基于确定性预报模式评估 2018 年夏季黄海海洋热浪的可预测性

IF 6.1 1区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Haiqing Yu , Hui Wang , Chunxin Yuan , Qinwang Xing
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引用次数: 0

摘要

了解海洋热浪(MHWs)的可预测性并确定其预报误差的来源对于提高其预报精度至关重要。2018年夏季,一场强烈的MHW袭击了黄海,给中国沿海地区的海参养殖业造成了重大经济损失。然而,预测此次MHW演变的能力仍不确定。本研究针对这一问题,基于确定性海洋预报模式进行了多次预报试验。结果表明,可以在不到 3 天的准备时间内有效预测这一 MHW。具体来说,在 3 天的预报时间内,平均 MHW 预报精度为 0.66,平均缺席/出现精度为 0.79。超过 3 天准备时间后,MHW 预报准确率逐渐下降,这主要是由于在其增长和衰减阶段预测过高的 "误报"。预测过高的 "误报 "主要归因于与经过黄海的两个台风有关的风力和气温预测的不确定性。此外,由大气强迫不确定性引起的异常海洋环流也可能通过平流引发 MHW 预报误差。建议今后在参数优化、海气耦合、集合预报以及与基于人工智能的天气预报相结合等方面做出努力,以改进对 MHW 的预报。我们的研究结果可为利益相关方提供一些启示,以便为黄海未来发生 MHW 做好准备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the predictability of the marine heatwave in the Yellow Sea during the summer of 2018 based on a deterministic forecast model

Understanding the predictability of marine heatwaves (MHWs) and identifying the sources of their forecast errors are essential for enhancing their forecast accuracy. In the summer of 2018, a powerful MHW struck the Yellow Sea, resulting in significant economic losses for the sea cucumber culture industry in China's coastal areas. However, the ability to predict the evolution of this MHW remains uncertain. In this study, several forecast experiments were conducted based on a deterministic ocean forecast model to address this issue. The results demonstrate that this MHW can be effectively predicted with a lead time of less than 3 days. Specifically, the mean MHW forecast accuracy is 0.66 and the mean absence/presence accuracy is 0.79 at a 3-day lead time. Beyond a 3-day lead time, the MHW forecast accuracy steadily decreases, which is primarily due to the overpredicted “False Alarms” during its growth and decay phases. The overpredicted “False Alarms” are largely attributed to uncertainties in predicting wind and air temperature related to two typhoons passing through the Yellow Sea. Additionally, anomalous ocean circulation induced by atmospheric forcing uncertainties may also trigger MHW forecast errors through advection. Future efforts involving parameter optimization, air-sea coupling, ensemble forecasts and integration with artificial intelligence-based weather forecasts are suggested to improve the prediction of MHWs. Our findings may provide implications for stakeholders in preparation for any future occurrences of MHWs in the Yellow Sea.

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来源期刊
Weather and Climate Extremes
Weather and Climate Extremes Earth and Planetary Sciences-Atmospheric Science
CiteScore
11.00
自引率
7.50%
发文量
102
审稿时长
33 weeks
期刊介绍: Weather and Climate Extremes Target Audience: Academics Decision makers International development agencies Non-governmental organizations (NGOs) Civil society Focus Areas: Research in weather and climate extremes Monitoring and early warning systems Assessment of vulnerability and impacts Developing and implementing intervention policies Effective risk management and adaptation practices Engagement of local communities in adopting coping strategies Information and communication strategies tailored to local and regional needs and circumstances
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