Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem

Yang, Qing; Fast, J. D.; Wang, H.; Easter, Richard C.; Morrison, Hugh

doi:10.5194/acpd-11-22663-2011

Cited by 32 publications

(46 citation statements)

References 59 publications

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“…In this sense, it is encouraging that for a given region, a single formula can be used across satellites and instruments (GOES imager, MODIS Aqua, and Terra) with excellent performance against N d in situ data, remarkably better than for other retrieved cloud properties (28,35). The activation parameterization and its assumptions represent another source of uncertainty (20), but again, comparisons with in situ and satellite measurements help better understand these limitations and their extent (6,7). Expanded applications of this approach (e.g., aerosol retrieval and assimilation under multilayer, convective, and ice clouds) may be possible, but additional research and testing is required on both retrieval and modeling sides.…”

Section: Discussionmentioning

confidence: 99%

“…Despite uncertainties (10) and challenges (20) in modeling aerosol-cloud interactions, recent studies have shown significant capabilities in predicting and explaining aerosol indirect effects in low cloud regimes (6,7,21). By building on this mechanistic understanding, observations of clouds may be used to infer aerosol physicochemical properties.…”

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confidence: 99%

“…Their role is dependent on their size, number, phase, and composition distributions, which vary significantly in space and time. There remain large uncertainties in predictions of aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal (5)(6)(7)(8)(9). These uncertainties in aerosol distributions lead to large uncertainties in weather and air-quality predictions and in estimates of health and climate-change impacts (10).…”

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confidence: 99%

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Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Saide

Carmichael

Scott

et al. 2012

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

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Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (N d ) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent N d observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and airquality, weather, and climate predictions.air quality | indirect effect | weather prediction | stratiform cloud | microphysics A mbient aerosols are important air pollutants with direct impacts on human health (1). They also play important roles in Earth's weather and climate systems through their direct (2), semi-direct (3), and indirect effects (4) on radiative transfer and clouds. Their role is dependent on their size, number, phase, and composition distributions, which vary significantly in space and time. There remain large uncertainties in predictions of aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal (5-9). These uncertainties in aerosol distributions lead to large uncertainties in weather and air-quality predictions and in estimates of health and climate-change impacts (10).Constraining ambient aerosol distributions with current Earthobserving systems is a difficult task. The most common approach is to assimilate satellite retrievals of aerosol optical depth (AOD) (11, 12), a quantity that represents total aerosol mass and composition in the atmospheric column. The requirement of cloudfree conditions for a successful retrieval and the remaining challenges of relating AOD to aerosol mass, size, and composition limit the utility of AOD retrievals (by themselves) in constraining aerosol mass and composition (13,14). These shortcomings are particularly true in regions of persistent marine stratocumulus, such as the southeast Pacific off the coast of Chile and Peru, where aerosol-cloud interactions are important to the energy balance (15), and limitations in current observing and modeling capabilities adversely impact regional and global weather and climate predictions (16). A typical MODIS AOD scene in this region ( Fig.

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Section: Discussionmentioning

confidence: 99%

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Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Saide

Carmichael

Scott

et al. 2012

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

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“…Fully coupled on-line models, where meteorological and chemical processes are solved together on the same grid and with the same physical parameterizations are able to simulate the complex aerosol-cloud-radiation feedback effects (Zhang, 2008). Recent case studies have shown that the inclusion of feedback effects can improve the model performance for specific cases and conditions Bangert et al, 2012;Yang et al, 2011;Forkel et al, 2012). More research and studies are needed to investigate how the inclusion of feedback effects within on-line air quality models affects the simulation results over Europe for a longer simulation episode.…”

Section: Introductionmentioning

confidence: 99%

“…Realistic simulation of the feedback effects requires the use of integrated meteorology-chemistry on-line models that include detailed treatment of aerosol life cycle and aerosol impacts on radiation (direct effects) and clouds (indirect effects) (Baklanov et al, 2014;Baklanov, 2011, Bangert et al, 2012;Yang et al, 2011). Historically, the study of these effects has been done separately in modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of feedback effects in CBMZ/MOSAIC chemical mechanism

