To Fight Climate Change, We Need More Powerful Supercomputers

To Fight Climate Change, We Need More Powerful Supercomputers

The second-best option, according to Dr. Palmer, is to randomize the sub-grid processes. Counter-intuitively, this additional randomness has the effect of stabilizing extreme weather conditions. Weather forecasts that take into account random (or “stochastic”) processes make more accurate predictions for the frequency of tropical cyclones, the duration of droughts and other weather phenomena, such as the long-lasting heat spell over Europe in the summer of 2018. It seems only reasonable, then, that long-term climate predictions should use this method too.

Climate scientists have begun to take note of Dr. Palmer’s argument. The new British climate model (UKESM1), in use since 2018, uses this method of randomness, and others are sure to follow. Björn Stevens, director of the Max Planck Institute for Meteorology in Hamburg, Germany, agrees with Dr. Palmer’s assessment. For the next generation of models, his institution “will be interested in exploring the role of stochastic treatments.”

But Dr. Palmer does not want to settle on the second best, and still hopes to bring the grid size of climate models down. A horizontal grid of about 1 km (0.6 mi), he believes, would significantly improve the accuracy of our climate models, and would give us the information we need to accurately gauge the risks posed by climate change.

To do this, we need supercomputers capable of performing these calculations. Centers of exascale supercomputers — computers able to perform at least a billion billion calculations per second — would be up to the task.

Read More At The New York Times

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