Abstract
Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.
Highlights
Changes in sea surface temperature (SST) vary regionally
The results had a root mean square error (RMSE) of 0.5 °C with a one-month lead time and 0.66 °C with a 12month lead time
The prediction accuracy was highest in the southeast of the study area, with an
Summary
Changes in sea surface temperature (SST) vary regionally. Most of the global oceans have experienced a trend of warming SST, while a small portion experienced cooling, e.g., the Atlantic to the south of Greenland and some areas in the equatorial Pacific [1]. Recent studies showed that the SST over the China Seas showed certain trends during the accelerated global warming period and the hiatus period, like a faster rising rate or a more significant downward trend than the global mean SST, indicating it is a sensitive area to global climate change [3,4,5]. These rapid warming trends can cause latitudinal shifts in species distributions, impact coral reefs and marine fisheries, and affect rainfalls over the middle and lower reaches of the Yangtze River [6,7]. The rapid flow as well as seasonal variations of these warm and cold currents create challenges for long-term and high-spatial-resolution SST prediction in this area
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