Preprints
https://doi.org/https://doi.org/10.48550/arXiv.2404.08336
https://doi.org/https://doi.org/10.48550/arXiv.2404.08336
19 Nov 2024
 | 19 Nov 2024
Status: this preprint is open for discussion and under review for Climate of the Past (CP).

Estimating breakpoints between climate states in the Cenozoic Era

Mikkel Bennedsen, Eric Hillebrand, Siem Jan Koopman, and Kathrine By Larsen

Abstract. This study presents a statistical time-domain approach for identifying transitions between climate states, referred to as breakpoints, using well-established econometric tools. We analyze a 67.1 million year record of the oxygen isotope ratio δ18O derived from benthic foraminifera. The dataset is presented in Westerhold et al. (2020), where the authors use recurrence analysis to identify six climate states. Fixing the number of breakpoints to five, our procedure results in breakpoint estimates that closely align with those identified by Westerhold et al. (2020). By treating the number of breakpoints as a parameter to be estimated, we provide the statistical justification for more than five breakpoints in the time series. Further, our approach offers the advantage of constructing confidence intervals for the breakpoints, and it allows for testing the number of breakpoints present in the time series.

Mikkel Bennedsen, Eric Hillebrand, Siem Jan Koopman, and Kathrine By Larsen

Status: open (until 14 Jan 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Mikkel Bennedsen, Eric Hillebrand, Siem Jan Koopman, and Kathrine By Larsen
Mikkel Bennedsen, Eric Hillebrand, Siem Jan Koopman, and Kathrine By Larsen

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Short summary
Our study presents a statistical approach for identifying transitions between climate states in the Cenozoic Era. We analyze a temperature proxy record covering the period from 67.1 million years ago to present time. Our analysis corroborates the transitions previously identified in the literature and provides statistical justification for additional transitions. Our approach allows for constructing confidence intervals, offering measures of uncertainty.