2015/10/08 by David P. Rowell, Ben Booth, Sharon E. Nicholson +1 · 11 citations
Environmental Science · Agricultural and Biological Sciences · #Climate variability and models #Climate change impacts on agriculture #Hydrology and Drought Analysis
paper · doi:10.1175/jcli-d-15-0140.1
openalex publication_date 2015/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Abstract The “long rains” season of East Africa has recently experienced a series of devastating droughts, whereas the majority of climate models predict increasing rainfall for the coming decades. This has been termed the East African climate paradox and has implications for developing viable adaptation policies. A logical framework is adopted that leads to six key hypotheses that could explain this paradox. The first hypothesis that the recent observed trend is due to poor quality data is promptly rejected. An initial judgment on the second hypothesis that the projected trend is founded on poor modeling is beyond the scope of a single study. Analysis of a natural variability hypothesis suggests this is unlikely to have been the dominant driver of recent droughts, although it may have contributed. The next two hypotheses explore whether the balance between competing forcings could be changing. Regarding the possibility that the past trend could be due to changing anthropogenic aerosol emissions, the results of sensitivity experiments are highly model dependent, but some show a significant impact on the patterns of tropical SST trends, aspects of which likely caused the recent long rains droughts. Further experiments suggest land-use changes are unlikely to have caused the recent droughts. The last hypothesis that the response to CO2 emissions is nonlinear explains no more than 10% of the contrast between recent and projected trends. In conclusion, it is recommended that research priorities now focus on providing a process-based expert judgment of the reliability of East Africa projections, improving the modeling of aerosol impacts on rainfall, and better understanding the relevant natural variability.