Saharan Air Layer Dust Loading: Effects on Convective Strength in Tropical Cloud Clusters

Thursday, 21 April 2016
Plaza Grand Ballroom (The Condado Hilton Plaza)
Randall J. Hergert, University of South Florida, Tampa, FL; and J. M. Collins, J. P. Dunion, and C. H. Paxton

The interaction between the Saharan Air Layer (SAL) and North Atlantic tropical cyclones has been a source of study for nearly 5 decades. The SAL's influences on developing African Easterly Waves (AEWs) have been repeatedly noted in several previous studies, either as a positive influence or a negative influence. In particular, SAL aerosols have been a variable that has been noted as positive in some cases, and negative in others.

In order to determine whether dust had an overall positive or negative effect on the development of AEWs, this study examined the role dust loading had on the mixing between the SAL and the moist marine boundary layer directly beneath the base of the SAL from the sea-level to ~800-850 hPa. Mixing between the SAL and the marine boundary layer would decrease the ability of the SAL to maintain its atmospheric characteristics. The possibility of dust being entrained into tropical cyclones and acting as cloud condensation nuclei (CCN), in turn enhancing the convective strength of these systems was also explored. The study area covered much of the eastern North Atlantic extending from from 0o – 30oN, 10o – 65oW.

An aerosol optical thickness (AOT) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Extended dataset (PATMOS-x) is correlated with total precipitable water (TPW) derived from microwave satellites and provided by Remote Sensing Systems (REMSS). In this correlation, gridded map data on a 1o x1o degree resolution was used to find a significant relationship between dust loading and TPW. A secondary monthly GIS analysis was conducted with three smaller regions between the same datasets to determine if there was any seasonal and spatial shift to this relationship. Although the correlation analysis revealed significant results, it struggled in the ability to explain any variance. The GIS analysis however did show that the relationship between AOT and TPW depended on the location in the study area as well as the month.

A correlational study was also conducted between the AOT data and Infrared Brightness (IRB) constructed from GridSat and confirmed on IBTrACS. From the IRB dataset also came derived cloud top temperatures; these were then averaged on a monthly basis and set to gridded map data in order to bring it into temporal and spatial consistence with the AOT dataset. A secondary monthly GIS analysis was done between these same two variables to reveal any seasonal shift in this relationship. Both the correlation analysis and GIS analysis revealed a persistent negative correlation between AOT and cloud-top temperatures, showing that increased dust concentrations were correlated to decreased cloud top temperatures or increased convection.

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