Spatio-temporal Multivariate Cluster Evolution Analysis for Detecting and Tracking Climate Impacts
This addresses the challenge of uncovering source-to-impact relationships in climate data for researchers and policymakers, though it is incremental as it builds on existing techniques.
The paper tackles the problem of detecting and tracking climate impacts from Earth System Models by developing an unsupervised data-driven approach that combines anomaly detection, clustering, and NLP, demonstrating its capability to detect known post-eruption impacts like those from the 1991 Mount Pinatubo eruption.
Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. This paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).