Mukhin Dmitry, Gavrilov Andrey, Loskutov Evgeny, Kurths Juergen, Feigin Alexander
Institute of Applied Physics of the Russian Academy of Sciences, 603950, Nizhny Novgorod, Russia.
Potsdam Institute for Climate Impact Research, 14412, Potsdam, Germany.
Sci Rep. 2019 May 13;9(1):7328. doi: 10.1038/s41598-019-43867-3.
Currently, causes of the middle Pleistocene transition (MPT) - the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before - are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δO records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.
目前，中更新世转型（MPT）的成因——即大振幅冰川变化的开始，其时间尺度为10万年，而非此前规律的4.1万年周期——是古气候学中一个具有挑战性的谜题。在此，我们展示了基于机器学习方法的贝叶斯数据分析如何有助于揭示更新世变化背后的主要机制，这些机制极有可能解释代用记录，并可用于检验现有理论。我们根据底栖δO记录（LR04堆叠）构建了一个贝叶斯数据驱动模型，该模型考虑了可能对更新世气候产生潜在影响的主要因素：内部气候动力学、渐变趋势、日照变化和千年尺度变化。与一些理论不同的是，我们发现，在长期气候趋势下，强烈的冰川周期是由千年尺度噪声引发的内部非线性振荡所致。我们发现，虽然轨道米兰科维奇强迫对MPT的开始并不重要，但倾角振荡通过日照的经向梯度使气候周期相位锁定。
