The Importance of Architecture Choice in Deep Learning for Climate Applications

arXiv:2402.13979v1 Announce Type: new
Abstract: Machine Learning has become a pervasive tool in climate science applications. However, current models fail to address nonstationarity induced by anthropogenic alterations in greenhouse emissions and do not routinely quantify the uncertainty of proposed projections. In this paper, we model the Atlantic Meridional Overturning Circulation (AMOC) which is of major importance to climate in Europe and the US East Coast by transporting warm water to these regions, and has the potential for abrupt collapse. We …

abstract applications architecture arxiv become climate climate science cs.ai cs.lg current deep learning emissions greenhouse importance machine machine learning paper science tool type uncertainty