Imperial College London

DrAdrianCallaghan

Faculty of EngineeringDepartment of Civil and Environmental Engineering

Senior Lecturer (Royal Society Shooter International Fellow)
 
 
 
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Contact

 

+44 (0)20 7594 6644a.callaghan Website

 
 
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Assistant

 

Miss Rebecca Naessens +44 (0)20 7594 5990

 
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Location

 

Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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20 results found

Callaghan A, 2018, On the relationship between the energy dissipation rate of surface breaking waves and oceanic whitecap coverage, Journal of Physical Oceanography, Vol: 48, Pages: 2609-2626, ISSN: 0022-3670

Wave breaking is the most important mechanism that leads to the dissipation of oceanic surface wave energy. A relationship between the energy dissipation rate associated with breaking wave whitecaps (Swcap) and the area of whitecap foam per unit area ocean surface (W) is expected, but there is a lack of consensus on what form this relationship should take. Here, mathematical representations of whitecap coverage (W), and growth phase whitecap coverage (Wgrowth) are derived, and an energy-balance approach is used to formulate W and Wgrowth in terms of Swcap. Both W and Wgrowth are found to be linearly proportional to Swcap, but also inversely proportional to the bubble plume penetration depth during active breaking. Since this depth can vary for breaking waves of different scales and slopes, there is likely no unique relationship between Swcap and either W or Wgrowth as bubble plume penetration depth must also be specified. Whitecap observations from the North Atlantic are used to estimate bubble plume penetration depth as a function of wind speed, and then used with W measurements to compute Swcap. An estimate of the relative magnitude of Swcap to the rate of energy input from the wind to the waves, Sin, is made. Above wind speeds of about 12 m/s, Sin is largely balanced by Swcap. At lower wind speeds the ratio Swcap/Sin quickly drops below unity with decreasing wind speed. It is proposed that sea state driven variability in both Swcap/Sin and bubble plume penetration depth are significant causes of variation in whitecap coverage datasets and parameterizations.

Journal article

Callaghan AH, Deane GB, Stokes MD, 2017, On the imprint of surfactant-driven stabilization of laboratory breaking wave foam with comparison to oceanic whitecaps, Journal of Geophysical Research: Oceans, Vol: 122, Pages: 6110-6128, ISSN: 2169-9275

Surfactants are ubiquitous in the global oceans: they help form the materially‐distinct sea surface microlayer (SML) across which global ocean‐atmosphere exchanges take place, and they reside on the surfaces of bubbles and whitecap foam cells prolonging their lifetime thus altering ocean albedo. Despite their importance, the occurrence, spatial distribution, and composition of surfactants within the upper ocean and the SML remains under‐characterized during conditions of vigorous wave breaking when in‐situ sampling methods are difficult to implement. Additionally, no quantitative framework exists to evaluate the importance of surfactant activity on ocean whitecap foam coverage estimates. Here we use individual laboratory breaking waves generated in filtered seawater and seawater with added soluble surfactant to identify the imprint of surfactant activity in whitecap foam evolution. The data show a distinct surfactant imprint in the decay phase of foam evolution. The area‐time‐integral of foam evolution is used to develop a time‐varying stabilization function, urn:x-wiley:21699275:media:jgrc22369:jgrc22369-math-0001 and a stabilization factor, urn:x-wiley:21699275:media:jgrc22369:jgrc22369-math-0002, which can be used to identify and quantify the extent of this surfactant imprint for individual breaking waves. The approach is then applied to wind‐driven oceanic whitecaps, and the laboratory and ocean urn:x-wiley:21699275:media:jgrc22369:jgrc22369-math-0003 distributions overlap. It is proposed that whitecap foam evolution may be used to determine the occurrence and extent of oceanic surfactant activity to complement traditional in‐situ techniques and extend measurement capabilities to more severe sea states occurring at wind speeds in excess of about 10 m/s. The analysis procedure also provides a framework to assess surfactant‐driven variability within and between whitecap coverage data sets.

