Abstract
This research introduces a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The scheme employs a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) to efficiently simulate aerosol activation, the vertical profile of supersaturation, and the activated cloud droplet number concentration (CDNC) near the cloud base. We evaluate the QDGE scheme by specifying observed environmental thermodynamic variables and aerosol information from 31 cloud cases as input and comparing the simulated CDNC with cloud observations. The average of mean relative error of the simulated CDNC for cloud cases in each campaign ranges from 17.30 % in Brazil to 25.90 % in China, indicating that the QDGE scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Additionally, we carried out an error analysis by calculating the Maximum Information Coefficient (MIC) between the mean relative error (MRE) and input variables for the individual campaigns and all cloud cases. MIC values are then sorted by aerosol properties, pollution level, environmental humidity, and dynamic condition according to their relative importance to MRE . Based on the error analysis we found that the magnitude of MRE is more relevant to the specification of input aerosol pollution level in marine regions and aerosol hygroscopicity in continental regions than to other variables in the simulation.
Highlights
Aerosols play an important role in affecting the radiation balance of the earth-atmosphere system by scattering and absorbing shortwave radiation and altering the cloud reflectivity and lifetime (Twomey, 1974, 1977; Ghan, 2013; Forster et al, 2016; Ramaswamy et al, 2019; Wang et al, 2020)
Cloud droplet number concentrations are underestimated for all cloud cases for the CL campaign (̅M̅B = mean relative error (M̅̅RE) = −19.36 %), which may be related to the high activation ratio (AR, the ratio of Na to CDNCO, see Table A1) in this 425 region
We introduce a numerically efficient aerosol activation scheme, which calculates the maximum cloud 475 supersaturation and cloud droplet number concentration (CDNC) by employing a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) scheme
Summary
Aerosols play an important role in affecting the radiation balance of the earth-atmosphere system by scattering and absorbing shortwave radiation and altering the cloud reflectivity and lifetime (Twomey, 1974, 1977; Ghan, 2013; Forster et al, 2016; Ramaswamy et al, 2019; Wang et al, 2020). Parameterizations of aerosol activation in climate models were based on observations and derived through parameter fitting, using the aerosol number or mass concentration or other Cloud Condensation Nuclei (CCN) proxies (e.g., sulfate 40 mass) to empirically determine the activated CDNC (Jones et al, 1994; Boucher and Lohmann, 1995; Jones and Slingo, 1996; Lohmann, 1997; Kiehl et al, 2000; Menon et al, 2002) These parameterizations have the advantages of convenience and low computational burden (Fountoukis et al, 2007), substantial uncertainties are resulting from limited spatiotemporal representativeness and unresolved variations in aerosol properties (Meskhidze et al, 2005). In the recent two decades, physically-based parameterization schemes of aerosol activation have emerged (Abdul-Razzak and Ghan, 2000; 45 Cohard et al, 2000; Fountoukis and Nenes, 2005; Ming et al, 2006; Kivekäs et al, 2008; Khvorostyanov and Curry, 2009; Shipway and Abel, 2010; Zhang et al, 2015). Abdul-Razzak et al (1998) Cohard et al (2000) Snider et al (2003) Fountoukis and Nenes (2005) Ming et al (2006) Kivekäs et al (2008) Khvorostyanov and Curry (2009) Shipway and Abel (2010)
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