Research Highlight: Assistant Professor Steve Meyers on the field of Astrochronology

The geologic record is the only available documentation of long-term environmental change, providing an opportunity to evaluate the causes and consequences of climate variability, and the evolving linkages between Earth System components, including the biosphere. In order to accurately interpret this record and assess rates of biologic, chemical, and physical change on our planet, we require a means to tell time as accurately as possible, often to a precision higher than that attainable by traditional geochronologic methods. My research program contributes to a frontier area of Earth Science known as Astrochronology, which utilizes the geologic record of climate oscillations – those ascribed to periodic changes in the Earth’s orbit and rotation – to measure the passage of time directly from repetitive sedimentary layers in rock. The basic concept is akin to a tree’s annual growth rings, used to reconstruct the chronology of its life. The ‘fastest’ of these astronomical rhythms occurs with a period of 20,000 years, and when such oscillations are reliably preserved in the stratigraphic record, they provide the most finely resolved time scale available for most deep time (> 1 million year old) strata. UW-Madison has substantial roots in this field, as one of the key scientific figures that recognized the potential of Astrochronology and helped to spur along its ‘modern’ renaissance is distinguished alumnus Alfred G. Fischer (B.A., 1939; M.S., 1940; see Outcrop 2008). 

The impact of Astrochronology on the quantification of deep time, and the evaluation of past perturbations to the Earth System (e.g., extinction events and climate change), has been truly revolutionary; the approach is now even employed to calibrate radioisotopic dating methods and test their veracity. But reading the periodic astronomical rhythm from the stratigraphic record is often challenging, as climate change is influenced by other factors, including random (‘stochastic’) processes. Further, climate is only one of numerous controls on the formation of the sedimentary rock record. Perhaps most troubling, the rate of formation of the rock record can be highly variable, and strata are often riddled with long and short pauses and omissions – gaps in the recording that can be difficult to quantify, or even identify. The result is a patchy amalgam of astronomical signal and noise, which requires a careful and sophisticated quantitative approach: part stratigraphy, part geophysics, part geochemistry, and part paleoclimatology. This intersection of fields is where I have focused much of my scientific effort, in developing a theoretical and computational framework for Astrochronology that extracts the signal from the noise, while using both components to constrain the dynamics of surface Earth processes. 

In 2008, I introduced a conceptual framework termed the ‘pathway of the orbital signal’ (Figure 1), which provides a broader context for the challenges outlined above, but also illustrates the potential prospects of Astrochronology (Meyers et al., 2008). This pathway recognizes the preserved climate signal as one that has been sequentially filtered through a wide array of processes, each of which serves to distort – and may even render unrecognizable – the climate response that we seek to quantify. Alternatively, if each step in this pathway is quantitatively constrained, the resulting ‘transfer functions’ yield information about a wide range of Earth surface processes, including paleoenvironmental change, but also the dynamics within depositional systems (ice sheets, continental margins, etc.) and their feedbacks with climate change. My first contribution on the specific topic of transfer functions was the introduction of a quantitative approach for evaluating the linkage between climate and deposition, using a statistical time-series analysis approach (Evolutive Harmonic Analysis) in tandem with stratigraphic modeling (Meyers et al., 2001). A follow-up paper (Meyers and Sageman, 2004) more thoroughly developed a new approach for identifying cryptic gaps in stratigraphy – the missing part of the recording – a fundamental problem that has served as a major challenge to the field of Astrochronology.

In these studies and subsequent publications, an underlying philosophy is that much of what has commonly been regarded as noise in the stratigraphic record is in fact untapped signal. Thus, the approach I’ve pursued is to develop and apply appropriate quantitative statistical methodologies and modeling techniques, which when combined with sound stratigraphic reasoning and high quality data, can accurately and precisely extract the signal from the noise. Subsequently, the integration of recovered astronomical signals with a wide array of geochemical, paleobiologic, and sedimentologic data, to assess rates of environmental and biologic change, provides a major advance in quantification of the surficial Earth System. It is important to stress that our goal is to understand rates, a time derivative, and traditional geochronologic precisions are not sufficient to provide rate information over short timescales (104 years); this is a major advantage of astrochronologic approaches to time.

