Search

Providing comprehensive regional- and local-scale information on changes observed in the climate system plays a vital role in planning effective and efficient climate change adaptation options, specifically over resource-limited regions. Here, we assess changes in temperature and heat waves over different regions of the African continent, with a focus on spatiotemporal trends and the time of emergence of change in hot extremes from natural variability. We analyse absolute and relative threshold indices. Data sets include temperatures from observations (CRUTS4.03 and BEST) and from three representative state-of-the-art reanalyses (ERA5, MERRA2 and JRA-55) for the common period 1980–2018. Statistically significant warming is observed over all regions of Africa in temperature time series from CRU observations and reanalysis data, although the trend strength varies between data sets. Also, extreme temperatures and heat wave indices from BEST observations and all reanalysis data sets reveal increasing trends over all regions of the African continent. However, there are differences in both trend strength and time evolution of heat wave indices between different reanalysis data sets. Most data sets agree in identifying 2010 as a peak heat year over Northern and Western Africa while Eastern and Southern Africa experienced the highest heat wave occurrence in 2016. Our results clearly reveal that heat wave occurrences have emerged from natural climate variability in Africa. The earliest time of emergence takes place in the Northern Africa region in the early 2000s while in the other African regions emergence over natural variability is found mainly after 2010. This also depends on the respective index metrics, where indices based on more consecutive days show later emergence of heat wave trends. Overall, significant warming and an increase in heat wave occurrence is found in all regions of Africa and has emerged from natural variability in the past one or two decades.

In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.Keywords: climate change, temperature, extreme events, attribution, CMIP6 Acknowledgement: This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)

Aiming to provide comprehensive information for climate change at regional level, we assess temperature and heat waves and their spatiotemporal trend and time of emergence over different regions of the African continent. We analyze observational data of Climate Research Unit Time Series version 4.03 (CRU TS) and the three state-of-the-art reanalysis datasets; European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), National Oceanic Atmospheric Administration’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) and the Japanese Meteorological Agency’s 55 years reanalysis (JRA-55). We assess changes in monthly mean temperature and the agreement between observations and reanalyses. Changes in heat waves are analyzed based on reanalysis datasets because of their high temporal resolution. Heat waves are defined using absolute and relative thresholds, the number of summer days, tropical nights, the percentage of days with maximum and mean temperature above the 90th percentile, the warm nights and the warm spell duration index. The results show increasing trends in monthly mean temperature in all four regions of Africa with different rate of change. A statistically significant trends in heat waves is found in all the regions. Years of highest heat wave occurrence are identified in 2010 for Northern and Western Africa and 2016 for Eastern and Southern Africa. Minimum-temperature based indices, tropical nights and warm nights, show the highest increase in decadal trends and earliest time of emergence, respectively.Key words: climate change; temperature; heat waves; time of emergence; reanalysis; Africa

Climate change will likely impact rainfall characteristics in particular locations; the amount, seasonality, variability and spatial patterns. In developing countries, this presents challenges for rural smallholder farmers as their livelihoods are largely based on rain-fed practices. Changes in climate patterns could increase farmers' vulnerability and the need for intervention. In this paper, we develop new metrics of analysis motivated by qualitative research with smallholder farmers. Previous research found that farmers' understanding of historical rainfall change is accurate, yet diverge from some research studies. We analyze meteorological station rainfall data using metrics that are familiar to smallholders. Farmers' perceptions of rainfall in southern Ethiopia were explored through interviews conducted in three communities. Our findings identified some forms of convergence, as well as divergence, in farmers' perception of rainfall trends and meteorological station data results. In asking the question ‘Why do data based on farmer experiences of rainfall variability differ from meteorological station data?’, we show that using existing data and applying farmer-influenced metrics can improve the information shared with farmers. We argue that, under further climate change, it will be increasingly important to convey meteorological information to farmers in ways that are relevant to them and their agricultural livelihoods.