A1457
Title: Detecting changepoints in globally-indexed functional time series
Authors: Drew Yarger - Purdue University (United States) [presenting]
Derek Tucker - Sandia National Laboratories (United States)
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. The context considered is where each time point is a year, and a function of temperature indexed by day of the year is observed. 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. Test statistics are proposed for detecting these changepoints, and performance is evaluated using varying levels of data complexity, including a simulation study, simplified climate model simulations, and climate reanalysis data. Changes in stratospheric temperature globally over 1984-1998 are evaluated. Such changes may be associated with the eruption of Mt. Pinatubo in 1991.
