A parameterization of aerosol activation 3. Sectional representation

Abdul-Razzak, Hayder; Ghan, Steven J.

doi:10.1029/2001jd000483

Cited by 280 publications

(231 citation statements)

References 24 publications

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“…The proposed approach is evaluated in the case of cloud droplet number concentration (CDNC) estimation from sectional aerosol particle size distribution using the cloud droplet formation parameterization by Abdul-Razzak and Ghan (2002). We consider the approximation errors caused by using a coarse sectional representation of the aerosol particle size distributions as well as the error caused by using the parameterization of aerosol activation instead of model actually simulating the process of aerosol growth to cloud droplets.…”

Section: Introductionmentioning

confidence: 99%

“…In Sect. 3, the cloud droplet formation parameterization by Abdul-Razzak and Ghan (2002) (ARG) and the air parcel model used in the simulations are briefly reviewed. In Sect.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, solving the cloud activation of the aerosol particles more accurately, would reduce the uncertainty in the estimated aerosol indirect effect. Current aerosol-climate models include parameterizations for calculating cloud activation of aerosol that use the above mentioned sectional approach (Abdul-Razzak and Ghan, 2002;Nenes and Seinfeld, 2003). These parameterizations introduce uncertainty in CDNC estimation due to highly simplified description of aerosol activation process.…”

Section: Introductionmentioning

confidence: 99%

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Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation

et al. 2013

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Abstract. In atmospheric models, due to their computational time or resource limitations, physical processes have to be simulated using reduced (i.e. simplified) models. The use of a reduced model, however, induces errors to the simulation results. These errors are referred to as approximation errors. In this paper, we propose a novel approach to correct these approximation errors. We model the approximation error as an additive noise process in the simulation model and employ the Random Forest (RF) regression algorithm for constructing a computationally low cost predictor for the approximation error. In this way, the overall simulation problem is decomposed into two separate and computationally efficient simulation problems: solution of the reduced model and prediction of the approximation error realisation. The approach is tested for handling approximation errors due to a reduced coarse sectional representation of aerosol size distribution in a cloud droplet formation calculation as well as for compensating the uncertainty caused by the aerosol activation parameterization itself. The results show a significant improvement in the accuracy of the simulation compared to the conventional simulation with a reduced model. The proposed approach is rather general and extension of it to different parameterizations or reduced process models that are coupled to geoscientific models is a straightforward task. Another major benefit of this method is that it can be applied to physical processes that are dependent on a large number of variables making them difficult to be parameterized by traditional methods.

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Section: Introductionmentioning

confidence: 99%

“…In Sect. 3, the cloud droplet formation parameterization by Abdul-Razzak and Ghan (2002) (ARG) and the air parcel model used in the simulations are briefly reviewed. In Sect.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation

et al. 2013

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“…There are also built-in options for various vertical profile assumptions to construct 3-D aerosol distributions. Moreover, CAR includes five parameterization schemes for the aerosol 1st indirect effect: those of Martin et al (1994), Ming et al (2006), Nenes and Seinfeld (2003), Chuang et al (2002), andAbdul andGhan (2002). In these schemes, cloud condensation nuclei (CCN) concentrations are parameterized as explicit functions of certain prescribed aerosol size distributions.…”

Section: Brief Model Descriptionmentioning

confidence: 99%

The cloud–aerosol–radiation (CAR) ensemble modeling system

Liang

Zhang

2013

Atmos. Chem. Phys.

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A cloud–aerosol–radiation (CAR) ensemble modeling system has been developed to incorporate the largest choices of alternate parameterizations for cloud properties (cover, water, radius, optics, geometry), aerosol properties (type, profile, optics), radiation transfers (solar, infrared), and their interactions. These schemes form the most comprehensive collection currently available in the literature, including those used by the world's leading general circulation models (GCMs). CAR provides a unique framework to determine (via intercomparison across all schemes), reduce (via optimized ensemble simulations), and attribute specific key factors for (via physical process sensitivity analyses) the model discrepancies and uncertainties in representing greenhouse gas, aerosol, and cloud radiative forcing effects. This study presents a general description of the CAR system and illustrates its capabilities for climate modeling applications, especially in the context of estimating climate sensitivity and uncertainty range caused by cloud–aerosol–radiation interactions. For demonstration purposes, the evaluation is based on several CAR standalone and coupled climate model experiments, each comparing a limited subset of the full system ensemble with up to 896 members. It is shown that the quantification of radiative forcings and climate impacts strongly depends on the choices of the cloud, aerosol, and radiation schemes. The prevailing schemes used in current GCMs are likely insufficient in variety and physically biased in a significant way. There exists large room for improvement by optimally combining radiation transfer with cloud property schemes

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“…Global aerosol models use approaches of different G.-J. Roelofs: Temperature responses of water vapor and aerosol lifetimes complexity to compute aerosol uptake in clouds during cloud formation, such as prescribed scavenging efficiencies for each cloud type (e.g., Stier et al, 2005), parameterizations derived from cloud parcel model simulations (e.g., Roelofs et al, 2006) or parameterizations based on aerosol activation theory (e.g., Fountoukis and Nenes, 2005;Abdul-Razzak and Ghan, 2002). A systematic analysis of the differences in computed (temperature responses of) aerosol lifetimes between models requires therefore a separate evaluation of effects associated with the hydrological cycle and with aerosol activation.…”

Section: Proposed Analysis Of Simulated Water Vapor and Aerosol Lifetmentioning

confidence: 99%

A steady-state analysis of the temperature responses of water vapor and aerosol lifetimes

Roelofs

2013

Atmos. Chem. Phys.

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The dominant removal mechanism of soluble aerosol is wet deposition. The atmospheric lifetime of aerosol, relevant for aerosol radiative forcing, is therefore coupled to the atmospheric cycling time of water vapor. This study investigates the coupling between water vapor and aerosol lifetimes in a well-mixed atmosphere. Based on a steady-state study by Pruppacher and Jaenicke (1995) we describe the coupling in terms of the processing efficiency of air by clouds and the efficiencies of water vapor condensation, of aerosol activation, and of the transfer from cloud water to precipitation. We extend this to expressions for the temperature responses of the water vapor and aerosol lifetimes. Previous climate model results (Held and Soden, 2006) suggest a water vapor lifetime temperature response of +5.3 ± 2.0% K⁻¹. This can be used as a first guess for the aerosol lifetime temperature response, but temperature sensitivities of the aerosol lifetime simulated in recent aerosol–climate model studies extend beyond this range and include negative values. This indicates that other influences probably have a larger impact on the computed aerosol lifetime than its temperature response, more specifically changes in the spatial distributions of aerosol (precursor) emissions and precipitation patterns, and changes in the activation efficiency of aerosol. These are not quantitatively evaluated in this study but we present suggestions for model experiments that may help to understand and quantify the different factors that determine the aerosol atmospheric lifetime

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A parameterization of aerosol activation 3. Sectional representation

Cited by 280 publications

References 24 publications

Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation

Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation

The cloud–aerosol–radiation (CAR) ensemble modeling system

A steady-state analysis of the temperature responses of water vapor and aerosol lifetimes

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