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arXiv 2403.08702physics.ao-ph
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数据驱动框架下气溶胶-云相互作用随机性的研究

On the Stochasticity of Aerosol-Cloud Interactions within a Data-driven Framework

Xiang-Yu Li, Hailong Wang, TC Chakraborty, Armin Sorooshian, Luke D. Ziemba, Christiane Voigt, Kenneth Lee Thornhill

AI总结:

本文利用ACTIVATE观测数据和机器学习模型研究气溶胶-云相互作用的随机性,发现气候平均Nc可预测但个别个例不可预测,表明ACI在细时空尺度上具有随机性。

摘要:

气溶胶-云相互作用(ACI)是气候预估中最大的不确定性来源。在理解ACI的诸多挑战中,ACI究竟是确定性的还是随机性的这一问题尚未被明确提出和探讨。本文尝试通过利用气溶胶数浓度Na和环境条件来预测云滴数浓度Nc,以回答这一问题。我们使用来自ACTIVATE野外观测(2020年至2022年)的气溶胶特性、垂直速度脉动w'以及气象状态(温度T和水汽混合比q_v)作为预测变量来估计Nc。研究表明,尽管ACI具有强非线性和多尺度特性,利用机器学习模型仍可成功预测气候平均意义上的Nc。然而,基于观测训练的机器学习模型无法预测个别个例中的Nc,却能成功预测覆盖广泛时空尺度的随机选取数据点的Nc,这表明ACI在细时空尺度上具有随机性。

英文摘要:

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projections. Among many challenges of understanding ACI, the question of whether ACI is deterministic or stochastic has not been explicitly formulated and asked. Here we attempt to answer this question by predicting cloud droplet number concentration Nc from aerosol number concentration Na and ambient conditions. We use aerosol properties, vertical velocity fluctuation w', and meteorological states (temperature T and water vapor mixing ratio q_v) from the ACTIVATE field observations (2020 to 2022) as predictor variables to estimate Nc. We show that the climatological Nc can be successfully predicted using a machine learning model despite the strongly nonlinear and multi-scale nature of ACI. However, the observation-trained machine learning model fails to predict Nc in individual cases while it successfully predicts Nc of randomly selected data points that cover a broad spatiotemporal scale, suggesting the stochastic nature of ACI at fine spatiotemporal scales.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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