Spatio-temporal Multivariate Cluster Evolution Analysis for Detecting and Tracking Climate Impacts

TL;DR

This paper introduces an unsupervised data-driven method combining anomaly detection, clustering, and NLP to identify and trace climate impacts and their source pathways in Earth System Model data, exemplified by the Mount Pinatubo eruption.

Contribution

The paper presents a novel approach that integrates anomaly detection, clustering, and NLP to detect and analyze climate impact pathways from complex Earth System Model data.

Findings

Successfully detected post-eruption impacts of Mount Pinatubo

Extracted meaningful sequences of impact events using NLP

Confirmed physical source-impact relationships in climate data

Abstract

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).