GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties

Area of Science:

• Climate Science
• Environmental Modeling
• Data Science

Background:

• High-resolution climate data is crucial for understanding climate change impacts.
• Existing datasets often lack sufficient resolution or ensemble size to capture internal climate variability.
• The need for robust climate datasets that account for internal variability is growing.

Purpose of the Study:

• Introduce the Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset.
• Provide a high-resolution, long-term climate dataset for the Contiguous U.S., Alaska, and Hawaii.
• Enable robust assessment of internal climate variability, forced climate signals, and time of emergence.

Main Methods:

• Statistically downscaling three CMIP6 global climate model large ensembles (CESM2, CanESM5, EC-Earth3).
• Utilizing the Generalized Analog Regression Downscaling method.
• Generating daily precipitation, mean temperature, and temperature range data at 12-km, 4-km, and 1-km resolutions.

Main Results:

• The GARD-LENS dataset provides a 150-year record of downscaled climate data.
• It is the first downscaled single model initial-condition large ensemble (SMILE) dataset.
• Offers comprehensive data for analyzing trends, extremes, and internal climate variability at high resolutions.

Conclusions:

• GARD-LENS is a valuable tool for scientists and practitioners.
• It facilitates the incorporation of internal climate variability into future climate analyses.
• Supports adaptation planning by providing robust, high-resolution climate information.