Effects of Short Scale Roughness and Wave Breaking Efficiency on Sea Spray Aerosol Production: Multisensor Field Observations

David J. Dowgiallo; Glendon M. Frick; Ivan B. Savelyev; Jeffrey A. Schindall; Justin P. Bobak; Magdalena D. Anguelova; Paul A. Hwang; Steve L. Means

arxiv: 1906.11201 · v1 · pith:5NTTKAICnew · submitted 2019-06-19 · ⚛️ physics.ao-ph

Effects of Short Scale Roughness and Wave Breaking Efficiency on Sea Spray Aerosol Production: Multisensor Field Observations

Paul A. Hwang , Ivan B. Savelyev , Steve L. Means , Magdalena D. Anguelova , Jeffrey A. Schindall , Glendon M. Frick , David J. Dowgiallo , Justin P. Bobak This is my paper

Pith reviewed 2026-05-25 19:43 UTC · model grok-4.3

classification ⚛️ physics.ao-ph

keywords sea spray aerosolocean surface roughnesswave breakingturbulent processesmultisensor observationswind seasmixed seasbubble breakup

0 comments

The pith

Turbulent processes at short ocean surface scales control sea spray aerosol properties more than wave breaking itself.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Simultaneous field measurements of sea spray aerosols, wind, waves, acoustics, and microwave brightness temperature are analyzed to identify key ocean surface processes. Parameters are formulated to represent surface conditions across length scales from tens of meters to a few centimeters. Correlations between aerosol number, volume, and flux and these parameters grow stronger toward the shortest scales. The results indicate that wave breaking supplies the necessary starting condition while turbulence linked to surface roughness determines the final aerosol state in the air. The data also show higher aerosol production efficiency in falling winds than in rising winds, linked to differences in air cavity sizes entrained by breakers of varying lengths.

Core claim

Analysis of multisensor data shows that correlation coefficients between sea spray aerosol properties and surface process parameters improve toward the shortest length scales. This indicates that the final state of atmospheric SSA properties is controlled primarily by turbulent processes characterized by the ocean surface roughness, although surface wave breaking remains a necessary initial and boundary condition. The data also reveal higher breaking production efficiency in low or falling winds, consistent with shorter breakers in mixed seas entraining smaller air cavities that undergo more efficient turbulent breakup.

What carries the argument

The improvement in correlation coefficients between SSA properties and surface process parameters as the represented length scale decreases to centimeters.

If this is right

SSA number, volume, and flux are set mainly by short-scale roughness after wave breaking begins the process.
Aerosol production efficiency is higher in low or falling winds than in rising winds.
Larger air cavities form in rising winds with wind seas and escape before full turbulent breakup into small bubbles.
Shorter breakers typical of falling winds with mixed seas trap smaller cavities that remain underwater longer for turbulence to act.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Aerosol flux models could shift emphasis from bulk wave statistics toward explicit short-scale roughness inputs.
Incorporating wind-direction history or sea-state type might refine predictions of SSA production rates.
Scale-dependent turbulence effects observed here may extend to other near-surface exchange processes such as gas transfer.

Load-bearing premise

The parameters formulated for surface processes at different length scales accurately capture those processes without significant influence from measurement artifacts or other factors.

What would settle it

A dataset in which SSA properties correlate more strongly with large-scale wave parameters than with short-scale roughness parameters, or show no scale-dependent improvement, would challenge the claim of primary turbulent control.

Figures

Figures reproduced from arXiv: 1906.11201 by David J. Dowgiallo, Glendon M. Frick, Ivan B. Savelyev, Jeffrey A. Schindall, Justin P. Bobak, Magdalena D. Anguelova, Paul A. Hwang, Steve L. Means.

