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Abstract

Uncertainty quantification (UQ) is a critical part in any computational model development. However, when dealing with complex climate models, canonical UQ methods face a range of challenges including
large number of input parameters

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In particular, we will describe polynomial chaos surrogate construction enabling efficient propagation of uncertainty and global sensitivity analysis via variance-based decomposition. Input dimensionality reduction is achieved by sparsity-imposing regularization while high-dimensional spatio-temporal output field is represented by Karhunen-Loeve Loève expansions. The inverse UQ is performed with Bayesian methods, via Markov chain Monte Carlo sampling. Bayesian machinery is well-suited to handle noisy and scarce observational data, while the required multiple model evaluations are alleviated by the pre-constructed surrogate usage. Furthermore, we will enhance the conventional Bayesian machinery to enable representation and propagation of uncertainties due to model structural errors. The overall framework allows efficient automated UQ with predictive uncertainty attributed to various sources such as parameter uncertainty, data noise and structural errors.

The developed workflow is connected to UQ Toolkit (www.sandia.gov/uqtoolkit). We will demonstrate the application of the methods to the E3SM land model (ELM) using observational data from selected FLUXNET sites.

PS: depending on the session, the talk/poster will highlight different components: generic methodology (Integrated session) or ELM application (E3SM session).