The importance of rare, high‐wind events for dust uplift in northern Africa

Abstract

Dust uplift is a nonlinear thresholded function of wind speed and therefore particularly sensitive to the long tails of observed wind speed probability density functions. This suggests that a few rare high‐wind events can contribute substantially to annual dust emission. Here we quantify the relative roles of different wind speeds to dust‐generating winds using surface synoptic observations of dust emission and wind from northern Africa. The results show that winds between 2 and 5 m s−1 above the threshold cause the most emission. Of the dust‐generating winds, 25% is produced by very rare events occurring only at 0.1 to 1.4% of the time, depending on the region. Dust‐producing winds are underestimated in ERA‐I, since it misses the long tail found in observations. ERA‐I overpredicts (underpredicts) the frequency of emission strength winds in the southern (northern) regions. These problems cannot be solved by simple tunings. Finally, we show that rare events make the largest contribution to interannual variability in dust‐generating winds and that ERA severely underestimates this interannual variability.

New Saharan wind observations reveal substantial biases in analysed dust‐generating winds

“…What thresholds to use with ERA‐I is also unclear since ERA‐I misses rare high wind‐speed events. Introducing a threshold would increase the effect of extreme events, increasing disagreements between ERA‐I and observations (Cowie et al, ).…”

Influence of dry‐season vegetation variability on Sahelian dust during 2002–2015

“…The emergence of these two time scales pertains to the distinct nature of the underlying mechanisms associated with the variability of the wind and vegetation cover, as emphasized by Zender and Kwon [2005]. Occasional strong winds profoundly affect monthly mean AOD [Cowie et al, 2015], while Geophysical Research Letters 10.1002/2016GL072317 the long-lasting vegetation anomalies systematically influence the series of erosion events arising throughout the whole dry season. In addition, the highest correlation with wind is found in well-known source areas, where wind is accelerated like in the Bodélé depression [Washington et al, 2006] and other [Schepanski et al, 2012] (Figure S4), and where vegetation is scarce or absent.…”

Identifying errors in dust models from data assimilation

“…The differences in modeled emissions are attributed to uncertainty in dust parameterizations (e.g., particle entrainment and suspension) [ Bagnold , ], the land surface [ Kok et al , ], and representation of model meteorology, often a dominating factor [e.g., Menut , ; Heinold et al , ]. The relationship between wind speed and dust emission is highly nonlinear and usually represented as the third power of the difference between surface wind speed and a fixed threshold friction velocity [e.g., Marticorena and Bergametti , ], making it sensitive to the tail of the wind speed distribution [ Cowie et al , ]. Global models are known to underestimate the dust‐lifting power of wind [ Marsham et al , ; Knippertz and Todd , ].…”