The model was developed by scientists with the U.S. Geological Survey and the Russian Academy of Sciences, Moscow.
The most dramatic losses in sea ice cover have occurred since 2003, and as scientists acquire newer data, they will apply the new model to study recent years of ice thickness and volume change.
This modeling approach uses sea ice motion data to follow parcels of ice backward in time at monthly intervals for up to 3 years while accumulating a history of the solar radiation and air temperature to which the ice was exposed. The model was constructed by fitting these data with an ice parcel's known thickness to determine how the thickness of sea ice changes in response to different environmental conditions. Data on the known thickness are obtained from measurements by submarine cruises and surface coring missions.
"Sea ice is affected by the accumulation of environmental factors to which it has been exposed," said USGS Director Mark Myers. "Understanding the natural variability of sea ice thickness is critical for improving global climate models. Sea ice regulates energy exchange and plays an important role in the Earth's climate system."
This model, built on historical observations, complements thermodynamic models that simulate ice thickness. Science benefits from having different models. Comparing different model outputs can help improve predictive capabilities. Many scientists worldwide are using satellite and ground observations of the Arctic's atmosphere, ice and ocean to gain a better understanding of how changes at the top of the world affect ecosystems both locally and globally.
The report "Fluctuating Arctic sea ice thickness changes estimated by an in-situ learned and empirically forced neural network model" was recently published in the Journal of Climate and can be found at the American Meteorological Society's journal site.
For additional information on this research, visit the USGS Remote Sensing and Sea Ice Research site.
Jessica Robertson | EurekAlert!
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