UNET-vs-Analogues_data

In Europe, temperature variations are mainly driven by the North Atlantic atmospheric circulation. Here we investigate a convolutional neural network (a UNET) for reconstructing daily temperature anomalies in Europe from sea level pressure as a proxy of the atmospheric circulation, and we compare the results with the traditional analogues approach. We show an excellent ability of the UNET to estimate temperature variations given information from sea level pressure only. This novel method outperforms the analogue method, at both daily and inter-annual time scales. Our study also shows that during the training, the UNET learns information such as the seasonal cycle of the relationship between sea-level pressure and temperature anomalies, which could explain part of its excellent scores. This work opens up promising prospects for estimating the contribution of atmospheric variability to temperature variations.欧洲地区的气温变化主要由北大西洋大气环流驱动。本研究针对以海平面气压作为大气环流替代指标、重建欧洲逐日气温距平的任务，探究了卷积神经网络（UNET）的应用效果，并将其结果与传统相似法进行对比。研究结果表明，仅依靠海平面气压信息，UNET即可出色地估算气温变化。该新型方法在逐日及年际时间尺度上均优于传统相似法。本研究还发现，在训练过程中，UNET能够学习到海平面气压与气温距平之间关系的季节循环特征，这可部分解释其优异的性能表现。本研究为量化大气变率对气温变化的贡献开辟了极具前景的研究方向。