Abstract
We present a novel methodology to estimate cloud condensation nuclei (CCN) concentrations from spaceborne CALIPSO lidar measurements. The algorithm utilizes (i) the CALIPSO-derived backscatter and extinction coefficient, depolarization ratio, and aerosol subtype information, (ii) the normalized volume size distributions and refractive indices from the CALIPSO aerosol model, and (iii) the MOPSMAP optical modelling package. For each CALIPSO height bin, we first select the aerosol-type specific size distribution and then adjust it to reproduce the extinction coefficient derived from the CALIPSO retrieval. The scaled size distribution is integrated to estimate the aerosol number concentration which is then used in the CCN parameterizations to calculate CCN concentrations at different supersaturations. To account for the hygroscopicity of continental and marine aerosols, we use the kappa parameterization and correct the size distributions before the scaling step. We have studied the sensitivity of the thus derived CCN concentration to the effect of variations of the initial size distributions. It is found that the uncertainty associated with the algorithm can range between a factor of 2 and 3. We have also compared our results with the POLIPHON and found comparable results for extinction coefficients larger than 0.05 km−1. An initial application to a case with coincident airborne in-situ measurements for independent validation shows promising results and illustrates the potential of CALIPSO for constructing a global height-resolved CCN climatology.
Highlights
15 Aerosol particles act as cloud condensation nuclei (CCN) and ice nucleating particles (INP) and provide a surface for the condensation of atmospheric water vapour to form cloud droplets
As most of the size distributions used in the CALIPSO aerosol model (CAMel) are derived from cluster analysis of the long term Aerosol Robotic Network (AERONET) measurements, they incorporate the errors associated with the AERONET inversion algorithm
In order to account for such errors and natural variability, we analyzed the sensitivity of CCN concentrations to the initial 225 normalized size distributions considered in our retrieval algorithm
Summary
15 Aerosol particles act as cloud condensation nuclei (CCN) and ice nucleating particles (INP) and provide a surface for the condensation of atmospheric water vapour to form cloud droplets. The rapid adjustments in clouds resulting from aerosol-cloud interactions (ACI) are not well understood and still remains the largest source of spread in global climate projections This challenge has motivated the scientific community to study ACI by using data from in-situ and satellite measurements as well as by means of modelling and simulations. Satellites provide long term global coverage that enables ACI studies with constrained meteorology and cloud regimes (Oreopoulos et al, 2017; Douglas and L’Ecuyer, 2019; Jia et al, 2021). Satellite-based ACI studies relate cloud parameters (cloud reflectivity or albedo, cloud optical depth, cloud fraction, cloud drop effective radius, liquid water path), aerosol properties
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