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;
- ESMs disagree on how much the Earth will warm for a given increase in atmospheric carbon dioxide (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 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. Therefore, the research project aims to improve the representation of these feedbacks in ESMs, reducing uncertainty in global warming projections. The project proposes 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.
Project Goals
The overarching goal of AI4PEX is to deliver enhanced knowledge on the Earth system by integrating Earth’s Observations (EO), AI, and ML into Earth system modelling and analysis in a yet unprecedented way to pave the way towards more reliable climate projections at global and regional scale.
AI4PEX aims to advance our collective understanding by improving the representation of processes in all domains – atmosphere, ocean and land – underpinning the main uncertainties in Earth system feedbacks and through this, support the development of robust climate mitigation and adaptation strategies from multi-decadal to longer time scales. AI4PEX will develop innovative techniques, model-data integration strategies and data-driven models to accurately and efficiently assess and study Earth system feedbacks and extremes. AI4PEX will address this challenge by establishing a deep interconnectivity between state-of-the-art ESMs and EO via AI and ML, leveraging the potential of big data science and advanced high-performance computing infrastructures.
AI4PEX will follow a synergistic approach to achieve this goal through the following objectives:
- Advancing process representation. Improve the understanding and representation of key processes underpinning the most uncertain feedbacks in the Earth system by maximising observational data utilisation via AI-enhanced physically-aware ESM development.
- Assessing future risks of extremes. Project future changes in climate extremes and associated risks based on the most up-to-date projections and ML-based approaches, supported by a better understanding of the links between climate variability and extremes, and their impacts on land and coastal waters.
- Delivering tools for resilience. Develop robust, agile, fast and exchangeable workflows for the integration of Earth’s observations and ML into Earth system science, modelling and analysis, including support for capacity building, transferability, and clear and engaging dissemination of new knowledge to society.
Role of SMHI
SMHI is a partner in the project. Ramon Fuentes Franco is the project leader for SMHI.
Project partners
There are 19 project partners across Europe and The Max Planck Society is the project coordinator.
Funding
AI4PEX has received funding from the European Union’s Horizon Europe research and innovation programme.
Timeline
The project starts January 2024 and finishes March 2028.