2017, Gordon, Hamish, Kirkby, Jasper, Baltensperger, Urs, Bianchi, Federico, Breitenlechner, Martin, Curtius, Joachim, Dias, Antonio, Dommen, Josef, Donahue, Neil M., Dunne, Eimear M., Duplissy, Jonathan, Ehrhart, Sebastian, Flagan, Richard C., Frege, Carla, Fuchs, Claudia, Hansel, Armin, Hoyle, Christopher R., Kulmala, Markku, Kürten, Andreas, Lehtipalo, Katrianne, Makhmutov, Vladimir, Molteni, Ugo, Rissanen, Matti P., Stozkhov, Yuri, Tröstl, Jasmin, Tsagkogeorgas, Georgios, Wagner, Robert, Williamson, Christina, Wimmer, Daniela, Winkler, Paul M., Yan, Chao, Carslaw, Ken S.

New particle formation has been estimated to produce around half of cloud-forming particles in the present-day atmosphere, via gas-to-particle conversion. Here we assess the importance of new particle formation (NPF) for both the present-day and the preindustrial atmospheres. We use a global aerosol model with parametrizations of NPF from previously published CLOUD chamber experiments involving sulfuric acid, ammonia, organic molecules, and ions. We find that NPF produces around 67% of cloud condensation nuclei at 0.2% supersaturation (CCN0.2%) at the level of low clouds in the preindustrial atmosphere (estimated uncertainty range 45–84%) and 54% in the present day (estimated uncertainty range 38–66%). Concerning causes, we find that the importance of biogenic volatile organic compounds (BVOCs) in NPF and CCN formation is greater than previously thought. Removing BVOCs and hence all secondary organic aerosol from our model reduces low-cloud-level CCN concentrations at 0.2% supersaturation by 26% in the present-day atmosphere and 41% in the preindustrial. Around three quarters of this reduction is due to the tiny fraction of the oxidation products of BVOCs that have sufficiently low volatility to be involved in NPF and early growth. Furthermore, we estimate that 40% of preindustrial CCN0.2% are formed via ion-induced NPF, compared with 27% in the present day, although we caution that the ion-induced fraction of NPF involving BVOCs is poorly measured at present. Our model suggests that the effect of changes in cosmic ray intensity on CCN is small and unlikely to be comparable to the effect of large variations in natural primary aerosol emissions.

2016, Kohn, Monika, Lohmann, Ulrike, Welti, André, Kanji, Zamin A.

The new Portable Immersion Mode Cooling chAmber (PIMCA) has been developed for online immersion freezing of single-immersed aerosol particles. PIMCA is a vertical extension of the established Portable Ice Nucleation Chamber (PINC). PIMCA immerses aerosol particles into cloud droplets before they enter PINC. Immersion freezing experiments on cloud droplets with a radius of 5–7 μm at a prescribed supercooled temperature (T) and water saturation can be conducted, while other ice nucleation mechanisms (deposition, condensation, and contact mode) are excluded. Validation experiments on reference aerosol (kaolinite, ammonium sulfate, and ammonium nitrate) showed good agreement with theory and literature. The PIMCA-PINC setup was tested in the field during the Zurich AMBient Immersion freezing Study (ZAMBIS) in spring 2014 in Zurich, Switzerland. Significant concentrations of submicron ambient aerosol triggering immersion freezing at T > 236 K were rare. The mean frozen cloud droplet number concentration was estimated to be 7.22·105 L−1 for T < 238 K and determined from the measured frozen fraction and cloud condensation nuclei (CCN) concentrations predicted for the site at a typical supersaturation of SS = 0.3%. This value should be considered as an upper limit of cloud droplet freezing via immersion and homogeneous freezing processes. The predicted ice nucleating particle (INP) concentration based on measured total aerosol larger than 0.5 μm and the parameterization by DeMott et al. (2010) at T = 238 K is INPD10=54 ± 39 L−1. This is a lower limit as supermicron particles were not sampled with PIMCA-PINC during ZAMBIS.

2018, Grosvenor, Daniel P., Sourdeval, Odran, Zuidema, Paquita, Ackerman, Andrew, Alexandrov, Mikhail D., Bennartz, Ralf, Boers, Reinout, Cairns, Brian, Chiu, J. Christine, Christensen, Matthew, Deneke, Hartwig, Diamond, Michael, Feingold, Graham, Fridlind, Ann, Hünerbein, Anja, Knist, Christine, Kollias, Pavlos, Marshak, Alexander, McCoy, Daniel, Merk, Daniel, Painemal, David, Rausch, John, Rosenfeld, Daniel, Russchenberg, Herman, Seifert, Patric, Sinclair, Kenneth, Stier, Philip, van Diedenhoven, Bastiaan, Wendisch, Manfred, Werner, Frank, Wood, Robert, Zhang, Zhibo, Quaas, Johannes

The cloud droplet number concentration (Nd) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol-cloud interactions. Current standard satellite retrievals do not operationally provide Nd, but it can be inferred from retrievals of cloud optical depth (τc) cloud droplet effective radius (re) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel-level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. Nd uncertainty is dominated by errors in re, and therefore, improvements in re retrievals would greatly improve the quality of the Nd retrievals. Recommendations are made for how this might be achieved. Some existing Nd data sets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative Nd estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.