Satellite‐Derived Characteristics of Saharan Cold Pool Outflows During Boreal Summer

Abstract

Cold pool outflows (CPOs) are thought to be the most significant meteorological mechanism of mineral dust emission from the world's largest source in the central and western Sahara in boreal summer. An absence of CPOs from numerical models and reanalyses used to simulate Saharan dust emission leads to considerable error in modelling of dust fluxes from the Sahara. As such, the role of CPOs in the observed variability of dust through the monsoon season remains unclear. To remedy these issues, an improved observational benchmark is needed. In this research, an automated approach to identify and track CPOs in dust imagery from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) is derived. The approach is found to flag 74.2% of events identified manually (26/35). 1,559 events are tracked for June, July and August of 2004-2017. CPOs follow a clear diurnal cycle, peaking at 1700-1900 UTC. Propagation speeds decay exponentially through their lifetime, but on average speeds are 1.5 m/s higher at night. 22.5% of the observed events exceed a total travelled distance of 300 km, with an overwhelming preference for northwestwards propagation. Common across the southern central and western Sahara, CPO activity shifts north through summer in line with observed dust emission. The exception to this is the development of an intense hotspot of CPO activity in southern Algeria in August, which does not parallel any known late season outbreaks of dust. The results underline the importance of the southernmost Saharan dust sources, activated by frequent CPO occurrence in early summer.

Quantifying Mechanisms of Aeolian Dust Emission: Field Measurements at Etosha Pan, Namibia

“…At Etosha, our data demonstrate that 39% ( n = 20) of all dust events (where aerosol concentration >0.5 mg/m 3 ) showed characteristics of being driven by the CPO mechanism (See Table S1 in Supporting Information S1). The short duration but high intensity dust emissions (See Table S2 in Supporting Information S1), and the presence of deep convective cloud associated with CPO‐driven events, makes them difficult to detect from remote sensing observation (Allen et al., 2015; Caton Harrison et al., 2021). However, the recognition of CPOs driving dust emissions at Etosha and other sites of significant dust activity is important because the small‐scale processes which generate them are not well constrained by emissions models (Bergametti et al., 2022), and so their contribution to total atmospheric aerosol load is therefore under‐represented globally (Caton Harrison et al., 2021; Marsham et al., 2013).…”

Dust Detection Over East Asia From Multispectral and Multi‐Temporal Himawari‐8/AHI Thermal Infrared Observations

“…The mean BTD (8.6–10.4 μm) of dust samples in the whole training data set has more significant increases than the mean BTD (12.4–10.4 μm) compared to the clear sky (5.3 and 3 k, respectively). In addition, BTD (12–10 μm) has been proven to be highly influenced by the surface parameters in desert areas (Ashpole & Washington, 2012; Legrand et al., 2001), and BTD (8–10 μm) is better suited for detecting blowing dust over desert areas and cold surfaces (Caton Harrison et al., 2021; Miller et al., 2017). Therefore, this study chooses BTD (8.6–10.4 μm) as the dust index for the ADD method.…”

Summer Dust Emissions From the Etosha Pan, Namibia: The Role of the Namib Anabatic‐Sea Breeze System

“…These key processes include the mix‐down of momentum from nocturnal low‐level jets (NLLJs) (Fiedler et al., 2013; Knippertz & Todd, 2012; Washington & Todd, 2005), downdrafts from convective storms (cold pool outflows) (Caton Harrison et al., 2021; Heinold et al., 2013; Marsham et al., 2013) and microscale dust devils or dry convective plumes (Jemmett‐Smith et al., 2015; Knippertz & Todd, 2012). The current state of knowledge regarding these emission mechanisms comes largely from work conducted in a Saharan context, either through intensive observational campaigns (Washington et al., 2006), remote sensing (Schepanski et al., 2007, 2009) or modeling studies (Heinold et al., 2013; Todd et al., 2008).…”