Journal article

Bell TG, Landwehr S, Miller SD, de Bruyn WJ, Callaghan AH, Scanlon B, Ward B, Yang M, Saltzman ESet al., 2017, Estimation of bubble-mediated air–sea gas exchange from concurrent DMS and CO2 transfer velocities at intermediate–high wind speeds, Atmospheric Chemistry and Physics, Vol: 17, Pages: 9019-9033, ISSN: 1680-7316

Simultaneous air–sea fluxes and concentration differences of dimethylsulfide (DMS) and carbon dioxide (CO2) were measured during a summertime North Atlantic cruise in 2011. This data set reveals significant differences between the gas transfer velocities of these two gases (Δkw) over a range of wind speeds up to 21ms−1. These differences occur at and above the approximate wind speed threshold when waves begin breaking. Whitecap fraction (a proxy for bubbles) was also measured and has a positive relationship with Δkw, consistent with enhanced bubble-mediated transfer of the less soluble CO2 relative to that of the more soluble DMS. However, the correlation of Δkw with whitecap fraction is no stronger than with wind speed. Models used to estimate bubble-mediated transfer from in situ whitecap fraction underpredict the observations, particularly at intermediate wind speeds. Examining the differences between gas transfer velocities of gases with different solubilities is a useful way to detect the impact of bubble-mediated exchange. More simultaneous gas transfer measurements of different solubility gases across a wide range of oceanic conditions are needed to understand the factors controlling the magnitude and scaling of bubble-mediated gas exchange.

Journal article

Callaghan AH, Deane GB, Stokes MD, 2016, Laboratory air-entraining breaking waves: Imaging visible foam signatures to estimate energy dissipation, Geophysical Research Letters, Vol: 43, Pages: 11320-11328, ISSN: 0094-8276

Oceanic air‐entraining breaking waves fundamentally influence weather and climate through bubble‐mediated ocean‐atmosphere exchanges, and influence marine engineering design by impacting statistics of wave heights, crest heights, and wave loading. However, estimating individual breaking wave energy dissipation in the field remains a fundamental problem. Using laboratory experiments, we introduce a new method to estimate energy dissipation by individual breaking waves using above‐water images of evolving foam. The data show the volume of the breaking wave two‐phase flow integrated in time during active breaking scales linearly with wave energy dissipated. To determine the volume time‐integral, above‐water images of surface foam provide the breaking wave timescale and horizontal extent of the submerged bubble plume, and the foam decay time provides an estimate of the bubble plume penetration depth. We anticipate that this novel remote sensing method will improve predictions of air‐sea exchanges, validate models of wave energy dissipation, and inform ocean engineering design.

Journal article

Deane GB, Stokes MD, Callaghan AH, 2016, The Saturation of Fluid Turbulence in Breaking Laboratory Waves and Implications for Whitecaps, Journal of Physical Oceanography, Vol: 46, Pages: 975-992, ISSN: 0022-3670

<jats:title>Abstract</jats:title> <jats:p>Measurements of energy dissipated in breaking laboratory waves, averaged over time and space and directly visualized with a bioluminescent technique, are presented. These data show that the energy dissipated in the crest of the breaking waves is constrained: average turbulence intensity within the crest saturates at around 0.5–1.2 W kg−1, whereas breaking crest volume scales with wave energy lost. These results are consistent with laboratory and field observations of the Hinze scale, which is the radius of the largest bubble entrained within a breaking crest that is stabilized against turbulent fragmentation. The Hinze scale depends on turbulence intensity but lies in the restricted range 0.7–1.7 mm over more than two orders of magnitude variation in underlying unbroken wave energy. The results have important implications for understanding the energetics of breaking waves in the field, the injection of turbulence into the upper ocean, and air–sea exchange processes in wind-driven seas.</jats:p>

Journal article

Scanlon B, Breivik Ø, Bidlot J-R, Janssen PAEM, Callaghan AH, Ward Bet al., 2016, Modeling Whitecap Fraction with a Wave Model, Journal of Physical Oceanography, Vol: 46, Pages: 887-894, ISSN: 0022-3670

<jats:title>Abstract</jats:title> <jats:p>High-resolution measurements of actively breaking whitecap fraction (WFA) and total whitecap fraction (WFT) from the Knorr11 field experiment in the Atlantic Ocean are compared with estimates of whitecap fraction modeled from the dissipation source term of the ECMWF wave model. The results reveal a strong linear relationship between model results and observed measurements. This indicates that the wave model dissipation is an accurate estimate of total whitecap fraction. The study also reveals that the dissipation source term is more closely related to WFA than WFT, which includes the additional contribution from maturing (stage B) whitecaps.</jats:p>

Journal article

Goddijn-Murphy L, Woolf DK, Callaghan AH, Nightingale PD, Shutler JDet al., 2016, A reconciliation of empirical and mechanistic models of the air-sea gas transfer velocity, Journal of Geophysical Research: Oceans, Vol: 121, Pages: 818-835, ISSN: 2169-9275

