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Status: Unchecked ×Topic: Arctic and Antarctic ice dynamics ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Arctic and Antarctic ice dynamics

   Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic

   Durand, Finn, Farchi, Bocquet, Boutin and Olason · ˜The œcryosphere · 2024

   The authors trained a neural network to imitate the neXtSIM sea-ice model for Arctic ice thickness, and it can be run forward for up to a year while outperforming simple baselines.

   Unchecked3 claims
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   1. UncheckedA neural-network stand-in for a sea-ice model beats a simple persistence forecast from 12 hours to about a year, with errors cut by up to 50 %.“The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error.”
   2. UncheckedThe neural-network surrogate's forecasts of Arctic sea-ice thickness beat the daily climatology for more than 6 months ahead.“Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months.”
   3. UncheckedWhen the neural-network surrogate is run repeatedly over time, it undergoes diffusion-like smoothing that loses fine-scale detail in sea-ice thickness.“While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures.”