Skip to main content

Thank you for visiting 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.

Foliar temperature acclimation reduces simulated carbon sensitivity to climate


Plant photosynthesis and respiration are the largest carbon fluxes between the terrestrial biosphere and the atmosphere1, and their parameterizations represent large sources of uncertainty in projections of land carbon uptake in Earth system models2,3 (ESMs). The incorporation of temperature acclimation of photosynthesis and foliar respiration, commonly observed processes, into ESMs has been proposed as a way to reduce this uncertainty2. Here we show that, across 15 flux tower sites spanning multiple biomes at various locations worldwide (10° S–67° N), acclimation parameterizations4,5 improve a model’s ability to reproduce observed net ecosystem exchange of CO2. This improvement is most notable in tropical biomes, where photosynthetic acclimation increased model performance by 36%. The consequences of acclimation for simulated terrestrial carbon uptake depend on the process, region and time period evaluated. Globally, including acclimation has a net effect of increasing carbon assimilation and storage, an effect that diminishes with time, but persists well into the future. Our results suggest that land models omitting foliar temperature acclimation are likely to overestimate the temperature sensitivity of terrestrial carbon exchange, thus biasing projections of future carbon storage and estimates of policy indicators such as the transient climate response to cumulative carbon emissions1.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Model improvement by acclimation.
Figure 2: Global influence of acclimation on photosynthesis and foliar respiration.
Figure 3: Global maps of the influence of acclimation on each process at the ends of the nineteenth, twentieth and twenty-first centuries.
Figure 4: Effect of acclimation on global simulated vegetation carbon in LM3.


  1. IPCC in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

    Google Scholar 

  2. Booth, B. B. et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).

    Article  Google Scholar 

  3. Ziehn, T., Kattge, J., Knorr, W. & Scholze, M. Improving the predictability of global CO2 assimilation rates under climate change. Geophys. Res. Lett. 38, L10404 (2011).

    Article  Google Scholar 

  4. Kattge, J. & Knorr, W. Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species. Plant Cell Environ. 30, 1176–1190 (2007).

    Article  CAS  Google Scholar 

  5. Atkin, O. K. et al. Using temperature-dependent changes in leaf scaling relationships to quantitatively account for thermal acclimation of respiration in a coupled global climate-vegetation model. Glob. Change Biol. 14, 2709–2726 (2008).

    Google Scholar 

  6. Arneth, A., Mercado, L., Kattge, J. & Booth, B. Future challenges of representing land-processes in studies on land-atmosphere interactions. Biogeosciences 9, 3587–3599 (2012).

    Article  Google Scholar 

  7. Shevliakova, E. et al. Historical warming reduced due to enhanced land carbon uptake. Proc. Natl Acad. Sci. USA 110, 16730–16735 (2013).

    Article  CAS  Google Scholar 

  8. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2011).

    Article  Google Scholar 

  9. Berry, J. & Björkman, O. Photosynthetic response and adaptation to temperature in higher plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 31, 491–543 (1980).

    Article  Google Scholar 

  10. Atkin, O. K., Bruhn, D., Hurry, V. M. & Tjoelker, M. G. The hot and the cold: unravelling the variable response of plant respiration to temperature. Funct. Plant Biol. 32, 87–105 (2005).

    Article  Google Scholar 

  11. Way, D. A. & Yamori, W. Thermal acclimation of photosynthesis: on the importance of adjusting our definitions and accounting for thermal acclimation of respiration. Photosynth. Res. 119, 89–100 (2014).

    Article  CAS  Google Scholar 

  12. Galbraith, D. et al. Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change. New Phytol. 187, 647–665 (2010).

    Article  Google Scholar 

  13. King, A. W., Gunderson, C. A., Post, W. M., Weston, D. J. & Wullschleger, S. D. Photosynthesis in balance with respiration? Response. Science 313, 917–918 (2006).

    Article  CAS  Google Scholar 

  14. Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2 . Glob. Change Biol. 19, 45–63 (2013).

    Article  Google Scholar 

  15. Slot, M. & Kitajima, K. General patterns of acclimation of leaf respiration to elevated temperatures across biomes and plant types. Oecologia 177, 885–900 (2014).

    Article  Google Scholar 

  16. Farquhar, G., von Caemmerer, S. & Berry, J. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    Article  CAS  Google Scholar 

  17. Atkin, O. K., Meir, P. & Turnbull, M. H. Improving representation of leaf respiration in large-scale predictive climate–vegetation models. New Phytol. 202, 743–748 (2014).

    Article  Google Scholar 

  18. Shevliakova, E. et al. Carbon cycling under 300 years of land use change: importance of the secondary vegetation sink. Glob. Biogeochem. Cycles 23, GB2022 (2009).

    Article  Google Scholar 

  19. Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).

    Article  Google Scholar 

  20. Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106, 7183–7192 (2001).

    Article  Google Scholar 

  21. Kruschke, J. K. Bayesian estimation supersedes the t test. J. Exp. Psychol. 142, 573–603 (2013).

    Article  Google Scholar 

  22. Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33–57 (2011).

    Article  CAS  Google Scholar 

  23. Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

    Article  Google Scholar 

  24. Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2012).

    Article  Google Scholar 

  25. Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Clim. Dynam. 40, 2123–2165 (2013).

    Article  Google Scholar 

  26. Collins, W. J. et al. Development and evaluation of an Earth-system model—HadGEM2. Geosci. Model Dev. Discuss. 4, 997–1062 (2011).

    Article  Google Scholar 

  27. Medlyn, B. E. et al. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ. 25, 1167–1179 (2002).

    Article  CAS  Google Scholar 

  28. IPCC in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).

    Google Scholar 

  29. Collatz, G. J., Ball, J. T., Grivet, C. & Berry, J. A. Physiological and environmental regulation of stomatal conductance, photosynthesis, and transpiration—a model that includes a laminar boundary layer. Agric. For. Meteorol. 54, 107–136 (1991).

    Article  Google Scholar 

  30. Moffat, A. M. et al. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agric. For. Meteorol. 147, 209–232 (2007).

    Article  Google Scholar 

  31. Papale, D. et al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3, 571–583 (2006).

    Article  CAS  Google Scholar 

  32. Papale, D. & Valentini, R. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Glob. Change Biol. 9, 525–535 (2003).

    Article  Google Scholar 

  33. Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).

    Article  Google Scholar 

Download references


This project was supported by student exchange funding for N.G.S. provided by the INTERFACE RCN (NSF DEB-0955771), a Purdue Climate Change Research Center graduate fellowship to N.G.S., and a NASA Earth and Space Science fellowship to N.G.S. (NNX13AN65H). S.L.M. acknowledges support from the National Oceanic and Atmospheric (US Department of Commerce) Grant NAOSOAR4320752. This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), CarboEuropeIP, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), and LBA. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California—Berkeley and the University of Virginia. This is publication 1605 of the Purdue Climate Change Research Center.

Author information

Authors and Affiliations



N.G.S., S.L.M., E.S. and J.S.D. designed the study. N.G.S. and S.L.M. performed the model simulations and analyses. All authors contributed to the interpretation of the results and writing of the manuscript.

Corresponding author

Correspondence to Nicholas G. Smith.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smith, N., Malyshev, S., Shevliakova, E. et al. Foliar temperature acclimation reduces simulated carbon sensitivity to climate. Nature Clim Change 6, 407–411 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing