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 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 the predicted climate is undermined by a number of uncertainties:

• ESMs disagree on how much the Earth will warm for a given increase in atmospheric CO2
• how much emitted CO2 will stay in the atmosphere to warm the planet and 
• how much excess heat in the Earth system will enter the ocean interior, delaying surface warming.

Central to these uncertainties are poorly understood, and poorly modelled, Earth system feedbacks, in particular cloud and 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. 

AI4PEX aims to improve the representation of these feedbacks in ESMs, reducing uncertainty in global warming projections. The project has a multidisciplinary approach, focused on “learning” how to accurately describe processes underpinning these feedbacks, through a fusion of observations with advanced machine learning and artificial intelligence. 

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.

A particular challenge in AI4PEX is the need for both ML/AI tools and Earth system models to operate efficiently together on the same, or connected, platforms. The role of CSC in the project is to coordinate and provide support and training for the deployment of tools and software on supercomputer platforms; provide support and services for data processing and hosting during the project; and provision for effective collaboration.