Sulfur scavenging in a mesoscale model with quasi‐spectral microphysics: Two‐dimensional results for continental and maritime clouds

Chaumerliac, Nadine; Richard, Eric; Pinty, Jean‐Pierre; Nickerson, Everett C.

doi:10.1029/jd092id03p03114

Cited by 68 publications

(29 citation statements)

References 30 publications

(11 reference statements)

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“…Simulations of winter-time orographic clouds suggest that the amount of orographic precipitation is sensitive to the available CCN and show decreasing precipitation for increasing CCN (Chaumerliac et al, 1987;Thompson et al, 2004). Similarly, Lynn et al (2007) find a decrease of orographic precipitation if the background aerosol conditions change from maritime to continental.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds

et al. 2010

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Abstract. Anthropogenic aerosols serve as a source of both cloud condensation nuclei (CCN) and ice nuclei (IN) and affect microphysical properties of clouds. Increasing aerosol number concentrations is hypothesized to retard the cloud droplet coalescence and the riming in mixed-phase clouds, thereby decreasing orographic precipitation.This study presents results from a model intercomparison of 2-D simulations of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds. The sensitivity of orographic precipitation to changes in the aerosol number concentrations is analysed and compared for various dynamical and thermodynamical situations. Furthermore, the sensitivities of microphysical processes such as coalescence, aggregation, riming and diffusional growth to changes in the aerosol number concentrations are evaluated and compared.The participating numerical models are the model from the Consortium for Small-Scale Modeling (COSMO) with bulk microphysics, the Weather Research and Forecasting (WRF) model with bin microphysics and the University of Wisconsin modeling system (UWNMS) with a spectral ice habit prediction microphysics scheme. All models are operated on a cloud-resolving scale with 2 km horizontal grid spacing.The results of the model intercomparison suggest that the sensitivity of orographic precipitation to aerosol modificaCorrespondence to: A. Muhlbauer (andreasm@atmos.washington.edu) tions varies greatly from case to case and from model to model. Neither a precipitation decrease nor a precipitation increase is found robustly in all simulations. Qualitative robust results can only be found for a subset of the simulations but even then quantitative agreement is scarce. Estimates of the aerosol effect on orographic precipitation are found to range from −19% to 0% depending on the simulated case and the model. Similarly, riming is shown to decrease in some cases and models whereas it increases in others, which implies that a decrease in riming with increasing aerosol load is not a robust result. Furthermore, it is found that neither a decrease in cloud droplet coalescence nor a decrease in riming necessarily implies a decrease in precipitation due to compensation effects by other microphysical pathways.The simulations suggest that mixed-phase conditions play an important role in buffering the effect of aerosol perturbations on cloud microphysics and reducing the overall susceptibility of clouds and precipitation to changes in the aerosol number concentrations. As a consequence the aerosol effect on precipitation is suggested to be less pronounced or even inverted in regions with high terrain (e.g., the Alps or Rocky Mountains) or in regions where mixed-phase microphysics is important for the climatology of orographic precipitation.

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Section: Introductionmentioning

confidence: 99%

Intercomparison of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds

et al. 2010

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“…A further complication is that the effects of anthropogenic constituents on the cloud (e.g., aerosols that act as cloud condensation nuclei or alter the thermodynamics of clouds) are nonlinear, and as of yet are not well understood. Numerous modeling studies have shown that cloud processes have potentially large effects on local, regional, and global distributions of sulfate aerosols, ozone, peroxides, and other key photochemical species (e.g., see Davis 1982, 1983;Jacob 1986;Walcek and Taylor 1986;Hegg et al 1986;Chaumerliac et al 1987;Lelieveld andCrutzen 1990, 1991;Liang and Jacob 1997;Matthijsen et al 1997;Walcek et al 1997;Barth et al 2002).…”

