Characterizing 15 years of Saharan Air Layer Properties in North Africa

The Saharan Air Layer (SAL) is a dry, well-mixed layer (WML) of warm and sometimes dusty air of nearly constant water vapor mixing ratio generated by the intense surface heating and strong, dry convection in the Sahara Desert, which has notable downstream impacts on the surface energy balance, organized convective system development, seasonal precipitation, and air quality. Characterizing both WMLs and SALs from the existing rawinsonde network has proven challenging because of its sparseness and inconsistent data reporting. Spurred on by this challenge, we previously created a detection methodology and supporting software to automate the identification and characterization of WMLs from multiple data sources including rawinsondes, remote sensing platforms, and model products. We applied our algorithm to each dataset at both its native and at a common (most coarse data product) vertical resolution to detect WMLs and their characteristics (temperature, mixing ratio, AOD, etc.) at each of the 53 rawinsonde launch sites in north Africa.

Our on-going work effort will be divided into two parts. First, assess the ability of each data product to capture WML detections, relative to rawinsonde observations, at a common vertical resolution. For each product, we will evaluate the height, thickness, and accuracy of WML detections and determine the existence of potential data biases (temperature, mixing ratio, etc.) for both Saharan and non-Saharan locations. The second part of our investigation will apply 5-day HySPLIT back trajectories for all WML detections from 2003 and 2018 using data from the best overall model or satellite product. Using these trajectory data, we will be able to determine whether each WML detection was of Saharan origin (i.e., can be classified as a SAL) or was generated from other sources (i.e., residual layers, subsidence inversion, or mid-latitude air streams) and layer-relative physical and meteorological properties. Unlike rawinsonde-only approaches, this investigation aims to provide a SAL climatology database utilizing a multi-year, continuous dataset that has consistent data frequency and data quality at each rawinsonde launch location. Further work will apply our detection methodology to model forecast and reanalysis products to permit analysis and prediction of SAL extent and layer properties across the entirety of north Africa.