Bayesian Calibration of the Community Land Model using Surrogates

Journal Article · Thu Jan 01 04:00:00 EST 2015 · SIAM/ASA Journal on Uncertainty Quantification, 3(1):199–233

DOI:https://doi.org/10.1137/140957998· OSTI ID:1182912

Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi; Sargsyan, K.; Swiler, Laura P.

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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1182912
• Report Number(s): PNNL-SA-101091; KP1703020; KJ0401000
• Journal Information: SIAM/ASA Journal on Uncertainty Quantification, 3(1):199–233, Journal Name: SIAM/ASA Journal on Uncertainty Quantification, 3(1):199–233
• Country of Publication: United States
• Language: English

Related Subjects

Bayesian calibration
Community Land Model
Markov chain Monte
structural error models
surrogate models