BibTeX

@article{2510.03305v1,
Author        = {Tian Zheng and Subashree Venkatasubramanian and Shuolin Li and Amy Braverman and Xinyi Ke and Zhewen Hou and Peter Jin and Samarth Sanjay Agrawal},
Title         = {Machine Learning Workflows in Climate Modeling: Design Patterns and
  Insights from Case Studies},
Eprint        = {2510.03305v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Machine learning has been increasingly applied in climate modeling on system
emulation acceleration, data-driven parameter inference, forecasting, and
knowledge discovery, addressing challenges such as physical consistency,
multi-scale coupling, data sparsity, robust generalization, and integration
with scientific workflows. This paper analyzes a series of case studies from
applied machine learning research in climate modeling, with a focus on design
choices and workflow structure. Rather than reviewing technical details, we aim
to synthesize workflow design patterns across diverse projects in ML-enabled
climate modeling: from surrogate modeling, ML parameterization, probabilistic
programming, to simulation-based inference, and physics-informed transfer
learning. We unpack how these workflows are grounded in physical knowledge,
informed by simulation data, and designed to integrate observations. We aim to
offer a framework for ensuring rigor in scientific machine learning through
more transparent model development, critical evaluation, informed adaptation,
and reproducibility, and to contribute to lowering the barrier for
interdisciplinary collaboration at the interface of data science and climate
modeling.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2510.03305v1},
File          = {2510.03305v1.pdf}
}