[A-AS05] Weather, Climate, and Environmental Science Studies using High-Performance Computing

[AAS05-P12] The spatial distribution of the influence of the vertical structure of aerosol climatology in ICON model in cloudless conditions.

*Aleksei Poliukhov^1,2, Anna Gvozdeva ¹, Daria Piskunova ^1,2 (1.Lomonosov Moscow State University, 2.Hydrometeorological Research Center of Russian Federation)

Keywords:radiation, aerosol, weather forecast, aerosol direct effect, ICON model

The error in the short-term forecast of surface air temperature is significant in regions with high aerosol concentrations. Additionally, the use of an aerosol climatology that incorrectly describes aerosol properties leads to significant errors in weather forecasts on both a global and regional scale. The purpose of this study is to assess the impact of the CAMS aerosol climate model on the accuracy of the calculation of net shortwave radiation and surface air temperature in the ICON model under cloudless conditions. We used the mesoscale NWP (Numerical Weather Prediction) ICON model with the Tanre, Tegen, and CAMS aerosol climatologies on the supercomputer of the Hydrometeorological Research Center of Russia. Calculations were performed at 11 stations: Moscow, Lindenberg, Nu-Alesynd, Eilat, Budapest-Lorinc, Cabauw, Cener, Palaiseau, Payerne, Sonnblick, and Toravere, under cloudless conditions. Using the CAMS aerosol model in the ICON simulation can significantly improve the accuracy of solar radiation calculations at the Earth's surface.. While using CAMS climate data, the error in calculating air temperature under cloudless conditions decreased by 0.06-0.08 °C. However, on several days, the error reached 0.3 °C at levels up to 850 hPa. The sensitivity of air temperature to changes in net shortwave radiation decreased from -0.9 ° C per 100 W/m² at 1,000 hPa to -0.3 ° C per 100 W/m² at 850 hPa using aerosol climate data compared to an aerosol-free atmosphere. The aerosol effect may have a cumulative impact with an increase in forecast lead time at a rate of 0.6 °C per 100 forecast hours. Our work emphasizes the critical importance of improving the accuracy of vertical aerosol concentration profiles for weather forecasting.
The work was supported by the Russian Science Foundation project #23-77-01030.