Prediction of Inter-annual Signal of Global Mean Surface Temperature Based on Deep Learning Approach

Global Mean Surface Temperature (GMST) research and prediction is still an essential theoretical basis for climate change and disaster prevention. Because GMST series contains multi-scale variation characteristics that are highly complex and nonlinear, the Ensemble Empirical Mode Decomposition (EEMD) was adopted to effectively decompose the GMST time series to Intrinsic Mode Functions (IMFs), which obtains different scales and different characteristics. The machine learning model Autoregressive Integrated Moving Average (ARIMA) and the deep learning model Long Short-Term Memory (LSTM) present substantial advantages in predicting long-term, complex, and nonlinear time series to carry out GMST inter-annual signal prediction research. The results reveal that the deep learning model fits and predicts the sub-sequences with strong long-term correlation (IMF2-6). IMF1, which represents the inter-annual scale change of GMST is affected by the Pacific Ocean and the Atlantic Ocean multi-climate signal. Three climate indexes should be added as forecast precursor factors into the prediction model to predict IMF1 more accurately. This paper finally selected the LSTM(ENSO) model that considers real-time ENSO to predict the inter-annual GMST signal in advance by comparing multiple sets of GMST data and found that 2020 will have a greater probability of becoming one of the hottest years in history.