Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning/artificial intelligence model that quantifies CCN from model-simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this machine learning model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. Machine learning extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust machine learning pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol–cloud interactions (ERF_aci) and improving confidence in assessment of anthropogenic contributions and climate change projections.

中文翻译：

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机器学习从化学和气象学中揭示气溶胶大小信息，以量化潜在的云形成粒子

云凝结核 (CCN) 是气溶胶-云相互作用的介质，它导致气候变化预测的最大不确定性。在这里，我们提出了一个机器学习/人工智能模型，该模型从模型模拟的气溶胶成分、大气痕量气体和气象变量中量化 CCN。涵盖对流层不同物理化学状态的综合多运动空中测量证实了该机器学习模型的有效性并有助于探索其内部工作原理：首次揭示不同范围的大气气溶胶成分和质量对应于不同的气溶胶数尺寸分布。机器学习从化学和气象学中提取这些信息，这对于准确量化 CCN 很重要。这可以在仅解析气溶胶成分的全球气候模型中提供物理化学可解释、计算效率高、稳健的机器学习途径；潜在地减轻由于气溶胶-云相互作用（ERF）引起的有效辐射强迫的不确定性_aci ) 并提高对人为贡献评估和气候变化预测的信心。