| Savvas Melidonis |
Forschungszentrum Juelich GmbH
Germany |
| Co-Author(s): Savvas Melidonis, Ankit Patnala, Belkis Asma Semcheddine, Jehangir Awan, Simon Grasse, Kacper Nowak, Aleksei Koldunov, Julius Polz, Enxhi Kreshpa, Illaria Luise, Christian Lessig, Nikolay Koldunov, Thomas Jung, Martin Schultz |
|
| Abstract: |
| Climate change presents critical challenges to ecosystems and human society. Accurate projections of climate change and its consequences are essential for assessing climate policies and developing proactive strategies to mitigate extreme weather events. While conventional climate models - grounded in fluid dynamics and radiative transfer physics - have yielded valuable insights, they suffer from several shortcomings: inherent biases, coarse spatial resolution, structural errors, and considerable computational inefficiency. Within Helmholtz Foundation Model Initiative (HFMI), we introduce HClimRep, an AI-driven foundation model designed to capture the intricate coupled dynamics of the atmosphere, stratosphere, ocean, and sea-ice and to generate realistic climate simulations. HClimRep builds on the WeatherGenerator prototype - a EU funded HORIZON weather forecasting project - by expanding its architecture and training regime to deliver true climate projection capabilities. In this presentation we will outline the most recent advances of our project in subseasonal to seasonal forecasting and showcase a atmosphere-ocean downscaling downstream application. By harnessing transformer-based modelling, GPU-level optimization, and the latest developments at the intersection of high performance computing and AI, our overarching goal is to deliver a breakthrough climate research tool that deepens our understanding of the Earth system and underpins evidence based climate adaptation and policy decisions. |
|