A Sensitivity-Enhanced Simulation Approach for Community Climate System Model

  • Jong G. Kim
  • Elizabeth C. Hunke
  • William H. Lipscomb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


A global sea-ice modeling component of the Community Climate System Model was augumented with automatic differentiation (AD) technology. The numerical experiments were run with two problem sets of different grid sizes. Rigid ice regions with high viscous properties cause computational difficulty in the propagation of AD-based derivative computation. Pre-tuning step was required to obtain successful convergence behavior. Various thermodynamic and dynamic parameters were selected for multivariate sensitivity analysis. The major parameters controlling the sea-ice thickness/volume computation were ice and snow densitives, albedo parameters, thermal conductivities, and emissivity constant. Especially, the ice and snow albedo parameters are found to have stronger effect during melting seasons. This high seasonal variability of the thermodynamic parameters underlines the importance of the multivariate sensitivity approach in global sea-ice modeling studies.


Community Climate System Model Multivariate Sensitivity Analysis High Seasonal Variability Source Code Transformation Albedo Parameter 


  1. 1.
    Bücker, H.M., Rasch, A., Slusanschi, E., Bischof, C.H.: Delayed Propagation of Derivatives in a Two-dimensional Aircraft Design Optimization Problem. In: Proceedings of the 17th Annual International Symposium on High Performance Computing Systems and Applications and OSCAR Symposium, Sherbrooke, Québec, Canada, May 11–14. NRC Research Press (2003)Google Scholar
  2. 2.
    Harder, M., Fischer, H.: Sea ice dynamics in the Weddell Sea simulation with an optimized model. J. Geophys. Res. 104, 11,151–11,162 (1999)Google Scholar
  3. 3.
    Hunke, E.C., Limpscomb, W.H.: CICE: the Los Alamos Sea Ice Model, Documentation and Software, LA-CC-98-16, Los Alamos National Laboratory, NM (2004)Google Scholar
  4. 4.
    Hascoet, L., Pascual, V.: TAPENADE 2.1 User’s Guide, INRIA Technical Report (2004), http://www-sop.inria.fr/tropics
  5. 5.
    Miller, P.A., Laxon, S.W., Feltham, D.L., Cresswell, D.J.: Optimization of a sea ice model using basin-wide observations of Arctic sea ice thickness, extent and velocity. J. Clim. (2005) (in press)Google Scholar
  6. 6.
    Parkinson, C.L., Washington, W.M.: A large-scale numerical model of sea ice. J. Geophys. Res. 84, 311–337 (1979)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jong G. Kim
    • 1
  • Elizabeth C. Hunke
    • 2
  • William H. Lipscomb
    • 2
  1. 1.MCS DivisionArgonne National LaboratoryArgonneU.S.A
  2. 2.Theoretical DivisionLos Alamos National LaboratoryLos AlamosU.S.A

Personalised recommendations