Ice Core Dating using Probabilistic Programming

TL;DR

This paper explores probabilistic programming for automatic ice core dating, aiming to improve accuracy, efficiency, and uncertainty quantification in establishing ice core chronologies, while addressing challenges like noisy data and spatial correlations.

Contribution

It introduces probabilistic models for ice core dating, demonstrating their use for automatic inference, prototyping, and handling uncertainties and spatial correlations.

Findings

Probabilistic models can effectively estimate ice core ages.

Uncertainty quantification improves confidence in dating results.

Identifies common failure modes of probabilistic tools in this context.

Abstract

Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, chemicals locked in the ice display quasi-periodic cycles that delineate annual layers. Manually counting these noisy seasonal patterns to infer the chronology can be an imperfect and time-consuming process, and does not capture uncertainty in a principled fashion. In addition, several ice cores may be collected from a region, introducing an aspect of spatial correlation between them. We present an exploration of the use of probabilistic models for automatic dating of ice cores, using probabilistic programming to showcase its use for prototyping, automatic inference and maintainability, and demonstrate common failure modes of these tools.