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 Abdul-Razzak 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 Nd to relevant input parameters are used to (i) unravel the spatially resolved contribution of aerosol number, mass, and chemical composition to changes in Nd between present-day and pre-industrial simulations and (ii) identify the key variables responsible for the differences in Nd 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 Nd 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 Nd accumulation mode aerosol geometric mean diameter. These two factors explain the different predictions of Nd 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.
Highlights
The impact of atmospheric aerosols on the energy budget of the earth and on cloud microphysical properties is a major contributor to climate prediction uncertainty and estimates of anthropogenic climate change (Intergovernmental Panel on Climate Change, 2007)
In order to make quantitative estimates of aerosol indirect effect (AIE) in global circulation models, it is necessary to realistically represent both the availability of atmospheric aerosol that can act as cloud condensation nuclei (CCN) as well as the activation process by which a subset of CCN activate into cloud droplets
Among the activation parameterizations included in this study, ARGα, FN, and FN-IL include the effect of noncontinuum effects in the condensation process through an explicit dependence on the accommodation coefficient, αc (Pruppacher and Klett, 1997)
Summary
The impact of atmospheric aerosols on the energy budget of the earth and on cloud microphysical properties is a major contributor to climate prediction uncertainty and estimates of anthropogenic climate change (Intergovernmental Panel on Climate Change, 2007). Evaluation of the impact of parametric uncertainty in climate model simulations has been typically done by performing model integrations with one parametric value perturbed to do a finite difference computation Such an approach has been used, for example, to quantify the sensitivity of CCN and cloud droplet number (CDNC) to the assumed hygroscopicity of secondary organic aerosol (Liu and Wang, 2010). Many studies have used similar approaches to asses the importance of the assumed split between primary and secondary organic emissions (e.g., Trivitayanurak and Adams, 2014) Another approach used to assess the problem of uncertainty in aerosol–cloud interactions consists of running an ensemble of simulations with perturbed parameters to construct a Bayesian process emulator (e.g., Lee et al, 2011). The final two sections are devoted to the application of the adjoint in the quantification of organic aerosol parametric uncertainty, by exploring the adjoint sensitivity to the assumed hygroscopicity of secondary organic aerosol (SOA) and POM
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