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Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

Journal Article · · Journal of Geophysical Research. Oceans
DOI:https://doi.org/10.1002/2015JC011558· OSTI ID:1325643
 [1];  [1];  [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations
Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.
Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1325643
Report Number(s):
LA-UR-15-29485
Journal Information:
Journal of Geophysical Research. Oceans, Journal Name: Journal of Geophysical Research. Oceans Journal Issue: 4 Vol. 121; ISSN 2169-9275
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (8)

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E3SMv0‐HiLAT: A Modified Climate System Model Targeted for the Study of High‐Latitude Processes journal August 2019
Emergent Relationships Among Sea Ice, Longwave Radiation, and the Beaufort High Circulation Exposed Through Parameter Uncertainty Analysis journal December 2019
Quality control for community-based sea-ice model development
  • Roberts, Andrew F.; Hunke, Elizabeth C.; Allard, Richard
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 376, Issue 2129 https://doi.org/10.1098/rsta.2017.0344
journal August 2018
The NUIST Earth System Model (NESM) version 3: description and preliminary evaluation journal January 2018
On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model journal January 2019
Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model journal January 2017
On the discretization of the ice thickness distributionin the NEMO3.6-LIM3 global ocean–sea ice model text January 2019

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Related Subjects

54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES
Earth Sciences
sea ice model
uncertainty quantification
global sensitivity analysis
CICE