Climate Change Research Spotlight

An Element of Randomness in Modeling Arctic Ice Cover

Incorporating random variation of temperature, humidity, and wind offers a computationally cheap alternative to improving resolution in an Earth system model when predicting when Arctic sea ice will disappear.

Source: Geophysical Research Letters


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Today, sea ice still covers much of the Arctic year-round, expanding during winter and shrinking in summer. But overall Arctic sea ice cover has been declining for decades, and it may one day disappear almost entirely. This decline has profound implications for ecology, shipping routes, and oil extraction throughout the region. However, climate models differ in their predictions of when, exactly, the Arctic might begin to experience ice-free summers and—decades later but more suddenly—ice-free winters.

Now Meccia et al. investigate a different strategy to assess uncertainties in Arctic sea ice predictions generated by a model known as EC-Earth. Their approach offers a computationally cheaper alternative to boosting the model’s resolution. Instead of trying to physically model air temperature, humidity, and wind on fine scales, the researchers introduced random, or stochastic, variations in these variables to help account for uncertainties in EC-Earth and provide predictions that could be more realistic.

The researchers ran the model for the years 1850 to 2160 with and without the stochastic variations under a “worst-case” greenhouse gas emissions scenario known as RCP8.5. Both approaches predicted an abrupt drop in wintertime Arctic sea ice cover around the year 2100. However, incorporating the random variability resulted in an approximately 10-year delay in the timing of this sudden collapse.

In the years approaching 2100, global temperatures predicted when the stochastic conditions were considered were lower than those predicted without the randomness. But after 2100, to the researchers’ surprise, global temperatures increased faster in the simulations with randomness than in those without. This acceleration may be due to an increase in high-altitude clouds seen in the simulations with stochastic variations after the disappearance of sea ice.

The research team is now exploring the underlying causes of the unexpected post-2100 temperature predictions. They also acknowledge that even with the introduction of the stochastic variations, significant uncertainties remain about climate feedbacks in EC-Earth, and further research is needed to understand and reduce them. (Geophysical Research Letters, https://doi.org/10.1029/2019GL085951, 2020)

—Sarah Stanley, Science Writer

Citation: Stanley, S. (2020), An element of randomness in modeling Arctic ice cover, Eos, 101, https://doi.org/10.1029/2020EO142715. Published on 14 April 2020.
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