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Title: On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites: SURROGATE-BASED MCMC FOR CLM

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesian model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically-average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modesmore » (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less
Authors:
 [1] ;  [2] ;  [3] ;  [3] ;  [1] ;  [4]
  1. Earth System Analysis and Modeling Group, Pacific Northwest National Laboratory, Richland Washington USA
  2. Sandia National Laboratories, Livermore California USA
  3. Hydrology Technical Group, Pacific Northwest National Laboratory, Richland Washington USA
  4. Sandia National Laboratories, Albuquerque New Mexico USA
Publication Date:
OSTI Identifier:
1322501
Report Number(s):
PNNL-SA-113356
Journal ID: ISSN 2169-897X; KJ0401000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Geophysical Research: Atmospheres; Journal Volume: 121; Journal Issue: 13
Publisher:
American Geophysical Union
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English