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Title: Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.

Abstract

Arctic sea ice is an important component of the global climate system and due to feedback effects the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice to model physical parameters. A new sea ice model that has the potential to improve sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of the Los Alamos National Laboratory CICE code and the MPM sea ice code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness, and central Arctic ice speed with respect to tenmore » different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.« less

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
1007324
Report Number(s):
SAND2010-6218
TRN: US201106%%900
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ADVECTION; CLIMATE MODELS; CLIMATES; DESIGN; FEEDBACK; FORECASTING; LAGRANGIAN FUNCTION; LANL; MATHEMATICAL MODELS; OPTIMIZATION; RHEOLOGY; SEAS; SENSITIVITY; SENSITIVITY ANALYSIS; SIMULATION; THERMODYNAMICS; THICKNESS; VELOCITY

Citation Formats

Peterson, Kara J., Bochev, Pavel Blagoveston, and Paskaleva, Biliana S. Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.. United States: N. p., 2010. Web. doi:10.2172/1007324.
Peterson, Kara J., Bochev, Pavel Blagoveston, & Paskaleva, Biliana S. Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.. United States. doi:10.2172/1007324.
Peterson, Kara J., Bochev, Pavel Blagoveston, and Paskaleva, Biliana S. Wed . "Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.". United States. doi:10.2172/1007324. https://www.osti.gov/servlets/purl/1007324.
@article{osti_1007324,
title = {Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.},
author = {Peterson, Kara J. and Bochev, Pavel Blagoveston and Paskaleva, Biliana S.},
abstractNote = {Arctic sea ice is an important component of the global climate system and due to feedback effects the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice to model physical parameters. A new sea ice model that has the potential to improve sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of the Los Alamos National Laboratory CICE code and the MPM sea ice code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness, and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.},
doi = {10.2172/1007324},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {9}
}

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