Added Value of a Dynamical Downscaling Approach for Simulating Precipitation and Temperature Over Tianshan Mountains Area, Central Asia

• Published in: Journal of geophysical research. Atmospheres 2019-11, Vol.124 (21), p.11051-11069
• Main Authors: Chen, Shuying, Hamdi, Rafiq, Ochege, Friday Uchenna, Du, Haoyang, Chen, Xi, Yang, Weikang, Zhang, Chi
• Format: Article
• Language: English
• Subjects: added value; Central Asia; Climate models; Climatic data; Climatology; Computer simulation; Datasets; dynamical downscaling; Ecological monitoring; Extreme weather; Future climates; Geophysics; Hydrologic studies; Hydrology; Mountains; Nesting; Orography; Precipitation; Regional climate models; Regional climates; Resolution; Seasonal variation; Simulation; Tianshan Mountains; Water towers; Weather forecasting; WRF
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1029/2019JD031016

Progresses in climatological, ecological, and hydrological studies that focused on the Tianshan mountains area (known as “the water tower of central Asia”) have been restricted by the availability of station observations as well as high resolution and quality data set. With the aim to overcome some of these difficulties, the high‐resolution Weather Research and Forecasting (WRF) regional climate model is run over the Tianshan mountains area driven by the ERA‐Interim reanalysis. Double nesting method was used with a horizontal resolution of 40 and 8 km covering the period 1980–2018. A decade of simulation from 1980 to 1989, a period when most abundant station observations are available, is considered for validation. These downscaled results are compared against station observations, ERA‐Interim reanalysis, and three widely used spatially interpolated products in order to investigate the added value of the dynamical downscaling approach. Results of these comparisons show that the WRF‐downscaled data outperform and add significant details to ERA‐Interim reanalysis. A remarkable improvement of the WRF simulation is found at reproducing the observed seasonal cycle of daily extreme temperatures and precipitation. Due to better representation of orography, WRF simulations are able to capture extreme precipitation events that are missing in the high‐quality interpolated products. Refining the resolution from 40 to 8 km further improves the model performance, particularly at depicting orographic enhancement of precipitation. The validated WRF model can be used in future climate projections studies, and this high‐resolution as well as high‐quality climatological data set we present here is useful for impact and further downstream studies. Key Points The Weather Research and Forecasting (WRF) regional climate model is evaluated over the Tianshan mountain areas Dynamical downscaling using WRF adds significant value in reproducing monthly mean daily extreme temperatures and precipitation Extreme precipitation events are absent in the high‐quality interpolated products but well simulated by WRF