Abstract
West Africa is a hot spot region for land–atmosphere coupling where atmospheric conditions and convective rainfall can strongly depend on surface characteristics. To investigate the effect of natural interannual vegetation changes on the West African monsoon precipitation, we implement satellite-derived dynamical datasets for vegetation fraction (VF), albedo and leaf area index into the Weather Research and Forecasting model. Two sets of 4-member ensembles with dynamic and static land surface description are used to extract vegetation-related changes in the interannual difference between August–September 2009 and 2010. The observed vegetation patterns retain a significant long-term memory of preceding rainfall patterns of at least 2 months. The interannual vegetation changes exhibit the strongest effect on latent heat fluxes and associated surface temperatures. We find a decrease (increase) of rainy hours over regions with higher (lower) VF during the day and the opposite during the night. The probability that maximum precipitation is shifted to nighttime (daytime) over higher (lower) VF is 12 % higher than by chance. We attribute this behaviour to horizontal circulations driven by differential heating. Over more vegetated regions, the divergence of moist air together with lower sensible heat fluxes hinders the initiation of deep convection during the day. During the night, mature convective systems cause an increase in the number of rainy hours over these regions. We identify this feedback in both water- and energy-limited regions of West Africa. The inclusion of observed dynamical surface information improved the spatial distribution of modelled rainfall in the Sahel with respect to observations, illustrating the potential of satellite data as a boundary constraint for atmospheric models.
Highlights
In recent decades, the investigation of land–atmosphere interactions evolved into a focal point of research on West African monsoon (WAM) variability
If not otherwise indicated, all analyses use the average of the perturbed DYN and climatological datasets (CLIM) four-member ensembles in order to reduce the noise from internal model variability and to get a more robust signal of the mean changes related to the new dynamical surface dataset
Where ρ is the air densitiy, Cp is the heat capacity of dry air (J m−3 K−1), Ch is the surface exchange coefficient, U is the wind speed (m s−1) and air is the potential air temperature (K). This summary of variable dependencies describes how state variables and surface fluxes directly react on changes in ALB, vegetation fraction (VF) or leaf area index (LAI): ALB has a direct effect on Rnet and impacts the available energy for the turbulent surface fluxes
Summary
The investigation of land–atmosphere interactions evolved into a focal point of research on West African monsoon (WAM) variability. We use a new set of satellite-derived data for green vegetation fraction (VF), albedo (ALB) and leaf area index (LAI) at high spatial resolution to address the question how interannual changes of vegetation patterns may affect surface variables, atmospheric circulations and rainfall patterns at a local to regional scale. This surface data set has been recently generated for the West African region including a novel high resolution land cover map. The comparability to observations and other studies is discussed
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