Abstract
Abstract. New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and model predicted cloud droplet size distribution is studied in a two-dimensional framework, and presented for pseudo-adiabatic cloud parcel model parameters that are pair-wise selected. From this response surface analysis it is shown that the susceptibility of cloud droplet size distribution to variations in different aerosol physiochemical parameters is highly dependent on the aerosol environment and meteorological conditions. In general the cloud droplet size distribution is most susceptible to changes in the updraft velocity. A shift towards an increase in the importance of chemistry for the cloud nucleating ability of particles is shown to exist somewhere between marine average and rural continental aerosol regimes. We also use these response surfaces to explore the feasibility of inverse modeling to determine cloud-aerosol interactions. It is shown that the "cloud-aerosol" inverse problem is particularly difficult to solve due to significant parameter interaction, presence of multiple regions of attraction, numerous local optima, and considerable parameter insensitivity. The identifiability of the model parameters will be dependent on the choice of the objective function. Sensitivity analysis is performed to investigate the location of the information content within the calibration data to confirm that our choice of objective function maximizes information retrieval from the cloud droplet size distribution. Cloud parcel models that employ a moving-centre based calculation of the cloud droplet size distribution pose additional difficulties when applying automatic search algorithms for studying cloud-aerosol interactions. To aid future studies, an increased resolution of the region of the size spectrum associated with droplet activation within cloud parcel models, or further development of fixed-sectional cloud models would be beneficial. Despite these improvements, it is demonstrated that powerful search algorithms remain necessary to efficiently explore the parameter space and successfully solve the cloud-aerosol inverse problem.
Highlights
A challenge currently facing the cloud-aerosol research community is quantifying the relative importance of aerosol size and composition for the activation of particles into cloud droplets (McFiggans et al, 2006)
We posit that a pseudo-adiabatic cloud parcel model is a sensible trade-off between processes accounted for, and computational speed necessary to perform the thousands of simulations required for the Markov Chain Monte Carlo simulation (MCMC) of a single cloud case
Greyscale shows the change in gradient of the Objective Function (OF) which provides a measure shows the change in gradient of t4h6e OF which provides a measure of droplet size distribution susceptibility: (A) marine Arctic; of droplet size distribution susceptibility: (A) marine Arctic; (B) marine average; (C) rural continental; (D) polluted continental. (B) marine average; (C) rural continental; (D) polluted continental
Summary
A challenge currently facing the cloud-aerosol research community is quantifying the relative importance of aerosol size and composition for the activation of particles into cloud droplets (McFiggans et al, 2006). The difficulties in accurately representing the development of a cloud droplet number concentration (CDNC) distribution using numerical approaches can be partly attributed to the current state of knowledge regarding which of the parameters describing the properties of an aerosol distribution are most important for the cloud nucleating ability of aerosol particles (Dusek et al, 2006). This capability is a function of the size of the particle, its composition and mixing state, and the supersaturation in the cloud (Fitzgerald, 1974; Hegg and Larson, 1990; Laaksonen et al, 1998; Feingold, 2003; Conant et al, 2004; Kanakidou et al, 2005; Andreae and Rosenfeld, 2008; Quinn et al, 2008). The susceptibility of cloud albedo and precipitation to aerosol perturbations (Platnick and Twomey, 1994; Noone et al, 2000; Sorooshian et al, 2009) could be investigated by using response surfaces to provide detailed insight to the structural response of certain calibration data to any calibration parameters of interest in two dimensions
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