On Tanzania’s Precipitation Climatology, Variability, and Future Projection

Krishna Borhara,Liping Deng,S.-Y Simon Wang,Binod Pokharel,Brennan Bean

doi:10.3390/cli8020034

Krishna Borhara, Liping Deng + Show 3 more

Open Access

https://doi.org/10.3390/cli8020034

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Journal: Climate	Publication Date: Feb 20, 2020
Citations: 30	License type: CC BY 4.0

Affiliation: Utah State University, Guangdong Ocean University

Abstract

We investigate historical and projected precipitation in Tanzania using observational and climate model data. Precipitation in Tanzania is highly variable in both space and time due to topographical variations, coastal influences, and the presence of lakes. Annual and seasonal precipitation trend analyses from 1961 to 2016 show maximum rainfall decline in Tanzania during the long rainy season in the fall (March–May), and an increasing precipitation trend in northwestern Tanzania during the short rainy season in the spring (September–November). Empirical orthogonal function (EOF) analysis applied to Tanzania’s precipitation patterns shows a stronger correlation with warmer temperatures in the western Indian Ocean than with the eastern-central Pacific Ocean. Years with decreasing precipitation in Tanzania appear to correspond with increasing sea surface temperatures (SST) in the Indian Ocean, suggesting that the Indian Ocean Dipole (IOD) may have a greater effect on rainfall variability in Tanzania than the El Niño-Southern Oscillation (ENSO) does. Overall, the climate model ensemble projects increasing precipitation trend in Tanzania that is opposite with the historical decrease in precipitation. This observed drying trend also contradicts a slightly increasing precipitation trend from climate models for the same historical time period, reflecting challenges faced by modern climate models in representing Tanzania’s precipitation.

Highlights

In Africa, the change in precipitation extremes affects agriculture and industries that either directly or indirectly rely on the replenishment of water resources
Observational data used in this study are the 0.25o Global Precipitation Climatology Centre (GPCC) monthly precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https://www.esrl.noaa.gov/psd/ [17,18]
Empirical orthogonal function (EOF) analyzes space-time datasets by reducing the data to spatial patterns known as EOFs that explain most of the data, and temporal patterns known as principal components (PC; [23]) that can be correlated with other variables [24]

Summary

Introduction

In Africa, the change in precipitation extremes affects agriculture and industries that either directly or indirectly rely on the replenishment of water resources. Many studies have identified a widespread decline in precipitation in several eastern and southern African countries and these trends were linked to global warming [1,2,3]. Droughts have become more frequent, longer, and more severe in the last two decades, the 2010–2011 East African drought when famine plunged several countries into a humanitarian crisis. This observed drying trend contradicts a projected increase in East African rainfall by climate models [2,7,8,9]. In recent years, East African countries have been plagued by frequent floods

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