Sea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long-term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment management of the Red Sea ecosystem. In this work, we build a data-driven Bayesian structural time series model and show its effectiveness in predicting future SST seasons with a high accuracy, and identifying the main predictive factors of future SST variability among a large number of factors, including regional SST and large-scale climate indices. The modeling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for five months ahead.

中文翻译：

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用于预测红海温度的贝叶斯结构时间序列方法

海面温度 (SST) 是影响珊瑚礁和导致红海白化事件的主要因素。因此，通过不确定性估计对温度模式进行长期预测对于红海生态系统的环境管理至关重要。在这项工作中，我们建立了一个数据驱动的贝叶斯结构时间序列模型，并展示了其在高精度预测未来海温季节的有效性，并在包括区域海温和区域海温在内的大量因素中识别未来海温变化的主要预测因素。大尺度气候指数。这里提出的建模方案应用了有效的层次聚类来识别在红海显示出不同 SST 变化的相互连接的子区域，和马尔可夫链蒙特卡罗算法，在训练时间序列模型的同时选择主要预测变量。特别是，数值结果表明可以可靠地预测未来五个月的每月 SST。