Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects

B. Gantt,X. Zhang,J. He,A. Nenes,Y. Zhang

doi:10.5194/acp-14-7485-2014

Abstract

Abstract. One of the greatest sources of uncertainty in the science of anthropogenic climate change is from aerosol–cloud interactions. The activation of aerosols into cloud droplets is a direct microphysical linkage between aerosols and clouds; parameterizations of this process link aerosol with cloud condensation nuclei (CCN) and the resulting indirect effects. Small differences between parameterizations can have a large impact on the spatiotemporal distributions of activated aerosols and the resulting cloud properties. In this work, we incorporate a series of aerosol activation schemes into the Community Atmosphere Model version 5.1.1 within the Community Earth System Model version 1.0.5 (CESM/CAM5) which include factors such as insoluble aerosol adsorption and giant cloud condensation nuclei (CCN) activation kinetics to understand their individual impacts on global-scale cloud droplet number concentration (CDNC). Compared to the existing activation scheme in CESM/CAM5, this series of activation schemes increase the computation time by ~10% but leads to predicted CDNC in better agreement with satellite-derived/in situ values in many regions with high CDNC but in worse agreement for some regions with low CDNC. Large percentage changes in predicted CDNC occur over desert and oceanic regions, owing to the enhanced activation of dust from insoluble aerosol adsorption and reduced activation of sea spray aerosol after accounting for giant CCN activation kinetics. Comparison of CESM/CAM5 predictions against satellite-derived cloud optical thickness and liquid water path shows that the updated activation schemes generally improve the low biases. Globally, the incorporation of all updated schemes leads to an average increase in column CDNC of 150% and an increase (more negative) in shortwave cloud forcing of 12%. With the improvement of model-predicted CDNCs and better agreement with most satellite-derived cloud properties in many regions, the inclusion of these aerosol activation processes should result in better predictions of radiative forcing from aerosol–cloud interactions.

Highlights

The interaction between cloud and aerosols is among the most uncertain aspects of anthropogenic climate change (Boucher et al, 2013)
CESM-CAM5 underpredicts (NMB < −66.7 %) column cloud condensation nuclei (CCN) concentrations at 0.5 % supersaturation compared to Moderate Resolution Imaging Spectroradiometer (MODIS)-derived values, the difficulty in using remote sensing measurements for the estimation of CCN abundances (Andreae, 2009) makes interpretation uncertain
Several process-based aerosol activation schemes are implemented into the Community Atmosphere Model version 5.1.1 within the Community Earth System Model version 1.0.5 (CESM/CAM5) to determine the global impacts of individual activation processes on cloud properties

Summary

Introduction

The interaction between cloud and aerosols is among the most uncertain aspects of anthropogenic climate change (Boucher et al, 2013). Anthropogenic CCN can inhibit drizzle production under certain conditions and lead to increased liquid water content, cloud lifetime, and cloud albedo (Albrecht, 1989). These two processes are referred to as the radiative forcing from aerosol–cloud interactions and adjustments and collectively constitute the effective radiative forcing from aerosol–cloud interactions in the Fifth Assessment Report from the Intergovernmental Panel on Climate Change (Boucher et al, 2013). Despite relatively strong relationships between CDNC and these aerosol parameters in several environments (Leaitch et al, 1992; Martin et al, 1994; Ramanathan et al, 2001), the empirical relationships do not explicitly account for the dependence of the droplet nucleation on aerosol size distribution, aerosol composition, or updraft velocity and are limited in their ability to accurately predict CDNC on a global scale

Objectives

Results

Conclusion