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

Abstract

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. Themore » 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. As a result, 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];  [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)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1259834
Alternate Identifier(s):
OSTI ID: 1262239
Report Number(s):
SAND-2015-7946J; SAND-2016-5746J
Journal ID: ISSN 2169-897X; 603839
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Name: Journal of Geophysical Research: Atmospheres; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; Community Land Model; MCMC-Bayesian; surrogate; flux tower

Citation Formats

Huang, Maoyi, Ray, Jaideep, Hou, Zhangshuan, Ren, Huiying, Liu, Ying, and Swiler, Laura. On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites. United States: N. p., 2016. Web. https://doi.org/10.1002/2015jd024339.
Huang, Maoyi, Ray, Jaideep, Hou, Zhangshuan, Ren, Huiying, Liu, Ying, & Swiler, Laura. On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites. United States. https://doi.org/10.1002/2015jd024339
Huang, Maoyi, Ray, Jaideep, Hou, Zhangshuan, Ren, Huiying, Liu, Ying, and Swiler, Laura. Wed . "On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites". United States. https://doi.org/10.1002/2015jd024339. https://www.osti.gov/servlets/purl/1259834.
@article{osti_1259834,
title = {On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites},
author = {Huang, Maoyi and Ray, Jaideep and Hou, Zhangshuan and Ren, Huiying and Liu, Ying and Swiler, Laura},
abstractNote = {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. As a result, 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.},
doi = {10.1002/2015jd024339},
journal = {Journal of Geophysical Research: Atmospheres},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {6}
}

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    Works referencing / citing this record:

    Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques
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