The Turkana Low-Level Jet—Links to Rainfall and Representation in Climate Models

The complex topography of East Africa poses challenges for the accurate modelling of the region’s climate. The Turkana Channel in northwestern Kenya is one of the region’s most significant physical features in terms of climate, because of the persistent low-level jet (LLJ) which blows through it. The LLJ’s role in the local climate is complex; jet-related patterns of convergence and divergence contribute towards the aridity of the surrounding area, but the LLJ is also a major pathway of moisture flux from the Indian Ocean into the interior of Africa. It may also have an important impact on rainfall in central and West Africa through the development of mesoscale convective systems. In reanalysis, the jet has an influence on regional rainfall variability and may also be weakening in strength on multidecadal timescales, with implications for model projections. We present the first analysis of the ability of CMIP5 models to represent the jet. While the seasonal cycle of the jet is reasonably well resolved in the ensemble mean, this is not representative of the individual models. Many have insufficiently high resolution or inaccurate topography, meaning that they are unable to represent the jet. Models are prone to conflating the Turkana LLJ with the Somali LLJ, leading to biases in the seasonal cycle. An important caveat to this study is the use of reanalysis data as a baseline. While four reanalysis datasets were used and all had the correct seasonal cycle, the spread in jet speed across the datasets was comparable to that in the models. Our results add to recent studies emphasising the importance of the Turkana LLJ. We contend that improving models’ underlying topography could lead to improvements in their representation of East African climate. Finally, noting the limitations of reanalysis data for such a study, we argue that a targeted field campaign to measure the LLJ directly could resolve uncertainties in the literature and help better constrain future generations of climate models.