skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Parameter and Prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length

Abstract

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarlymore » reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.« less

Authors:
 [1];  [1];  [2]
  1. ORNL
  2. Indiana University
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1081757
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Journal of Geophysical Research
Additional Journal Information:
Journal Volume: 116
Country of Publication:
United States
Language:
English

Citation Formats

Post, Wilfred M, King, Anthony Wayne, and Dragoni, Danilo. Parameter and Prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length. United States: N. p., 2011. Web.
Post, Wilfred M, King, Anthony Wayne, & Dragoni, Danilo. Parameter and Prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length. United States.
Post, Wilfred M, King, Anthony Wayne, and Dragoni, Danilo. Sat . "Parameter and Prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length". United States.
@article{osti_1081757,
title = {Parameter and Prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length},
author = {Post, Wilfred M and King, Anthony Wayne and Dragoni, Danilo},
abstractNote = {Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.},
doi = {},
url = {https://www.osti.gov/biblio/1081757}, journal = {Journal of Geophysical Research},
number = ,
volume = 116,
place = {United States},
year = {2011},
month = {1}
}