Quantifying land surface temperature variability for two Sahelian mesoscale regions during the wet season

Land-atmosphere feedbacks play an important role in the weather and climate of many semi-arid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. We used spectral analysis to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth Observations (EO). We analysed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (< 5–day) spectral variance, notably a shift towards lower frequency variability in forest pixels relative to non-forest areas, and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, we found that areas of forest, and to a lesser extent grassland regions, warm up more slowly than sparsely vegetated or barren pixels. We applied the same spectral analysis method to simulated LST data from the the Joint UK Land Environment Simulator (JULES) land surface model. We found a reasonable level of agreement with the EO spectral analysis, for two contrasting land surface regions. However JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EO. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events.