The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.
翻译:厄尔尼涅诺南方涛动(ENSO)是热带中太平洋和东太平洋热带中、中太平洋和东太平洋海洋海面温度(SST)的半周期性波动,通过长距离依赖或远程连接影响世界各地区域水文学年际变异,通过长距离依赖或远程连接影响世界各地区域水文的年际变化。最近的研究表明,深层学习(DL)方法对于改进厄尔尼尼诺预测以及复杂网络对远程连接的了解具有价值。然而,在预测理解厄尔尼诺-南方涛动河流流(ENSO)时存在差距,包括DL的黑盒性质,使用简单的厄尔尼诺指数来描述一个复杂的现象,将基于DL的厄尔尼诺流预测转化为河流流预测。在这里,我们显示,基于突出的地图,可变DL(XDL)方法可以提取全球SST(DL)中包含的可解释的预测信息,并发现SST信息区域和与河流流相关的依赖结构,这些与气候网络建设一道,能够改进预测。我们的结果揭示了全球SST对厄尔尼指数以外的全球SST的更多信息内容,了解SST如何影响河流流流流流流流流流流流流流流流流流流流流流流,并形成,并生成模型和测测测测测,并产生更好的数据,包括根据地测测测测测测算。