Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

Abstract

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.climate | aerosol−cloud effects | general circulation models | radiative forcing | satellite observations Clouds play a key role in Earth's radiation budget, and aerosols serve as the seeds upon which cloud droplets form. Anthropogenic activity has led to an increase in aerosol particle concentrations globally and an increase in those particles that act as cloud condensation nuclei (CCN) and ice nucleating particles (INP). The effect of an increase in aerosols on cloud optical properties, and associated radiative forcing, is the most uncertain component of historical radiative forcing of Earth's climate caused by greenhouse gases (GHGs) and aerosols. The Intergovernmental Panel on Climate Change (IPCC) AR5 assessment of climate forcing factors (Fig. S1) ascribes "high" confidence to the estimate of direct aerosol radiative forcing (mean

Metrics to quantify the importance of mixing state for CCN activity

“…One of the reasons for this is the challenge to represent the many microscale aerosol processes in large-scale global climate models (Seinfeld et al, 2016). Using a highfidelity aerosol model, our study provides quantitative support that mixing state is important for determining the aerosol impact on clouds.…”

The impact of precipitation evaporation on the atmospheric aerosol distribution in EC-Earth v3.2.0

“…Even though the fundamental understanding of the interaction between aerosols and clouds has strongly improved over the past decade, translating and combining the wide range of contributing processes into parameterisations that can be applied in GCMs introduces large uncertainties (Seinfeld et al, 2016) and dominates the uncertainty in climate projections (e.g. Fan et al, 2016;Boucher et al, 2013).…”

What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?

“…To date, radiative forcing through aerosol-cloud interactions constitutes the least understood anthropogenic influence on climate (IPCC, 2013): the uncertainty in aerosolinduced radiative forcing of ± 0.70 W m -2 (from a mean of -0.55 W m -2 ) is twice the uncertainty for CO 2 (± 0.35, mean +1.68 W m -2 ). This uncertainty propagates through to e.g., the calculation of climate sensitivity, a variable that is needed to predict global temperature increase for given emission scenarios (Andreae et al, 2005;Seinfeld et al, 2016). It remains a significant 10 challenge to reduce these uncertainties and to increase thereby our confidence in global and regional climate scenarios (IPCC, 2013;Lee et al, 2013;Seinfeld et al, 2016).…”

“…This uncertainty propagates through to e.g., the calculation of climate sensitivity, a variable that is needed to predict global temperature increase for given emission scenarios (Andreae et al, 2005;Seinfeld et al, 2016). It remains a significant 10 challenge to reduce these uncertainties and to increase thereby our confidence in global and regional climate scenarios (IPCC, 2013;Lee et al, 2013;Seinfeld et al, 2016).…”

“…To improve model performance, data from measurements of particle number size distribution, CCN number concentrations, aerosol particle chemical composition and hygroscopicity are needed (Carslaw et al, 2013;Ghan and Schwartz, 2007;Rosenfeld et al, 2014;Seinfeld et al, 2016). Satellite observations, covering large scales and longtime horizons, can provide proxies of these variables.…”