Synopsis

Predicting the Thickness of Sea Ice

Physics 8, s113
A new solution to an old equation will make it easier to model the evolution of sea-ice thickness.
Norbert Untersteiner

The thinning of polar sea ice is one of the most visible signals of changing climate, so scientists need reliable ways to both measure ice thickness and model its evolution. This second effort is moving forward, thanks to a new solution to a forty-year-old equation that predicts the distribution in sea-ice thickness over space and time. A key to the solution was realizing that the processes by which ice sheets deform to make thicker or thinner ice are reminiscent of the molecular collisions that lead to Brownian motion.

In the 1970s, geophysicists derived an equation for the thickness distribution of sea ice, whose solution is a curve that gives the probability of finding ice with a particular thickness range. But the equation is difficult to solve analytically because it contains an intractable term that describes how colliding ice sheets can overlap or buckle to form high ridges.

John Wettlaufer and Srikanth Toppaladoddi of Yale University realized this mechanical jostling was, conceptually, similar to the random molecular collisions a particle in water experiences. Working from this idea, they recast the ice-thickness equation into one analogous to that used for Brownian motion, which physicists know how to solve. They were then able to predict an ice thickness distribution close to that observed by NASA’s Ice, Cloud and land Elevation Satellite (ICESat), an instrument that, between 2003 and 2010, used lasers to map sea ice in the polar regions. In the future, such predictions could be used to estimate processes that affect the evolution of ice distribution.

This research is published in Physical Review Letters.

–Jessica Thomas


Subject Areas

GeophysicsStatistical Physics

Related Articles

Network Science Applied to Urban Transportation
Computational Physics

Network Science Applied to Urban Transportation

A simple model based on network theory can reproduce the complex structures seen in urban transportation networks. Read More »

Strange Kinetics Shape Network Growth
Statistical Physics

Strange Kinetics Shape Network Growth

A connection between time-varying networks and transport theory opens prospects for developing predictive equations of motion for networks. Read More »

Improving Assessments of Climate Tipping Points
Complex Systems

Improving Assessments of Climate Tipping Points

Statistical properties of fluctuations of certain parameters describing a complex system can reveal when that system is approaching a tipping point. Read More »

More Articles