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Abstract The predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) time scales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability; in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on nonextreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and nonextreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events and also that, while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization. Significance Statement We studied weather model forecasts of changes in Arctic sea ice extent on day-to-day time scales in different regions and seasons. We were especially interested in extreme sea ice loss days, or days in which sea ice melts very quickly or is reduced due to diverging forces such as winds, ocean currents, and waves. We find that forecast models generally capture the observed timing of extreme sea ice loss days. We also find that forecasts of sea ice extent are worse on extreme sea ice loss days compared to typical days, and that forecast errors remain elevated following extreme sea ice loss events.
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High‐Frequency Sea Ice Variability in Observations and Models
Abstract We characterize high‐frequency variability of sea ice extent (HFVSIE) in observations and climate models. We find that HFVSIE in models is biased low with respect to observations, especially at synoptic timescales (<20 days) in the Arctic year‐round and at monthly timescales (30–60 days) in Antarctica in winter. Models show large spread in HFVSIE, especially in Antarctica. This spread is partly explained by sea ice mean‐state while model biases in sea level pressure (SLP) and wind variability do not appear to play a major role in HFVSIE spread. Extreme sea ice extent (SIE) changes are associated with SLP anomaly dipoles aligned with the sea ice edge and winds directed on‐ice (off‐ice) during SIE loss (gain) events. In observations, these events are also associated with distinct ocean wave states during the cold season, when waves are greater (smaller) and travel toward (away from) the sea ice edge during SIE loss (gain) events.
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- PAR ID:
- 10374895
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 48
- Issue:
- 14
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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