José

Pérez

Balzarini

et al. 2015

Atmospheric Environment

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a bstr a ctTo investigate the impact of the aerosol effects on meteorological variables and pollutant concentrations two simulations with the WRF-Chem model have been performed over Europe for year 2010. We have performed a baseline simulation without any feedback effects and a second simulation including the direct as well as the indirect aerosol effect. The paper describes the full configuration of the model, the simulation design, special impacts and evaluation. Although low aerosol particle concentrations are detected, the inclusion of the feedback effects results in an increase of solar radiation at the surface over cloudy areas (North-West, including the Atlantic) and decrease over more sunny locations (SouthEast). Aerosol effects produce an increase of the water vapor and decrease the planet boundary layer height over the whole domain except in the Sahara area, where the maximum particle concentrations are detected. Significant ozone concentrations are found over the Mediterranean area. Simulated feedback effects between aerosol concentrations and meteorological variables and on pollutant distributions strongly depend on the aerosol concentrations and the clouds. Further investigations are necessary with higher aerosol particle concentrations. WRF-Chem variables are evaluated using available hourly ob-servations in terms of performance statistics. Standardized observations from the ENSEMBLE system webinterface were used. The research was developed under the second phase of Air Quality Model Evaluation International Initiative (AQMEII). WRF-Chem demonstrates its capability in capturing tem-poral and spatial variations of the major meteorological variables and pollutants, except the wind speed over complex terrain. The wind speed bias may affect the accuracy in the chemical predictions (NO2, SO2). The analysis of the correlations between simulated data sets and observational data sets indicates that the simulation with aerosol effects performs slightly better. These results indicate potential importance of the aerosol feedback effects and an urgent need to further improve the representations in current atmospheric models to reduce uncertainties at all scales.

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The effect of smoke emission amount on changes in cloud properties and precipitation: A case study of Canadian boreal wildfires of 2007

Sokolik

2013

JGR Atmospheres

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[1] We investigate the influence of wildfire smoke aerosols on cloud microphysics and precipitation using a coupled aerosol-cloud microphysics-meteorology model WRF-Chem-SMOKE. The Wildfire Automated Biomass Burning Algorithm products are used to compute "online" hourly size-and composition-resolved smoke emission fluxes during Canadian boreal wildfires in the summer of 2007. Comparisons with Moderate Resolution Imaging Spectroradiometer aerosol optical depth, Ozone Monitoring Instrument aerosol index, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation vertical feature mask demonstrate that WRF-Chem-SMOKE captures both the horizontal and vertical spatial distribution of smoke. However, estimated smoke emissions result in much lower aerosol optical depth values than those of observations (by about tenfold). Modeling experiments with varying amounts of smoke emissions of 5 to 10 times as high as the original load reveal that low smoke load favors the collision-coalescence process at a certain stage, leading to either positive or negative changes in the cloud water path (CWP) relative to smoke-free conditions. For high smoke emissions, changes in CWP are positive, as large as 0.5 kg/m 2 . A domain-integrated increase in CWP is proportional to smoke loading. By contrast, both positive and negative changes in the rain water path (RWP) and the snow water path (SWP) are found. While domain-integrated changes in RWP are negative, those in SWP go from negative to positive under a high smoke load. Higher smoke loadings suppress precipitation initially, because of smoke-induced reduction of the collision-coalescence and riming processes, but ultimately cause an invigoration of precipitation. We found that precipitation is highly sensitive to 3-D smoke fields and varies in a nonlinear manner with smoke loads.

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Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem

Cited by 32 publications

References 59 publications

Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Sensitivity of feedback effects in CBMZ/MOSAIC chemical mechanism

The effect of smoke emission amount on changes in cloud properties and precipitation: A case study of Canadian boreal wildfires of 2007

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