发表机构
University of Western Australia（西澳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对全球气候模式SST降尺度的计算瓶颈，提出残差修正神经网络（RCNN），结合U-Net与残差修正方法，将澳大利亚西海岸SST分辨率从25km提升至2km，可准确捕捉细尺度特征，支持沿海与海洋生态研究。
AI 中文摘要
全球气候模式提供的大尺度海洋与大气预报通常分辨率不足，无法准确捕捉沿海海洋对大气强迫的响应，以及驱动海表温度（SST）细尺度变异性的沿海环流。动力降尺度应用于漫长海岸线、预测集合或长时段时，计算成本过高。因此，本研究利用机器学习技术，对季节耦合海气预报系统ACCESS-S2的海表温度（SST）开展统计降尺度。本研究提出一种新型深度学习框架，采用U-Net生成初始高分辨率SST估计值，随后通过残差修正方法优化结果；目标SST场来自区域海洋模式系统（ROMS）。这一名为残差修正神经网络（RCNN）的两步方法，通过在每一步纳入动态缩放的残差逐步优化U-Net初始预测，可准确捕捉大范围模式及涡旋、锋面等细尺度特征。研究还引入定制损失辅助的RCNN变体，以提升极端事件下的性能——这些极端事件可能因SST极值的气候驱动变化而未出现在训练数据中。该框架可高效对澳大利亚西海岸的SST进行降尺度；2011年海洋热浪案例研究显示，RCNN将ACCESS-S2 SST预测的水平分辨率从25km提升至2km，可识别ACCESS-S2数据集未解析的细尺度异常。这种计算效率与精度的平衡，为沿海影响评估及海洋生态系统研究提供了应用支持。
英文摘要
The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability. Dynamical downscaling is computationally prohibitive, when applied to extensive coastlines, predictive ensembles, or long time periods. Therefore, this work presents a statistical downscaling of sea surface temperature (SST) from the seasonal coupled ocean-atmosphere forecast system (ACCESS-S2) using machine learning techniques. This study proposes a novel deep learning framework that uses a U-Net to generate an initial high-resolution SST estimate, which is subsequently refined using a residual corrective approach. The target SST fields are derived from the Regional Ocean Modeling System (ROMS). This two step approach called Residual Corrective Neural Network (RCNN) progressively refines initial U-Net predictions by incorporating dynamically scaled residuals at each step, enabling accurate capture of broad patterns and fine-grained features such as eddies and fronts. We also introduce a custom loss-assisted RCNN variant to improve performance during extreme events, which may be absent from training data due to climate-driven shifts in SST extremes. The framework efficiently downscales SST along the west coast of Australia. A 2011 marine heatwave case study shows that the RCNN improves ACCESS-S2 SST predictions by increasing horizontal resolution from 25 km to 2 km, enabling identification of fine-scale anomalies unresolved in the ACCESS-S2 dataset. This balance between computational efficiency and accuracy supports applications in coastal impact assessment and marine ecosystem studies.
