RSST-ARGM: A Data-Driven Approach to Long-term Sea Surface Temperature Prediction

Abstract

For the purpose of exploring the long-term variation of regional SST, this paper studies the historical SST in local sea areas and the emission pattern of greenhouse gases and proposes a gray model of regional SST based on atmospheric reflection which can be used to predict SST variation in a long time span. By studying the grey systematic relationship between historical SST data, the model obtains the development law of temperature change, and further introduces different future greenhouse gas emission scenarios as the index coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper, and its accuracy is as high as 0.99984. The model predicts that the local SST could reach a maximum of 15.3℃ by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness.