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Improving the predictability of global CO2 assimilation rates under climate change

Ziehn, T; Kattge, J; Knorr, W and Scholze, Marko LU (2011) In Geophysical Research Letters 38. p.10404-10404
Abstract
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... (More)
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. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
in
Geophysical Research Letters
volume
38
pages
10404 - 10404
publisher
American Geophysical Union
external identifiers
  • scopus:79957966429
ISSN
1944-8007
DOI
10.1029/2011GL047182
project
BECC
language
English
LU publication?
no
id
78f78f92-f3c6-461f-8f50-74e676727ede (old id 4448638)
date added to LUP
2014-05-23 12:11:22
date last changed
2017-10-01 04:13:24
@article{78f78f92-f3c6-461f-8f50-74e676727ede,
  abstract     = {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.},
  author       = {Ziehn, T and Kattge, J and Knorr, W and Scholze, Marko},
  issn         = {1944-8007},
  language     = {eng},
  pages        = {10404--10404},
  publisher    = {American Geophysical Union},
  series       = {Geophysical Research Letters},
  title        = {Improving the predictability of global CO2 assimilation rates under climate change},
  url          = {http://dx.doi.org/10.1029/2011GL047182},
  volume       = {38},
  year         = {2011},
}