The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages ~46.7°C (T_crit) ¹.  However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. We found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers, and remote sensing (ECOSTRESS) have midday-peak temperatures of ~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 T_crit 0.01% of the time. Further, upper-canopy leaf warming experiments (+2, 3, and 4°C in Brazil, Puerto Rico, and Australia) increased leaf temperatures non-linearly with peak leaf temperatures exceeding T_crit 1.3% of the time (11% >43.5°C, 0.3% >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 collapse in metabolic function, but the remaining uncertainty in our understanding of T_crit could reduce this to 2.6 ± 0.6°C.  The 4.0°C estimate is within the “worst case scenario” (RCP-8.5) of climate change predictions² for tropical forests and therefore it is still within our power to decide (e.g., by not taking the RCP 8.5 route) the fate of these critical realms of carbon, water, and biodiversity ^3,4.

Field Data - We estimate canopy temperature at the km 83 eddy covariance tower in the Tapajos region of Brazil ^1–3 using a pyrgeometer (Kipp and Zonen, Delft, Netherlands) mounted at 64 m to measure upwelling longwave radiation (L↑ in W m^-2) with an estimated radiative-flux footprint of 8,000 m^{2 4}.  Data were collected every 2 seconds and averaged over 30-minute intervals between August 2001 and March 2004. We estimated canopy temperature with the following equation:

Eq 1 – Canopy temperature (°C) = (L↑/(E*5.67e-8))^0.25-273.15

We chose an emissivity value (E) of 0.98 for the tower data, as this was the most common value used in the ECOSTRESS data (SDS_Emis1-5 (ECO2LSTE.001) and the broader literature for tropical forests ⁵. We compared canopy temperature derived from the pyrgeometer to eddy covariance derived latent heat fluxes (flux footprint ~1 km²), air temperature at 40 m, which is the approximate canopy height (model 076B, Met One, Oregon, USA; and model 107, Campbell Scientific, Logan, Utah, USA) and soil moisture at depths of 40 cm (model CS615, Campbell Scientific, Logan, Utah, USA). Further details on instrumentation and eddy covariance processing can be found in ^1,3. This site was selectively logged, which had a minor overall impact on the forest ⁶, but did not affect any trees near the tower.

Leaf thermocouple data - We measured canopy leaf temperature at a 30 m canopy walk-up tower between July to December of 2004 and July to December of 2005 at the same site. We initially placed 50 thermocouples on canopy-exposed leaves of Sextonia rubra, Micropholis sp., Lecythis lurida) (originally published in Doughty and Goulden 2008). Fine wire thermocouples (copper constantan 0.005 Omega, Stamford, CT) were attached to the underside of leaves by threading the wire through the leaf and inserting the end of the thermocouple into the abaxial surface. The thermocouples were wired into a multiplexer attached to a data logger (models AM25T and 23X, Campbell Scientific, Logan, UT, USA) and the data were recorded at 1 Hz. Additional upper-canopy leaf thermocouple data from Brazil⁷, Puerto Rico⁸, Panama⁹, Atlantic forest Brazil¹⁰ and Australia ¹¹, were generally collected in a similar manner. 

