ClimateSOM: A Visual Analysis Workflow for Climate Ensemble Datasets

2025-08-08

AI Summary

Interpreting spatiotemporal variability patterns across climate model ensembles remains challenging due to high dimensionality and structural complexity. Method: This paper proposes a visualization analytics workflow integrating Self-Organizing Maps (SOM) with Large Language Models (LLMs). SOM reduces dimensionality and clusters high-dimensional climate time series while preserving spatial structure; LLMs semantically interpret clustering outcomes, generating scientifically grounded, human-readable descriptions of climate patterns. The integrated system supports interactive exploration of variability magnitude, spatial configurations, and inter-model grouping relationships. Contribution/Results: Evaluated on precipitation projections over California and the U.S. Pacific Northwest, the method enables accurate identification of dominant variability modes, reveals model consensus and divergence, and produces expert-validated, interpretable insights. This work represents the first deep integration of LLMs into an SOM-driven climate ensemble analysis pipeline, significantly enhancing cognitive efficiency and interpretability of complex ensemble variability structures.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Ensemble datasets are ever more prevalent in various scientific domains. In climate science, ensemble datasets are used to capture variability in projections under plausible future conditions including greenhouse and aerosol emissions. Each ensemble model run produces projections that are fundamentally similar yet meaningfully distinct. Understanding this variability among ensemble model runs and analyzing its magnitude and patterns is a vital task for climate scientists. In this paper, we present ClimateSOM, a visual analysis workflow that leverages a self-organizing map (SOM) and Large Language Models (LLMs) to support interactive exploration and interpretation of climate ensemble datasets. The workflow abstracts climate ensemble model runs - spatiotemporal time series - into a distribution over a 2D space that captures the variability among the ensemble model runs using a SOM. LLMs are integrated to assist in sensemaking of this SOM-defined 2D space, the basis for the visual analysis tasks. In all, ClimateSOM enables users to explore the variability among ensemble model runs, identify patterns, compare and cluster the ensemble model runs. To demonstrate the utility of ClimateSOM, we apply the workflow to an ensemble dataset of precipitation projections over California and the Northwestern United States. Furthermore, we conduct a short evaluation of our LLM integration, and conduct an expert review of the visual workflow and the insights from the case studies with six domain experts to evaluate our approach and its utility.

Problem

• Analyze variability in climate ensemble model projections
• Visualize patterns and clusters in ensemble model runs
• Integrate LLMs for interpreting climate data variability

Innovation

• Uses self-organizing map for ensemble data variability
• Integrates LLMs for interactive sensemaking support
• Visual workflow for exploring and clustering model runs