Abstract
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.
Highlights
R ED Sea coral reefs are experiencing bleaching, the most problematic change that may have happened to this ecosystem [1]–[3]
sea surface temperature (SST) predictions must incorporate, in addition to information accounting for its past spatiotemporal variability, potential global, and regional predictors, such as the El Nino Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO) (e.g., [6]–[10])
We calibrated different Bayesian structural time series (BSTS) models depending on prior specifications, using a training dataset starting from the beginning of the time series (January 1979) up to time t = 396 (December 2012)
Summary
R ED Sea coral reefs are experiencing bleaching, the most problematic change that may have happened to this ecosystem [1]–[3]. Bleaching events are believed to be significantly influenced, at a first level, by thermal stress from sea surface temperature (SST) [4]. Any decision making related to management strategies of Red Sea ecosystem must rely on SST predictions. The Red Sea (see Fig. 1) is located in an area that lies in a transitional region with potential influence from the Atlantic, Indian Ocean, and Pacific Ocean [5]. SST predictions must incorporate, in addition to information accounting for its past spatiotemporal variability, potential global, and regional predictors, such as the El Nino Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO) (e.g., [6]–[10]).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