Journal article

Wain DJ, Lilly JM, Callaghan AH, Yashayaev I, Ward Bet al., 2015, A breaking internal wave in the surface ocean boundary layer, Journal of Geophysical Research: Oceans, Vol: 120, Pages: 4151-4161, ISSN: 2169-9275

Journal article

Walesby K, Vialard J, Minnett PJ, Callaghan AH, Ward Bet al., 2015, Observations indicative of rain‐induced double diffusion in the ocean surface boundary layer, Geophysical Research Letters, Vol: 42, Pages: 3963-3972, ISSN: 0094-8276

Journal article

Callaghan AH, Ward B, Vialard J, 2014, Influence of surface forcing on near-surface and mixing layer turbulence in the tropical Indian Ocean, Deep Sea Research Part I: Oceanographic Research Papers, Vol: 94, Pages: 107-123, ISSN: 0967-0637

Journal article

Callaghan AH, Stokes MD, Deane GB, 2014, The effect of water temperature on air entrainment, bubble plumes, and surface foam in a laboratory breaking-wave analog, Journal of Geophysical Research: Oceans, Vol: 119, Pages: 7463-7482, ISSN: 2169-9275

Journal article

Ward B, Fristedt T, Callaghan AH, Sutherland G, Sanchez X, Vialard J, Doeschate ATet al., 2014, The Air–Sea Interaction Profiler (ASIP): An Autonomous Upwardly Rising Profiler for Microstructure Measurements in the Upper Ocean, Journal of Atmospheric and Oceanic Technology, Vol: 31, Pages: 2246-2267, ISSN: 0739-0572

<jats:title>Abstract</jats:title> <jats:p>The upper few meters of the ocean form a critical layer for air–sea interaction, but because of observational challenges this region is undersampled. However, the physical processes controlling momentum transfer, gas exchange, and heat transfer are all concentrated in the uppermost region of the ocean. To study this region, the Air–Sea Interaction Profiler (ASIP) was developed. This is an autonomous microstructure vertical profiling instrument that provides data from a maximum depth of 100 m to the ocean surface and allows measurements to be performed in an undisturbed environment. The core sensor package on ASIP includes shear probes, microstructure and CTD-quality temperature and conductivity sensors, a photosynthetically active radiation (PAR) sensor, and an oxygen optode providing a repeated high-resolution dataset immediately below the air–sea interface. Autonomous profiling is accomplished with thrusters that submerge the positively buoyant instrument. Once the desired depth is reached, ASIP ascends through the water column acquiring data. At the surface, ASIP acquires its position and transmits this over the Iridium satellite network. ASIP is then placed in a low-power mode for a specified period, whereupon it repeats the profile cycle. Two-way communication over the Iridium network allows mission parameters to be changed in real time. ASIP has been used to study several scientific questions, such as the impact of diurnal warming on atmospheric processes, turbulence scaling in the upper ocean, parameterizing air–sea gas exchange, salinity gradients in the ocean surface boundary layer (OSBL), and consequences for remote sensing.</jats:p>

Journal article

Meskhidze N, Petters MD, Tsigaridis K, Bates T, O'Dowd C, Reid J, Lewis ER, Gantt B, Anguelova MD, Bhave PV, Bird J, Callaghan AH, Ceburnis D, Chang R, Clarke A, de Leeuw G, Deane G, DeMott PJ, Elliot S, Facchini MC, Fairall CW, Hawkins L, Hu Y, Hudson JG, Johnson MS, Kaku KC, Keene WC, Kieber DJ, Long MS, Mårtensson M, Modini RL, Osburn CL, Prather KA, Pszenny A, Rinaldi M, Russell LM, Salter M, Sayer AM, Smirnov A, Suda SR, Toth TD, Worsnop DR, Wozniak A, Zorn SRet al., 2013, Production mechanisms, number concentration, size distribution, chemical composition, and optical properties of sea spray aerosols, Atmospheric Science Letters, Vol: 14, Pages: 207-213, ISSN: 1530-261X

Journal article

Callaghan AH, 2013, An improved whitecap timescale for sea spray aerosol production flux modeling using the discrete whitecap method, Journal of Geophysical Research: Atmospheres, Vol: 118, Pages: 9997-10,010, ISSN: 2169-897X

Journal article

Callaghan AH, Deane GB, Stokes MD, 2013, Two Regimes of Laboratory Whitecap Foam Decay: Bubble-Plume Controlled and Surfactant Stabilized, Journal of Physical Oceanography, Vol: 43, Pages: 1114-1126, ISSN: 0022-3670

