The history of Earth’s climate is penned on ice. Interpreting it is a matter of decoding the complex signals extracted from isotopes accumulated tens of thousands of years and frozen miles below the surface of Antarctica.
When researchers try to understand the enormous amount of information embedded into an ice core, they face a forensic difficulty: what is the best possible way to isolate the useful information from the corrupt.
A new study published in the Entropy journal demonstrates the way tools from information theory, a branch of complexity science, can overcome this difficulty by quickly homing in on portions of the data that need further investigation.
“With this kind of data, we have limited opportunities to get it right,” stated Joshua Garland, a mathematician at the Santa Fe Institute who works with 68,000 years of data from the West Antarctic Ice Sheet Divide ice core. “Extracting the ice and processing the data takes hundreds of people, and tons of processing and analysis. Because of resource constraints, replicate cores are rare.”
By the time Garland and his colleagues got access to the data, more than a decade had passed from the initial drilling of the ice core to the reporting of the dataset it included. Teams from multiple universities funded by the National Science Foundation extracted the two-mile ice core over five seasons from 2007 to 2012. The core from the field camp in West Antarctica was packaged and shipped to the National Science Foundation Ice Core Facility in Colorado, and eventually to the University of Colorado. A sophisticated processing facility at the Stable Isotope Lab at the Institute of Arctic and Alpine Research helped researchers pull water isotope records from the ice.
The outcome was a highly resolved, complex dataset. In comparison to earlier ice core data, which enabled analysis for every 5 cm, the WAIS Divide core allows analysis at millimeter resolution.
One of the exciting thing about ice core research in the last decade is we’ve developed these lab systems to analyze the ice in high resolution. Quite a while back we were limited in our ability to analyze climate because we couldn’t get enough data points, or if we could it would take too long. These new techniques have given us millions of data points, which is rather difficult to manage and interpret without some new advances in our [data] processing.
Tyler Jones, Paleoclimatologist, University of Colorado Boulder
According to Garland, in earlier cores, decades, even centuries, were assembled into a single point. In contrast, at times, the WAIS data offers over 40 data points per year. However, as researchers start analyzing the data at shorter time scales, even small anomalies can cause problems.
As fine-grained data becomes available, fine-grained analyses can be performed. But it also makes the analysis susceptible to fine-grained anomalies.
Joshua Garland, Mathematician, Santa Fe Institute
In order to quickly recognize the anomalies that need further investigation, the researchers use information theoretic methods to measure the amount of complexity appearing at each point in the time sequence. A sudden increase in the complexity could suggest that there was either a major, unanticipated climate event (for example, a super volcano) or that there was a problem in the data or the data processing pipeline.
This kind of anomaly would be invisible without a highly detailed, fine-grained, point-by-point analysis of the data, which would take a human expert many months to perform. Even though information theory can’t tell us the underlying cause of an anomaly, we can use these techniques to quickly flag the segments of the data set that should be investigated by paleoclimate experts.
Elizabeth Bradley, Computer Scientist, University of Colorado Boulder; External Professor, Santa Fe Institute
She compares the dataset from the ice core with a Google search that returns a million pages. “It’s not that you couldn’t go through those million pages,” stated Bradley. “But imagine if you had a technique that could point you toward the ones that were potentially meaningful?” During the analysis of large, real-world datasets, information theory would have the ability to spot differences in the data that signal either a significant climate event or a processing error.
In the Entropy paper, the researchers have described the way information theory was used to recognize and repair a problematic stretch of data from the original ice core. Their analysis ultimately led to the need for a resampling of the archival ice core—the longest resampling of a high-resolution ice core until now. Upon resampling and reprocessing that portion of the ice, the researchers could resolve an anomalous increase in entropy from approximately 5000 years ago.
“It’s vitally important to get this area right,” stated Garland, “because it contains climate information from the dawn of human civilization.”
“I think climate change is the most pressing problem ever to face humanity, and ice cores are undoubtedly the best record of Earth’s climate going back hundreds of thousands of years,” stated Jones. “Information theory helps us sift through the data to make sure what we’re putting out into the world is the absolute best and most certain product we can.”
Detecting Climate Record Anomalies with Complexity Science
The West Antarctic Divide ice core contains over a million data points, and 68,000 years of Earth’s climate history. Joshua Garland, a mathematician at the Santa Fe Institute, explains how his team uses information theory to quickly flag which portions of massive data set stand out. (Video credit: National Science Foundation, Santa Fe Institute, Mountain Creative)