Fluctuating Arctic Sea Ice Thickness Changes Estimated by an In Situ Learned and Empirically Forced Neural Network Model

Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm tr...

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Bibliographic Details
Published in:Journal of climate 2008-02, Vol.21 (4), p.716-729
Main Authors: Belchansky, G. I., Douglas, D. C., Platonov, N. G.
Format: Article
Language:English
Subjects:
Air temperature
Atmosphere
Atmospheric circulation
Atmospherics
Climate change
Climate models
Global climate
Ice
Ice drift
Ice thickness
Meteorology
Neural networks
Oceans
Pixels
Sea ice
Seas
Studies
Surface temperature
Thickness measurement
Time series
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Summary:Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel’s previous 3-yr drift track (or less if the ice was
ISSN:0894-8755
1520-0442
DOI:10.1175/2007JCI1787.1