南京信息工程大学张荣华教授的课题组近日在研究代表热带太平洋上空风应力异常的 U-Net 模式，及其与ENSO研究的中间耦合模式整合中取得新进展。2023年12月9日出版的《大气科学进展》杂志发表了这项成果。

研究人员建立了 U-Net 模型来描述热带太平洋海表温度与风应力异常之间的关系；从U-Net 导出的 τ 模型表示为 τ_UNet ，然后用于替换中间耦合模型(ICM)的原始基于奇异值分解的 τ 模型，形成一个新的集成人工智能的中间耦合模型，表示为 ICM-UNet。从 ICM-UNet 模型获得的模拟结果证明了其在反映赤道太平洋海洋和大气异常场的时空变化方面的能力。

在仅海洋的案例研究中，τ_UNet 导出的风应力异常场用于推进中间耦合模型的海洋分量，也表明了典型厄尔尼诺—南方涛动事件的合理模拟。研究结果强调了将人工智能衍生模型与基于物理的动力学模型相结合进行厄尔尼诺—南方涛动建模研究的可行性。此外，海洋动力模式与基于人工智能的大气风模式的成功整合为海洋—大气相互作用模式研究提供了一条新的途径。

据悉，厄尔尼诺—南方涛动(EI Nino-Southern Oscillation, ENSO)是影响热带太平洋海洋—大气耦合系统的最强烈的年际气候模型，已经建立了许多动力和统计模式对其进行模拟和预测。在一些简化的海洋—大气耦合模式中，可以通过使用奇异值分解(SVD)等统计方法来构建海表温度(SST)异常和风应力(τ)异常之间的关系。近年来，人工智能(AI)在气候建模中的应用显示出良好的前景，人工智能模型与动态模型的整合这一方向掀起了一阵研究热潮。

附：英文原文

Title: U-Net models for representing wind stress anomalies over the tropical Pacific and their integrations with an intermediate coupled model for ENSO studies

Author: Shuangying Du, Rong-Hua Zhang

Issue&Volume: 2023-12-09

Abstract: El Nio-Southern Oscillation (ENSO) is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific, and numerous dynamic and statistical models have been developed to simulate and predict it. In some simplified coupled ocean-atmosphere models, the relationship between sea surface temperature (SST) anomalies and wind stress (τ) anomalies can be constructed by statistical methods, such as singular value decomposition (SVD). In recent years, the applications of artificial intelligence (AI) to climate modeling have shown promising prospects, and the integrations of AI-based models with dynamic models are active areas of research. This study constructs U-Net models for representing the relationship between SSTAs and τ anomalies in the tropical Pacific; the UNet-derived τ model, denoted as τ_UNet, is then used to replace the original SVD-based τ model of an intermediate coupled model (ICM), forming a newly AI-integrated ICM, referred to as ICM-UNet. The simulation results obtained from the ICM-UNet demonstrate its ability in representing the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacic. In the ocean-only case study, the τ_UNet-derived wind stress anomaly fields are used to force the ocean component of the ICM, also indicating reasonable simulations of typical ENSO events. These results demonstrate the feasibility of integrating AI-derived model with physics-based dynamical model for ENSO modeling studies. Furthermore, the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

DOI: 10.1007/s00376-023-3179-2

Source: http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-3179-2viewType=HTML