High-resolution dynamic downscaling of historical and future climate projections over Central Asia

Abstract

Climate change poses various challenges for agriculture and water management practices in Central Asia (CA). Central to these challenges are cryosphere dynamics, fragile mountain ecosystems, and ongoing natural hazards that highlight the need for robust projections of regional climate change. For the first time, dynamic downscaling was conducted in Central Asia at a spatial resolution of 5 km. This produced a regional dataset that incorporated periods between 1980 and 2000 and 2076 to 2096. Results show that dynamic downscaling significantly improves the simulation of temperature and precipitation across CA compared to General Circulation Models (GCMs) and other Regional Climate Models (RCMs) due to better representation of topography and related meteorological fields. Our analysis shows that there will be a significant warming trend in Central Asia with a projected increase of 6°C under the Shared Socioeconomic Pathway (SSP) scenario SSP5-8.5 from 2076 to 2096. Pronounced warming is detected over mountainous areas of CA from autumn to spring, which can be explained by the snow-albedo feedback. Precipitation increases are projected from winter to spring and decreases are projected from summer to autumn.

Keywords

• central asia
• dynamic downscaling
• high-resolution dataset
• regional climate projections