The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Marko Scholze

Senior lecturer

Default user image.

Improving the predictability of global CO2 assimilation rates under climate change

Author

  • T Ziehn
  • J Kattge
  • W Knorr
  • Marko Scholze

Summary, in English

Feedbacks between the terrestrial carbon cycle and the atmosphere have the potential to greatly modify expected rates of future climate change. This makes it all the more urgent to exploit all existing data for the purpose of accurate modelling of the underlying processes. Here we use a Bayesian random sampling method to constrain parameters of the Farquhar model of leaf photosynthesis and a model of leaf respiration against a comprehensive set of plant trait data at the leaf level. The resulting probability density function (PDF) of model parameters is contrasted with a PDF derived using a conventional "expert knowledge" approach. When running the Biosphere Energy Transfer Hydrology (BETHY) scheme with a 1000- member sub-sample of each of the two PDFs for present climate and a climate scenario, we find that the use of plant trait data is able to reduce the uncertainty range of simulated net leaf assimilation (NLA) by more than a factor of two. Most of the remaining variability is caused by only four parameters, associated with the acclimation of photosynthesis to plant growth temperature and to how leaf stomata react to atmospheric CO2 concentration. We suggest that this method should be used extensively to parameterize Earth system models, given that data bases on plant traits are increasingly being made available to the modelling community. Citation: Ziehn, T., J. Kattge, W. Knorr, and M. Scholze (2011), Improving the predictability of global CO2 assimilation rates under climate change, Geophys. Res. Lett., 38, L10404, doi:10.1029/2011GL047182.

Department/s

  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2011

Language

English

Pages

10404-10404

Publication/Series

Geophysical Research Letters

Volume

38

Document type

Journal article

Publisher

American Geophysical Union (AGU)

Topic

  • Physical Geography

Status

Published

ISBN/ISSN/Other

  • ISSN: 1944-8007