Although the potential power of Astrochronology is clear, a challenge that still hinders many studies is the inability to unambiguously assign observed stratigraphic rhythms (measured in meters) to astronomical cycles (measured in years) using available radioisotopic age data; in such circumstances the presence of specific astronomical cycles must be assumed. This results in a potential circularity in derived geologic timescales, and has led some to question the veracity of particular astrochronologies. To help resolve this issue, a new computational method for astrochronologic testing termed average spectral misfit (ASM), has been developed (Meyers and Sageman, 2007; Meyers et al., 2012a). The technique comprehensively evaluates all plausible astronomical interpretations (time scales) while also providing a formal statistical test of the null hypothesis of ‘no astronomical signal’ via Monte Carlo simulation. The method has now been applied to address long-standing cyclostratigraphic controversies, from the Triassic, Cretaceous and Eocene (Figure 2; Meyers and Sageman, 2007; Meyers, 2008; Meyers et al., 2012a). 

Over the coming years, the direction of my research program will be guided by a recently funded NSF CAREER award, “Deciphering the Beat of a Timeless Rhythm: The Future of Astrochronology”. This project builds upon the theoretical and computational advances outlined above, to address fundamental challenges to the development of accurate and precise deep time astrochronologies. Its major topics include (1) statistical astrochronologic testing (e.g., further development of ASM), (2) integration with radioisotopic data (including Bayesian statistical approaches; Meyers et al., 2012b), (3) refining orbital and rotational models for the Earth, and (4) transfer function assessment for evaluation of Earth surface processes. One objective is to provide a standard quantitative methodology for Astrochronology – which is presently lacking – and its dissemination through software (such as ‘R’), professional workshops, and graduate student training.

My ultimate goal is to bring attention to Astrochronology as an important and evolving approach for investigating Earth history as a whole. In addition to this work, I have established an x-ray fluorescence scanning laboratory that provides much of the geochemical data for ongoing projects (see Outcrop 2010), and I maintain a broader involvement in sedimentary geology and paleoclimatology/paleoceanography research. A common theme in all this work is the integration of data with modeling and statistical techniques, to unravel the history of the climate system, oceans and geosphere.


Fischer, A.G., and Roberts, L.T., 1991, Cyclicity in the Green River Formation (lacustrine Eocene) of Wyoming: Journal of Sedimentary Petrology, v. 61, p. 1146–1154.

Meyers, S.R., Sageman, B., and Hinnov, L., 2001, Integrated quantitative stratigraphy of the Cenomanian-Turonian Bridge Creek Limestone Member using Evolutive Harmonic Analysis and stratigraphic modeling: Journal of Sedimentary Research, v. 71, p. 627-643. (JSR Outstanding Paper of 2001)

Meyers, S.R., and Sageman, B.B., 2004, Detection, quantification, and significance of hiatuses in pelagic and hemipelagic strata: Earth and Planetary Science Letters, v. 224, p. 55-72.

Meyers, S.R., and Sageman, B.B., 2007, Quantification of Deep-Time Orbital Forcing by Average Spectral Misfit: American Journal of Science, v. 307, p. 773-792.

Meyers, S.R., 2008, Resolving Milankovitchian Controversies: The Triassic Latemar Limestone and Eocene Green River Formation: Geology, v. 36, p. 319-322.

Meyers, S.R., Sageman, B.B., and Pagani, M., 2008, Resolving Milankovitch: Consideration of Signal and Noise: American Journal of Science, v. 308, p. 770-786.

Meyers, S.R., Sageman, B.B. and Arthur, M.A., 2012a, Obliquity forcing of organic matter accumulation during Oceanic Anoxic Event 2, Paleoceanography, 27, PA3212, doi:10.1029/2012PA002286.

Meyers, S.R., and Siewert, S.E., Singer, B.S., Sageman, B.B., Condon, D.J., Obradovich, J.D., Jicha, B.R., and Sawyer, D.A., 2012b, Intercalibration of radioisotopic and astrochonologic time scales for the Cenomanian-Turonian boundary interval, Western Interior Basin, USA, Geology, v. 40, p. 7-10.


Transfer Function Figure_v7

Figure 1. Theoretical pathway of the orbital signal, from insolation changes to proxy data series employed for time-series analysis (Meyers et al., 2008).


Figure 2. Upper graph: Average Spectral Misfit results for oil yield data from the Currant Creek Ridge No. 1 core (Wyoming, USA), Eocene Green River Formation. The astronomical interpretation of Fischer and Roberts (1991) is confirmed. Lower graph: A segment of the analyzed oil yield data from the Currant Creek Ridge No. 1 core illustrating the identified short eccentricity (~100 ka) and long eccentricity (~400 ka) cycles.  Results from Meyers (2008).