Abstract
Aerosol-cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (Nd). Even though rigorous first-principle approaches exist to calculate Nd, the cloud and aerosol research community also relies on empirical approaches such as relating Nd to aerosol mass concentration. Here we analyze relationships between Nd and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 80 chemical species. Single- and multispecies log-log linear regressions were performed to predict Nd using chemical composition. Single-species regressions reveal that the species that best predicts Nd is total sulfate (). Multispecies regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multispecies regressions that produce the highest correlation with Nd was that most included sulfate (either total or non-sea-salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed for examination of the effect of these environmental factors on the composition-Nd correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between Nd with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with Nd for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea-salt sulfate and sodium correlated best with Nd at cloud top, whereas iron and oxalate correlated best with Nd at cloud base.
Highlights
To assess the degree to which humans have altered Earth’s climate, it is necessary to quantify the effect that particles in the air have on clouds
The four campaigns addressed in this study are the Eastern Pacific Emitted Aerosol Cloud Experiment (EPEACE) (Russell et al, 2013; Wonaschütz et al, 2013), the Nucleation in California Experiment (NiCE) (Crosbie et al, 2016; Maudlin et al, 2015), the Biological and Oceanic Atmospheric Study (BOAS) (Wang et al, 2016), and the Fog and Stratocumulus Evolution (FASE) experiment (Dadashazar et al, 2017; MacDonald et al, 2018)
We analyze which of the nine species filtered out in Sect. 2.4 best predicts Nd by itself without binning by external factors
Summary
To assess the degree to which humans have altered Earth’s climate, it is necessary to quantify the effect that particles in the air (i.e., aerosols) have on clouds. Some fraction of aerosols (called cloud condensation nuclei, CCN) activate into cloud droplets, impacting the cloud droplet number concentration (Nd). For warm marine boundary layer (MBL) clouds at fixed liquid water, higher Nd values result in (i) higher cloud albedo ( cooling the Earth and counteracting the greenhouse effect) (Twomey, 1977), (ii) delayed and/or reduced precipitation (Albrecht, 1989), and (iii) en-. MacDonald et al.: Cloud chemistry and droplet number concentration hanced entrainment at cloud top (Ackerman et al, 2004). The complex interactions and feedback mechanisms between aerosols, meteorology, and clouds leads to aerosol–cloud interactions as the largest source of uncertainty in climate models (IPCC, 2013; Bellouin et al, 2020)
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