%0 Conference Paper
%A Melidonis, Savvas
%A Patnala, Ankit
%A Semcheddine, Asma
%A Schultz, Martin
%A Polz, Julius
%A Nowak, Kacper
%T HClimRep: AI Climate Model for Capturing the Atmosphere, Ocean, and Sea Ice Interactions
%M FZJ-2025-05426
%D 2025
%X 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
%B AI in Science Summit
%C 3 Nov 2025 - 4 Nov 2025, Copenhagen (Denmark)
Y2 3 Nov 2025 - 4 Nov 2025
M2 Copenhagen, Denmark
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/1049641