ProjectAI4PEX – Artificial Intelligence for Enhanced Representation of Processes and Extremes in Earth System Models
Basic data
Acronym:
AI4PEX
Title:
Artificial Intelligence for Enhanced Representation of Processes and Extremes in Earth System Models
Duration:
01/04/2024 to 31/03/2028
Abstract / short description:
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 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 and artificial intelligence. Such data and approaches, constrained
by the laws of physics, will deliver a step change in the accuracy of Earth system models.
AI4PEX Artificial Intelligence and machine learning for enhanced representation of Processes and EXtremes in Earth system models:
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.
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 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 and artificial intelligence. Such data and approaches, constrained
by the laws of physics, will deliver a step change in the accuracy of Earth system models.
AI4PEX Artificial Intelligence and machine learning for enhanced representation of Processes and EXtremes in Earth system models:
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.
Involved staff
Managers
Department of Informatics
Faculty of Science
Faculty of Science
Department of Informatics
Faculty of Science
Faculty of Science
Contact persons
Department of Informatics
Faculty of Science
Faculty of Science
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Department of Informatics, Faculty of Science
Cluster of Excellence: Machine Learning: New Perspectives for Science (CML)
Centers or interfaculty scientific institutions
Centers or interfaculty scientific institutions
Tübingen AI Center
Department of Informatics, Faculty of Science
Department of Informatics, Faculty of Science
Local organizational units
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics
Faculty of Science
Faculty of Science
Funders
Brüssel, Belgium