Abstract
Abstract. Much of the large uncertainty in estimates of anthropogenic aerosol effects on climate arises from the multi-scale nature of the interactions between aerosols, clouds and dynamics, which are difficult to represent in conventional general circulation models (GCMs). In this study, we use a multi-scale aerosol-climate model that treats aerosols and clouds across multiple scales to study aerosol indirect effects. This multi-scale aerosol-climate model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each vertical column of a GCM grid. The extension allows a more physically-based treatment of aerosol-cloud interactions in both stratiform and convective clouds on the global scale in a computationally feasible way. Simulated model fields, including liquid water path (LWP), ice water path, cloud fraction, shortwave and longwave cloud forcing, precipitation, water vapor, and cloud droplet number concentration are in reasonable agreement with observations. The new model performs quantitatively similar to the previous version of the MMF model in terms of simulated cloud fraction and precipitation. The simulated change in shortwave cloud forcing from anthropogenic aerosols is −0.77 W m−2, which is less than half of that (−1.79 W m−2) calculated by the host GCM (NCAR CAM5) with traditional cloud parameterizations and is also at the low end of the estimates of other conventional global aerosol-climate models. The smaller forcing in the MMF model is attributed to a smaller (3.9 %) increase in LWP from preindustrial conditions (PI) to present day (PD) compared with 15.6 % increase in LWP in stratiform clouds in CAM5. The difference is caused by a much smaller response in LWP to a given perturbation in cloud condensation nuclei (CCN) concentrations from PI to PD in the MMF (about one-third of that in CAM5), and, to a lesser extent, by a smaller relative increase in CCN concentrations from PI to PD in the MMF (about 26 % smaller than that in CAM5). The smaller relative increase in CCN concentrations in the MMF is caused in part by a smaller increase in aerosol lifetime from PI to PD in the MMF, a positive feedback in aerosol indirect effects induced by cloud lifetime effects from aerosols. The smaller response in LWP to anthropogenic aerosols in the MMF model is consistent with observations and with high resolution model studies, which may indicate that aerosol indirect effects simulated in conventional global climate models are overestimated and point to the need to use global high resolution models, such as MMF models or global CRMs, to study aerosol indirect effects. The simulated total anthropogenic aerosol effect in the MMF is −1.05 W m−2, which is close to the Murphy et al. (2009) inverse estimate of −1.1±0.4 W m−2 (1σ) based on the examination of the Earth's energy balance. Further improvements in the representation of ice nucleation and low clouds in MMF are needed to refine the aerosol indirect effect estimate.
Highlights
Clouds are an extremely important climate regulator
We have recently developed a new aerosol-climate model (Wang et al, 2011), which is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud resolving model (CRM) within each grid column of a general circulation models (GCMs) (Khairoutdinov et al, 2008)
The smaller increase in liquid water path (LWP) due to anthropogenic aerosol in the MMF model than in CAM5 is consistent with Lohmann (2008), who showed a slightly smaller response in LWP in a version of the ECHAM including aerosol effects on convective clouds using cumulus parameterization (8.2 %) compared to a version of the ECHAM without aerosol effects on convective clouds (9.7 %) though conventional cloud parameterizations are used for both stratiform and convective clouds
Summary
Clouds are an extremely important climate regulator. They have a large impact on the Earth’s energy budget and play a central role in the hydrological cycle. Uncertainties in estimates of the anthropogenic aerosol indirect effect still dominate uncertainties in the estimates of radiative forcing of past and future climate change, despite more than a decade of effort on this issue (Forster et al, 2007; IPCC, 2007; Lohmann et al, 2010) Much of this uncertainty arises from the multi-scale nature of the interactions between aerosols, clouds, and dynamics (e.g., Rosenfeld et al, 2006; Wood et al, 2011). Cloud statistics diagnosed from the CRM component are used to drive the aerosol and trace gas processing by clouds This multi-scale aerosol-climate model, hereafter called PNNL-MMF, allows us to simulate aerosol-cloud interactions in both stratiform and convective clouds in a more physically-based manner.
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