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Title: Bayesian Calibration of the Community Land Model using Surrogates

Abstract

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, conditioned 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 accurate surrogate models can be created for CLM in most cases. The posterior distributions lead to better prediction 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 potentially be used to identify physical processes that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1182912
Report Number(s):
PNNL-SA-101091
KP1703020; KJ0401000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: SIAM/ASA Journal on Uncertainty Quantification, 3(1):199–233
Country of Publication:
United States
Language:
English
Subject:
Bayesian calibration; Community Land Model; surrogate models; structural error models; Markov chain Monte

Citation Formats

Ray, Jaideep, Hou, Zhangshuan, Huang, Maoyi, Sargsyan, K., and Swiler, Laura P. Bayesian Calibration of the Community Land Model using Surrogates. United States: N. p., 2015. Web. doi:10.1137/140957998.
Ray, Jaideep, Hou, Zhangshuan, Huang, Maoyi, Sargsyan, K., & Swiler, Laura P. Bayesian Calibration of the Community Land Model using Surrogates. United States. doi:10.1137/140957998.
Ray, Jaideep, Hou, Zhangshuan, Huang, Maoyi, Sargsyan, K., and Swiler, Laura P. Thu . "Bayesian Calibration of the Community Land Model using Surrogates". United States. doi:10.1137/140957998.
@article{osti_1182912,
title = {Bayesian Calibration of the Community Land Model using Surrogates},
author = {Ray, Jaideep and Hou, Zhangshuan and Huang, Maoyi and Sargsyan, K. and Swiler, Laura P.},
abstractNote = {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, conditioned 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 accurate surrogate models can be created for CLM in most cases. The posterior distributions lead to better prediction 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 potentially be used to identify physical processes that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.},
doi = {10.1137/140957998},
journal = {SIAM/ASA Journal on Uncertainty Quantification, 3(1):199–233},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}