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@INPROCEEDINGS{Melidonis:1049641,
author = {Melidonis, Savvas and Patnala, Ankit and Semcheddine, Asma
and Schultz, Martin and Polz, Julius and Nowak, Kacper},
title = {{HC}lim{R}ep: {AI} {C}limate {M}odel for {C}apturing the
{A}tmosphere, {O}cean, and {S}ea {I}ce {I}nteractions},
reportid = {FZJ-2025-05426},
year = {2025},
abstract = {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},
month = {Nov},
date = {2025-11-03},
organization = {AI in Science Summit, Copenhagen
(Denmark), 3 Nov 2025 - 4 Nov 2025},
subtyp = {After Call},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511) / Earth System Data
Exploration (ESDE) / HClimRep2024050120270431 - Helmholtz
Representation Model for Climate Science (HClimRep)
(HClimRep2024050120270431)},
pid = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE /
G:(DE-HGF)HClimRep2024050120270431},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/1049641},
}