Satellite data - ECOSTRESS data (ECO2LSTE.001) – The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission is a thermal infrared (TIR) multispectral scanner with five spectral bands at 8.28, 8.63, 9.07, 10.6, and 12.05 µm. The sensor has a native spatial resolution of 38 m x 68 m, resampled to 70 m x 70 m, and a swath width of 402 km (53°). Data are collected from an average altitude of 400 ± 25 km on the International Space Station (ISS). ECOSTRESS is an improvement over other thermal sensors because no other sensors provide TIR data with sufficient spatial, temporal, and spectral resolution to reliably estimate LST at the local-to-global scale for a diurnal cycle ¹². To ensure the highest quality data, we used ECOSTRESS quality flag 3520, which identifies the best quality pixels (no cloud detected), a minimum-maximum difference (MMD) indicative of vegetation or water (Kealy and Hook 1993), and nominal atmospheric opacity. We accessed ECOSTRESS LST data through the AppEEARS website (https://lpdaac.usgs.gov/tools/appeears/) for the following products and periods: SDS_LST (ECO2LSTE.001) from a long longitudinal swath of the Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a red box) and then a larger area of the western Amazon for 18 September to 29 September 2019 (SI Fig 1a green box), Central Africa for 1 August to 30 August 2019 (SI Fig 1b), and SE Asia for 15 January to 30 February 2020 (SI Fig. 1c). The dates were chosen as all ECOSTRESS data available at the start of the study for the smaller regions and for warm periods with low soil moisture for the larger areas. We calculated “peak median,” which is defined as the average of the highest three medians of each granule (i.e., for the Amazon SI Fig. 1a, there were 934 granules) for each hour period.

Comparison of LST data – We compared ECOSTRESS LST to VIIRS LST (VNP21A1D.001) and MODIS LST (MYD11A1.006). A more detailed comparison and description of these sensors can be found in Hulley et al 2021¹³. Details for the sensors and quality flags used are given in Table S1. Broadly, G1 for ECOSTRESS and VIIRS is classified as vegetation (using emissivity) and of medium quality. G2 is classified as vegetation, but of the highest quality. MODIS landcover classifies this region as almost entirely broadleaf evergreen vegetation, but using MMD (emissivity) only 18% (VIIRS) and 12% (ECOSTRESS) of the data are classified as vegetation, rather than as soils and rocks (Table S2). Therefore, we use the vegetation classification (from MMD) as a very conservative estimate of complete forest canopy cover and not farms, urban, or degraded forest where rocks or soils are more likely to appear to satellites. 

SMAP data – To estimate pantropical soil moisture, we use the Soil Moisture Active Passive (SMAP) sensor and the product Geophysical_Data_sm_rootzone (SPL4SMGP.005).  SMAP measurements provide remote sensing of soil moisture in the top 5 cm of the soil ¹⁴ and the L4 products combine SMAP observations and complementary information from a variety of sources. We accessed SMAP data from the AppEEARS website for the following products and periods: Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a), Central Africa for 25 December 2019 to 20 July 2020 (SI Fig 1b), and Borneo for 25 December 2018 to 20 July 2020 (SI Fig 1c).

Warming experiments – For model validation, we used the results of three upper-canopy leaf and branch warming experiments of 2°C (Brazil), 3°C (Puerto Rico), and 4°C (Australia).  The first experiment (Brazil), was 4 individual leaf-resistant heaters on each of 6 different upper-canopy species at the Floresta National (FLONA) do Tapajos as part of the Large-Scale Biosphere–Atmosphere Ecology Program (LBA-ECO) in Santarem, Brazil¹⁴. On the same six species, black plastic passively heated branches by an average ~2°C.  Initially, heat balance sap flow sensors and the passive heaters were added to 40 branches, but we had confidence in the data from 9 heated and 4 control in the final analysis.  The second experiment (Puerto Rico) had two species (Ocotea sintenisii (Mez) Alain and Guarea guidonia (L.) Sleumer where leaves were heated by 3 °C at the Tropical Responses to Altered Climate Experiment (TRACE) canopy tower site at Sabana Field Research Station, Luquillo, Puerto Rico⁸. The final experiment (Australia), which increased leaf temperatures by 4 °C, was conducted at Daintree Rainforest Observatory (DRO) in Cape Tribulation, Far North Queensland, Australia. Leaf heaters were installed using a pair of 30-gauge copper-constantan thermocouples, one reference leaf, and one heated with a target temperature differential of 4°C. There were two pairs in the upper canopy of each tree crown installed in 2–3 individuals across four species with the thermocouples installed on the underside of the leaves. Two absolute 36-gauge copper-constantan thermocouples were installed in each species to measure the leaf temperatures of the reference leaves. Thermocouple wires connected into an AM25T multiplexer from Campbell Scientific connected to a CR1000 Campbell datalogger.  More details about the experiment and sensors can be found in ¹¹.

