Modelling the influence of soil moisture on the Turkana jet

Low‐level jets (LLJs) are sensitive to continental‐scale pressure gradients. Soil moisture influences these gradients by altering turbulent flux partitioning and near‐surface temperatures, thereby affecting LLJ characteristics. The Turkana jet, a strong southeasterly LLJ flowing through a channel between the Ethiopian and East African Highlands, is an important feature of the East African water cycle. Previous work has shown that the jet is sensitive to soil‐moisture‐induced pressure gradients driven by the Madden–Julian oscillation. Here, we build on this finding through using convection‐permitting UK Met Office Unified Model simulations to isolate the role of soil moisture in shaping jet characteristics. Modelling experiments reveal that the Turkana jet is highly sensitive to soil‐moisture‐induced temperature gradients across the channel's exit. Prescribing realistic dry soils intensifies the local surface‐induced thermal low and strengthens the jet. A maximum jet sensitivity of up to occurs when comparing dry and wet surface states within 750 km downstream of the exit, highlighting the significant influence of soil moisture on jet dynamics, given typical speeds of 8–. The impact of soil moisture on the jet is most pronounced when synoptic forcing is weak and skies are clear. Notably, despite a substantial impact on LLJ strength, we find a minor sensitivity of the vertically integrated moisture transport. We speculate that this minimal sensitivity is linked to model errors in the representation of boundary‐layer turbulence, which affects midtropospheric moisture and the strength of elevated nocturnal inversions. This study highlights that the Turkana channel is a hotspot for surface–jet interactions, due to the strong sensitivity of surface fluxes to soil moisture near a topographically constrained LLJ. Future research should continue examining surface‐driven predictability, particularly in regions where land–atmosphere interactions influence dynamical atmospheric conditions, and evaluate such processes in weather prediction models.