Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model-Reference-Cited by-同舟云学术

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Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

Published:2022-08 Issue:8 Volume:14 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Tezaur Irina¹^ORCID,Peterson Kara²,Powell Amy³,Jakeman John⁴,Roesler Erika⁵

Affiliation:

1. Quantitative Modeling & Analysis Department Sandia National Laboratories Livermore CA USA

2. Computational Mathematics Department Sandia National Laboratories Albuquerque NM USA

3. Systems Design & Architecture Department Sandia National Laboratories Albuquerque NM USA

4. Optimization & UQ Department Sandia National Laboratories Albuquerque NM USA

5. Atmospheric Sciences Department Sandia National Laboratories Albuquerque NM USA

Funder

Sandia National Laboratories

National Nuclear Security Administration

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002831

Reference69 articles.

1. Adams B. Bauman L. Bohnhoff W. Dalbey K. Ebeida M. Eddy J. et al. (2013).DAKOTA a multilevel parallel object‐oriented framework for design optimization parameter estimation uncertainty quantification and sensitivity analysis: Version 5.4 user’s manual(Tech. Rep.).

2. Global sensitivity analysis in an ocean general circulation model: a sparse spectral projection approach

3. Beyond bifurcation: using complex models to understand and predict abrupt climate change

4. Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling;Environmental Modelling & Software;2023-12

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