Author(s)

Vincent Ezikornwor Weli^1*, Moses Okemini Nwagbara², Famous Ozabor¹

¹Department of Geography and Environmental Management, University of Port-Harcourt, Choba, Nigeria.
²Department of Soil Science and Meteorology, Michael Okpara University of Agriculture, Umudike, Nigeria.

This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley assumption of 2014 was adopted. However, the Bruckner circle assumption was adopted in selecting the normal under review. Secondary data of minimum and maximum temperatures for Port Harcourt were collected from the archive of Nigerian meteorological agency (NIMET). The grid cell of the HadCM3 corresponding to the Port Harcourt meteorological station was selected from the HadCM3 website to generate the largescale predictors. Data for temperature was there after normalized for the period of calibration. To analyze data, ANOVA and Paired t tests were used. Result showed that, the model was significant at p < 0.05 implying that the largescale predictors of the HadCM3 have performed significantly and that temperature pattern in the area is significantly dependent on them. The Duncan statistics showed that in the A2 scenario Maximum temperature will rise with a mean difference of 3.1°C from 1960-2080, while for B2 the increase will be 0.18°C for same period. For minimum temperature, the ANOVA also showed a difference of 0.21°C and 0.11°C for A2 and B2 respectively. The paired t test statistics showed that these variations in temperatures for both maximum and minimum at A2 and B2 scenarios are significant at p < 0.05. Reforestation, afforestation, carbon sequestration are strongly advocated.

HadCM3, Temperature, Downscaling, Port–Harcourt, Normal

Weli, V. , Nwagbara, M. and Ozabor, F. (2017) The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria. Atmospheric and Climate Sciences, 7, 424-435. doi: 10.4236/acs.2017.74031.