The impact of aerosols on forecasting short range temperature over South Africa
Rambuwani, Tshifhiwa Gift
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The impact of biomass burning and dust aerosols on the performance of forecasting short-range near surface temperature over South Africa was investigated using the UK Met Office Unified Model (MetUM) nesting suite. Aerosol climatologies were used to provide initialisation of aerosol information in the model. Among other reasons, the two (biomass burning and dust) aerosol climatologies were used so that the computational cost in terms of disk space (for the run outputs), sufficient memory to run the reconfiguration tasks, and forecast run time can be reduced. Monthly mean simulated total aerosol climatology mixing ratios (for biomass burning and dust) from the HadGEM2 were compared with aerosol optical depth (AOD) observations (500 nm AERONET and 550 nm MODIS) at AERONET sites across South Africa. Overall, the simulated biomass burning climatology has minimum mixing ratio values between December and April relative to other months, and peaks during late winter (August) and early spring (September). The simulated dust climatology shows maximum peaks during the spring season (September-October-November) for all other selected stations, except in Simonstown IMT station where the maximum peak is in June. The AOD observations show maximum values in September or October as compared to all other months, which might be due to high biomass burning aerosol loading during those months. The MetUM nesting suite was run in two scenarios, namely i) with and ii) without monthly mean aerosol climatologies, to produce 48-hour lead time temperature simulations for every day of September 2015 over South Africa. The model was set up to run with a horizontal resolution of approximately 4.4 km and with 70 vertical levels. The parametrisations of biomass burning and dust aerosol processes were catered for through the Coupled Large-scale Aerosol Simulator for Studies in Climate (CLASSIC) aerosol scheme within the MetUM. Results show that including aerosol climatologies produces a slight difference in forecasted surface temperatures between the simulations of the two mentioned scenarios above. The subjective verification at stations shows that the addition of aerosols into the model simulations makes an average temperature difference of not more than 0.3o C between the scenarios. Furthermore, the overall subjective verification at stations across South Africa shows that both scenarios’ simulations are able to predict the near surface temperature much better over the inland stations compared to coastal ones. The calculated verification scores show that including aerosol climatologies makes a slight improvement in near surface temperature prediction over the domain, with a calculated difference in average bias and root mean square error (RMSE) values of not more than 0.08 and 0.01, respectively, between the scenarios. The calculated p-values of verification scores (bias, RMSE and Pearson correlation coefficient (r)), using a linear regression t-test at 5 % significance level between the two scenarios, shows a significant linear relationship between the calculated bias, and between the calculated RMSE, and between calculated r scores of scenarios’ simulations. It was further shown that the bias of the simulated temperature is cold (negative) during the day light hours and warm (positive) during the night hours, which is in agreement with the previous NWP verification studies.