Section: The Need For Improved Physical Un-derstanding Planetary Boumentioning

confidence: 99%

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Dabberdt¹,

Carroll²,

Baumgardner³

et al. 2004

Bulletin of the American Meteorological Society

110

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The U.S. Weather Research Program convenes expert working groups on a one-time basis to identify critical research needs in various problem areas. The most recent expert working group was charged to “identify and delineate critical meteorological research issues related to the prediction of air quality.” In this context, “prediction” is denoted as “forecasting” and includes the depiction and communication of the present chemical state of the atmosphere, extrapolation or nowcasting, and numerical prediction and chemical evolution on time scales up to several days. Emphasis is on the meteorological aspects of air quality.The problem of air quality forecasting is different in many ways from the problem of weather forecasting. The latter typically is focused on prediction of severe, adverse weather conditions, while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary layer structure and wind direction are perhaps the two most poorly determined meteorological variables for regional air quality prediction. Meteorological observations are critical to effective air quality prediction, yet meteorological observing systems are designed to support prediction of severe weather, not the subtleties of adverse air quality. Three-dimensional meteorological and chemical observations and advanced data assimilation schemes are essential. In the same way, it is important to develop high-resolution and self-consistent databases for air quality modeling; these databases should include land use, vegetation, terrain elevation, and building morphology information, among others. New work in the area of chemically adaptive grids offers significant promise and should be pursued. The quantification and effective communication of forecast uncertainty are still in their early stages and are very important for decision makers; this also includes the visualization of air quality and meteorological observations and forecasts. Research is also needed to develop effective metrics for the evaluation and verification of air quality forecasts so that users can understand the strengths and weaknesses of various modeling schemes. Last, but not of least importance, is the need to consider the societal impacts of air quality forecasts and the needs that they impose on researchers to develop effective and useful products.

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“…The geometric standard deviation of the cloud droplet size distribution is fixed to 0.28 and 0.15 for maritime and continental air masses, respectively, while it remains fixed at 0.547 for the raindrop size distribution regardless of the air mass type (Chaumerliac et al, 1987). In contrast, the median diameters of the distributions are calculated at each time step from the respective water mixing ratios.…”

Section: Evolution Of the Cloud Droplet Distributionrain Formationmentioning

confidence: 99%

“…For any of the microphysical processes included in the previous equations (autoconversion, accretion, sedimentation), the corresponding rate of change can be written in the form of a proportionality relationship (Chaumerliac et al, 1987;Hegg et al, 1984):…”

Section: Coupling the Chemistry Model With The Microphysical Schemementioning

confidence: 99%

Modeling the partitioning of organic chemical species in cloud phases with CLEPS (1.1)

Chaumerliac¹,

Deguillaume²,

Perroux³

et al. 2018

Atmos. Chem. Phys.

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Abstract. The new detailed aqueous-phase mechanism Cloud Explicit Physico-chemical Scheme (CLEPS 1.0), which describes the oxidation of isoprene-derived watersoluble organic compounds, is coupled with a warm microphysical module simulating the activation of aerosol particles into cloud droplets. CLEPS 1.0 was then extended to CLEPS 1.1 to include the chemistry of the newly added dicarboxylic acids dissolved from the particulate phase. The resulting coupled model allows the prediction of the aqueousphase concentrations of chemical compounds originating from particle scavenging, mass transfer from the gas-phase and in-cloud aqueous chemical reactivity. The aim of the present study was more particularly to investigate the effect of particle scavenging on cloud chemistry. Several simulations were performed to assess the influence of various parameters on model predictions and to interpret long-term measurements conducted at the top of Puy de Dôme (PUY, France) in marine air masses. Specific attention was paid to carboxylic acids, whose predicted concentrations are on average in the lower range of the observations, with the exception of formic acid, which is rather overestimated in the model. The different sensitivity runs highlight the fact that formic and acetic acids mainly originate from the gas phase and have highly variable aqueous-phase reactivity depending on the cloud acidity, whereas C 3 -C 4 carboxylic acids mainly originate from the particulate phase and are supersaturated in the cloud.

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Sulfur scavenging in a mesoscale model with quasi‐spectral microphysics: Two‐dimensional results for continental and maritime clouds

Cited by 68 publications

References 30 publications

Intercomparison of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds

Intercomparison of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Modeling the partitioning of organic chemical species in cloud phases with CLEPS (1.1)

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