Adaptive-mesh Grid Techniques for Climate Modeling on Millennial
||This visualization shows
small-scale flow impinging a mountain using three levels of
refinements on the cubed-sphere mesh. Operator integration factor
splitting is employed for advancing the solution in time, enabling
large time steps (360 seconds) even at a fine resolution of 70 km
squared. This simulation approach has the potential to improve
forecast accuracy and reduce model running time on current and
upcoming supercomputer architectures.
(106 MB mpeg).
||Steady-state solution to the
shallow water equations is compared in two different adaptive dynamical
cores. Introducing a refinement into the balanced steady-state flow for
the finite volume dynamical core (left) produces a counterintuitive
result: the error grows! This does not occur with the spectral-element
dynamical core (right, finer scale), which means this new core shows
great promise as a replacement for the dynamical core used in the
Community Climate Simulation Model (CCSM) and other atmospheric models
currently in production.
Weather and climate simulation are extremely complex problems
that require many long-duration computer simulations of the Earth's
atmosphere. Recently, attempts have been made to improve the accuracy
of long-duration models by simulating localized flow structures such
as hurricanes. Simulating such details requires higher resolution and is
required by applications in the atmospheric and air-quality communities,
at operational centers attempting to forecast significant weather events,
and for research endeavors that focus on the dynamics of severe storms
Recent developments in computational mathematics techniques are
being applied to existing models to help them simulate local structures
such as storms and fronts. We are applying adaptive-mesh grid techniques
to atmospheric models, and this promotes collaboration between the
mathematicians who develop these methods and meteorologists who model
and study such events. Weather modelers worldwide are striving
to increase model resolution, but their methods have been limited to
only reducing the spacing between grid points everywhere, which
drastically increases computer time. U.S. modelers are currently
trailing in this approach because it requires such close interaction
between atmospheric modelers, computational scientists, and computer
scientists. Our new method employs techniques designed to leapfrog
that strategy: we are developing more efficient models that can
automatically hone in on localized structures with high precision
while simulating other areas with lower resolution. Further, this
approach is tailored to operate efficiently on current and upcoming
This new methodology was compared with a low-order dynamical core
for standard test cases, and the results were startling. Our new
high-order approach is not only more efficient computationally, but
for the same number of grid points, it was more precise and less
diffusive. Less diffusivity allows simulations of thousands of years
of atmospheric processes without degradation. Also, our tests of the
time stepping showed greatly improved efficiency. Efforts are now
being applied to a new adaptive global weather model that will run
on petascale supercomputers.
This effort supports NCAR's strategic priorities of "Conducting
research in computer science, applied mathematics, statistics, and
numerical methods," "Developing community models," and "Improving
prediction of weather, climate, and other atmospheric phenomena."
It will yield more efficient and accurate models, help reinforce
NCAR's image as a leader in cutting-edge numerical methods, and may
set the standard for next-generation climate and weather models.
By using the best applications available, NCAR's atmospheric
modeling community can concentrate their efforts on improving
our understanding of the planet.
This project exploring adaptive-mesh grid techniques in
spectral-element dynamical cores is supported by the NSF with awards
0222282, 0530845, and Core funding.