The importance of numerical time-stepping errors
Update: 2010-12-09
Description
Comprehensive assessments of uncertainty in climate prediction models should in principle consider contributions from the discretised numerical schemes, as well as from the parameterised physics and other sources. The numerical contributions are often assumed to be negligible, however. This talk reviews the evidence for uncertainty arising from time-stepping schemes, and suggests a possible avenue for progress to reduce it.
The context for the progress is that many climate models use the simple leapfrog scheme in concert with the Robert-Asselin filter. Unfortunately, this filter introduces artificial damping and degrades the formal accuracy, because of a conservation problem. A simple modification to the filter is proposed, which fixes the conservation problem, thereby reducing the artificial damping and increasing the formal accuracy.
The Robert-Asselin-Williams (RAW) filter may easily be incorporated into existing climate models, via the addition of only a few lines of code that are computationally very inexpensive. Results will be shown from recent implementations of the RAW filter in various models. The modification will be shown to reduce model biases and to significantly improve the predictive skill.
The context for the progress is that many climate models use the simple leapfrog scheme in concert with the Robert-Asselin filter. Unfortunately, this filter introduces artificial damping and degrades the formal accuracy, because of a conservation problem. A simple modification to the filter is proposed, which fixes the conservation problem, thereby reducing the artificial damping and increasing the formal accuracy.
The Robert-Asselin-Williams (RAW) filter may easily be incorporated into existing climate models, via the addition of only a few lines of code that are computationally very inexpensive. Results will be shown from recent implementations of the RAW filter in various models. The modification will be shown to reduce model biases and to significantly improve the predictive skill.
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