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The trends and variability in surface air temperatures over selected eco-climatic zones in Nigeria were assessed using Merra-2 datasets from 1981 to2018. A total of 15 stations spread across the eco-climatic zones in Nigeria were used for this study. The Mann-Kendall, linear trend and Sen’s slope trend test, time series plots and descriptive statistics were used. The coefficients of variability of surface air maximum temperature showed low variability for the Mangrove-swamp rainforest and moderate variability for the Guinea-wooded, Sudan and Sahel savannas. Similarly, the coefficients of variability of surface air minimum temperature showed moderate variability for all the selected eco-climatic zones. The M-K trend test showed that 14 stations had upward trends and 1 downward trend, with 13 stations having statistically significant trend in air surface maximum temperature. All the stations had statistically significant upward trends in air surface minimum temperature. The average increase in maximum and minimum air surface temperatures is estimated to be about 0.035°C and 0.036°C per year, respectively. For Nigeria, the average air surface temperature is estimated to increase by about 0.036°C per year, and the average air surface temperature is estimated to have increased by about 1.4°C over the 38 years. This study then gives a linear trend projection of about 4.3°C increase in estimated mean air surface temperature by year 2100 in Nigeria.Citation: King, L. E., Udo, S. O., Ewona, I. O., Amadi, S. O., Ebong, E. D., & Emeka, C. N. (2024). TREND AND VARIATIONS OF SURFACE AIR TEMPERATURES ACROSS SOME ECO-CLIMATIC ZONES IN NIGERIA. Trends in Renewable Energy, 10, 170-209. doi:10.17737/tre.2024.10.2.00172

We evaluated and compared the performance of simulated Angström-Prescott (AP) and Hargreaves-Samani (HS) models on monthly and annual timescales using generalized datasets covering the entire West African region. The fitted AP model yielded more efficient parameters of a = 0.366 and b = 0.459, whereas the HS model produced a 0.216 coefficient based on an annual timescale, which is more suitable in the region compared to coefficients recommended by the Food and Agriculture Organization (FAO) (a = 0.25 and b = 0.5) and HS (0.17), respectively. Employing the FAO and HS recommended coefficients will introduce a relative percentage error (RPE) of 18.388% and 27.19% compared to the RPEs of 0.0014% and 0.1036% obtained in this study, respectively. When considering time and resource availability in the absence of ground-measured datasets, the coefficients obtained in this study can be used for predicting global solar radiation within the region. According to the AP and HS coefficients, the polycrystalline module (p-Si) is more reliable than the monocrystalline module (m-Si) because the p-Si module has a higher tendency to withstand the high temperatures projected to affect the region due to its higher intrinsic properties based on the AP and HS coefficients assessment in the region.Citation: Agbor, M. E., Udo, S. O., Ewona, I. O., Nwokolo, S. C., Ogbulezie, J. C., Amadi, S. O., and Billy, U. A. (2023). Effects of Angstrom-Prescott and Hargreaves-Samani Coefficients on Climate Forcing and Solar PV Technology Selection in West Africa. Trends in Renewable Energy, 9, 78-106. DOI: 10.17737/tre.2023.9.1.00150

Temperature forecasts and trend analyzes were carried out for several locations in Mali as an important tool for warning of potentially threatening weather events such as severe heat waves, storms, droughts and floods, which could pose a great risk to humans and their environment. Five locations (Segou, Sikasso, Kayes, Gao and Taoudenni) across Mali (170 00’N – 40 00’W) were chosen for this research work. Satellite data of annual temperature obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) database for 35 years (1985-2019) was used for this work. The Mann-Kendall trend test was carried out for various locations to observe and study the trend. Four Models including Auto Regressive and Integrated Moving Average (ARIMA), Exponential smoothening (ETS), TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components) and the linear model were employed to forecast average temperature for 10 years for all the locations. The model that produces the best forecast at the 95% confidence level is expected to have the lowest Root Mean Square Error (RMSE) value. The results showed that no significant trends were recorded at the considered locations. The linear model produced the best forecast for Segou, Kayes and Taoudenni, while the TBATS model produced the best forecast for Gao and the ARIMA model produced the best forecast for Sikasso.Citation: Billy, U., Udo, S., Ewona, I., Umoh, M., & Mfongang, A. (2023). Temperature Forecasting as a Means of Mitigating Climate Change and Its Effects: A Case Study of Mali. Trends in Renewable Energy, 9(2), 167-179. doi:http://dx.doi.org/10.17737/tre.2023.9.2.00158