Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies

2025-09-30

AI Summary

This paper addresses core challenges in applying machine learning (ML) to climate modeling—including physical inconsistency, difficulties in multi-scale coupling, data sparsity, poor generalization robustness, and low integration with scientific workflows—by proposing a synergistic ML workflow framework that jointly leverages physical priors and simulation data. Methodologically, it integrates surrogate modeling, ML-driven parameterization, probabilistic programming, simulation-based inference, and physics-informed transfer learning, prioritizing interpretability and scientific rigor. Key contributions include: (i) distilling cross-task workflow design patterns; (ii) establishing a scientific ML practice framework that supports transparent development, critical evaluation, and reliable integration; and (iii) significantly enhancing model trustworthiness and interdisciplinary reproducibility while lowering technical barriers to deep integration between data science and climate modeling.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Developing ML workflows for climate modeling applications
• Ensuring physical consistency in machine learning climate models
• Integrating scientific workflows with data-driven climate approaches

Innovation

• Surrogate modeling for system emulation acceleration
• Physics-informed transfer learning for robust generalization
• Simulation-based inference integrating observational data