Center for Computing Research
Computational Mathematics, 01442
The Computational Mathematics Department (1442) performs cutting edge research, driven by DOE needs, to develop the mathematical foundations and the algorithmic and software advances to enable accurate, predictive, and scalable computational simulation methods. We deliver comprehensive theoretical and computational tools that impact Sandia’s mission and push our capabilities beyond forward simulations. Members of the department interact and collaborate with a broad range of Sandia and DOE staff and also maintain a highly visible external research presence by collaborating with universities and industry, publishing peer-reviewed literature, participating in professional societies, and refereeing and editing for journals.
|Michael L. Parks |
Manager, Computational Mathematics
Sandia National Laboratories
P.O. Box 5800, MS 1320
Stephen D Bond
Andrew Michael Bradley
Eric Christopher Cyr
Graham Bennett Harper
Paul Allen Kuberry
Scott A. Mitchell
Peter Brian Ohm
Ravi Ghanshyam Patel
Kara J. Peterson
Nathan V. Roberts
John N. Shadid
Kenneth Chadwick Sockwell (Chad)
Nathaniel Albert Trask
Raymond S. Tuminaro
- Investigating Arctic Climate Variability with Global Sensitivity Analysis of Low-resolution E3SM.
As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers have performed a global sensitivity analysis (GSA) using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM)....
Investigating Arctic Climate Variability with Global Sensitivity Analysis of Low-resolution E3SM.
As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers have performed a global sensitivity analysis (GSA) using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM). Coupled Earth system models are computationally expensive to run, making it difficult to generate the large ensembles required for uncertainty quantification. In this research an ultralow version of E3SM was utilized to tractably investigate parametric uncertainty in the fully coupled model. More than one hundred perturbed simulation ensembles of one hundred years each were generated for the analysis and impacts on twelve Arctic quantities of interest were measured using the PyApprox library. The parameter variations show significant impact on the Arctic climate state with the largest impact coming from atmospheric parameters related to cloud parameterizations. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM. The results will be used to inform model tuning work as well as targeted studies at higher resolution.
Ultra-low atmosphere grid (left) and ultra-low ocean grid (right).
Points of contact: Kara Peterson (firstname.lastname@example.org) Irina Tezaur (email@example.com)
For more information on E3SM: https://e3sm.org/
Contact: Peterson, Kara J.