垂直风速变化的趋势揭示了云微物理反.
Donifan Barahona1, Katherine H Breen2,3, Derek Ngo4
1Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA. donifan.o.barahona@nasa.gov.
Nature communications
|December 22, 2025
概括
机器学习预测大气流的增强,通过增加的垂直风速标准偏差 (σW) 表示. 这种流影响云的形成,并且自1900年以来略有缓解了温室气体变暖.
科学领域:
- 大气科学 大气科学
- 气候科学 气候科学
- 云物理 云物理
背景情况:
- 垂直空气运动对于气溶激活成云滴和冰晶至关重要.
- 大气模型难以准确地表示亚千米级的风运动,影响云模拟.
- 机器学习为模拟这些微观大气过程提供了潜在的解决方案.
研究的目的:
- 使用生成机器学习技术预测垂直风速 (σW) 的空间标准偏差.
- 识别大气动荡的趋势及其驱动因素.
- 量化相关云微物理变化的辐射影响.
主要方法:
- 利用一种结合风暴解决模拟,观测数据和气候再分析的生成技术.
- 应用机器学习来预测垂直风速 (σW) 的空间标准偏差.
- 分析了 σW 的趋势,并将其与水蒸气,温度和对流的变化联系起来.
主要成果:
- 观察到 σW 的显著趋势,海洋地区的增幅高达 1%yr−1,表明大气流增加.
- 他将这些趋势归因于水蒸气,温度和对流的全球变化.
- 确定了变暖,流和云水流星激活之间的反循环.
结论:
- 由全球气候变化驱动的增强大气动荡会影响云层形成.
- 自1900年以来,这种效应导致了大约-0.10 ± 0.04 Wm-2的辐射冲击.
- 云层中的微物理变化略有缓解了温室气体变暖.
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