Model – We created a model of individual leaves on a tree (100 by 100 grid where each leaf is a pixel) to estimate the upper limit of tropical canopy temperatures with projected changes in climate.  At the start of the simulation, we randomly applied the measured distribution (ambient Fig 1c) of canopy leaf temperatures >31.2 °C (chosen to give a mean canopy temperature of 33.2 ± 0.4 °C, matching the canopy average Fig 1b) to the entire grid.  Each year we increased the mean air temperatures by 0.03°C to simulate a warming planet. As air temperatures reached +2, 3, and 4°C, we applied the leaf temperature distributions (but subtracted out the air temperature increases) from the different warming experiments (+2°C (Brazil), +3°C (Puerto Rico), and +4°C (Australia), respectively (Fig S7)).  We ran the model at a daily time step with leaves flushing once a year (all dead leaves reset to living each year). 

In addition, to take into account the effect of climate inter-annual variation - specifically drought, these mean canopy temperatures were further increased or decreased by deviations from mean maximum air temperatures at 40 m pulled each day from the Tapajos eddy covariance tower^1–3 and soil moisture at 40 cm depth  (m³ m^-3) which controlled canopy temperatures following equation 2 (Fig S6). 

Eq 2 – Canopy temperature (°C)  = 46.5-33.6*soil moisture (m³ m^-3) 

For example, in a non-drought year, on a day when max air temperatures were 0.1 °C higher than average and soil moisture was 0.01 m³ m^-3 lower than average (which would add 0.3 °C to canopy temperatures (Eq 2)), we would add 0.4 °C to the grid canopy temperature that day. Every year, there was a 10% random probability of either a minor (80% probability) drought which reduced soil moisture by 0.1 m³ m^-3 and increased air temperatures by 0.5 °C or severe drought (20% probability), which reduced soil moisture by 0.2 m³ m^-3 and increased air temperatures by 1 °C. This is similar to the Amazon-wide temperature increases during the last El Niño ¹⁵. 

If an individual leaf temperature increases to above 46.7 °C (T_crit) the leaf died, following Slot et al. (2021). Prior research has suggested that irreversible damage could begin at 45 °C ¹⁶ and T₅₀ for tropical species is 49.9 °C ¹⁷, and we use these values in a sensitivity study. We further explore the impact of duration of T_crit on mortality in a sensitivity study (ranging between needing a single exposure to four exposures to T_crit to die). Over the season, if a leaf died, then it did not contribute towards canopy evapotranspiration.  We ran simulations as a 3D canopy with an LAI of 5 where if the top leaf died, then it was replaced by a shade-adapted leaf with a T_crit 1 °C lower ¹⁸. If each of the 5 LAIs died, then all leaves in that grid cell were dead and canopy evaporative cooling decreased by that percentage. Several lines of evidence suggest that under normal hydraulic conditions, when radiation load increases from ~350 to 1100 W m^-2 (e.g. between shady and sunny conditions) average canopy temperature increases by ~3 °C and therefore, evaporative cooling for a full 1100 W m^-2 is ~4.4°C^4,19 (we vary this in a sensitivity study between 3.7 and 5.1°C). For example, if, over a year, 1000 leaves (10% of all leaves) surpass T_crit and die, evaporative cooling for all leaves in the grid will be reduced by 10% (1000/(100 by 100 grid)) or 0.44 °C and 0.44 °C will be added to mean canopy temperature. Therefore, mean canopy temperature could heat up by a maximum of 4.4°C either due to a reduction of soil moisture or from an increase in dead leaves. We ran each simulation until the point where all leaves were dead and repeated this 30 times. We assumed loss of tree function following the death of all leaves, but we discuss this further in the discussion. We then ran sensitivity studies for several of the key variables (bold indicates the standard model parameter) including: drought (0.05, 0.1, to 0.2 m³ m^-3 decrease in soil moisture), change in T_crit (T_crit: 45, 46.7, 49.9 °C), T_crit range (100 by 100 grid =random distribution of 46.7±2, 100 by 100 grid =46.7±0), Max evaporative cooling (3.7, 4.4°C), (T_crit duration (exceed Tcrit once, exceed Tcrit more than 3 times) and soil moisture coefficient (-33.6 -38.2; i.e. change the slope from Fig S6 by ± 1 sd).

