GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania

Paper No. 254-13
Presentation Time: 4:50 PM

TOWARD AN OMNI-PROXY RECONSTRUCTION OF CENOZOIC CO2 (Invited Presentation)


BOWEN, Gabriel1, HARPER, Dustin1, DA, Jiawei2, HÖNISCH, Bärbel3 and MONTANEZ, Isabel4, (1)Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, (2)School of Earth Sciences and Engineering, Nanjing University, 163 Xianlin Boulevard, Qixia District, Nanjing University (Xianlin Campus), Nanjing, 210023, China, (3)Lamont-Doherty Earth Observatory and Dept. of Earth and Environmental Sciences, Columbia University, Palisades, NY 10964, (4)Department of Earth and Planetary Sciences, University of California, Davis, Davis, CA 95616

Atmospheric CO2 links Earth’s carbon cycle, climate, and biotic systems. As such, knowing how CO2 concentrations have varied over Earth history is fundamental to understanding and learning from the geologic record. In the absence of direct evidence prior to the ice core record, numerous paleo-CO2 proxies have been developed and used to reconstruct paleo-CO2 on multi-million-year time scales. Each of these proxy systems involves complex, interactive sensitivities to multiple environmental and biological factors. As a result, quantitative interpretation and robust error propagation are challenging for individual proxies, let alone multi-proxy reconstructions.

As a component of the community-engaged CO2 Proxy Integration Project (CO2PIP, https://paleo-co2.org/) we are working toward comprehensive, multi-proxy reconstructions of paleo-CO2 change within the Bayesian Joint Proxy Inversion (JPI) framework. The core of these reconstructions is a set of Proxy System Models (PSMs) that describe the response of four widely used classes of paleo-CO2 proxies to physical and biological forcing. Development of the PSMs has involved re-framing and integrating existing perspectives on proxy interpretation and highlights opportunities for future refinement of our understanding of these systems. Our Bayesian analysis consists of PSMs coupled with time- and/or space-explicit models of forcing variables, including atmospheric CO2 concentration, and a model for the age and sampling uncertainty of proxy observations. This system is inverted using Bayes’ Theorem to produce a posterior sample of the model state-space conditioned on compiled proxy data.

I will illustrate results from early applications of JPI to 1) reconstruct the Cenozoic CO2 history using reduced-order PSMs, and 2) reconstruct early Paleogene CO2 timeseries using a multi-proxy marine dataset. These examples show the power of Bayesian proxy inversion to facilitate propagation of uncertainty within and across these complex proxy systems and develop quantitative reconstructions. The results suggest strong and consistent Earth System Sensitivity to CO2 forcing across a range of Cenozoic timescales and show that it is likely that modern levels of CO2 (~420 ppm) have not occurred on Earth since the mid-Miocene.