ClimateSOM: A Visual Analysis Workflow for Climate Ensemble Datasets
This work addresses the challenge for climate scientists in understanding patterns and variability in ensemble model runs, though it appears incremental as it combines existing techniques (SOM and LLMs) in a new application.
The authors tackled the problem of analyzing variability in climate ensemble datasets by developing ClimateSOM, a visual analysis workflow that uses self-organizing maps and LLMs to enable interactive exploration and interpretation, as demonstrated through a case study on precipitation projections over California and the Northwestern United States with evaluation by six domain experts.
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.