Computational Power for Truly Long-Term Forecasts

Monte Carlo simulation is often referred to as a computational space hog.  But how much space a simulation hogs, of course, is a matter of how much data, how many variables, and the complexity of the statistical analysis.  Climate prediction is a wonderful case in point. 

When it comes to predicting atmospheric events, the TV weather guys are pretty good at getting it right for the next few days.  That’s because their forecasts are based on accurate models, and one of the factors in their accuracy is that they account for geographical space in small increments.  For short-term forecasting, the grid spacing is a few tens of kilometers.

But climate change models attempt look a hundred years ahead–as well as a hundred years back–and these truly long-term environmental risk predictions are not nearly so accurate, even with the widespread use of Monte Carlo software.  The inaccuracies result from the fact that because of the limitations on computing power at individual institutions, the present climate change models must use expanded grid spacing.  Otherwise, computation for the statistical analysis involved would overwhelm the computers trying to run the models That’s the reason that Oxford University professor Tim Palmer has proposed a "global" facility to meet the computational needs of climate scientists.  "We do not," he says, have the computing power to solve the known partial differential equations of climate science with sufficient accuracy."  
Particle physicists also have the need for huge models, including those with mammoth Monte Carlo simulations, and they have met it with CERN, the European Organization for Nuclear Research. It is Palmer’s idea that a parallel organization in which national climate change centers could collaborate on an international climate prediction would support significant advances in our understanding of climate change.  At this facility dedicated computer power would allow scientists to squeeze down the grid scale, still be able to run Monte Carlo software to control for approximations at this level of detail, and reveal in much higher resolution how the earth’s climate is changing and how human activities affect this.  

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