Detecting changepoints in globally-indexed functional time series

TL;DR

This paper introduces statistical methods for detecting changepoints in complex, spatially-indexed functional climate data, with applications to global temperature changes related to volcanic eruptions.

Contribution

It proposes new test statistics for identifying changepoints in spatially-indexed functional time series, addressing challenges of multiple change points across locations.

Findings

Effective detection of changepoints in climate data demonstrated

Methods successfully applied to real climate reanalysis data

Identified temperature changes linked to Mt. Pinatubo eruption

Abstract

In environmental and climate data, there is often an interest in determining if and when changes occur in a system. Such changes may result from localized sources in space and time like a volcanic eruption or climate geoengineering events. Detecting such events and their subsequent influence on climate has important policy implications. However, the climate system is complex, and such changes can be challenging to detect. One statistical perspective for changepoint detection is functional time series, where one observes an entire function at each time point. We will consider the context where each time point is a year, and we observe a function of temperature indexed by day of the year. Furthermore, such data is measured at many spatial locations on Earth, which motivates accommodating sets of functional time series that are spatially-indexed on a sphere. Simultaneously inferring changes that can occur at different times for different locations is challenging. We propose test statistics for detecting these changepoints, and we evaluate performance using varying levels of data complexity, including a simulation study, simplified climate model simulations, and climate reanalysis data. We evaluate changes in stratospheric temperature globally over 1984-1998. Such changes may be associated with the eruption of Mt. Pinatubo in 1991.