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Climate data induced uncertainty in model-based estimations of terrestrial primary productivity

Wu, Zhendong LU ; Ahlström, Anders LU ; Smith, Benjamin LU ; Ardö, Jonas LU ; Eklundh, Lars LU ; Fensholt, Rasmus and Lehsten, Veiko LU (2017) In Environmental Research Letters 12(6).
Abstract
Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9 % of mean GPP). We also... (More)
Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9 % of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32 % of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Climate datasets, GPP, Uncertainty, LPJ-GUESS, Apparent model sensitivity, Climate data range, Global carbon cycle
in
Environmental Research Letters
volume
12
issue
6
publisher
IOP Publishing
external identifiers
  • scopus:85011372234
ISSN
1748-9326
DOI
10.1088/1748-9326/aa6fd8
language
English
LU publication?
yes
id
278a3dcf-af82-4d99-9ef8-260fa10eebf6
date added to LUP
2017-06-20 12:11:36
date last changed
2017-08-04 14:50:39
@article{278a3dcf-af82-4d99-9ef8-260fa10eebf6,
  abstract     = {Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9 % of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32 % of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.},
  articleno    = {064013},
  author       = {Wu, Zhendong and Ahlström, Anders and Smith, Benjamin and Ardö, Jonas and Eklundh, Lars and Fensholt, Rasmus and Lehsten, Veiko},
  issn         = {1748-9326},
  keyword      = {Climate datasets,GPP,Uncertainty,LPJ-GUESS,Apparent model sensitivity,Climate data range,Global carbon cycle},
  language     = {eng},
  month        = {06},
  number       = {6},
  publisher    = {IOP Publishing},
  series       = {Environmental Research Letters},
  title        = {Climate data induced uncertainty in model-based estimations of terrestrial primary productivity},
  url          = {http://dx.doi.org/10.1088/1748-9326/aa6fd8},
  volume       = {12},
  year         = {2017},
}