Sensitivity Analysis and Parameter Tuning Scheme for Global Sea-Ice Modeling
Automatic differentiation (AD) is used to perform a multiple parameter sensitivity analysis for the Los Alamos sea-ice model CICE. Numerical experiments are run by six-hourly, 1997 forcing data with a two-hour time step, and the AD-based sensitivity scheme is validated by comparison with derivatives...
| Main Authors: | , , |
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.379.5538 http://info.mcs.anl.gov/pub/tech_reports/reports/P1282.pdf |
| Summary: | Automatic differentiation (AD) is used to perform a multiple parameter sensitivity analysis for the Los Alamos sea-ice model CICE. Numerical experiments are run by six-hourly, 1997 forcing data with a two-hour time step, and the AD-based sensitivity scheme is validated by comparison with derivatives calculated using the conventional finite-difference approach. Twenty-two thermodynamic and dynamic parameters are selected for simultaneous analysis. Of these, the most important for controlling the simulated average sea-ice thickness is ice density; albedos and emissivity predominate in summer, while ice thickness is most sensitive to the ice conductivity in winter. The ice-ocean drag parameter and maximum ice salinity significantly affect the simulation year-round. Gradient information computed by the AD-based sea-ice code is then used in an experiment designed to assess the efficacy of this technique for tuning the parameters against observational data. Preliminary results, obtained with a bound-constrained minimization method and with simulated observational data, show that satisfactory convergence is obtained. |
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