Droplet nucleation: Physically-based parameterizations and comparative evaluation

Ghan, Steven J.; Abdul-Razzak, Hayder; Nenes, Athanasios; Ming, Yi; Liu, Xiaohong; Ovchinnikov, Mikhail; Shipway, Ben; Meskhidze, N.; Xu, Jiele; Shi, Xiangjun

doi:10.1029/2011ms000074

Cited by 178 publications

(278 citation statements)

References 187 publications

(422 reference statements)

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“…Two similar schemes are those proposed by Abdul-Razzak et al (1998) and also by Fountoukis and Nenes (2005), which have been developed further since the original papers to include additional effects. These schemes have been shown to perform well (Ghan et al, 2011), and since they are physically based, there is the opportunity to develop them further to include the process that is the focus of this paper.…”

Section: P J Connolly Et Al: Cloud Drop Activationmentioning

confidence: 99%

A parameterisation for the activation of cloud drops including the effects of semi-volatile organics

et al. 2014

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Abstract. We present a parameterisation of aerosol activation, including co-condensation of semi-volatile organics, for warm clouds that has applications in large-scale numerical models. The scheme is based on previously developed parameterisations that are in the literature, but has two main modifications. The first is that the total aerosol mass is modified by the condensation of organic vapours entering cloud base, whereas the second is that this addition of mass acts to modify the median diameter and the geometric standard deviation of the aerosol size distribution. It is found that the scheme is consistent with parcel model calculations of cocondensation under different regimes. Such a parameterisation may find use in evaluating important feedbacks in climate models.

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Section: P J Connolly Et Al: Cloud Drop Activationmentioning

confidence: 99%

A parameterisation for the activation of cloud drops including the effects of semi-volatile organics

et al. 2014

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“…[10][11][12][13][14]. The focus tends to be uncertainty analysis, or how uncertain parameters affect the cloud hydrometeor number and cloud radiative forcing.…”

mentioning

confidence: 99%

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Sullivan

Lee

Oreopoulos

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Understanding how dynamical and aerosol inputs affect the temporal variability of hydrometeor formation in climate models will help to explain sources of model diversity in cloud forcing, to provide robust comparisons with data, and, ultimately, to reduce the uncertainty in estimates of the aerosol indirect effect. This variability attribution can be done at various spatial and temporal resolutions with metrics derived from online adjoint sensitivities of droplet and crystal number to relevant inputs. Such metrics are defined and calculated from simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) and the National Center for Atmospheric Research Community Atmosphere Model Version 5.1 (CAM5.1). Input updraft velocity fluctuations can explain as much as 48% of temporal variability in output ice crystal number and 61% in droplet number in GEOS-5 and up to 89% of temporal variability in output ice crystal number in CAM5.1. In both models, this vertical velocity attribution depends strongly on altitude. Despite its importance for hydrometeor formation, simulated vertical velocity distributions are rarely evaluated against observations due to the sparsity of relevant data. Coordinated effort by the atmospheric community to develop more consistent, observationally based updraft treatments will help to close this knowledge gap.global models | clouds | vertical velocity | sensitivity | attribution C loud radiative forcing remains one of the largest sources of uncertainty in the overall terrestrial radiative budget (1). Incloud phase partitioning, or the fraction of liquid versus ice hydrometeors, can be as important as cloud cover in the calculation of cloud radiative forcing (2). Cloud long-wave emissivity depends on cloud water path and hydrometeor sizes, along with cloud height and temperature. Cloud short-wave albedo also depends on particle size, because more and smaller hydrometeors yield a higher optical depth for the same water path (1). Global climate models (GCMs) predict a diversity of liquid and ice water paths (3), as well as cloud hydrometeor sizes, and the treatment of initial hydrometeor formation, i.e., droplet activation or ice nucleation, contributes to this spread for all cloud types (4, 5).The available supersaturation of a cloudy air parcel determines how many hydrometeors can form therein. Supersaturation strongly depends on aerosol and dynamical parameters. Updraft, or vertical velocity, is especially important because it is the driver of supersaturation generation, owing to the induced expansion cooling during air mass ascent. Aerosol particle surfaces upon which vapor can condense or deposit, called cloud condensation nuclei (CCN) or ice nucleating particles (INP), respectively, act as a sink of supersaturation. The balance between supersaturation generation and loss eventually determines the number of hydrometeors that form in the cloud. As part of the increasing trend to track both cloud hydrometeor mass and number density in GCM cloud modules (6-9), most state o...

