Open AccessA global sensitivity analysis and Bayesian inference framework for improving the parameter estimation and prediction of a process‐based Terrestrial Ecosystem Model
Author(s) -
Tang Jinyun,
Zhuang Qianlai
Publication year - 2009
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2009jd011724
Subject(s) - ecosystem respiration , bayesian inference , fluxnet , environmental science , eddy covariance , bayesian probability , sensitivity (control systems) , primary production , atmospheric sciences , statistics , mathematics , ecosystem , ecology , physics , electronic engineering , engineering , biology
A global sensitivity analysis and Bayesian inference framework was developed for improving the parameterization and predictability of a monthly time step process‐based biogeochemistry model. Using a Latin Hypercube sampler and an existing Terrestrial Ecosystem Model (TEM), a set of 500,000 Monte Carlo ensemble simulations was conducted for a black spruce forest ecosystem. A global sensitivity analysis was then conducted to identify the key model parameters and examine the interaction structures among TEM parameters. Bayesian inference analysis was also performed using these ensemble simulations and eddy flux data of carbon, latent heat flux, and MODIS gross primary production (GPP) to reduce the uncertainty of parameter estimation and prediction of TEM. We found that (1) the simulated carbon fluxes are mostly affected by parameters of the maximum rate of photosynthesis (CMAX), the half‐saturation constant for CO 2 uptake by plants ( k c ), the half‐saturation constant for Photosynthetically Active Radiation used by plants ( k i ), and the change in autotrophic respiration due to 10°C temperature increase (RHQ10); (2) the effect of parameters on seasonal carbon dynamics varies from one parameter to another during a year; (3) to well constrain the uncertainties of TEM predictions and parameters using the Bayesian inference technique, at least two different fluxes of NEP, GPP, and ecosystem respiration (RESP) are required; and (4) different assumptions of the error structures of the flux data used in the Bayesian inference analysis result in different uncertainty bounds of the posterior parameters and model predictions. We further found that, using the Bayesian framework and eddy flux and satellite data, the uncertainty of simulated carbon fluxes has been remarkably reduced. The developed global sensitivity analysis and Bayesian framework could further be used to analyze and improve the predictability and parameterization of relatively coarse time step biogeochemistry models when the eddy flux and satellite data are available for other terrestrial ecosystems.