Leveraging Deep Learning for Climate Change Prediction Models : A Dive into Cutting-Edge Techniques

Gowrisankar Krishnamoorthy, Sai Mani Krishna Sistla

Journal of Knowledge Learning and Science Technology · 30 September 2023

In one sentence. Proposes deep ensembles to improve uncertainty in deep-learning statistical downscaling of climate projections, tested with pseudo-reality experiments for non-stationary future climates.

Abstract

In recent years, deep learning has emerged as a promising approach for statistical downscaling, which involves generating high-resolution climate data from coarse low-resolution variables. However, concerns persist regarding the ability of these models to generalize to future climate change scenarios, primarily due to the assumption of stationarity. In this study, we propose the use of deep ensembles as a straightforward method to enhance the uncertainty quantification of statistical downscaling models. By improving the representation of uncertainty, these models offer superior planning capabilities against extreme weather events, which can have significant negative social and economic impacts. Given the absence of observational future data, we rely on pseudo-reality experiments to evaluate the effectiveness of deep ensembles in quantifying the uncertainty of climate change projections. The adoption of deep ensembles facilitates more robust risk assessment, addressing critical needs in various sectors for adapting to climate change challenges.

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