New research demonstrates that machine-learning techniques can be used to accurately represent clouds and their atmospheric heating and moistening, and better represent clouds in coarse resolution climate models, with the potential to narrow the range of climate prediction. This could be a major advance in accurate predictions of global warming in response to increased greenhouse gas concentrations that are essential for policy-makers (e.g. the Paris climate agreement).
A new study examined the global carbon cycle and suggests that scientists may have misgauged how carbon is distributed around the world, particularly between the northern and southern hemispheres. The results could change projections of how, when and where the currently massive levels of atmospheric carbon will result in environmental changes such as ocean acidification.
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