<jats:title>Abstract</jats:title> <jats:p>A laboratory experiment to quantify whitecap foam decay time in the presence or absence of surface active material is presented. The investigation was carried out in the glass seawater channel at the Hydraulics Facility of Scripps Institution of Oceanography. Whitecaps were generated with focused, breaking wave packets in filtered seawater pumped from La Jolla Shores Beach with and without the addition of the surfactant Triton X-100. Concentrations of Triton X-100 (204 μg L−1) were chosen to correspond to ocean conditions of medium productivity. Whitecap foam and subsurface bubble-plume decay times were determined from digital images for a range of wave scales and wave slopes. The experiment showed that foam lifetime is variable and controlled by subsurface bubble-plume-degassing times, which are a function of wave scale and breaking wave slope. This is true whether or not surfactants are present. However, in the presence of surfactants, whitecap foam is stabilized and persists for roughly a factor of 3 times its clean seawater value. The range of foam decay times observed in the laboratory study lie within the range of values observed in an oceanic dataset obtained off Martha’s Vineyard in 2008.</jats:p>

Journal article

Callaghan AH, Deane GB, Stokes MD, Ward Bet al., 2012, Observed variation in the decay time of oceanic whitecap foam, Journal of Geophysical Research: Oceans, Vol: 117, Pages: n/a-n/a, ISSN: 0148-0227

Journal article

Goddijn-Murphy L, Woolf DK, Callaghan AH, 2011, Parameterizations and Algorithms for Oceanic Whitecap Coverage, Journal of Physical Oceanography, Vol: 41, Pages: 742-756, ISSN: 0022-3670

<jats:title>Abstract</jats:title> <jats:p>Shipboard measurements of fractional whitecap coverage W and wind speed at 10-m height, obtained during the 2006 Marine Aerosol Production (MAP) campaign, have been combined with ECMWF wave model and Quick Scatterometer (QuikSCAT) satellite wind speed data for assessment of existing W parameterizations. The wind history trend found in an earlier study of the MAP data could be associated with wave development on whitecapping, as previously postulated. Whitecapping was shown to be mainly wind driven; for high wind speeds (&amp;gt;9 m s−1), a minor reduction in the scatter of in situ W data points could be achieved by including sea state conditions or by using parameters related to wave breaking. The W values were slightly larger for decreasing wind/developed waves than for increasing wind/developing waves, whereas cross-swell conditions (deflection angle between wind and swell waves between ±45° and ±135°) appeared to dampen whitecapping. Tabulated curve fitting results of the different parameterizations show that the errors that could not be attributed to the propagation of the standard error in U10 remained largely unexplained. It is possible that the counteracting effects of wave development and cross swell undermine the performance of the simple parameterizations in this study.</jats:p>

Journal article

Callaghan AH, White M, 2009, Automated Processing of Sea Surface Images for the Determination of Whitecap Coverage, Journal of Atmospheric and Oceanic Technology, Vol: 26, Pages: 383-394, ISSN: 0739-0572

<jats:title>Abstract</jats:title> <jats:p>Sea surface images have been collected to determine the percentage whitecap coverage (W) since the late 1960s. Image processing methods have changed dramatically since the beginning of whitecap studies. An automated whitecap extraction (AWE) technique has been developed at the National University of Ireland, Galway, that allows images to be analyzed for percentage whitecap coverage without the need of a human analyst. AWE analyzes digital images and determines a suitable threshold with which whitecaps can be separated from unbroken background water. By determining a threshold for each individual image, AWE is suitable for images obtained in conditions of changing ambient illumination. AWE is also suitable to process images that have been taken from both stable and nonstable platforms (such as towers and research vessels, respectively). Using techniques based on derivative analysis, AWE provides an objective method to determine an appropriate threshold for the identification of whitecaps in sea surface images without the need for a human analyst. The automated method allows large numbers of images to be analyzed in a relatively short amount of time. AWE can be used to analyze hundreds of images per individual W data point, which produces more convergent values of W.</jats:p>

Journal article

Callaghan A, de Leeuw G, Cohen L, O'Dowd CDet al., 2008, Relationship of oceanic whitecap coverage to wind speed and wind history, Geophysical Research Letters, Vol: 35, ISSN: 0094-8276

Journal article

Callaghan AH, Deane GB, Stokes MD, 2008, Observed physical and environmental causes of scatter in whitecap coverage values in a fetch-limited coastal zone, Journal of Geophysical Research, Vol: 113, ISSN: 0148-0227

Journal article

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