DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution

TL;DR

DeepSD leverages a super-resolution CNN framework with multi-scale inputs to improve statistical downscaling of climate variables, enabling higher resolution climate projections from coarse Earth System Models.

Contribution

This work introduces DeepSD, a novel stacked super-resolution CNN approach with multi-scale inputs for climate downscaling, outperforming traditional methods.

Findings

DeepSD outperforms Bias Correction Spatial Disaggregation and other statistical methods.

It effectively downscales daily precipitation from 1 degree to 1/8 degrees.

Framework can be applied to multiple Earth System Models and emission scenarios.

Abstract

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.