通过深度学习发现每日降水中的人类指纹
Yoo-Geun Ham1, Jeong-Hwan Kim2, Seung-Ki Min3,4
1Department of Oceanography, Chonnam National University, Gwangju, South Korea. ygham@jnu.ac.kr.
Nature
|August 30, 2023
概括
深度学习可以在每日降雨中检测到气候变化信号. 自2010年代中期以来,观测到的降水数据偏离了自然变化,表明全球变暖
科学领域:
- 气候科学
- 机器学习在环境科学中的应用
- 水文气象学
背景情况:
- 气候模型预测由于温室气体变暖而导致的全球降雨变化和极端情况.
- 由于区域范围内的自然降雨波动,对这些预测的观测验证具有挑战性.
- 区分气候变化信号与降雨数据的自然变化仍然是一个重大障碍.
研究的目的:
- 在观察到的每日降水数据中检测气候变化信号的深度学习能力.
- 确定人为变暖对降水影响已经明显的特定地区和时间范围.
- 使用观测证据验证气候模型对强化降雨变化的预测.
主要方法:
- 使用来自气候模型模拟的每日降水场和全球平均表面空气温度数据来训练卷积神经网络 (CNN).
- 将训练的深度学习算法应用于历史的观测降水数据.
- 使用可解释的人工智能框架分析CNN的发现并确定敏感区域和时间尺度.
主要成果:
- 深度学习成功地在观测记录中的每日降水场中识别出新出现的气候变化信号.
- 观测到的每日降水数据成为地球变暖的强有力的预测指标,显示自2010年代中期以来的自然变化.
- 在热带东太平洋和中度风暴轨道的天气时间尺度 (<10天) 上的降水变化对人为变暖的敏感性很高.
结论:
- 全球变暖对每日水文波动的影响已变得明显,即使长期年平均降水变化被自然变化所掩盖.
- 在复杂的环境数据集中检测微妙的气候变化信号提供了强大的工具.
- 这些发现提供了支持气候模型预测降雨变化和极端情况的观测证据.
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