Statistical Downscaling of Global Climate Models for Temperature Trend Analysis in Calgary

Abstract

Climate change, particularly global warming, is a significant environmental issue that has gained widespread attention in recent decades. This study aimed to complement the model for the future by utilizing Global Climate Models (GCMs) data. The shallow-layered Artificial Neural Network (ANN) and deep-based Long Short-Term Memory (LSTM) network was applied to extract the historical temperature trend of Calgary, Canada. Mutual Information (MI) was employed for screening purposes to ensure the quality of the input variables. The results of the study indicate that the LSTM model, which relied on the data screening method using MI, achieved RMSE of 0.01 °C, DC of 0.93, a CC of 0.75 and a Bias of 1.89, and has superiority over the ANN method in the Alberta region.

Keywords

• climate change
• temperature
• statistical downscaling
• Artificial Neural Network
• Long Short-Term Memory
• mutual information