TY  - CONF
AU  - Melidonis, Savvas
AU  - Patnala, Ankit
AU  - Semcheddine, Asma
AU  - Schultz, Martin
AU  - Polz, Julius
AU  - Nowak, Kacper
TI  - HClimRep: AI Climate Model for Capturing the Atmosphere, Ocean, and Sea Ice Interactions
M1  - FZJ-2025-05426
PY  - 2025
AB  - Climate change poses a significant threat to ecosystems and human society. Accurate climate projections are crucial for developing effective policies for mitigating extreme weather events that are expected to increase due to global warming. However, traditional climate models have limitations, including biases and high computational costs. Under the Helmholtz Foundation Model Initiative (HFMI), we propose a new data-driven climate model, namely HClimRep, which uses foundation model principles and machine learning to analyze diverse climate datasets. This approach enables flexible and customizable outputs, providing a versatile tool for climate applications. Keywords: Foundation Model, AI Model, Deep Learning, Climate Modelling, Climate Simulations
T2  - AI in Science Summit
CY  - 3 Nov 2025 - 4 Nov 2025, Copenhagen (Denmark)
Y2  - 3 Nov 2025 - 4 Nov 2025
M2  - Copenhagen, Denmark
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1049641
ER  -