Global distribution and seasonal dependence of satellite‐based whitecap fraction

Salisbury, Dominic J.; Anguelova, Magdalena D.; Brooks, Ian M.

doi:10.1002/2014gl059246

Cited by 40 publications

(42 citation statements)

References 53 publications

(73 reference statements)

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“…Grythe et al (2014) noted a strong influence of sea surface temperature on sea spray aerosol production when they compared existing sea spray aerosol source functions with a global database of sea spray aerosol mass concentration measurements. Salisbury et al (2014) noted large differences between a commonly used whitecap fraction parameterisation (Monahan and O'Muircheartaigh, 1980) derived almost entirely in low-latitude regions and a satellite estimate derived over the entire globe. The authors postulate that the weaker wind speed dependence observed in their global data set may in part be due to the influence of secondary factors that co-vary with the wind geographically, such as sea surface temperature.…”

Section: Introductionmentioning

confidence: 89%

“…Salter et al (2014) have shown that the interfacial bubble flux and bubble size spectra are strongly dependent on water temperature and that these are strongly correlated to total particle number flux in a laboratory setting. Grythe et al (2014) noted a strong influence of sea surface temperature on sea spray aerosol production when they compared existing sea spray aerosol source functions with a global database of sea spray aerosol mass concentration measurements.…”

Section: Introductionmentioning

confidence: 99%

“…A series of laboratory systems designed to replicate sea spray aerosol production by whitecaps have shown that the number production flux increases markedly as water temperatures are decreased (e.g. Salter et al, 2014;Zábori et al, 2013;Bowyer et al, 1990). In contrast, observational data from the field, such as chemical analysis of particulate matter smaller than 10 µmin diameter (PM 10 ) or total suspended mass, have often been used to infer that sea spray aerosol production increases with higher sea surface temperatures due to higher observed concentrations at lower latitudes (e.g.…”

Section: Introductionmentioning

confidence: 99%

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An empirically derived inorganic sea spray source function incorporating sea surface temperature

et al. 2015

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Abstract.We have developed an inorganic sea spray source function that is based upon state-of-the-art measurements of sea spray aerosol production using a temperature-controlled plunging jet sea spray aerosol chamber. The size-resolved particle production was measured between 0.01 and 10 µm dry diameter. Particle production decreased non-linearly with increasing seawater temperature (between −1 and 30 • C) similar to previous findings. In addition, we observed that the particle effective radius, as well as the particle surface, particle volume and particle mass, increased with increasing seawater temperature due to increased production of particles with dry diameters greater than 1 µm. By combining these measurements with the volume of air entrained by the plunging jet we have determined the size-resolved particle flux as a function of air entrainment. Through the use of existing parameterisations of air entrainment as a function of wind speed, we were subsequently able to scale our laboratory measurements of particle production to wind speed. By scaling in this way we avoid some of the difficulties associated with defining the "white area" of the laboratory whitecap -a contentious issue when relating laboratory measurements of particle production to oceanic whitecaps using the more frequently applied whitecap method.The here-derived inorganic sea spray source function was implemented in a Lagrangian particle dispersion model (FLEXPART -FLEXible PARTicle dispersion model). An estimated annual global flux of inorganic sea spray aerosol of 5.9 ± 0.2 Pg yr −1 was derived that is close to the median of estimates from the same model using a wide range of existing sea spray source functions. When using the source function derived here, the model also showed good skill in predicting measurements of Na + concentration at a number of field sites further underlining the validity of our source function.In a final step, the sensitivity of a large-scale model (NorESM -the Norwegian Earth System Model) to our new source function was tested. Compared to the previously implemented parameterisation, a clear decrease of sea spray aerosol number flux and increase in aerosol residence time was observed, especially over the Southern Ocean. At the same time an increase in aerosol optical depth due to an increase in the number of particles with optically relevant sizes was found. That there were noticeable regional differences may have important implications for aerosol optical properties and number concentrations, subsequently also affecting the indirect radiative forcing by non-sea spray anthropogenic aerosols.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An empirically derived inorganic sea spray source function incorporating sea surface temperature

et al. 2015

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“…There remains, however, an order of magnitude uncertainty in the parameterization of the whitecap fraction, and there is increasing evidence that neither the production of aerosol per unit area whitecap nor the lifetime of a whitecap are independent of the scale of wave breaking or other water properties (Norris et al, 2013a;Callaghan, 2013;Spada et al, 2013;Salter et al, 2014b;Salter et al, 2015). Recent work on satellite retrievals of the whitecaps (Anguelova and Webster, 2006;Gaiser, 2011, 2013) shows significant promise as a means of providing this driving parameter for sea spray source functions and implicitly accounting for the wide range of important controlling factors in addition to wind speed (Salisbury et al, 2013(Salisbury et al, , 2014. It might also ultimately allow a source function to be specified directly in terms of the satellite measurements.…”

Section: User Requirements For Marine Aerosol Particle Emissionsmentioning

confidence: 99%

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

et al. 2018

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Abstract. Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite-and groundbased platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of ...

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“…There remains, however, an order of magnitude uncertainty in the parameteriza-660 tion of the whitecap fraction, and there is increasing evidence that neither the production of aerosol per unit area whitecap nor the lifetime of a whitecap are independent of the scale of wave breaking or other water properties (Norris et al (2013a); Callaghan (2013); Spada et al (2013), Salter et al (2014Salter et al ( , 2015). Recent work on satellite retrievals of the whitecaps (Anguelova and Webster, 2006;Gaiser, 2011, 2013) shows significant promise as a means of providing this driving 665 parameter for sea spray source functions, and implicitly accounting for the wide range of important controlling factors in addition to wind speed (Salisbury et al, 2013(Salisbury et al, , 2014. It might, also, ultimately allow a source function to be specified directly in terms of the satellite measurements.…”

Section: User Requirements For Emissions: Marine Aerosol Particlesmentioning

confidence: 99%

Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements

Benedetti¹,

Reid²,

Baklanov³

et al. 2018

Preprint

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Abstract. Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centres due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, providers of climate services, and health professionals. The prediction of aerosol particle properties in Numerical Weather Prediction (NWP) models faces a number of challenges owing to 5 the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions. Errors in aerosol prediction concern all processes involved in the aerosol life cycle.These include errors on the source terms (for both anthropogenic and natural emissions), errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), as well as errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transfor-10 mation and growth, hygroscopicity). The main goal of current research on aerosol forecast consists in prioritizing these errors and trying to reduce the most important ones through model development and data assimilation. Aerosol particle observations from satellite and ground-based platforms have been crucial to guide model development of the recent years, and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol parti-15 cle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near-surface, and aircraft) and freely shared. This white paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centres. Some of the requirements are equally applicable to aerosol-climate research. However, the focus here is on the global operational prediction 20 of aerosol properties such as mass concentrations and optical parameters. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability to simulate the size information.However the next generation of aerosol operational models will have the capability to predict both mass and number density which will provide a more complete description of the aerosols properties. 25A brief overview of the state-of-the-art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.

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Global distribution and seasonal dependence of satellite‐based whitecap fraction

Cited by 40 publications

References 53 publications

An empirically derived inorganic sea spray source function incorporating sea surface temperature

An empirically derived inorganic sea spray source function incorporating sea surface temperature

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements

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