Gao Yuchun, Xiu Yongwu, Nie Yafei, Luo Hao, Yang Qinghua, Zampieri Lorenzo, Lv Xianqing, Uotila Petteri
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory Ocean University of China Qingdao China.
Laboratory for Ocean Dynamics and Climate Qingdao Marine Science and Technology Center Qingdao China.
J Geophys Res Oceans. 2024 Nov;129(11):e2024JC021499. doi: 10.1029/2024JC021499. Epub 2024 Nov 21.
In this study, the subseasonal Antarctic sea ice edge prediction skill of the Copernicus Climate Change Service (C3S) and Subseasonal to Seasonal (S2S) projects was evaluated by a probabilistic metric, the spatial probability score (SPS). Both projects provide subseasonal to seasonal scale forecasts of multiple coupled dynamical systems. We found that predictions by individual dynamical systems remain skillful for up to 38 days (i.e., the ECMWF system). Regionally, dynamical systems are better at predicting the sea ice edge in the West Antarctic than in the East Antarctic. However, the seasonal variations of the prediction skill are partly system-dependent as some systems have a freezing-season bias, some had a melting-season bias, and some had a season-independent bias. Further analysis reveals that the model initialization is the crucial prerequisite for skillful subseasonal sea ice prediction. For those systems with the most realistic initialization, the model physics dictates the propagation of initialization errors and, consequently, the temporal length of predictive skill. Additionally, we found that the SPS-characterized prediction skill could be improved by increasing the ensemble size to gain a more realistic ensemble spread. Based on the C3S systems, we constructed a multi-model forecast from the above principles. This forecast consistently demonstrated a superior prediction skill compared to individual dynamical systems or statistical observation-based benchmarks. In summary, our results elucidate the most important factors (i.e., the model initialization and the model physics) affecting the currently available subseasonal Antarctic sea ice prediction systems and highlighting the opportunities to improve them significantly.
在本研究中，哥白尼气候变化服务（C3S）和次季节到季节（S2S）项目的次季节南极海冰边缘预测技能通过一种概率度量——空间概率得分（SPS）进行了评估。这两个项目都提供了多个耦合动力系统的次季节到季节尺度预测。我们发现，单个动力系统的预测在长达38天内仍具技巧性（即欧洲中期天气预报中心系统）。在区域上，动力系统在预测南极西部的海冰边缘方面比在南极东部表现更好。然而，预测技能的季节变化部分取决于系统，因为一些系统存在冻结季节偏差，一些存在融化季节偏差，还有一些存在与季节无关的偏差。进一步分析表明，模型初始化是次季节海冰预测具备技巧性的关键前提。对于那些初始化最为逼真的系统，模型物理特性决定了初始化误差的传播，进而决定了预测技能的时间长度。此外，我们发现通过增加集合规模以获得更逼真的集合离散度，可以提高以SPS表征的预测技能。基于C3S系统，我们依据上述原则构建了一个多模型预测。与单个动力系统或基于统计观测的基准相比，这个预测始终展现出卓越的预测技能。总之，我们的结果阐明了影响当前可用的次季节南极海冰预测系统的最重要因素（即模型初始化和模型物理特性），并突出了大幅改进这些系统的机会。
