https://arxiv.org/pdf/2308.05915

*Authors*
Drew Yarger, J. Derek Tucker

*11 August 2023*

https://doi.org/10.48550/arXiv.2308.05915


*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.
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*Source: arXiv*

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