**Figure 4.** Figure 4: Processed results of the 10.7 GHz microwave brightness temperature Tb measurements: (a) Time series of wind speed U10, and the Tb deviation Tbp (from the flat surface value), subscript p is polarization (H: horizontal, V: vertical). (b) The Tbp dependence on wind speed. The modeled curves (Hwang 2012) separating the foam and roughness contributions and the sum are illustrated for comparison. (c) The diff… view at source ↗

**Figure 7.** Figure 7: The SSA size spectra: (a) Volume dV/dlnr; (b) Number dN/dlnr; (c) Flux computed with (3) and (4): dFis/dlnr; and (d) Flux computed with (5) and (6): dFU/dlnr [PITH_FULL_IMAGE:figures/full_fig_p050_7.png] view at source ↗

**Figure 16.** Figure 16: (a) Episodic behavior of the SSA flux showing differences in the rate of change as a function of Et in rising wind, falling wind and quasi-steady conditions; and (b) Time series of U10, Et and F, with 6 episodes identified. The general trend of rising and falling winds in F(Et) is indicated by the arrows in (a); effects of time lags between U10, Et and F are illustrated with dotted lines connecting 2R … view at source ↗

**Figure 2.** Figure 2: Comparisons of wind and wave measurements onboard FLIP with those of four buoys identified in [PITH_FULL_IMAGE:figures/full_fig_p054_2.png] view at source ↗

**Figure 3.** Figure 3: Sea state, swell and wind-sea separation, and wind-sea energy dissipation computation: (a) Temporal variation of the wave spectrum, for reference, U10 and fpw are superimposed; (b) The significant wave heights of the wind-sea and swell components: Hsw and Hss, respectively; and (c) The spectral peak periods of the Wind-sea and swell components: Tpw and Tps, respectively; (d) Significant wave height Hs and … view at source ↗

**Figure 5.** Figure 5: The underwater acoustic noise in the frequency range between 1250 and [PITH_FULL_IMAGE:figures/full_fig_p057_5.png] view at source ↗

**Figure 9.** Figure 9 [PITH_FULL_IMAGE:figures/full_fig_p061_9.png] view at source ↗

**Figure 10.** Figure 10: Empirical model function (EMF) of dFU/dlnr, and its comparison with data and other model functions (Monahan et al. 1986: M86; Smith et al. 1993: S93; Lewis and Schwartz 2004: L04). Plotting symbols circle, plus and triangle show data at 5, 10 and 15 m s -1 , respectively; solid, dashed, and dashed-dotted curves show various models (described in the legend with different colors) at 5, 10 and 15 m s-1 , res… view at source ↗

**Figure 16.** Figure 16: (a) Episodic [PITH_FULL_IMAGE:figures/full_fig_p068_16.png] view at source ↗

read the original abstract

Simultaneous measurements of sea spray aerosol (SSA), wind, wave, underwater acoustic noise, and microwave brightness temperature are obtained in the open ocean. These data are analyzed to clarify the ocean surface processes important to SSA production. Parameters are formulated to represent surface processes with characteristic length scales over a broad range, from tens of meters to a few centimeters. The result shows that the correlation coefficients between SSA properties (number, volume and flux) and surface process parameters improve toward the shortest length scale. This suggests that whereas surface wave breaking is a necessary initial and boundary condition, the final state of the atmospheric SSA properties is controlled primarily by turbulent processes characterized by the ocean surface roughness. The investigation also reveals distinct differences of the SSA properties in rising winds and falling winds, with higher efficiency of breaking production in low or falling winds. Previous studies show that the length scale of breaking waves is shorter in mixed seas than in wind seas. Combining the observations together, it is suggestive that larger air cavities are entrained in rising winds (with wind seas more likely). The larger air cavities escape before they can be fully broken down into small bubbles for the subsequent SSA production. In contrast, the shorter breakers in low or falling winds (with mixed seas more likely) trap smaller air cavities that stay underwater longer for more efficient bubble breakup by turbulence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper's multisensor field data show tighter SSA correlations at short roughness scales than longer wave scales, but the turbulence-dominance claim needs explicit wind-speed controls to stand.

