skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Bayesian calibration of the Community Land Model using surrogates

Technical Report ·
DOI:https://doi.org/10.2172/1204075· OSTI ID:1204075
 [1];  [2];  [2];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditional on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural error in CLM under two error models. We find that surrogate models can be created for CLM in most cases. The posterior distributions are more predictive than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters' distributions significantly. The structural error model reveals a correlation time-scale which can be used to identify the physical process that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.

Research Organization:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
DOE Contract Number:
AC04-94AL85000
OSTI ID:
1204075
Report Number(s):
SAND2014-0867; 498573
Country of Publication:
United States
Language:
English

Similar Records

Bayesian Calibration of the Community Land Model using Surrogates
Journal Article · 2015 · SIAM/ASA Journal on Uncertainty Quantification, 3(1):199–233 · OSTI ID:1204075

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
Journal Article · 2016 · Journal of Geophysical Research: Atmospheres · OSTI ID:1204075

On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites
Journal Article · 2016 · Journal of Geophysical Research: Atmospheres · OSTI ID:1204075

Related Subjects