1 from Supplementary Table 1 (middle) and QF g2 (right). We used three levels of quality flags (ECOSTRESS - G1 - 3522 and 3520, G2 =3520, VIIRS - G1 - 12001, 15841, 11745, 32225 and G2 = 32225, and MODIS - G1 - 0 and 65 and G2 -0) for the region depicted in Extended Data Fig. 1a during the same period (18 September to 28 September 2019). Quality flags were complex with 136 for ECOSTRESS and 229 for VIIRS (but only 8 for MODIS). Extended Data Fig. 10 Histogram of LST temperature data. (top) We show log10 histograms of LST data for three sensors (VIIRS, MODIS, and ECOSTRESS) for similar time periods (Sept 18-28, 2019) for similar areas in the Amazon basin. The difference between the left, middle and right are different data quality flags for no flag (left), QF g1 from Supplementary Table 1 (middle) and QF g2 (right). We used three levels of quality flags (ECOSTRESS - G1 - 3522 and 3520, G2 = 3520, VIIRS - G1 - 12001, 15841, 11745, 32225 and G2 = 32225, and MODIS - G1 - 0 and 65 and G2 -0) for the region depicted in Extended Data Fig. 1a during the same period (18 September to 28 September 2019). (bottom) - A scaled in comparison for the same dataset showing the much higher resolution of ECOSTRESS versus VIIRS and MODIS LST. Supplementary information Supplementary Information This file contains supplementary text, methods and Tables 1, 2. Reporting Summary Peer Review File Source data Source Data Fig. 1 Source Data Fig. 2 Source Data Fig. 3 Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and Permissions About this article Check for updates. Verify currency and authenticity via CrossMark Cite this article Doughty, C.E., Keany, J.M., Wiebe, B.C. et al. Tropical forests are approaching critical temperature thresholds. Nature (2023). https:// doi.org/10.1038/s41586-023-06391-z Download citation * Received: 31 August 2021 * Accepted: 30 June 2023 * Published: 23 August 2023 * DOI: https://doi.org/10.1038/s41586-023-06391-z Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative Comments By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. 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