Effect of aerosol subgrid variability on aerosol optical depth and cloud condensation nuclei: implications for global aerosol modelling

Abstract

Abstract. A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid boxes, which can lead to discrepancies in simulated aerosol climate effects between high- and low-resolution models. This study investigates the impact of neglecting subgrid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. We compare WRF-Chem (Weather and Research Forecast model) runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. The processes most affected by neglecting aerosol subgrid variability are gas-phase chemistry and aerosol uptake of water through aerosol–gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol properties when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of subgrid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.

Will a perfect model agree with perfect observations? The impact of spatial sampling

“…In situ measurements cover even less of the atmosphere surrounding them; yet, observed aerosol fields are known to exhibit variations over relatively short distances of 10 to 100 km (Anderson et al, 2003;Kovacs, 2006;Santese et al, 2007;Shinozuka and Redemann, 2011;Schutgens et al, 2013). Note that the spatial resolution of global models also impacts global model data due to the non-linear nature of many physical and chemical processes (Qian et al, 2010;Gustafson et al, 2011;Stroud et al, 2011;Weigum et al, 2016); but that is not the topic of this paper.…”

On the spatio-temporal representativeness of observations

“…Since the landmark study by Anderson et al (2003), we know aerosol varies over hours and tens of kilometres; see also Kovacs (2006), Santese et al (2007), Shinozuka and Redemann (2011), Schutgens et al (2013) and Weigum et al (2016). Aerosol studies are likely to show a very clear impact from spatio-temporal sampling.…”

Spatial and temporal CCN variations in convection-permitting aerosol microphysics simulations in an idealised marine tropical domain

“…Ghan et al, 2001;Adams and Seinfeld, 2002;Spracklen et al, 2005) and several have explored aerosol-cloud interactions in regional-scale models (e.g. Bangert et al, 2011;Zubler et al, 2011;Yang et al, 2012), only a few studies (Ekman et al, 2004(Ekman et al, , 2006Wang et al, 2011;Archer-Nicholls et al, 2016;Possner et al, 2016;Weigum et al, 2016) have explored the microphysical properties of aerosols, and their potential interactions with clouds, at resolutions of ∼ 1 km where convection is resolved.…”