Seasonal forecasts of the East African long rains: insight from atmospheric relaxation experiments

Abstract

The impacts of recent droughts and floods over East Africa may have been avoided with accurate and timely early warnings. However skillful predictions for the long rains season from dynamical seasonal forecasts have long proved elusive and understanding of the drivers of interannual variability of this season is incomplete. Although recent work has highlighted several candidates for key drivers of variability during March–April, the representation of East African precipitation and links to remote processes in seasonal climate models is relatively unknown. This is investigated here through use of the atmospheric relaxation technique in coupled seasonal climate hindcast experiments, which also provide an estimate of the upper bound of seasonal predictability from remote sources. Results highlight the key role of the lower troposphere in the northwest Indian Ocean in controlling interannual variability, particularly in March and April. This is in support of recent work suggesting ascent-induced boundary-layer heating this region as a key driver of interannual variability. Results from single-variable relaxation experiments also reveal the importance of correct simulation of humidity for the proper representation of this link. Processes in the southwest Indian Ocean provide a control on May precipitation over southwest Kenya and northern Tanzania, highlighting the role of Somali jet variability in long rains cessation. Relaxation in more remote regions over the Pacific is unable to improve the representation of interannual variability over East Africa in general, although variability in the east Pacific appears to provide a weak control on March rainfall, consistent with previous hypotheses linking decaying ENSO events to early season rainfall. Finally, modelled precipitation anomalies are found to be insufficiently constrained to the coast of Africa. Relaxation (particularly in the northwest Indian Ocean) can improve these spatial biases, however the variance explained by these modes is systematically underestimated in the model and appears insensitive to remote processes. Inadequate representation of local processes over East Africa is proposed as the cause of this underestimation and several candidates are outlined.