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“…The underlying equation set on which the parametrization is built makes a number of physical assumptions, which are common to many well used activation parametrizations (Ghan et al, 2011). These assumptions and the resulting consequences are discussed, but the accuracy and computational efficiency of the resulting parametrization make it suitable for inclusion in both GCMs and high resolution models that use a modal representation of aerosol.…”

Section: Discussionmentioning

confidence: 99%

“…(11), where y may be taken as a continuous variable or, if the modal representation of the aerosol has fixed dispersion characteristics, can simply use appropriate discrete values. Results from other well-used parametrization schemes are also shown, together with results from the parcel model used in Ghan et al (2011). Each panel provides results from different aerosol scenarios as described by Whitby (1978) and range from relatively clean marine conditions to highly polluted conditions.…”

Section: A Revised Parametrization To Determine S Maxmentioning

confidence: 99%

“…(1) it is assumed that (i) the initial particle size is negligible compared to the droplet size, (ii) particles grow in equilibrium with the environmental relative humidity, and (iii) kinetic and inertial limitations to droplet growth are ignored. These and further approximations are discussed in Ghan et al (2011), while more detail surrounding the derivation and approximations used to obtain Eq. (1) is provided in Appendix A.…”

Section: Introductionmentioning

confidence: 99%

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Revisiting Twomey's approximation for peak supersaturation

Shipway

2015

Atmos. Chem. Phys.

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Abstract. Twomey's seminal 1959 paper provided lower and upper bound approximations to the estimation of peak supersaturation within an updraft and thus provides the first closed expression for the number of nucleated cloud droplets. The form of this approximation is simple, but provides a surprisingly good estimate and has subsequently been employed in more sophisticated treatments of nucleation parametrization. In the current paper, we revisit the lower bound approximation of Twomey and make a small adjustment that can be used to obtain a more accurate calculation of peak supersaturation under all potential aerosol loadings and thermodynamic conditions. In order to make full use of this improved approximation, the underlying integro-differential equation for supersaturation evolution and the condition for calculating peak supersaturation are examined. A simple rearrangement of the algebra allows for an expression to be written down that can then be solved with a single lookup table with only one independent variable for an underlying lognormal aerosol population. While multimodal aerosol with N different dispersion characteristics requires 2N +1 inputs to calculate the activation fraction, only N of these one-dimensional lookup tables are needed. No additional information is required in the lookup table to deal with additional chemical, physical or thermodynamic properties. The resulting implementation provides a relatively simple, yet computationally cheap, physically based parametrization of droplet nucleation for use in climate and Numerical Weather Prediction models. BackgroundAtmospheric aerosols are fundamental to the formation of clouds. They provide the substrate onto which liquid droplets can form, overcoming the energy barriers associated with clustering of water molecules (see Mason, 1971; Pruppacher and Klett, 1997, hereafter PK97). Not only do they facilitate the formation of cloud, but they also determine the initial sizes and numbers of droplets as the cloud forms. Variations in the underlying aerosol population; through numbers, sizes, dispersion characteristics and chemical composition; combined with variations in the dynamic and thermodynamic properties of the ambient air; lead to wide ranging differences in the number concentrations of droplets within clouds. Differences in number further impact the physical evolution of the clouds, their lifetime and their interaction with radiation. These impacts are most famously seen through the Twomey (1977) and Albrecht (1989) effects, but also through other mechanisms once feedbacks with the dynamics (e.g. Ackerman et al., 2004;Jiang et al., 2006;Wang and Feingold, 2009) or the ice phase (Lohmann and Feichter, 2005) are taken into account. Collectively, the interactions between aerosol and clouds represent a significant source of uncertainty in our ability to model the climate system and, moreover, to estimate the impacts of anthropogenic climate change (IPCC, Summary for Policymakers, 2007).The approach generally used to ascertain how many of the to...

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Droplet nucleation: Physically-based parameterizations and comparative evaluation

Cited by 178 publications

References 187 publications

A parameterisation for the activation of cloud drops including the effects of semi-volatile organics

A parameterisation for the activation of cloud drops including the effects of semi-volatile organics

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Revisiting Twomey's approximation for peak supersaturation

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