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Abstract. Mineral aerosols (i.e., dust) can affect climate and weather by absorbing and scattering shortwave and longwave radiation in the Earth's atmosphere, the direct radiative effect. Yet understanding of the direct effect is so poor that the sign of the net direct effect at top of the atmosphere (TOA) is unconstrained, and thus it is unknown if dust cools or warms Earth's climate. Here we develop methods to estimate the instantaneous shortwave direct effect via observations of aerosols and radiation made over a 3-year period in a desert region of the southwestern US, obtaining a direct effect of -14±1 and -9±6 W m−2 at the surface and TOA, respectively. We also generate region-specific dust optical properties via a novel dataset of soil mineralogy from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), which are then used to model the dust direct radiative effect in the shortwave and longwave. Using this modeling method, we obtain an instantaneous shortwave direct effect of -21±7 and -1±7 W m−2. The discrepancy between the model and observational direct effect is due to stronger absorption in the model, which we interpret as an AVIRIS soil iron oxide content that is too large. Combining the shortwave observational direct effect with a modeled longwave TOA direct effect of 1±1 W m−2, we obtain an instantaneous TOA net effect of -8±6 W m−2, implying a cooling effect of dust. These findings provide a useful constraint on the dust direct effect in the southwestern United States. 

Abstract Here we present retrievals of aerosol optical depth τ from an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals of τ and more than 95% of retrievals when τ > 0.1 are rejected, resulting in a factor-of-2 reduction in dust-storm averaged τ . We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability in τ during the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites that are located close to dust source regions may require ancillary measurements to aid in the identification of dust. Significance Statement In this study we demonstrate that, during dust storms, measurements made with a sun photometer at an AERONET site in the western Sonoran Desert are frequently classified as cloud contaminated by the network’s processing algorithm. We identify the various algorithmic tests that result in the misclassification and discuss the physical reasons why dust typically fails those tests. We then present a method to restore these data that utilizes measurements from a collocated ceilometer. This work highlights the challenges, and one solution, to operating an AERONET site in a region that is close to the sources of airborne dust.