Exceedance and Return Period of High Temperature in the African Region

Alemtsehai A Turasie

doi:10.3390/cli9040053

Abstract

Several studies have indicated that the social, economic and other impacts of global warming can be linked with changes in the frequency and intensity of extreme weather/climate events. Developing countries, particularly in the African region, are highly affected by extreme events such as high temperature, usually followed/accompanied by drought. Therefore, studying the probability of occurrence and return period of extreme temperatures, and possible change in these parameters, is of high importance for climate-related policy making and preparedness works in the region. This study aims to address these issues by assessing probability of exceedance and return period of extremes in annual maximum and annual mean temperatures. The analyses of historical data in this study showed that extremes in both annual maximum and mean temperature are highly likely to be exceeded more often in the future compared to the past. For the extreme event marker (threshold) defined in this study, probability of 3 exceedances in the following 19 years (for instance), at any gridpoint, is estimated to be at least 10% for extremes in annual maxima and at least 15% for those in annual means. Most places in the region, however, have much higher (up to 20%) probability of exceedance. The estimated probability of exceedance has shown increasing tendency with time. Return period, based on the most recent data, of extremes in annual maximum temperature is found to be less than 6.5 years at about 48% of the gridpoints in the region. Similarly, return period of extremes in annual mean temperature is estimated to be less than 5.5 years at about 82% of places in the region. These estimates have also shown a strong tendency of getting shorter as time goes on. On average, extremes in annual mean temperature were found to have shorter return periods (4–7 years) compared to those in annual maximum temperature (6–10 years), at 95% confidence. The empirical results presented in this study are generally in agreement with IPCC’s projections of increased warming trend. This data-driven, robust method is used in the present study and the results can also be considered as an alternative approach for detecting changes in climate via estimating and assessing possible changes in frequency of extreme events with time.

Highlights

Climate extreme is the occurrence of a value of a weather or climate variable above a threshold value near the upper ends of the range of observed values of the variable [1] (p. 5)
It would be of high importance to assess likelihoods of the occurrence of extreme climate events and their return periods using data-driven robust methods that we used in this study
Return periods of extreme in annual mean temperatures are as short as 4.1 years based on ECDF2 and as short as 5.9 years based on ECDF1 at a considerable number of places. These results generally indicate that the return periods of extremes in both series are getting shorter with time, which in turn imply that the once extreme event is becoming common phenomena in recent/future times

Summary

Introduction

Climate extreme is the occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends of the range of observed values of the variable [1] (p. 5). Developing countries are more vulnerable to extremes of normal climatic variability, since climate change is likely to increase the frequency and magnitude of some extreme weather events and disasters [11,12,13] and [14] 7–73) projected, with medium to high confidence, that climate-related extreme events have multidimensional impacts globally, and on the African region in particular. These include: increases in droughts, floods, stress on water resources, food security, human health, and infrastructure; and sea level rise that would in turn affect coastal settlements and the like. This, in turn, can support preparedness for and to improve the management strategies of risks associated with such extreme events

Objectives

Results

Conclusion