Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic
- 1Princeton University (Princeton, United States of America)
Abstract
This study evaluates the impact of sea ice concentration (SIC) data assimilation (DA) on subseasonal forecasts of Antarctic sea ice by comparing reforecast (aka, hindcast) experiment suites initialized with and without SIC DA. The two initial conditions (ICs) are evaluated against NSIDC SIC observations from 1992 to 2017. Assimilating SIC reduces mean biases and root-mean-square-errors (RMSE) and enhances anomaly correlation coefficients (ACC) of SIC. The improvement in sea ice ICs is greater in the Antarctic than in the Arctic. After SIC DA, the sea ice thickness (SIT) field becomes mostly thinner except in the interior Weddell and Ross sectors. Results from reforecast experiments show that SIC DA improves the subseasonal forecast skill of Antarctic SIC, as indicated by enhanced detrended ACC, in nearly all initialization months except December and January, when the initial improvement is quickly overtaken by a bias likely linked to the thin SIT bias. SIC DA also improves the probabilistic prediction of the sea ice edge position, as measured by the Spatial Probability Score (SPS) at subseasonal time scales. The forecast skill improvement is largest in spring, followed by winter and summer, and shows minor differences in autumn. Consistent with the IC improvement, the reforecast skill gain associated with SIC DA is more pronounced in the Antarctic than the Arctic. Our study demonstrates the critical role of SIC DA in improving subseasonal prediction of Antarctic sea ice.
Versions
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BibTeX
@article{zhang2026subseasonal,
title = {{Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic}},
author = {Yong-Fei Zhang and Mitchell Bushuk and Michael Winton and William Gregory and Bill Hurlin and Liwei Jia and Feiyu Lu},
journal = {The Cryosphere},
year = {2026},
month = oct,
doi = {10.5194/tc-20-5867-2026},
url = {https://tc.copernicus.org/articles/20/5867/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic
AU - Yong-Fei Zhang
AU - Mitchell Bushuk
AU - Michael Winton
AU - William Gregory
AU - Bill Hurlin
AU - Liwei Jia
AU - Feiyu Lu
PY - 2026
DA - 2026/10/13
JO - The Cryosphere
DO - 10.5194/tc-20-5867-2026
UR - https://tc.copernicus.org/articles/20/5867/2026/
AB - This study evaluates the impact of sea ice concentration (SIC) data assimilation (DA) on subseasonal forecasts of Antarctic sea ice by comparing reforecast (aka, hindcast) experiment suites initialized with and without SIC DA. The two initial conditions (ICs) are evaluated against NSIDC SIC observations from 1992 to 2017. Assimilating SIC reduces mean biases and root-mean-square-errors (RMSE) and enhances anomaly correlation coefficients (ACC) of SIC. The improvement in sea ice ICs is greater in the Antarctic than in the Arctic. After SIC DA, the sea ice thickness (SIT) field becomes mostly thinner except in the interior Weddell and Ross sectors. Results from reforecast experiments show that SIC DA improves the subseasonal forecast skill of Antarctic SIC, as indicated by enhanced detrended ACC, in nearly all initialization months except December and January, when the initial improvement is quickly overtaken by a bias likely linked to the thin SIT bias. SIC DA also improves the probabilistic prediction of the sea ice edge position, as measured by the Spatial Probability Score (SPS) at subseasonal time scales. The forecast skill improvement is largest in spring, followed by winter and summer, and shows minor differences in autumn. Consistent with the IC improvement, the reforecast skill gain associated with SIC DA is more pronounced in the Antarctic than the Arctic. Our study demonstrates the critical role of SIC DA in improving subseasonal prediction of Antarctic sea ice.
SN - 1994-0424
ER -