Efficient uncertainty quantification methodologies for high-dimensional climate land models

Technical Report · Tue Nov 01 04:00:00 EDT 2011

DOI:https://doi.org/10.2172/1113860· OSTI ID:1113860

Sargsyan, Khachik ^[1]; Safta, Cosmin ^[1]; Berry, Robert Dan ^[1]; Ray, Jaideep ^[1]; Debusschere, Bert J. ^[1]; Najm, Habib N. ^[1]

---

1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)

In this report, we proposed, examined and implemented approaches for performing efficient uncertainty quantification (UQ) in climate land models. Specifically, we applied Bayesian compressive sensing framework to a polynomial chaos spectral expansions, enhanced it with an iterative algorithm of basis reduction, and investigated the results on test models as well as on the community land model (CLM). Furthermore, we discussed construction of efficient quadrature rules for forward propagation of uncertainties from high-dimensional, constrained input space to output quantities of interest. The work lays grounds for efficient forward UQ for high-dimensional, strongly non-linear and computationally costly climate models. Moreover, to investigate parameter inference approaches, we have applied two variants of the Markov chain Monte Carlo (MCMC) method to a soil moisture dynamics submodel of the CLM. The evaluation of these algorithms gave us a good foundation for further building out the Bayesian calibration framework towards the goal of robust component-wise calibration.

• Research Organization: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1113860
• Report Number(s): SAND2011--8757; 456836
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES