用神经网络建模海气通量不确定性，提升气候模拟精度。
Data-Driven Probabilistic Air-Sea Flux Parameterization
- 基于输入变量构建高斯分布，用神经网络估计通量均值与方差。
- 在单柱海洋模型中验证，通量变化影响海表温度与混合层深度。
- 春季层化期随机模拟的波动最明显，适合需要不确定性的研究者。
准确量化海气通量对理解海气相互作用和改进耦合天气与气候系统至关重要。本文提出一种概率框架，以捕捉海气通量的高度可变性，弥补确定性通量算法的不足。假设通量服从以输入变量为条件的高斯分布，利用人工神经网络与涡动相关观测数据，通过最小化负对数似然损失来估计均值和方差。训练后的神经网络提供替代现有批量算法的通量均值估计，并量化均值周围的不确定性。可通过从预测分布中采样构建海气湍流通量的随机参数化。在单柱强迫上层海洋模型中的测试表明，通量算法的变化会季节性影响海表温度与混合层深度。随机模拟的集合展宽在春季层化期最为显著。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。