التفاصيل البيبلوغرافية
| العنوان: |
U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies. |
| المؤلفون: |
Du, Shuangying, Zhang, Rong-Hua |
| المصدر: |
Advances in Atmospheric Sciences; Jul2024, Vol. 41 Issue 7, p1403-1416, 14p |
| مصطلحات موضوعية: |
EL Nino, OCEAN temperature, SINGULAR value decomposition, OCEAN-atmosphere interaction, ATMOSPHERIC models |
| Abstract (English): |
El Niño-Southern Oscillation (ENSO) is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific, and numerous dynamical 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 dynamical 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 τUNet, 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 ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific. In the ocean-only case study, the τUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM, the results of which also indicate reasonable simulations of typical ENSO events. These results demonstrate the feasibility of integrating an AI-derived model with a 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
摘 要: 厄尔尼诺-南方涛动(ENSO)是热带太平洋海气耦合系统中最显著的气候变率信号, 国际上已开发了许多动力和统计模式用于模拟和预测ENSO事件. 在一些简单的海气耦合模式中, 海表温度(SST)异常与风应力(τ)异常之间的关系可以通过统计方法来构建, 如奇异值分解(SVD)等. 近年来, 人工智能(AI)在气候模式研究方面展现出广阔的应用前景, AI模型与动力模式的融合也是备受关注的研究方向. 在本研究中, 我们构建了表征热带太平洋海表温度异常(SSTA)与风应力异常之间非线性关系的U-Net模型(τUNet), 并用其代替中间型耦合模式(ICM)中基于SVD构建的τ模型, 形成一个与AI融合的新型ICM, 简称ICM-UNet. 所构建的ICM-UNet成功再现热带太平洋海区海洋和大气异常场的时空变化. 在个例研究中, 我们利用τUNet模型得到的风应力异常场来强迫ICM的海洋部分(IOM), 所得到的模拟结果也能够合理地表征典型的厄尔尼诺事件. 这些结果表明, 在ENSO模拟研究中, 将AI模型与基于物理的动力模型进行融合是可行的. 此外, 海洋动力模型与基于AI的大气风应力模型的成功融合也为海气相互作用的研究提供了一种新方法. [ABSTRACT FROM AUTHOR] |
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