Climate researchers have no shortage of scientific issues on which to expend computer power. The biggest problem is choosing which one to tackle first.
IF You’re uSINg a computer to solve a scientific problem, it goes without saying that additional computer power will help answer the problem faster and more accurately. Or will it? For the community of researchers who use vast computer models to simulate Earth’s climate in all its glorious intricacy, greater computational capacity is always welcome, but choosing where to apply that power can be contentious. Is it better to compute existing models in finer detail, or to make the models bigger by adding more scientific content? There’s no single best answer to that conundrum, and in practice the research community pursues as wide a variety of goals as it can, in the hope that a consensus will eventually emerge.
image courtesy tony rosati anD tom Delworth, noaa/gfDl
Today’s so-called General Circulation Models (GCMs) include interlinked components that attempt to capture the behavior of atmosphere, oceans, sea ice, and land surface in determining Earth’s climate. In computational terms, a GCM is essentially an enormous and intricately interlinked collection of ordinary and partial differential equations that calculate air and ocean currents and their associated heat flows; the absorption of the sun’s heat (which depends on cloud cover and the amount of snow
and ice covering the planet’s surface, among other things); the radiation of heat from land and sea ice back into the atmosphere; humidity and precipitation; and a great deal more. Typically, these models cover the planet’s surface by calculating at grid points spaced approximately 100 kilometers apart, and divide the atmosphere, up to a height of some 15 kilometers, into perhaps 20 layers. From a global perspective, with Earth’s total surface area amounting to just more than a half-billion square kilometers, that’s a lot of grid points, but
it takes no scientific expertise to understand that weather conditions can vary significantly across hundred-kilometer distances. As a result, many medium-scale phenomena in current GCMs cannot be calculated directly but must be dealt with by “parameterization,” meaning that important aspects of small-scale physics are in essence approximated and averaged over grid cells.
An obvious use of greater computer power is to decrease the distance between grid points. That’s particularly valuable in ocean modeling, says Ben Kirtman of the Rosenstiel School of Marine and Atmospheric Science at the University of Miami, because calculating on a grid spacing of a few kilometers would directly capture important heat and current flows, without parameterization. Kirtman is working with a project recently funded by the National
a u.s. national oceanic and atmospheric administration climate change model that couples a 25 km-resolution ocean model with a 100 km-resolution atmosphere model.
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