Bayesian Calibration of the Community Land Model using Surrogates


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 the CLM under two error models. We find that accurate surrogate models could be created for the CLM in three out of the four cases we investigated. 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 the structural error. While the calibrated CLM has a higher predictive skill, the calibration is underdispersive.

SIAM/ASA Journal on Uncertainty Quantification