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Title: Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.
Authors:
 [1] ;  [2] ;  [1] ;  [1] ;  [1] ;  [3] ;  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Edinburgh, Scotland (United Kingdom)
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 8; Journal Issue: 7; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES
OSTI Identifier:
1335322

Safta, C., Ricciuto, Daniel M., Sargsyan, Khachik, Debusschere, B., Najm, H. N., Williams, M., and Thornton, Peter E.. Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model. United States: N. p., Web. doi:10.5194/gmd-8-1899-2015.
Safta, C., Ricciuto, Daniel M., Sargsyan, Khachik, Debusschere, B., Najm, H. N., Williams, M., & Thornton, Peter E.. Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model. United States. doi:10.5194/gmd-8-1899-2015.
Safta, C., Ricciuto, Daniel M., Sargsyan, Khachik, Debusschere, B., Najm, H. N., Williams, M., and Thornton, Peter E.. 2015. "Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model". United States. doi:10.5194/gmd-8-1899-2015. https://www.osti.gov/servlets/purl/1335322.
@article{osti_1335322,
title = {Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model},
author = {Safta, C. and Ricciuto, Daniel M. and Sargsyan, Khachik and Debusschere, B. and Najm, H. N. and Williams, M. and Thornton, Peter E.},
abstractNote = {In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.},
doi = {10.5194/gmd-8-1899-2015},
journal = {Geoscientific Model Development (Online)},
number = 7,
volume = 8,
place = {United States},
year = {2015},
month = {7}
}