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Overview

"Several sciences are often necessary to form the groundwork of a single art" - Mills, 1843

"Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something it is an art to deal with it" - Knuth, 1974

In the spirit of Mills and Knuth, this class will develop an approach to modeling complex systems, such as those of climate, based on the rigorous understanding of the underlying processes we understand and on exploiting our insight and creativity for those we do not. We will explore and use various numerical methods, develop computing skills, and deal with data handing as a means to and end of quantifying climate system behavior.

Instructor: David Noone<dcn@colorado.edu>

When: Spring 2006; Tuesday (lecture) 2:30-3:45pm,  Thursday (lab) 2:30-4:45pm

Where: Stadium 136C (Hart lab)

Prerequisites: ATOC 5050 (5720), ATOC 5060 desirable

Grading: in-class labs (20%), mid-term assignment (30%), modeling project (50%)

Office hours: TBD

Exam: none

Textbook: none

Useful books

Jacobson, D., Fundamentals of atmospheric modeling, Cambridge, 1998.

Kalnay, E., Atmospheric modeling, data assimilation and predictability, Cambridge, 2003.

McKuffie, K., and A. Henderson-Sellers, A climate modeling primer, 2nd ed., John Wiley and Sons, 2005.

Randall, D., General Circulation Model development, Academic press, 2000.

Robinson, W., Modeling dynamic climate systems, Springer, 2001

Salzmann, B., Dynamic paleoclimatology, Elsevier Academic Press, 2004

Trenberth, K., Climate System Modeling, Cambridge, 1992.

Washington, W., and C. Parking, An introduction of three-dimensional climate modeling, 2nd ed., University Science Books, 2004.

Course outline

  1. Box models
  2. Multi-box models
  3. Predictability and chaos
  4. Finite differences
  5. Stability analysis of 1-d advection. (Why does my model crash?)
  6. Explicit and implicit methods: 1-d diffusion equation.
  7. Solving Poisson's equation and other elliptic problems (in 2+ dimensions)
  8. Spectral methods
  9. Transport models
  10. Dynamic models
  11. Lagrangian methods
  12. Weather prediction
  13. Atmospheric general circulation models
  14. The black art of parameterization
  15. Climate models
  16. Climate modeling
  17.  
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