Classifier accuracy between adjacent time windows estimates a total variation distance between news content distributions, and its peaks mark changepoints that align with major historical events.
Is a Recent Surge in Global Warming Detectable?
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abstract
The global mean surface temperature is widely studied to monitor climate change. A current debate centers around whether there has been a recent (post-1970s) surge/acceleration in the warming rate. This paper addresses whether an acceleration in the warming rate is detectable from a statistical perspective. We use changepoint models, which are statistical techniques specifically designed for identifying structural changes in time series. Four global mean surface temperature records over 1850-2023 are scrutinized within. Our results show limited evidence for a warming surge; in most surface temperature time series, no change in the warming rate beyond the 1970s is detected. As such, we estimate minimum changes in the warming trend for a surge to be detectable in the near future.
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Neural Total Variation Distance Estimators for Changepoint Detection in News Data
Classifier accuracy between adjacent time windows estimates a total variation distance between news content distributions, and its peaks mark changepoints that align with major historical events.