Spatiotemporal Patterns Of the Sea Surface Temperature Using Statistical Anomaly Detection Across the 18-Year-Old Global Scale

Climate change impacts the temperature and acidity of the oceans, seas and lakes through the air-water interface. Worldwide patterns of the sea surface temperature (SST) can be monitored using spaceborne observations of the 4 $\mu$m wavebands during nighttime since July 2002 by Aqua-MODIS. Despite the spatial collection, most studies examine the part of the time-series as lineplots. This research proposes to decipher the 18-year-old SST trends, from July 2002 to December 2020, by focusing on the spatiotemporal detections of the SST anomalies, in order to decompose, hindcast and forecast the time-series. An array of >200 Level-3 SST4 global maps was subject to the Reed-Xiaoli Anomaly Detector, then high, moderate and low anomaly areas were decomposed (seasonal effect removal) and modelled using state space smoothing. Southern Newfoundland and Eastern Hokkaido were representatives of ice-free high anomalies, while the Philippine Sea and Northern Canary were identified as moderately and slightly anomalous, respectively. The modelling accuracy was very satisfactory for both western boundary currents, and excellent for less anomalous regions. The global analysis better fitted with the anomalous Southern Newfoundland than the other regions, indicating an overall rapid increase in SST.