Antarctica’s hidden ice dynamics will redefine sea level projections

As the planet heats up and Antarctica’s ice sheet melts at a faster pace, scientists warn of a steady rise in sea levels that could put coastal communities at risk. 

Home to enough frozen water to increase global sea levels by 190 feet if completely melted, Antarctica remains a focus of intense scientific scrutiny. 

Yet most climate models struggle to capture the complexities of its ice movement due to limited direct observations and the interplay between ocean currents, atmospheric conditions, and the frozen surface.

In a paper published in the journal Science, a team from Stanford University utilized machine learning to analyze high-resolution remote-sensing data of Antarctic ice movements. 

Insights into the behavior of ice 

By combining large volumes of satellite imagery with established physical principles, their approach identifies fundamental processes governing the large-scale flow of ice on the continent.

The resulting insights could help refine how existing models estimate Antarctica’s future and the associated threat of accelerating sea-level rise.

Ching-Yao Lai is an assistant professor of geophysics at the Stanford Doerr School of Sustainability and senior author of the study. 

“A vast amount of observational data has become widely available in the satellite age,” said Professor Lai. “We combined that extensive observational dataset with physics-informed deep learning to gain new insights about the behavior of ice in its natural environment.”

A critical component of global climate

Antarctica’s ice sheet is Earth’s largest reservoir of frozen water, nearly twice the size of Australia. Acting like a vast sponge for the planet, it plays a pivotal role in regulating global sea levels.

Understanding how this sheet moves and melts is essential as its rate of shrinkage increases every year.

Historically, researchers have relied on laboratory experiments to approximate Antarctic ice’s mechanical properties. However, simulating real Antarctic ice in a lab can be deceptively simplistic, explained Professor Lai. 

Finding patterns in Antarctica’s ice

Factors such as variations between seawater-derived ice and snow-compacted ice, or the presence of cracks, air voids, and other irregularities, all contribute to ice movement in nature.

“These differences influence the overall mechanical behavior, the so-called constitutive model, of the ice sheet in ways that are not captured in existing models or in a lab setting,” Lai said.

Rather than tackling every variable individually, the Stanford-led team used a machine learning model to assess large-scale patterns in the movement and thickness of ice.

This model used satellite images and airplane radar data from 2007 to 2018, while adhering to basic physical laws that regulate ice flow.

Variations in ice shelf dynamics

The researchers examined five Antarctic ice shelves – floating extensions of land-based glaciers. These shelves maintain stable levels of continental ice by keeping large amounts of it from sliding outright into the ocean. 

The experts discovered that areas of the shelves closer to land experience strong compression, a characteristic that aligns with typical lab-derived models for ice dynamics.

However, ice located farther offshore experiences extension, causing it to respond differently in different directions – a phenomenon known as anisotropy.

Study first author Yongji Wang completed the project as a postdoctoral researcher in Lai’s lab and is now a postdoctoral researcher at New York University. 

“Our study uncovers that most of the ice shelf is anisotropic,” said Wang. “The compression zone – the part near the grounded ice – only accounts for less than 5% of the ice shelf. The other 95% is the extension zone and doesn’t follow the same law.”

This finding undermines a key assumption in many climate models that treat ice as the same in every direction. Real conditions, Wang’s analysis indicates, are considerably more nuanced.

“People thought about this before, but it had never been validated,” Wang added. “Now, based on this new method and the rigorous mathematical thinking behind it, we know that models predicting the future evolution of Antarctica should be anisotropic.”

Consequences for coastal areas

Understanding these complex ice dynamics is crucial, especially as polar regions warm rapidly. Rising seas already cause intensifying floods, coastal erosion, and severe storm damage. 

Models that overgeneralize the ice’s properties may underestimate or misjudge how fast Antarctica’s ice sheets can collapse or produce icebergs – a process called calving.

Moreover, the scientists note that many existing models have assumed the Antarctic ice sheet’s mechanical behavior is uniform. This oversimplification could translate into significant inaccuracies in projections about melt rates and sea-level rise.

New understanding of Earth processes

This project exemplifies an emerging strategy that merges “big data” with fundamental physics in Earth science research. 

Professor Lai emphasized that while machine learning can discover patterns from enormous sets of satellite images, scientists must still ensure the results adhere to the time-tested laws of physics that govern planetary processes.

“We are trying to show that you can actually use AI to learn something new,” Lai said. “It still needs to be bound by some physical laws, but this combined approach allowed us to uncover ice physics beyond what was previously known and could really drive new understanding of Earth and planetary processes in a natural setting.”

Since the data used here extend only to 2018, the team’s next steps include refining their technique with newly available images and radar signals. 

The researchers also plan to apply the method to other parts of the Antarctic continent and possibly beyond, gleaning more clues about how these massive frozen landscapes might evolve under accelerating climate change.

Guiding adaptation strategies 

As researchers plug these results into broader climate simulations, the hope is that predictions about rising oceans will incorporate Antarctica’s real-world complexities more faithfully. 

Policymakers and coastal communities around the globe stand to benefit from improved forecasts to guide adaptation strategies – from planning dikes to restoring coastal wetlands.

Ultimately, the fusion of machine learning with physical knowledge marks a powerful advance in unraveling how Antarctica’s ice moves and melts. 

By better capturing small-scale differences across the continent’s ice shelves, scientists can glean deeper insights into the largest reservoir of freshwater on the planet – and how its future may shape the global coastline in coming decades.

Image Credit: NASA’s Goddard Space Flight Center Scientific Visualization Studio

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