Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in Earth System Models

Global warming continues at an alarming rate, presenting unprecedented challenges to society that require urgent, science-led mitigation and adaptation. Earth system models (ESMs) are essential tools for projecting climate change, providing important information to decision makers. However, confidence in predicted climate change is undermined by a number of uncertainties; (i) ESMs disagree on how much the Earth will warm for a given increase in atmospheric carbon dioxide (CO2) (Earth’s equilibrium climate sensitivity); (ii) how much emitted CO2 will stay in the atmosphere to warm the planet (half the CO2 emitted by humans has been absorbed by the land and ocean) and (iii) how much excess heat in the Earth system will enter the ocean interior, delaying surface warming (~90 % of the heat in the Earth system goes into the ocean). Central to these uncertainties are poorly understood, and poorly modelled, Earth system feedbacks, in particular cloud feedbacks, carbon cycle feedbacks and ocean heat uptake. Poor representation of these phenomena degrades the accuracy of ESM projections, with implications for anticipating future climate extremes and societal impacts. We aim to improve the representation of these feedbacks in ESMs, reducing uncertainty in global warming projections. We propose a multidisciplinary approach, focused on “learning” how to accurately describe processes underpinning these feedbacks, through a fusion of observations with advanced machine learning (ML) and artificial intelligence (AI). Such data and approaches, constrained by the laws of physics, will deliver a step change in the accuracy of Earth system models. AI4PEX will place Europe at the forefront of a revolution in Earth system modelling, leading to increased accuracy of climate change projections and superior support for implementation of the Paris Climate Agreement and the European Green Deal.