Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections

TL;DR

This paper introduces a neural network-based super-resolution method for downscaling climate model data, significantly improving high-resolution precipitation projections and extreme event prediction over India.

Contribution

It presents a novel auxiliary variables informed spatio-temporal neural architecture for statistical downscaling of climate variables, outperforming existing methods.

Findings

Achieved better accuracy than three state-of-the-art baselines.

Improved prediction of extreme climate events.

Enabled high-resolution climate projections over India.

Abstract

Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (~115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India. We showcase significant improvement gain against three popular state-of-the-art baselines with a better ability to predict extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.