Emerging AI-based weather prediction models as downscaling tools

TL;DR

This paper explores using AI-based numerical weather prediction systems for global climate data downscaling, offering a computationally efficient alternative to traditional methods with promising results in high-resolution climate projections.

Contribution

It introduces a novel application of AI-NWP systems for global downscaling of climate data, demonstrating their ability to produce detailed, high-quality forecasts from low-resolution models.

Findings

AI-NWP can generate detailed forecasts with a one-day lead time.

The method produces high-quality, long-term climate datasets.

Potential for bias correction and improved climate data accuracy.

Abstract

The demand for high-resolution information on climate change is critical for accurate projections and decision-making. Presently, this need is addressed through high-resolution climate models or downscaling. High-resolution models are computationally demanding and creating ensemble simulations with them is typically prohibitively expensive. Downscaling methods are more affordable but are typically limited to small regions. This study proposes the use of existing AI-based numerical weather prediction systems (AI-NWP) to perform global downscaling of climate information from low-resolution climate models. Our results demonstrate that AI-NWP initalized from low-resolution initial conditions can develop detailed forecasts closely resembling the resolution of the training data using a one day lead time. We constructed year-long atmospheric fields using AI-NWP forecasts initialized from smoothed ERA5 and low-resolution CMIP6 models. Our analysis for 2-metre temperature indicates that AI-NWP can generate high-quality, long-term datasets and potentially perform bias correction, bringing climate model outputs closer to observed data. The study highlights the potential for off-the-shelf AI-NWP to enhance climate data downscaling, offering a simple and computationally efficient alternative to traditional downscaling techniques. The downscaled data can be used either directly for localized climate information or as boundary conditions for further dynamical downscaling.