read the letter

The main takeaway is that simultaneous open-ocean measurements of SSA, wind, waves, acoustic noise, and microwave brightness temperature yield higher correlation coefficients as the surface-process length scale shrinks from tens of meters down to centimeters. The authors interpret this as evidence that wave breaking supplies the initial bubbles while turbulence, indexed by short-scale roughness, sets the final aerosol number, volume, and flux. They also note higher breaking efficiency in falling or low winds, which they link to mixed seas and smaller entrained air cavities that survive longer for turbulent breakup. The dataset itself is a clear strength: real simultaneous multisensor records rather than separate campaigns or lab proxies, and the scale-dependent parameter formulation is a straightforward way to test the length-scale hypothesis. The rising-versus-falling wind contrast adds an observational angle that prior work has not always captured in the same runs. That said, the central inference is vulnerable on exactly the point the stress-test note flags. Short-scale proxies (microwave brightness temperature, acoustic noise) are themselves tightly coupled to local wind stress, so the reported correlation gains could simply reflect better wind resolution rather than an independent turbulent control after breaking. Without partial correlations or regression models that hold wind speed fixed, the claim that turbulence rather than breaking governs the final SSA state does not yet follow cleanly. The air-cavity size argument is likewise suggestive but rests on indirect inference from sea-state differences. The abstract gives no sample sizes, exact coefficients, or significance levels, so it is hard to judge how robust the patterns are once those controls are applied. This is observational work aimed at air-sea interaction and aerosol modeling groups. Readers who need updated parameterizations for SSA source functions will find the scale and wind-history angles useful even if the mechanistic interpretation requires tightening. The paper deserves peer review because the field measurements are non-trivial and the questions it raises are directly relevant to climate and weather models; referees will likely press on the confounding issue and ask for the missing statistical details, but the underlying data merit that scrutiny rather than a desk rejection.

Referee Report

2 major / 2 minor

Summary. The manuscript reports simultaneous open-ocean measurements of sea spray aerosol (SSA) number, volume and flux together with wind, waves, underwater acoustic noise and microwave brightness temperature. Parameters are constructed to represent surface processes at length scales ranging from tens of meters down to centimeters; the authors find that correlation coefficients between these parameters and SSA properties increase toward the shortest scales. From this they conclude that, while wave breaking supplies the necessary initial condition, the final atmospheric SSA state is controlled primarily by turbulent processes tied to surface roughness. They additionally report higher SSA production efficiency in falling winds than in rising winds and interpret this in terms of differences in entrained air-cavity size between wind-sea and mixed-sea conditions.

Significance. If the reported scale-dependent correlations survive appropriate controls for wind speed, the work would supply direct observational support for shifting the emphasis in SSA source functions from wave-breaking metrics to small-scale roughness and turbulence. The multisensor data set itself is a useful resource for the community.

major comments (2)

[correlation analysis (results)] The central inference that short-scale roughness parameters exert primary control independent of wind forcing is not yet supported. Acoustic noise and microwave brightness temperature are themselves strongly coupled to local wind stress; no partial-correlation analysis, multivariate regression that includes wind speed as a covariate, or stratification by wind-speed bins is described that would demonstrate the correlation gain survives after wind is partialled out. Without such controls the improvement at centimeter scales could simply reflect a higher-resolution proxy for the same wind forcing that drives both roughness and initial breaking.
[wind-history comparison (results)] The rising/falling wind asymmetry is presented without controls for co-varying factors such as wave age, significant wave height, or sea-state classification. The claim that larger air cavities are entrained in rising winds therefore rests on an untested assumption that wind history is the dominant distinguishing variable.

minor comments (2)

[abstract and results] Abstract and main text should report the actual Pearson or Spearman coefficients, sample sizes (N), and p-values for each scale and each SSA property so that readers can judge the magnitude and statistical significance of the reported improvements.
[methods] The precise mathematical definitions of the short-scale parameters (e.g., how microwave brightness temperature or acoustic noise are converted into roughness or breaking-efficiency proxies) need to be stated explicitly, including any empirical constants or filtering steps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and will revise the manuscript to incorporate additional statistical controls where feasible.

read point-by-point responses

Referee: [correlation analysis (results)] The central inference that short-scale roughness parameters exert primary control independent of wind forcing is not yet supported. Acoustic noise and microwave brightness temperature are themselves strongly coupled to local wind stress; no partial-correlation analysis, multivariate regression that includes wind speed as a covariate, or stratification by wind-speed bins is described that would demonstrate the correlation gain survives after wind is partialled out. Without such controls the improvement at centimeter scales could simply reflect a higher-resolution proxy for the same wind forcing that drives both roughness and initial breaking.

Authors: We agree that the parameters derived from acoustic noise and microwave brightness temperature are coupled to wind stress, and that the manuscript does not present partial-correlation or multivariate analyses controlling for wind speed. While the scale-dependent improvement in correlations is consistent with our interpretation of turbulent control at short scales, additional controls are needed to strengthen the claim of independence from wind forcing. We will add partial-correlation analysis (controlling for wind speed) and wind-speed-binned stratification to the revised manuscript. revision: yes
Referee: [wind-history comparison (results)] The rising/falling wind asymmetry is presented without controls for co-varying factors such as wave age, significant wave height, or sea-state classification. The claim that larger air cavities are entrained in rising winds therefore rests on an untested assumption that wind history is the dominant distinguishing variable.

