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Abstract Understanding the variability of Antarctic sea ice is an ongoing challenge given the limitations of observed data. Coupled climate model simulations present the opportunity to examine this variability in Antarctic sea ice. Here, the daily sea ice extent simulated by the newly released National Center for Atmospheric Research (NCAR) Community Eart h System Model Version 2 (CESM2) for the historical period (1979–2014) is compared to the satellite‐observed daily sea ice extent for the same period. The comparisons are made using a newly developed suite of statistical metrics that estimates the variability of the sea ice extent on timescales ranging from the long‐term decadal to the short term, intraday scales. Assessed are the annual cycle, trend, day‐to‐day change, and the volatility, a new statistic that estimates the variability at the daily scale. Results show that the trend in observed daily sea ice is dominated by subdecadal variability with a weak positive linear trend superimposed. The CESM2 simulates comparable subdecadal variability but with a strong negative linear trend superimposed. The CESM2's annual cycle is similar in amplitude to the observed, key differences being the timing of ice advance and retreat. The sea ice begins its advance later, reaches its maximum later and begins retreat later in the CESM2. This is confirmed by the day‐to‐day change. Apparent in all of the sea ice regions, this behavior suggests the influence of the semiannual oscillation of the circumpolar trough. The volatility, which is associated with smaller scale dynamics such as storms, is smaller in the CESM2 than observed.
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Modeling the annual cycle of daily Antarctic sea ice extent
Abstract. The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In thispaper we propose a mathematical and statistical decomposition of this temporal variation inSIE. Each component is interpretable and, when combined,gives a complete picture of the variation in the sea ice. We consider timescales varying from the instantaneous and not previously defined to themulti-decadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about thetiming and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We definea number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors toSIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation indaily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation thanthe traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that theanomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend inAntarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend thatbegan around 2011 and continued until 2016.
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- Award ID(s):
- 1745089
- PAR ID:
- 10185986
- Date Published:
- Journal Name:
- The Cryosphere
- Volume:
- 14
- Issue:
- 7
- ISSN:
- 1994-0424
- Page Range / eLocation ID:
- 2159 to 2172
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/tc-14-2159-2020
