Homogeneity of Monthly Mean Air Temperature of the United Republic of Tanzania with HOMER

Copyright © 2014 Philbert M. Luhunga et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accordance of the Creative Commons Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intellectual property Philbert M. Luhunga et al. All Copyright © 2014 are guarded by law and by SCIRP as a guardian. The long-term climate datasets are widely used in a variety of climate analyses. These datasets, however, have been adversely impacted by inhomogeneities caused by, for example relocations of meteorological station, change of land use cover surrounding the weather stations, substitution of meteorological station, changes of shelters, changes of instrumentation due to its failure or damage, and change of observation hours. If these inhomogeneities are not detected and adjusted properly, the results of climate analyses using these data can be erroneous. In this paper for the first time, monthly mean air temperatures of the United Republic of Tanzania are homogenized by using HOMER software package. This software is one of the most recent homogenization software and exhibited the best results in the comparative analysis performed within the COST Action ES0601 (HOME)

Spatial and Temporal Analysis of Rainfall and Temperature Extreme Indices in Tanzania

Climate extreme indices in Tanzania for the period 1961-2015 are analyzed using quality controlled daily rainfall, maximum and minimum temperatures data. RClimdex and National Climate Monitoring Products (NCMP) software developed by the commission for Climatology of the World Meteorological Organization (WMO) were used for the computation of the indices at the respective stations at monthly and annual time scales. The trends of the extreme indices averaged over the country were computed and tested for statistical significance. Results showed a widespread statistical significant increase in temperature extremes consistent with global warming patterns. On average, the annual timescale indicate that mean temperature anomaly has increased by 0.69˚C, mean percentage of warm days has increased by 9.37%, and mean percentage of warm nights has increased by 12.05%. Mean percentage of cold days and nights have decreased by 7.64% and 10.00% respectively. A non-statistical significance decreasing trends in rainfall is depicted in large parts of the country. Increasing trend in percentage of warm days and warm nights is mostly depicted over the eastern parts of the country including areas around Kilimanjaro, Dar-es-Salaam, Zanzibar, Mtwara, and Mbeya regions. Some parts of the Lake Victoria Basin are also characterized by increasing trend of warm days and warm nights. However, non-statistical significant decreasing trends in the percentage of warm days and warm nights are depicted in the western parts of the country including Tabora and Kigoma regions and western side of the lake Victoria. These results indicate a clear dipole pattern in temperature dynamics between the eastern side of the country mainly influenced by the Indian Ocean and the western side of the country largely influenced by the moist Congo air mass associated with westerly winds. The results also indicate that days and nights are both getting warmer, though, the warming trend is much faster in the minimum temperature than maximum temperature.The paper is publishedThe authors wish to thank The Tanzania Meteorological Agency for providing data used in this study and WMO for providing guidance in the analysis of climate extreme in climate time series