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On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites

Journal Article · · Journal of Geophysical Research: Atmospheres
DOI:https://doi.org/10.1002/2015jd024339· OSTI ID:1259834
 [1];  [2];  [1];  [1];  [1];  [3]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
+ Show Author Affiliations
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 modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Lastly, 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.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1259834
Alternate ID(s):
OSTI ID: 1322501
OSTI ID: 1262239
Report Number(s):
SAND--2015-7946J; SAND--2016-5746J; 603839
Journal Information:
Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres; ISSN 2169-897X
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English

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Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation journal January 2018
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques journal January 2019

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Related Subjects

54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES
Community Land Model
MCMC-Bayesian
flux tower
surrogate