Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

Abstract

Abstract. Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloudaerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA.Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds ( − 0.28 ± 0.26 W m −2 ) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m −2 ). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

“…Aerosol reanalysis from MERRA2 is used to gain insight into speciation and vertical distribution that is not provided by remote-sensing analyses that use column-integrated CCN proxies such as aerosol index (AI) or aerosol optical depth (AOD). It has been demonstrated that model-simulated AI accurately predicts changes in CDNC in contrast to AOD (Gryspeerdt et al, 2017), but observations of AI are still subject to near-cloud retrieval artifacts (Christensen et al, 2017). The aerosol species considered in the present analysis are dust (DU), sea salt (SS), black carbon (BC), organic carbon (OC), and sulfate (SO 4 ).…”

Bounding Global Aerosol Radiative Forcing of Climate Change

“…Constructs such as the albedo susceptibility (Platnick & Twomey, ) or precipitation susceptibility (Sorooshian et al, ) are useful in that they survey globally the regions of the Earth that have the potential to generate large responses to aerosol perturbations while controlling for key meteorologically driven variables. Progress in accounting for spurious correlations (i.e., correlations that do not imply a causal aerosol effect on the respective cloud property) has been made using statistical techniques (Gryspeerdt et al, ), careful sampling (Christensen et al, ) and through combination with reanalysis data (Koren, Feingold, & Remer, ; McCoy, Bender et al, ).…”

“…Even if in general, the horizontal scale of variance of the aerosol is large compared to that of clouds (Anderson, Charlson, Winker, et al, ), this assumption may be weak in the proximity of precipitating clouds. In addition, sampling the aerosol radiative properties in close vicinity to clouds leads to errors (Christensen et al, ) due to the humidity swelling of the aerosol (Quaas et al, ) and misclassification of cloud as aerosols (Zhang et al, ). The most straightforward remote sensing aerosol retrieval is the AOD, τ a . However, τ a does not scale very well with the relevant CCN concentration at cloud base, because it is a column‐integrated quantity, is affected by humidity, and by aerosols that may not act as CCN (Stier, ).…”

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

“…It should be noted that all aerosol samples are under cloud-free conditions and are selected in close proximity to cloud pixels. Retrievals of aerosol properties from passive sensors and lidar observation are both affected by clouds near the aerosol, and thereby result in overestimation for aerosol properties Christensen et al, 2017;Tackett and Di Girolamo, 2009). The extent of this overestimation may be different among different sensors and depends on how far aerosol pixels are chosen from the corresponding cloud pixels (Christensen et al, 2017).…”

“…Retrievals of aerosol properties from passive sensors and lidar observation are both affected by clouds near the aerosol, and thereby result in overestimation for aerosol properties Christensen et al, 2017;Tackett and Di Girolamo, 2009). The extent of this overestimation may be different among different sensors and depends on how far aerosol pixels are chosen from the corresponding cloud pixels (Christensen et al, 2017). This effect, however, would likely impact all metrics in a similar way and we would not expect this effect to impact qualitative comparisons between different metrics.…”