A pinboard by
Charles Chisanga

PhD student, University of Zambia


Statistical Downscaling of Precipitation and Temperature

The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Circulation Models (GCM). It was evaluated to determine its suitability in generating synthetic weather data for 2020 and 2055 according to the projections of HadCM3 and BCCR-BCM2 GCMs under SRB1 and SRA1B scenarios at Mount Makulu (Latitude: 15.550o S, Longitude: 28.250o E, Elevation: 1200 meter), Zambia. Three weather parameters - precipitation, minimum and maximum temperature were simulated using LARS-WG v5.5 for observed station and AgMERRA reanalysis data for Mount Makulu. Monthly means and variances of observed and generated daily precipitation, maximum temperature and minimum temperature were used to evaluate the suitability of LARS-WG. Other climatic conditions such as wet and dry spells, seasonal frost and heat spells distributions were also used to assess the performance of the model. The results showed that these variables were modeled with good accuracy and LARS-WG could be used with high confidence to reproduce the current and future climate scenarios. Mount Makulu did not experience any seasonal frost. The average temperature for the baseline (Observed station data: 1981-2010 and AgMERRA reanalysis: 1981-2010) was 21.36oC and 22.20oC, respectively. Using the observed station data the average temperature under SRB1 (2020), SRA1B (2020), SRB1 (2055), SRA1B (2055) would be 21.90oC, 21.95oC, 22.84oC and 23.18oC, respectively. Under the AgMERRA reanalysis the average temperatures would be 22.79oC (SRB1:2020), 22.85oC (SRA1B:2020), 23.73oC (SRB1:2055) and 24.12oC (SRA1B:2055). The HadCM3 and BCM2 GCMs ensemble mean showed that the number of days with precipitation would reduce by 4-5 while precipitation amount in 2020s and 2055s would increase under observed station data and reduce under AgMERRA reanalysis data, respectively.