Production of sea spray aerosol in the surf zone

Leeuw, G. de; Neele, Filip; Hill, M. P.; Smith, Michael H.; Vignati, E.

doi:10.1029/2000jd900549

Cited by 249 publications

(176 citation statements)

References 34 publications

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“…Biogenic emissions were generated using the Biogenic Emissions Inventory System v3.14 (BEIS3.14), which includes a 1 km resolution land cover database with 230 different vegetation types (Vukovich and Pierce, 2002). Sea-salt emissions were generated online based on the formulation described by de Leeuw et al (2000) for the surf zone and the formulation described by Gong (2003) for the open ocean. Emissions from wildfires and open burning at 1 km × 1 km resolution were obtained from the Fire INventory from NCAR (FINN) (Hodzic et al, 2007;Wiedinmyer et al, 2011).…”

Section: Meteorology and Emissionsmentioning

confidence: 99%

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Zhang

Ying

et al. 2015

Atmos. Chem. Phys.

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Abstract. For the first time, a ∼ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O 3 , PM 2.5 , elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM 2.5 , EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92 % of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http: //faculty.engineering.ucdavis.edu/kleeman/.

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Section: Meteorology and Emissionsmentioning

confidence: 99%

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Zhang

Ying

et al. 2015

Atmos. Chem. Phys.

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“…This method accounts for the hygroscopic uptake of water from sea salt particles for RH values between 45% and 99%. Coastline sea salt flux is also parameterized in the model following the work of Leeuw et al (2000) and Gong et al (2002). Sea salt particle spectrum is represented with a bimodal lognormal distribution assuming a mean diameter of 0.36 µm for the first (accumulated) mode and a mean diameter of 2.85 µm for the second (coarse) mode.…”

Section: A1 Sea Salt Spraymentioning

confidence: 99%

An integrated modeling study on the effects of mineral dust and sea salt particles on clouds and precipitation

et al. 2011

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Abstract. This report addresses the effects of pollution on the development of precipitation in clean ("pristine") and polluted ("hazy") environments in the Eastern Mediterranean by using the Integrated Community Limited Area Modeling System (ICLAMS) (an extended version of the Regional Atmospheric Modeling System, RAMS). The use of this model allows one to investigate the interactions of the aerosols with cloud development. The simulations show that the onset of precipitation in hazy clouds is delayed compared to pristine conditions. Adding small concentrations of GCCN to polluted clouds promotes early-stage rain. The addition of GCCN to pristine clouds has no effect on precipitation amounts. Topography was found to be more important for the distribution of precipitation than aerosol properties. Increasing by 15% the concentration of hygroscopic dust particles for a case study over the Eastern Mediterranean resulted in more vigorous convection and more intense updrafts. The clouds that were formed extended about three kilometers higher, delaying the initiation of precipitation by one hour. Prognostic treatment of the aerosol concentrations in the explicit cloud droplet nucleation scheme of the model, improved the model performance for the twenty-four hour accumulated precipitation. The spatial distribution and the amounts of precipitation were found to vary greatly between the different aerosol scenarios. These results indicate the large uncertainty that remains and the need for more accurate description of aerosol feedbacks in atmospheric models and climate change predictions.

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“…It measures particles of sizes from 0.5-48 µm at 36 size ranges. This type of laser counters has been a standard device in numerous experiments (de Leeuw et al 2000;Jensen et al 2001;Gathman 1983;Zielinski 2004). Significant differences in size distribution measurements may occur between different optical instruments (Jensen et al 2001).…”

Section: Area and Measurement Methodsmentioning

confidence: 99%

Coarse Aerosol Concentration Over the North Polar Waters of the Atlantic

Petelski

2005

Aerosol Science and Technology

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This paper presents the data obtained during the polar research cruises of the r/v Oceania in 1995-2001. The aerosol size distributions were measured using the laser particle counter CSASP-100-HV-SP. The size spectral density between diameters 0.5-15 µm was found to fit the following form: dN/dr = n(2 r ) = exp(a2 r + b), where: a = −0.29 and b are functions of wind speed. Additionally, poor precision of the sea spray emission determination using only the aerosol concentration data measured at one altitude was confirmed.

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Production of sea spray aerosol in the surf zone

Cited by 249 publications

References 34 publications

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

An integrated modeling study on the effects of mineral dust and sea salt particles on clouds and precipitation

Coarse Aerosol Concentration Over the North Polar Waters of the Atlantic

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