The role of greenhouse gases in past climatic variations : an approach based on accurate chronologies of deep polar ice cores

• Main Author: Beeman, Jai Chowdhry
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ), Université Grenoble Alpes, Frédéric Parrenin, Amaëlle Landais, Emmanuel Witrant
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: HAL CCSD 2019
• Subjects: Glaciology; Paleoclimatology; Dating; Greenhouse gases; Automation; Bayesian methods; Glaciologie; Paleoclimatologie; Datation; Gaz à effet de serre; Automatisation; Méthodes bayésiennes; [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; Antarctic; Antarc*; ice core
• Online Access: https://theses.hal.science/tel-02450783; https://theses.hal.science/tel-02450783/document; https://theses.hal.science/tel-02450783/file/BEEMAN_2019_diffusion.pdf

Deep polar ice cores contain records of both past climate and trapped air that reflects past atmospheric compositions, notably of greenhouse gases. This record allows us to investigate the role of greenhouse gases in climate variations over eight glacial-interglacial cycles. The ice core record, like all paleoclimate records, contains uncertainties associated both with the relationships between proxies and climate variables, and with the chronologies of the records contained in the ice and trapped air bubbles. In this thesis, we develop a framework, based on Bayesian inverse modeling and the evaluation of complex probability densities, to accurately treat uncertainty in the ice core paleoclimate record. Using this framework, we develop two studies, the first about Antarctic Temperature and CO2 during the last deglaciation, and the second developing a Bayesian synchronization method for ice cores. In the first study, we use inverse modeling to identify the probabilities of piecewise linear fits to CO2 and a stack of Antarctic Temperature records from five ice cores, along with the individual temperature records from each core, over the last deglacial warming, known as Termination 1. Using the nodes, or change points in the piecewise linear fits accepted during the stochastic sampling of the posterior probability density, we discuss the timings of millenial-scale changes in trend in the series, and calculate the phasings between coherent changes. We find that the phasing between Antarctic Temperature and CO2 likely varied, though the response times remain within a range of ~500 years from synchrony, both between events during the deglaciation and accross the individual ice core records. This result indicates both regional-scale complexity and modulations or variations in the mechanisms linking Antarctic temperature and CO2 accross the deglaciation. In the second study, we develop a Bayesian method to synchronize ice cores using corresponding time series in the IceChrono inverse chronological model. Tests show that ...