clMost of us think of them as puffy and picturesque, but for climate modelers, clouds are a major thorn in the side, and are the hard question. Scientists do know clouds wield an outsize influence on climate: Reducing Earth’s cloud cover by just 5% would have the same planet-wide warming effect as all the CO2 released into the atmosphere since the start of the industrial age. The big problem, however, is that as the earth warms, the effect of clouds changes too, and after decades of research and debate scientists can’t agree exactly how. Most think clouds will warm the earth overall, others think they could cool it, and no one is sure by how much either way. Not knowing how these potentially huge cloud influences will unfold makes predicting climate change very difficult. The best estimate of temperature increase due to a doubling of atmospheric CO2 lies anywhere between +1.5C (2.7F) and +4.5C (8.1F), according to the U.N. Intergovernmental Panel on Climate Change (IPCC). Most of that gigantic uncertainty owes to the difficulty of modeling clouds. Cumulonimbus cloud over Warsaw, Poland. Image by Kamil Nowacki. Now that uncertainty range may have been narrowed down to a sliver, based on the findings of a new study by researchers at Imperial College, London. The team took a novel approach, sidestepping finicky cloud modeling schemes altogether, and turning to machine learning to predict climate “cloud feedback” instead. Climate models work by slicing the world up into a three-dimensional grid and assigning values…This article was originally published on Mongabay Läs mer

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