A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics

arXiv:2402.18484v1 Announce Type: cross
Abstract: Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain computationally intractable, policy makers must rely on coarse-models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored "sub-grid" scales. We propose a framework to non-intrusively debias coarse-resolution climate predictions using neural-network (NN) correction operators. Previous efforts have attempted to train such operators …

abstract arxiv climate cs.lg events framework machine machine learning makers physics.ao-ph policy risk simulations statistics type weather