印度-太平洋极端降雨的传播途径是由太平洋海面温度调节的
Felix M Strnad1, Jakob Schlör2, Ruth Geen3
1Machine Learning in Climate Science, University of Tübingen, Tübingen, Germany. felix.strnad@uni-tuebingen.de.
Nature communications
|September 15, 2023
概括
玻里亚夏季季内振荡 (BSISO) 驱动印度-太平洋地区的极端降雨. 机器学习识别了三种BSISO模式,揭示了太平洋海面温度如何影响其轨道,以改善极端天气预报.
科学领域:
- 大气科学 大气科学
- 气候动力学 气候动力学
- 气象学 天气学
背景情况:
- 印度-太平洋地区降雨极端的季内变化主要是由北极夏季季内振荡 (BSISO) 驱动的.
- BSISO涉及从印度洋到西太平洋的对流降雨的准周期性向东北传播.
- 准确预测BSISO的时空位置对于季后预测极端降雨事件至关重要,但由于对其传播动态的不完全理解,仍然具有挑战性.
研究的目的:
- 为了描述与BSISO相关的极端降雨的传播途径,在印度-太平洋地区.
- 使用无监督机器学习识别BSISO传播的不同模式.
- 调查太平洋海面温度对BSISO传播模式的影响及其对极端降雨预测的影响.
主要方法:
- 应用无监督机器学习技术来分析降雨数据并识别极端事件传播模式.
- 将BSISO传播的特征分为三个不同的模式:东北向,东方阻塞和准静止.
- 分析太平洋海面温度模式 (例如,类似厄尔尼诺现象,类似拉尼娜现象) 与已识别的BSISO传播模式之间的关系.
主要成果:
- 确定三种不同的BSISO传播模式:东北向,东方阻塞和准静止.
- 证明太平洋海面温度显著调节BSISO的传播.
- 类似厄尔尼诺的情况有利于准静止模式,而类似拉尼娜的情况有利于向东阻塞模式,通过局部翻转循环的湿静态能量变化来调解.
结论:
- 无监督机器学习有效地描述了BSISO传播模式,提高了我们对极端降雨驱动因素的理解.
- 太平洋海面温度在调节BSISO路径方面发挥着关键作用,为区域气候变化提供了洞察力.
- 这些发现为开发印度-太平洋极端降雨预警系统奠定了基础,提前四周可预警.
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