Methods References

1. Miller, S. D. et al. Biometric and micrometeorological measurements of tropical forest carbon balance. Ecol. Appl. 14, 114–126 (2004).
2. da Rocha, H. R. et al. Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia. Ecol. Appl. 14, 22–32 (2004).
3. Goulden, M. L. et al. Diel and seasonal patterns of tropical forest co2 exchange. Ecol. Appl. 14, 42–54 (2004).
4. Kivalov, S. N. & Fitzjarrald, D. R. Observing the Whole-Canopy Short-Term Dynamic Response to Natural Step Changes in Incident Light: Characteristics of Tropical and Temperate Forests. Boundary-Layer Meteorol. 173, 1–52 (2019).
5. Jin, M. & Liang, S. An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations. J. Clim. 19, (2006).
6. Miller, S. D. et al. Reduced impact logging minimally alters tropical rainforest carbon and energy exchange. Proc. Natl. Acad. Sci. 108, 19431 LP – 19435 (2011).
7. Doughty, C. E. An In Situ Leaf and Branch Warming Experiment in the Amazon. Biotropica 43, 658–665 (2011).
8. Carter, K. R., Wood, T. E., Reed, S. C., Butts, K. M. & Cavaleri, M. A. Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation. Plant. Cell Environ. 44, 2879–2897 (2021).
9. Rey-Sanchez, A. C., Slot, M., Posada, J. & Kitajima, K. Spatial and seasonal variation of leaf temperature within the canopy of a tropical forest. Clim. Res. 71, 75–89 (2016).
10. Fauset, S. et al. Differences in leaf thermoregulation and water use strategies between three co-occurring Atlantic forest tree species. Plant. Cell Environ. 41, 1618–1631 (2018).
11. Crous K Y, A W Cheesman, K Middleby, Rogers Eie, A Wujeska-Klause, A Y M Bouet, D S Ellsworth, M J Liddell, L A Cernusak, C V M Barton, Similar patterns of leaf temperatures and thermal acclimation to warming in temperate and tropical tree canopies., Tree Physiology, 2023;, tpad054, https://doi.org/10.1093/treephys/tpad054.
12. Xiao, J., Fisher, J. B., Hashimoto, H., Ichii, K. & Parazoo, N. C. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 7, 877–887 (2021).
13. Hulley, G. C. et al. Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product. IEEE Trans. Geosci. Remote Sens. 60, 1–23 (2022).
14. Reichle, R., Lannoy, G. De, Koster, R. D., Crow, W. T. & 2017., J. S. K. SMAP L4 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 3. Boulder, Color. USA. NASA Natl. Snow Ice Data Cent. Distrib. Act. Arch. Center. doi https//doi.org/10.5067/B59DT1D5UMB4. (2017).
15. Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016).
16. Berry, J. & Bjorkman, O. Photosynthetic Response and Adaptation to Temperature in Higher Plants. Annu. Rev. Plant Physiol. 31, 491–543 (1980).
17. 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, (2021).
18. Slot, M., Krause, G. H., Krause, B., Hernández, G. G. & Winter, K. Photosynthetic heat tolerance of shade and sun leaves of three tropical tree species. Photosynth. Res. 141, 119–130 (2019).
19. Doughty, C. E. & Goulden, M. L. Are tropical forests near a high temperature threshold? J. Geophys. Res. Biogeosciences (2009) doi:10.1029/2007JG000632.