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

Abstract

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:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1335322
Grant/Contract Number:  
AC05-00OR22725
Resource 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
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

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., 2015. 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. Wed . "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}
}

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Works referenced in this record:

Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations
journal, February 2005


The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
journal, October 2009


Strictly Proper Scoring Rules, Prediction, and Estimation
journal, March 2007

  • Gneiting, Tilmann; Raftery, Adrian E.
  • Journal of the American Statistical Association, Vol. 102, Issue 477
  • DOI: 10.1198/016214506000001437

An Adaptive Metropolis Algorithm
journal, April 2001

  • Haario, Heikki; Saksman, Eero; Tamminen, Johanna
  • Bernoulli, Vol. 7, Issue 2
  • DOI: 10.2307/3318737

The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research
journal, September 2011


Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle: ASSIMILATING CO
journal, October 2002

  • Kaminski, T.; Knorr, W.; Rayner, P. J.
  • Global Biogeochemical Cycles, Vol. 16, Issue 4
  • DOI: 10.1029/2001GB001463

Markov Chain Monte Carlo in Practice: A Roundtable Discussion
journal, May 1998


Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling
journal, August 2005


Estimation of global sensitivity indices for models with dependent variables
journal, April 2012

  • Kucherenko, S.; Tarantola, S.; Annoni, P.
  • Computer Physics Communications, Vol. 183, Issue 4
  • DOI: 10.1016/j.cpc.2011.12.020

Constraining a global ecosystem model with multi-site eddy-covariance data
journal, January 2012


Facilitating feedbacks between field measurements and ecosystem models
journal, May 2013

  • LeBauer, David S.; Wang, Dan; Richter, Katherine T.
  • Ecological Monographs, Vol. 83, Issue 2
  • DOI: 10.1890/12-0137.1

Bayesian Posterior Predictive Checks for Complex Models
journal, February 2004


Comparing observations and process-based simulations of biosphere-atmosphere exchanges on multiple timescales: MODEL EVALUATION ON MULTIPLE TIMESCALES
journal, April 2010

  • Mahecha, M. D.; Reichstein, M.; Jung, M.
  • Journal of Geophysical Research: Biogeosciences, Vol. 115, Issue G2
  • DOI: 10.1029/2009JG001016

Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS): CARBON FLUXES FROM DATA ASSIMILATION SYSTEM
journal, June 2005

  • Rayner, P. J.; Scholze, M.; Knorr, W.
  • Global Biogeochemical Cycles, Vol. 19, Issue 2
  • DOI: 10.1029/2004GB002254

A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty: BAYESIAN CARBON CYCLE MODEL CALIBRATION
journal, June 2008

  • Ricciuto, Daniel M.; Davis, Kenneth J.; Keller, Klaus
  • Global Biogeochemical Cycles, Vol. 22, Issue 2
  • DOI: 10.1029/2006GB002908

Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length
journal, January 2011

  • Ricciuto, Daniel M.; King, Anthony W.; Dragoni, D.
  • Journal of Geophysical Research, Vol. 116, Issue G1
  • DOI: 10.1029/2010JG001400

Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis
journal, November 2011


Making best use of model evaluations to compute sensitivity indices
journal, May 2002


Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing
journal, January 2014


Measuring and testing dependence by correlation of distances
journal, December 2007

  • Székely, Gábor J.; Rizzo, Maria L.; Bakirov, Nail K.
  • The Annals of Statistics, Vol. 35, Issue 6
  • DOI: 10.1214/009053607000000505

Influence of carbon-nitrogen cycle coupling on land model response to CO 2 fertilization and climate variability : INFLUENCE OF CARBON-NITROGEN COUPLING
journal, December 2007

  • Thornton, Peter E.; Lamarque, Jean-François; Rosenbloom, Nan A.
  • Global Biogeochemical Cycles, Vol. 21, Issue 4
  • DOI: 10.1029/2006GB002868

OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models
journal, January 2007

  • Trudinger, Cathy M.; Raupach, Michael R.; Rayner, Peter J.
  • Journal of Geophysical Research, Vol. 112, Issue G2
  • DOI: 10.1029/2006JG000367

Factors controlling CO 2 exchange on timescales from hourly to decadal at Harvard Forest
journal, January 2007

  • Urbanski, S.; Barford, C.; Wofsy, S.
  • Journal of Geophysical Research, Vol. 112, Issue G2
  • DOI: 10.1029/2006JG000293

An improved analysis of forest carbon dynamics using data assimilation
journal, January 2005


A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC)
journal, August 2011


    Works referencing / citing this record:

    On the effect of model parameters on forecast objects
    journal, January 2018

    • Marzban, Caren; Jones, Corinne; Li, Ning
    • Geoscientific Model Development, Vol. 11, Issue 4
    • DOI: 10.5194/gmd-11-1577-2018

    On the effect of model parameters on forecast objects
    journal, January 2018

    • Marzban, Caren; Jones, Corinne; Li, Ning
    • Geoscientific Model Development, Vol. 11, Issue 4
    • DOI: 10.5194/gmd-11-1577-2018