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基于机器学习的极端事件归因.
Jared T Trok1, Elizabeth A Barnes2, Frances V Davenport3
1Department of Earth System Science, Stanford University, Stanford, CA, USA.
Science advances
|August 21, 2024
概括
机器学习,使用卷积神经网络,将极端天气事件归因于全球变暖. 这种方法提供了对气候变化对热浪影响的快速,低成本分析.
科学领域:
- 气候科学 气候科学
- 机器学习 机器学习
- 极端天气事件 极端天气事件
背景情况:
- 全球气温上升正在增加极端天气事件的频率和强度.
- 准确地将这些事件归因于人类造成的气候变化对于理解影响至关重要.
- 已建立的归因方法可能耗时且资源密集.
研究的目的:
- 开发和验证一种基于机器学习的新型方法,用于极端事件归因.
- 量化全球平均温度 (GMT) 对特定极端高温事件的影响.
- 评估机器学习提供快速和成本效益的事件归因的潜力.
主要方法:
- 利用卷积神经网络 (CNN) 来创建动态一致的反事实场景.
- 将CNN模型应用于最近北美中南部 (2023) 的极端高温事件和历史事件.
- 将机器学习衍生的归因估计与已知方法的结果进行比较.
主要成果:
- 据估计,2023年北美中南部热带活动期间的温度因全球变暖而增加1.18°C-1.42°C.
- 预计类似事件每年发生0.140.60次,温度在工业化前GMT的2.0°C以上.
- 每日温度和GMT之间的学习关系受季节性和每日气象条件的影响.
结论:
- 机器学习,特别是CNN,为极端事件归因提供了可行的和高效的工具.
- 这些发现与已建立的归因技术保持一致,验证了ML方法.
- 这种方法可以更快,更容易地分析气候变化在极端天气中的作用.
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