Lin Yichen, Nakayama Yoshihiro, Liang Kaixin, Huang Yongtao, Chen Dake, Yang Qinghua
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), 519082, Zhuhai, China.
Institute of Low Temperature Science, Hokkaido University, N19W8, Sapporo, Japan.
Sci Data. 2024 Sep 17;11(1):1006. doi: 10.1038/s41597-024-03848-2.
Polynyas play a critical role in the formation of Antarctic Bottom Water and the enhancement of polar primary productivity. Accurate and exhaustive identification of Antarctic polynyas is fundamental to advancing in-depth research. However, due to methodological limitations, previous studies paid more attention to frequent polynyas and infrequent polynyas have not been investigated much despite that they could be vulnerable to climate change. Inspired by a cyclone tracking algorithm, we develop a novel method to overcome challenges identifying all types of polynyas satisfying spatiotemporal criteria and tracing their daily evolution, extracting from an extensive amount of sea ice concentration data. Based on it, we establish a dataset called "Daily Edge of Each Polynya in Antarctica" (DEEP-AA). Validation against remote sensing and ship-based observations confirms DEEP-AA's reliability. Compared to existing maps, the DEEP-AA identifies a threefold number of polynyas and reveals the seasonal area recovery of infrequent polynyas is earlier than frequent ones.
冰间湖在南极底层水的形成以及极地初级生产力的增强过程中发挥着关键作用。准确且详尽地识别南极冰间湖是推进深入研究的基础。然而，由于方法上的局限性，以往的研究更多地关注频繁出现的冰间湖，而对不频繁出现的冰间湖研究较少，尽管它们可能更容易受到气候变化的影响。受气旋跟踪算法的启发，我们开发了一种新方法，以克服在识别满足时空标准的各类冰间湖并追踪其每日演变方面所面临的挑战，该方法从大量海冰浓度数据中提取信息。在此基础上，我们建立了一个名为“南极每个冰间湖的每日边界”（DEEP - AA）的数据集。与遥感和船基观测结果的验证证实了DEEP - AA的可靠性。与现有地图相比，DEEP - AA识别出的冰间湖数量增加了两倍，并揭示出不频繁出现的冰间湖的季节性面积恢复比频繁出现的冰间湖更早。