Authors: We acknowledge that the rising/falling wind comparison does not explicitly control for wave age, significant wave height, or sea-state classification. The interpretation linking efficiency differences to air-cavity size draws on prior literature regarding breaker lengths in wind-sea versus mixed-sea conditions, but the referee is correct that direct controls on co-varying factors are absent. In revision we will add stratification by wave age and significant wave height (where data permit) and qualify the air-cavity discussion accordingly. revision: partial

Circularity Check

0 steps flagged

No circularity: purely observational correlations from field data

full rationale

This is an observational study that formulates parameters representing surface processes at varying length scales and reports empirical correlation coefficients with SSA properties. No equations, derivations, or predictions are present that reduce by construction to fitted inputs or self-citations. The central claim follows directly from the measured correlation trends without any self-definitional steps, fitted-input predictions, or load-bearing self-citation chains. The paper is self-contained against external benchmarks as a data-driven analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Since only the abstract is available, the ledger is based on the described approach. The claim rests on domain assumptions about parameterizing surface processes by length scales and interpreting correlations as indicating primary control mechanisms.

axioms (1)

domain assumption Surface wave breaking is a necessary initial and boundary condition for SSA production
Stated explicitly in the abstract as background for the analysis.

pith-pipeline@v0.9.0 · 5813 in / 1163 out tokens · 49880 ms · 2026-05-25T19:43:30.555832+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

correlation coefficients between SSA properties ... improve toward the shortest length scale. This suggests that whereas surface wave breaking is a necessary initial and boundary condition, the final state ... is controlled primarily by turbulent processes characterized by the ocean surface roughness.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Parameters are formulated to represent surface processes with characteristic length scales over a broad range, from tens of meters to a few centimeters.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages

[1]

D., 2008: Complex relative permittivity of sea foam at microwave frequencies

Anguelova, M. D., 2008: Complex relative permittivity of sea foam at microwave frequencies. J. Geophys. Res., 113, C08001, doi:10.1029/2007JC004212. Bock, E. J., T. Hara, N. M. Frew, and W. R. McGillis, 1999: Relationship between air ‐sea gas transfer and short wind waves. J. Geophys. Res., 104, 25821-25831. Buckingham, M. J., and J. R. Potter (eds.), 199...

work page doi:10.1029/2007jc004212 2008
[2]

Monahan, E. C., D. E. Spiel, and K. L. Davidson, 1986: A model of marine aerosol generation via whitecaps and wave disruption. In Oceanic whitecaps and their role in air -sea exchange processes, edited by E. C. Monahan and G. MacNiocaill, 167– 174, D. Reidel, Norwell, Mass. O’Dowd, C. D., and G. de Leeuw, 2007: Marine aerosol production: a review of the c...

work page doi:10.1029/2011jd017379 1986
[3]

JPO/31Oct2014 44 FLIPexptR0Noline.docx Prosperetti, A., L. A. Crum, and H. C. Pumphrey, 1989: The underwater noise of rain. J. Geophy. Res., 94, 3255-3259. Pumphrey, H. C., 1989: Sources of ambient noise in the ocean: An experimental investigation. Ph D dissertation, Univ. Miss., 96 pp. Pumphrey, H. C., and P. A. Elmore, 1990: The entrainment of bubbles b...

work page 1989
[4]

T., 1974: Microwave radiometer measurements of the Cape Cod Canal

Swift, C. T., 1974: Microwave radiometer measurements of the Cape Cod Canal . Radio Sci., 9, 641-653. Tang, I. N., A. C. Tridico, and K. H. Fung, 1997: Thermodynamic and optical properties of sea salt aerosol. J. Geophys. Res., 102, 23269-23275. Ulaby, F., R. Moore, and A. Fung, 1981: Microwave remote sensing: active and passive, Microwave Remote Sensing ...

work page 1974
[5]

