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Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition
Dmitry Mukhin1, Andrey Gavrilov2, Evgeny Loskutov2
1Institute of Applied Physics of the Russian Academy of Sciences, 603950, Nizhny Novgorod, Russia. mukhin@ipfran.ru.
Scientific Reports
|May 16, 2019
Summary
The Middle Pleistocene Transition (MPT) saw the climate shift to large glacial cycles. Machine learning reveals these shifts were driven by internal climate oscillations, not orbital forcing.
Area of Science:
- Paleoclimatology
- Climate Science
- Machine Learning Applications in Earth Science
Background:
- The Middle Pleistocene Transition (MPT) marks a significant change in Earth's glacial cycles, shifting from 41,000-year cycles to larger 100,000-year cycles.
- Understanding the drivers of the MPT is a key challenge in paleoclimatology, with existing theories focusing on various factors including orbital forcing and internal climate dynamics.
Purpose of the Study:
- To investigate the underlying mechanisms responsible for the Middle Pleistocene Transition (MPT) using advanced data analysis.
- To test existing theories on Pleistocene climate variability by developing a data-driven model.
- To reveal how internal climate dynamics and external forcings interact to produce observed glacial cycles.
Main Methods:
- Construction of a Bayesian data-driven model utilizing benthic δ18O records (LR04 stack).
- Application of machine learning approaches for analyzing complex climate data.
- Inclusion of key factors influencing Pleistocene climate: internal dynamics, trends, insolation variations, and millennial variability.
Main Results:
- The study reveals that strong glacial cycles during the MPT emerged from internal nonlinear oscillations, amplified by millennial noise, rather than solely orbital forcing.
- Long-term climate trends played a crucial role in enabling these internal oscillations to manifest as large-amplitude glacial cycles.
- While not the primary driver for the MPT onset, obliquity (a form of orbital forcing) phase-locks the climate cycles via the meridional insolation gradient.
Conclusions:
- The Middle Pleistocene Transition was primarily driven by internal climate system nonlinearities, modulated by millennial-scale variability and long-term trends.
- Orbital Milankovitch forcing, particularly obliquity, plays a secondary role in phase-locking these cycles rather than initiating the transition.
- Bayesian data analysis and machine learning provide powerful tools for deciphering complex paleoclimate dynamics and testing climate theories.
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