Tropical forests are approaching critical temperature thresholds

Mar 25, 2024

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The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages approximately 46.7 °C (Tcrit)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 °C during dry periods, with a long high-temperature tail that can exceed 40 °C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper canopy leaves exceed Tcrit 0.01% of the time. Furthermore, upper canopy leaf warming experiments (+2, 3 and 4 °C in Brazil, Puerto Rico and Australia, respectively) increased leaf temperatures non-linearly, with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% for more than 43.5 °C, and 0.3% for more than 49.9 °C). 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 °C increase in air temperatures before a potential tipping point in metabolic function, but remaining uncertainty in the plasticity and range of Tcrit in tropical trees and the effect of leaf death on tree death could drastically change this prediction. The 4.0 °C estimate is within the ‘worst-case scenario’ (representative concentration pathway (RCP) 8.5) of climate change predictions2 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 biodiversity3,4.

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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.

Data and code to produce all figures are available at https://doi.org/10.5061/dryad.fqz612jx1.

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Support was provided by the ECOSTRESS mission and NASA Research Opportunities in Space and Earth Science grant numbers 80NSSC20K0216, 80NSSC19K0206 and 80NSSC21K0191. S.F. and E.G. acknowledge Natural Environmental Research Council grant NE/V008366/1. K.C. acknowledges the Australian Research Council grant DE160101484.

School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

Christopher E. Doughty, Jenna M. Keany & Benjamin C. Wiebe

Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA

Camilo Rey-Sanchez

College of Forest Resources and Environmental Sciences, Michigan Technological University, Houghton, MI, USA

Kelsey R. Carter

Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA

Kelsey R. Carter

Centre for Tropical Environmental and Sustainability Science, James Cook University, Cairns, Queensland, Australia

Kali B. Middleby & Alexander W. Cheesman

Department of Earth System Science, University of California, Irvine, CA, USA

Michael L. Goulden

Departamento de Ciencias Atmosfericas, Universidade de São Paulo, São Paulo, Brazil

Humberto R. da Rocha

Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY, USA

Scott D. Miller

Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK

Yadvinder Malhi & Imma Oliveras Menor

School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK

Sophie Fauset

University of Leeds, Leeds, UK

Emanuel Gloor

Smithsonian Tropical Research Institute, Balboa, Ancon, Republic of Panama

Martijn Slot

AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France

Imma Oliveras Menor

Western Sydney University, Hawkesbury Institute for the Environment, Penrith, New South Wales, Australia

Kristine Y. Crous

Schmid College of Science and Technology, Chapman University, Orange, CA, USA

Gregory R. Goldsmith & Joshua B. Fisher

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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.

Correspondence to Christopher E. Doughty.

The authors declare no competing interests.

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

(A) Linear regression of canopy temperature versus soil moisture (40 cm depth) at the km 83 eddy covariance tower (r2 = 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 (r2 = 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 °C) periods (r2 = 0.50, P = 0.009, N = 11) at the km 83 eddy covariance tower in the Tapajos region of Brazil. (D) Linear regression (r2 = 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).

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.

Canopy top tropical leaf thermocouple measurements for normal (blue) and warmed leaves (red) for Brazil (+2 °C), Puerto Rico (+3 °C), and Australia (+4 °C). Insets show the long tail distribution of temperatures and text records the highest leaf temperature.

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 °C 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 °C higher than the Atlantic forest.

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.

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 (m3 m−3) data showing periods of dry weather.

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 (m3 m−3) showing periods of dry weather.

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).

(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.

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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

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Received: 31 August 2021

Accepted: 30 June 2023

Published: 23 August 2023

DOI: https://doi.org/10.1038/s41586-023-06391-z

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