Recent findings, as highlighted in SciTechDaily, suggest there may be a significant oversight in some of the most influential climate models predicting future sea-level rise. According to glaciologist Neal Iverson of Iowa State University, the misinterpretation stems from how glacial ice deforms under milder, or “temperate,” conditions close to its melting point.

In Iverson’s words, “The new value when used in the flow law ‘will tend to predict increases in flow velocity that are much smaller in response to increased stresses caused by ice sheet shrinkage as the climate warms.’” That would mean models show less ice flowing into the oceans, therefore projecting less sea-level rise. If proven widely applicable, this discovery indicates that vast swaths of glacial ice may be more stable and less prone to rapid melt than climate scientists previously assumed.

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What They Found

Researchers investigating temperate ice discovered that a key parameter in the standard “flow law” equation, often referred to as Glen’s Flow Law, was being misapplied under warm-ice conditions. Glen’s Flow Law is typically written as:

ε ̇ = Aτⁿ

where:

ε˙ = strain rate (how quickly ice deforms),
A = a temperature-dependent coefficient,
τ = stress acting on the ice,
ⁿ = the stress exponent (commonly assumed to be ~3).

The newly identified issue centers on how A changes at temperatures close to 0°C. Because temperate ice contains small amounts of liquid water, its internal friction and microstructure behave differently from what the original equation assumed. If A is set too high, ice appears to flow or deform more readily than it really does, causing models to overestimate how quickly glaciers discharge water into the ocean. This “critical flaw” effectively inflates sea-level rise predictions by magnifying the melt rate whenever the equation applies to ice near its melting point.

How a Misapplied Exponent Can Inflate Melting

If the true value of ⁿ near the melting point differs from the standard assumption (usually ⁿ≈3), model results can be off by large factors:

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