Although El Nino-Southern Oscillation (ENSO) is the most prominent interannual variability signal in the climate system and its teleconnection with Antarctic sea ice variability has been extensively studied, its impact on sea ice predictability remains largely unexplored. This knowledge gap has limited the development of Antarctic sea ice prediction models.
To address this issue, the research team led by Prof. LI Xiaofeng from the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) employed the deep learning model sea ice prediction network (SIPNet) and its linear counterpart to analyze the influence of El Nino and La Nina events on both linear and nonlinear predictability of Antarctic sea ice across different lead times.
The study was published in npj Climate and Atmospheric Science on Feb. 27.
As a crucial component of the global climate system, Antarctic sea ice plays a vital role in regulating water exchange between major oceans, facilitating heat transport from the tropics to the poles, and contributing to the global thermohaline circulation. Over the past decade, increased variability in Antarctic sea ice extent has heightened the demand for research on sea ice predictability.
This study reveals that ENSO exerts cross-timescale influences on the subseasonal predictability of Antarctic sea ice. Within a lead time of three weeks, sea ice predictability is primarily determined by the persistence of sea ice anomalies, with minimal influence from ENSO. However, as the lead time increases, the contribution of sea ice persistence declines rapidly, while ENSO's influence gradually strengthens and becomes dominant beyond four weeks.
Additionally, El Nino events exert a greater overall impact on sea ice predictability than La Nina events. Specifically, El Nino enhances linear predictability, improving sea ice predictability in the Amundsen–Bellingshausen Sea, Ross Sea, and Indian Ocean sector by 25.6%, 19.6%, and 30.4%, respectively, at an 8-week lead time. In contrast, La Nina primarily enhances nonlinear predictability, particularly improving overall sea ice predictability in the Ross Sea.
Nevertheless, both El Nino and La Nina events significantly reduce predictability in the western Pacific sector. ENSO influences Antarctic climate through teleconnections, generating larger and more persistent sea ice anomalies that provide additional predictability signals. "SIPNet effectively captures and expresses these signals, enhancing our understanding of Antarctic sea ice predictability and providing scientific guidance for improving sea ice prediction models," said Prof. WANG Yunhe, first author of the study.
"These findings offer valuable insights for advancing polar climate forecasting capabilities," said Prof. LI.
Regional model skill under different ENSO phases. (Image by IOCAS)
(Text by Prof. WANG Yunhe)
Media Contact:
ZHANG Yiyi
Institute of Oceanology
E-mail: zhangyiyi@qdio.ac.cn
(Editor: ZHANG Yiyi)