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@INPROCEEDINGS{Melidonis:1049704,
author = {Melidonis, Savvas and Patnala, Ankit and Semcheddine, Asma
and Grasse, Simon and Schultz, Martin},
title = {{HC}lim{R}ep: {A} {F}oundation {M}odel for {C}apturing the
{A}tmosphere, {O}cean, and {S}ea {I}ce {I}nteractions},
reportid = {FZJ-2025-05488},
year = {2025},
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 traditional climate models
based on fluid dynamics and radiative transfer have provided
valuable information, they face limitations such as inherent
biases, coarse resolution, and structural errors. Moreover,
these models are computationally intensive, dependentof
physical constraints or governing equations, and it is
therefore impossible to explore a wide range of policy
scenarios and generate actionable climate information at the
desired high resolutions. Building on the success of
AtmoRep, a foundation model for atmospheric dynamics, we
propose HClimRep, a novel fully data-driven global climate
model that seeks to capture complex interactions of the
atmosphere, ocean, and sea ice to create a realistic climate
simulation, including the stratospheric ozone. Upon
completion of phase 1 of the model development, we have
successfully built a first HClimRep prototype that
integrates unstructured ocean gridded data and includes
stratospheric dynamics. The phase 2 of the model development
leverages insights gained from phase 1, combined with the
advancements of the newly launched ECMWF WeatherGenerator
(WGen) prototype, to drive further progress. In particular,
we use the WGen prototype to significantly facilitate the
incorporation of new climate data modalities and achieve
long iterative forecasts (rollouts). Further key challenges
of our initiative include the establishment of stable
seasonal-to-decadal climate forecasts, the accurate
simulation of future climates by learning CO2 forcing as
well as the employment of petabyte-scale data on multiple
resolutions and grids. Our main project objective is the
creation of a generalizable large-scale foundation model,
which will serve as a basis for various downstream
climate-related applications and products from stratospheric
warmings forecasts.to tropical cyclone climatology and
hydrological downscaling.},
month = {Jun},
date = {2025-06-03},
organization = {Helmholtz AI Conference, Karlsruhe
(Germany), 3 Jun 2025 - 5 Jun 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/1049704},
}