3Q: Machine learning and climate modeling
Today, predicting what the future has in store for Earth’s climate means dealing in uncertainties. For example, the core climate projections from the Intergovernmental Panel on Climate Change (IPCC) has put the global temperature bump from a doubling of atmospheric CO2 levels — referred to as “climate sensitivity” — anywhere between 1.5 degrees C and 4.5 C. That gap, which has not budged since the first IPCC report in 1990, has profound implications for the type of environmental events humanity may want to prepare for. Part of the uncertainty arises because of unforced variability — changes that would occur even in the absence of increases in CO2 — but part of it arises because of the need for models to simulate complex processes like clouds and convection. Recently, climate scientists have tried to narrow the ranges of the uncertainty in climate models by using a recent revolution in computer science. Machine learning, which is already being deployed for...