Mechanisms of Regional Arctic Sea Ice Predictability in Two Dynamical Seasonal Forecast Systems
Journal
Journal of Climate
Journal Volume
35
Journal Issue
13
Pages
4207-4231
Date Issued
2022
Author(s)
Bushuk M
Zhang Y
Winton M
Hurlin B
Delworth T
Lu F
Jia L
Zhang L
Cooke W
Harrison M
JOHNSON N.C
Kapnick S
Mchugh C
Murakami H
Rosati A
WITTENBERG A.T
Yang X
Zeng F.
Abstract
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan- Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems. © 2022 American Meteorological Society.
Subjects
Arctic; Climate models; Climate variability; Data assimilation; Sea ice; Seasonal forecasting
Other Subjects
Dynamical systems; Earth (planet); Earth system models; Economic and social effects; Forecasting; Sea ice; Arctic; Arctic sea ice; Climate variability; Data assimilation; Forecast systems; Ocean heat content; Sea ice extent; Seasonal forecasting; Seasonal forecasts; Upper ocean; Climate models; climate modeling; climate variation; data assimilation; prediction; sea ice; temperature anomaly; weather forecasting; Arctic Ocean
Type
journal article