Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data

Abstract

The impact of climate change on the environment has become increasingly visible today, and foreseeing future climate events, which involves long-term prediction of climate variables (e.g., temperature, wind speed, precipitation, etc.) at a local small scale in a local region, is crucial for disaster risk management. General Circulation Models (GCMs) allow for the simulation of multiple climate variables, decades into the future (often till the year 2100). GCM simulations, however, are at a global large scale (from 100 km to 600 km) and are too coarse to monitor climate change at the local small scale. Statistical downscaling approaches are often applied to the GCM simulations to allow the evaluation of the GCM outputs at the local scale. Machine learning-based techniques are popular approaches for statistical downscaling. In this paper, we provide an overview of GCM downscaling with machine learning and present a case study that leverages deep learning to downscale weekly averages of the daily minimum and maximum temperatures in the Hackensack–Passaic watershed in New Jersey.