High-resolution climate simulations over High-Mountain Asia: Focus on the Central Himalaya and Karakoram

Abstract ID: 3.9683 | Not reviewed | Requested as: Talk | TBA | TBA

Tika Gurung (1)
Liang, Chen (1)

(1) Department of Earth and Atmospheric Sciences, University of Nebraska Lincoln, 68588, Lincoln, NE, USA

Categories: Atmosphere, Snow & Ice
Keywords: Hydroclimate, High Mountain Asia, Climate Modeling

Categories: Atmosphere, Snow & Ice
Keywords: Hydroclimate, High Mountain Asia, Climate Modeling

Abstract

High Mountain Asia (HMA) region, often referred as the Third Pole, plays a crucial role in the global water cycle, as it contains the largest reserves of freshwater outside polar regions, providing water supplies to the millions of people downstream. Accurate modeling of hydroclimatic dynamics in HMA is crucial at higher elevations as it regulates these pristine water resources, where orographic gradients and peculiar climate system create substantial variations in precipitation and temperature. Data from lower elevations may not accurately reflect the unique climatic conditions above 3000 m elevation as there is limited observational data at these high altitudes, and the region’s steep terrain requires finer spatial resolution to effectively represent hydroclimatic variables. This study presents the high-resolution simulations of high-altitude hydroclimatic conditions using the Weather Research and Forecasting (WRF) model forced with the ERA5 reanalysis data at a horizontal grid spacing of 12 km and 4 km, span two hydrological years from October 2016 to September 2018. The simulations are evaluated using data collected from observed stations above 3000 m elevation and available gridded products (CHIRPS, CMORPH, ERA5L). The analysis focuses on precipitation and temperature variations across annual to daily scales in the Central Himalaya and Karakoram regions, known for their contrasting glacial environments. The model reasonably captures temperature and precipitation’s spatial and temporal variability, focusing on monsoon and winter periods for the Central Himalaya and Karakoram, respectively. In general, WRF outperforms ERA5L, providing more realistic spatial patterns. Inter-comparison of precipitation gridded products and WRF outputs show inconsistencies with over-and-underestimation depending on the reference dataset. Performance metrics (R2 and RMSE) indicate station-specific variations in WRF and ERA5L. Overall, probability density function and quantile comparison of daily precipitation and temperature demonstrate WRF outputs align better with in-situ data than ERA5L. These means that integration of multiple-data sources with advanced statistical techniques to better evaluate the model output to capture the regional complexities.