Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic framework to represent the highly variable nature of air-sea fluxes, which is missing in deterministic bulk algorithms. Assuming Gaussian distributions conditioned on the input variables, we use artificial neural networks and eddy-covariance measurement data to estimate the mean and variance by minimizing negative log-likelihood loss. The trained neural networks provide alternative mean flux estimates to existing bulk algorithms, and quantify the uncertainty around the mean estimates. Stochastic parameterization of air-sea turbulent fluxes can be constructed by sampling from the predicted distributions. Tests in a single-column forced upper-ocean model suggest that changes in flux algorithms influence sea surface temperature and mixed layer depth seasonally. The ensemble spread in stochastic runs is most pronounced during spring restratification.

翻译：准确量化海气通量对于理解海气相互作用及改进耦合天气气候系统至关重要。本研究提出了一种概率框架来表征海气通量高度变化的特性，这是确定性总体算法所缺失的。在假设输入变量条件下服从高斯分布的前提下，我们通过最小化负对数似然损失函数，利用人工神经网络和涡动协方差测量数据来估计均值和方差。训练完成的神经网络可为现有总体算法提供替代性的平均通量估计，并量化均值估计的不确定性。通过从预测分布中采样，可构建海气湍流通量的随机参数化方案。在单柱强迫上层海洋模型中的测试表明，通量算法的改变会季节性影响海表温度和混合层深度。随机模拟中的集合离散度在春季重层化期间最为显著。