Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover
复制标题
机器学习揭示气溶胶的气候强迫是由云量增加主导的
DOI：
10.1038/s41561-022-00991-6
复制
发表时间：
2022-08-01
影响因子：
18.3
通讯作者：
Lohmann, Ulrike
中科院分区：
文献类型：
--
作者：
Chen, Ying;Haywood, Jim;Lohmann, Ulrike
基于卫星的机器学习分析的扩散性火山爆发表明，气溶胶气候强迫是由云量的变化，而不是云亮度的变化，气溶胶-云的相互作用对气候有潜在的巨大影响，但量化不足，从而在气候预测中贡献了大量和长期的不确定性。来自气候模式的影响是不受观测约束，因为检索气溶胶-云相互作用的强大的大尺度信号经常受到气象协变相关的相当大的噪声的阻碍。2014年冰岛Holuhraun喷发导致了大量的气溶胶羽流，否则接近原始环境，因此提供了一个理想的自然实验来量化云对气溶胶扰动的响应。在这里，我们使用基于卫星的机器学习方法从气象协变的噪声中分离出重要的信号。我们的分析表明，火山爆发产生的气溶胶使云层覆盖增加了约10%，这似乎是气候强迫的主要原因，而不是之前认为的云层增亮。我们发现，火山气溶胶确实通过减小液滴大小来使云变亮，但这比云分数的变化具有明显较小的辐射影响。这些结果增加了大量的观测约束气溶胶的冷却影响。这些限制对于改进气候模型至关重要，因为气候模型仍然不能充分反映气溶胶-云相互作用的复杂宏观物理和微观物理影响。
Satellite-based machine-learning analysis of a diffusive volcanic eruption suggests that aerosol climate forcing is dominated by changes in cloud cover, rather than changes in cloud brightness.Aerosol-cloud interactions have a potentially large impact on climate but are poorly quantified and thus contribute a substantial and long-standing uncertainty in climate projections. The impacts derived from climate models are poorly constrained by observations because retrieving robust large-scale signals of aerosol-cloud interactions is frequently hampered by the considerable noise associated with meteorological co-variability. The 2014 Holuhraun effusive eruption in Iceland resulted in a massive aerosol plume in an otherwise near-pristine environment and thus provided an ideal natural experiment to quantify cloud responses to aerosol perturbations. Here we disentangle significant signals from the noise of meteorological co-variability using a satellite-based machine-learning approach. Our analysis shows that aerosols from the eruption increased cloud cover by approximately 10%, and this appears to be the leading cause of climate forcing, rather than cloud brightening as previously thought. We find that volcanic aerosols do brighten clouds by reducing droplet size, but this has a notably smaller radiative impact than changes in cloud fraction. These results add substantial observational constraints on the cooling impact of aerosols. Such constraints are critical for improving climate models, which still inadequately represent the complex macro-physical and microphysical impacts of aerosol-cloud interactions.