Uncertainty quantification for large-scale ocean circulation predictions.
Abstract
Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO{sub 2} forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.
- Authors:
- Publication Date:
- Research Org.:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1008117
- Report Number(s):
- SAND2010-6203
TRN: US201108%%133
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; ATLANTIC OCEAN; CLIMATE MODELS; CLIMATES; POLYNOMIALS; SENSITIVITY
Citation Formats
Safta, Cosmin, Debusschere, Bert J, Najm, Habib N, and Sargsyan, Khachik. Uncertainty quantification for large-scale ocean circulation predictions.. United States: N. p., 2010.
Web. doi:10.2172/1008117.
Safta, Cosmin, Debusschere, Bert J, Najm, Habib N, & Sargsyan, Khachik. Uncertainty quantification for large-scale ocean circulation predictions.. United States. https://doi.org/10.2172/1008117
Safta, Cosmin, Debusschere, Bert J, Najm, Habib N, and Sargsyan, Khachik. 2010.
"Uncertainty quantification for large-scale ocean circulation predictions.". United States. https://doi.org/10.2172/1008117. https://www.osti.gov/servlets/purl/1008117.
@article{osti_1008117,
title = {Uncertainty quantification for large-scale ocean circulation predictions.},
author = {Safta, Cosmin and Debusschere, Bert J and Najm, Habib N and Sargsyan, Khachik},
abstractNote = {Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO{sub 2} forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.},
doi = {10.2172/1008117},
url = {https://www.osti.gov/biblio/1008117},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Sep 01 00:00:00 EDT 2010},
month = {Wed Sep 01 00:00:00 EDT 2010}
}