|Title||Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Seinfeld JH, Bretherton C., Carslaw K.S, Coe H., DeMott PJ, Dunlea E.J, Feingold G., Ghan S., Guenther A.B, Kahn R., Kraucunas I., Kreidenweis S.M, Molina MJ, Nenes A, Penner JE, Prather KA, Ramanathan V, Ramaswamy V., Rasch PJ, Ravishankara A.R, Rosenfeld D, Stephens G., Wood R.|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|Type of Article||Article|
|Keywords||aerosol-cloud effects; aerosols; anthropogenic; boundary-layer; ccn activity; climate; community atmosphere model; condensation nuclei; convective clouds; Forcing; General circulation models; heterogeneous ice nucleation; Mixing; radiative; satellite; Satellite observations; state; vocals-rex|
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.