Modern Earth System Models (ESMs) operate on horizontal scales far larger than typical cloud features, requiring stochastic subcolumn generators to represent subgrid horizontal and vertical cloud variability. Traditional physically-based generators often rely on analytical cloud overlap paradigms, such as exponential-random decorrelation, which can struggle to capture the complex, anti-correlated behavior of non-contiguous cloud layers. In this study, we introduce a novel two-stage machine learning subcolumn generator for the GEOS atmospheric model, utilizing a Conditional Variational Autoencoder combined with a Generative Adversarial Network (CVAE-GAN) and a U-Net architecture. Trained on a merged CloudSat-CALIPSO height-resolved cloud optical depth dataset, the ML generator creates 56 stochastic subcolumns representing cloud occurrence and optical depth profiles. Evaluated against the established Räisänen, the ML approach accurately reproduces bimodal cloud overlap distributions, significantly reduces biases in grid-mean statistics, and halves the root-mean-square error in ISCCP-style cloud-top pressure and optical thickness joint histograms. The improvements brought by our deep generative models translate into more accurate offline radiative transfer calculations, reducing the global-mean shortwave top-of-atmosphere cloud radiative effect bias by a factor of three. Provided that the generator can be accelerated on CPUs, this offers a practical pathway to reduce structural errors at the cloud-radiation interface.

翻译：现代地球系统模式(ESMs)的运行水平尺度远大于典型云特征，需要随机子柱生成器来表征次网格尺度的水平和垂直云变率。传统基于物理的生成器通常依赖解析云重叠范式（如指数-随机去相关），难以捕捉非连续云层复杂的反相关行为。本研究为GEOS大气模式引入了一种新型两阶段机器学习子柱生成器，采用条件变分自编码器结合生成对抗网络(CVAE-GAN)与U-Net架构。该机器学习生成器基于融合的CloudSat-CALIPSO高度分辨云光学厚度数据集训练，可生成56个表征云出现概率及光学厚度廓线的随机子柱。与成熟的Räisänen方法相比，机器学习方法能准确再现双峰云重叠分布，显著降低网格平均统计偏差，并将ISCCP风格的云顶气压与光学厚度联合直方图的均方根误差减半。深度生成模型带来的改进可转化为更精确的离线辐射传输计算，使全球平均短波大气顶云辐射效应偏差降低三分之二。若该生成器能在CPU上实现加速，将为减少云-辐射界面结构误差提供可行路径。