Abstract
Understanding and characterizing sources of uncertainty in climate modeling is an important task. Because of the ever increasing sophistication and resolution of climate modeling it is increasing important to develop uncertainty quantification methods that minimize the computational cost that occurs when these methods are added to climate modeling. This research explores the application of sparse stochastic collocation with polynomial edge detection to characterize portions of the probability space associated with the Earth's radiative budget in the Community Earth System Model (CESM). Specifically, we develop surrogate models with error estimates for a range of acceptable input parameters that predict statistical values of the Earth's radiative budget as derived from the CESM simulation.
| Original language | English |
|---|---|
| Pages (from-to) | 1014-1020 |
| Number of pages | 7 |
| Journal | Procedia Computer Science |
| Volume | 9 |
| DOIs | |
| State | Published - 2012 |
| Event | 12th Annual International Conference on Computational Science, ICCS 2012 - Omaha, NB, United States Duration: Jun 4 2012 → Jun 6 2012 |
Funding
The submitted manuscript has been authored in part by contractors [UT-Battelle LLC, manager of Oak Ridge National Laboratory (ORNL)] of the U.S. Government under Contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.
Keywords
- Climate modeling
- Error estimation
- Uncertainty quantification