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 sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predic...

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Bibliographic Details
Published in:EURASIP Journal on Wireless Communications and Networking
Main Authors: Linqian Zhu, Qi Liu, Xiaodong Liu, Yonghong Zhang
Format: Article in Journal/Newspaper
Language:English
Published: SpringerOpen 2021
Subjects:
Long-term prediction
Regional SST
Temperature variation
Grey model
Atmospheric reflection
Telecommunication
TK5101-6720
Electronics
TK7800-8360
North Atlantic
Online Access:https://doi.org/10.1186/s13638-021-02044-9
https://doaj.org/article/8c45638a24a54114af7648bb2617f296
Description
Summary:Abstract For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST 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 variation, and further introduces different greenhouse gas emission scenarios in the future as the indexes 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. The accuracy of the model is as high as 0.99984. The model predicts that the regional SST could reach a maximum of $$15.3\,^{\circ }{\mathrm {C}}$$ 15.3 ∘ C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness.

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