Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Abstract

Abstract. Aerosol indirect effects in climate models strongly depend on the representation of the aerosol activation process. In this study, we assess the process-level differences across activation parameterizations that contribute to droplet number uncertainty by using the adjoints of the AbdulRazzak and Ghan (2000) and Fountoukis and Nenes (2005) droplet activation parameterizations in the framework of the Community Atmospheric Model version 5.1 (CAM5.1). The adjoint sensitivities of N d to relevant input parameters are used to (i) unravel the spatially resolved contribution of aerosol number, mass, and chemical composition to changes in N d between present-day and pre-industrial simulations and (ii) identify the key variables responsible for the differences in N d fields and aerosol indirect effect estimates when different activation schemes are used within the same modeling framework. The sensitivities are computed online at minimal computational cost. Changes in aerosol number and aerosol mass concentrations were found to contribute to N d differences much more strongly than chemical composition effects. The main sources of discrepancy between the activation parameterizations considered were the treatment of the water uptake by coarse mode particles, and the sensitivity of the parameterized N d accumulation mode aerosol geometric mean diameter. These two factors explain the different predictions of N d over land and over oceans when these parameterizations are employed. Discrepancies in the sensitivity to aerosol size are responsible for an exaggerated response to aerosol volume changes over heavily polluted regions. Because these regions are collocated with areas of deep clouds, their impact on shortwave cloud forcing is amplified through liquid water path changes. The same framework is also utilized to efficiently explore droplet number uncertainty attributable to hygroscopicity parameter of organic aerosol (primary and secondary). Comparisons between the parameterization-derived sensitivities of droplet number against predictions with detailed numerical simulations of the activation process were performed to validate the physical consistency of the adjoint sensitivities.

Role of updraft velocity in temporal variability of global cloud hydrometeor number

“…A simulation (DEF-C, denoting default CAM-5 simulation) with the National Center for Atmospheric Research Community Atmosphere Model, Version 5.1 (CAM5.1) is used to compare the results between GCMs. Droplet number variability within this framework has also been considered by Morales-Betancourt and Nenes (19).…”

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

“…So far, uncertainty quantification approaches have been used mostly for global models, but they may also provide a way to explore uncertainty in aerosol−cloud interactions in models confined to represent smaller scales (63). Adjoint sensitivity approaches provide another pathway to obtain uncertainty information (64,65).…”

Droplet activation parameterization: the population-splitting concept revisited

“…Even though coarse-mode particles typically contribute a small number of concentration to the CCN population, they represent an important sink for water vapor, effectively modulating the parcel s max (e.g., Ghan et al, 1998;Barahona et al, 2010;Morales Betancourt and Nenes, 2014). This means that even modest increases in either the number or the hygroscopicity of these large particles can cause a significant decrease in s max , often leading to lower droplet concentrations (Morales Betancourt and Nenes, 2014).…”

“…After this is done, the number of activated cloud droplets, N d , is equal to the concentration of CCN with a critical supersaturation, s c , lower than s max . A number of activation parameterizations have been developed using this approach (e.g., Feingold and Heymsfield, 1992;Ghan et al, 1993;Nenes and Seinfeld, 2003;Pinsky et al, 2012), and many have been incorporated into Global Circulation Models (GCMs) and regional models to compute aerosol indirect effects (e.g., Abdul-Razzak and Ghan, 2000;Fountoukis and Nenes, 2005;Ming et al, 2006;Shipway and Abel, 2010).…”