The new model will be built by a consortium of researchers led by Caltech, in partnership with MIT; the Naval Postgraduate School (NPS); and the Jet Propulsion Laboratory (JPL), which Caltech manages for NASA. The consortium, dubbed the Climate Modeling Alliance (CliMA), plans to fuse Earth observations and high-resolution simulations into a model that represents important small-scale features, such as clouds and turbulence, more reliably than existing climate models. The goal is a climate model that projects future changes in critical variables such as cloud cover, rainfall, and sea ice extent more accurately — with uncertainties at least half the size of those in existing models. "Projections with current climate models — for example, of how features such as rainfall extremes will change — still have large uncertainties, and the uncertainties are poorly quantified," says Tapio Schneider, Caltech's Theodore Y. Wu Professor of Environmental Science and Engineering, senior research scientist at JPL, and principal investigator of CliMA. "For cities planning their stormwater management infrastructure to withstand the next 100 years' worth of floods, this is a serious issue; concrete answers about the likely range of climate outcomes are key for planning." .... Current climate modeling relies on dividing up the globe into a grid and then computing what is going on in each sector of the grid, as well as how the sectors interact with each other. The accuracy of any given model depends in part on the resolution at which the model can view the Earth — that is, the size of the grid's sectors. Limitations in available computer processing power mean that those sectors generally cannot be any smaller than tens of kilometers per side. But for climate modeling, the devil is in the details — details that get missed in a too-large grid. For example, low-lying clouds have a significant impact on climate by reflecting sunlight, but the turbulent plumes that sustain them are so small that they fall through the cracks of existing models. Similarly, changes in Arctic sea ice have been linked to wide-ranging effects on everything from polar climate to drought in California, but it is difficult to predict how that ice will change in the future because it is sensitive to the density of cloud cover above the ice and the temperature of ocean currents below, both of which cannot be resolved by current models
I recall decades ago examining random fields, my doctoral thesis, and how complex a problem they are. Now they are totally redoing models. Hopefully accuracy rather than precision is the end result. Then again should we question what we are being told, it is precise but is it accurate? Just a thought.
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