Power -law relationship of the SSA size spectral components: (a) Volume dV/dlnr; (b) Number dN/dlnr; (c) Flux computed with (3) and (4): dFis/dlnr; and (d) Flux computed with (5) and (6): dFU/dlnr. Fig. 9: The coefficients of power -law empirical model functions for the SSA size spectra, / lndN d r , / lndV d r , / lnisdF d r , and / lnUdF d r : (a) propo...

work page 1986
[6]

(b) The ratio 1 80dryrr  as a function of RH based on Gerber (1985). Fig

work page 1985
[7]

Power -law relationship of the SSA size spectral components: (a) Volume dV/dlnr; (b) Number dN/dlnr; (c) Flux computed with (3) and (4): dFis/dlnr; and (d) Flux computed with (5) and (6): dFU/dlnr. Fig. 9: The coefficients of power -law empirical model functions for the SSA size spectra, 1 / lndN d r , / lndV d r , / lnisdF d r , and / lnUdF d r : (a) pro...

work page 1986
[8]

(b) The ratio 1 80dryrr  as a function of 2 RH based on Gerber (1985). 3 4

work page 1985

[1] [1]

D., 2008: Complex relative permittivity of sea foam at microwave frequencies

Anguelova, M. D., 2008: Complex relative permittivity of sea foam at microwave frequencies. J. Geophys. Res., 113, C08001, doi:10.1029/2007JC004212. Bock, E. J., T. Hara, N. M. Frew, and W. R. McGillis, 1999: Relationship between air ‐sea gas transfer and short wind waves. J. Geophys. Res., 104, 25821-25831. Buckingham, M. J., and J. R. Potter (eds.), 199...

work page doi:10.1029/2007jc004212 2008

[2] [2]

Monahan, E. C., D. E. Spiel, and K. L. Davidson, 1986: A model of marine aerosol generation via whitecaps and wave disruption. In Oceanic whitecaps and their role in air -sea exchange processes, edited by E. C. Monahan and G. MacNiocaill, 167– 174, D. Reidel, Norwell, Mass. O’Dowd, C. D., and G. de Leeuw, 2007: Marine aerosol production: a review of the c...

work page doi:10.1029/2011jd017379 1986

[3] [3]

JPO/31Oct2014 44 FLIPexptR0Noline.docx Prosperetti, A., L. A. Crum, and H. C. Pumphrey, 1989: The underwater noise of rain. J. Geophy. Res., 94, 3255-3259. Pumphrey, H. C., 1989: Sources of ambient noise in the ocean: An experimental investigation. Ph D dissertation, Univ. Miss., 96 pp. Pumphrey, H. C., and P. A. Elmore, 1990: The entrainment of bubbles b...

work page 1989

[4] [4]

T., 1974: Microwave radiometer measurements of the Cape Cod Canal

Swift, C. T., 1974: Microwave radiometer measurements of the Cape Cod Canal . Radio Sci., 9, 641-653. Tang, I. N., A. C. Tridico, and K. H. Fung, 1997: Thermodynamic and optical properties of sea salt aerosol. J. Geophys. Res., 102, 23269-23275. Ulaby, F., R. Moore, and A. Fung, 1981: Microwave remote sensing: active and passive, Microwave Remote Sensing ...

work page 1974

[5] [5]

Power -law relationship of the SSA size spectral components: (a) Volume dV/dlnr; (b) Number dN/dlnr; (c) Flux computed with (3) and (4): dFis/dlnr; and (d) Flux computed with (5) and (6): dFU/dlnr. Fig. 9: The coefficients of power -law empirical model functions for the SSA size spectra, / lndN d r , / lndV d r , / lnisdF d r , and / lnUdF d r : (a) propo...

work page 1986

[6] [6]

(b) The ratio 1 80dryrr  as a function of RH based on Gerber (1985). Fig

work page 1985

[7] [7]

Power -law relationship of the SSA size spectral components: (a) Volume dV/dlnr; (b) Number dN/dlnr; (c) Flux computed with (3) and (4): dFis/dlnr; and (d) Flux computed with (5) and (6): dFU/dlnr. Fig. 9: The coefficients of power -law empirical model functions for the SSA size spectra, 1 / lndN d r , / lndV d r , / lnisdF d r , and / lnUdF d r : (a) pro...

work page 1986

[8] [8]

(b) The ratio 1 80dryrr  as a function of 2 RH based on Gerber (1985). 3 4

work page 1985