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Whitecaps, sea-salt aerosols, and climate

Dissertation defense 5 June, 2002. Whitecaps, sea-salt aerosols, and climate. Magdalena D. Anguelova. Advisor: Prof. F. Webster. College of Marine Studies, University of Delaware. Outline. Background and objectives; Work and results:

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Whitecaps, sea-salt aerosols, and climate

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  1. Dissertation defense 5 June, 2002 Whitecaps, sea-salt aerosols, and climate Magdalena D. Anguelova Advisor: Prof. F. Webster College of Marine Studies, University of Delaware

  2. Outline • Background and objectives; • Work and results: • Whitecap coverage estimation; • Whitecap coverage database; • Generation of sea-salt aerosols; • Conclusions.

  3. The challenge in climate models

  4. Aerosol effects IPCC, 2001

  5. Aerosol radiative forcing • Defined as… • Assessment: Effect of anthropogenic aerosols = • Effect of all aerosols – Effect of natural aerosols natural

  6. Natural aerosols; Baseline of an unperturbed atmosphere. Background atmosphere

  7. Background baseline • Natural aerosols; • Baseline of an unperturbed atmosphere. Sea-salt aerosols Sea-salt aerosols are the dominant aerosol species in background atmosphere.

  8. Climate effects of sea-salt aerosols -- cooling. • Direct effect • Indirect effect: • Halogen chemistry: • Dominate the activation of CCN; • Compete with SO42-aerosols. • Reactive Cl and Br; • Tropospheric O3: • Sink of S.

  9. Motivation Sea-salt aerosol effects must be accounted for in climate models.

  10. Formation of sea-salt aerosols • Sea spray; • Droplet sizes: • 0.5-500 m; • < 20 m; • Sea-salt aerosols: • Phase state; • Sizes: 0.025 to 20 m.

  11. Modeling sea-salt aerosols Generation • Generation; • Transport; • Diffusion and convection; • Chemical and physical transformations: • in clear air; • in clouds; • below clouds; • Wet and dry deposition.

  12. (Monahan and O’Muircheartaigh, 1980) Explicit forms for 4 size regions covering 1.6 to 500 m range. Andreas (2001) Sea spray generation function Rate of production of sea spray per unit area per increment of droplet radius, r (s-1 m-2m-1). Best

  13. r0 1.6 m Measurements (Monahan and O’Muircheartaigh, 1986) W(U10, T, Ts , S, f , d , C) ) ,Ts ,f ,C) ,S ,d Improved generation function? 0.1 , Ts , f , C) , T , S , d Whitecap coverage

  14. Need for a database W (U10 , T, Ts , S, f , d , C) 10 2 16 11 15 12 4 13 7 6 5 1 3 9 14

  15. 307 points 477 points Need of a database W (U10 , T, Ts , S, f , d , C) New method 10 2 16 11 15 12 4 13 7 6 5 1 3 9 14

  16. Objectives • Whitecap coverage estimation • Whitecap coverage database • Generation of sea-salt aerosols • Develop a method for global estimates • Build; • Examine spatial and temporal characteristics; • Evaluate climate effects; • Parameterize W(Ts) and W(S); • Modify the existing generation function; • Estimate sea-salt aerosol loading; • Investigate spatial and temporal characteristics; • Evaluate contribution to climate processes.

  17. Outline • Background and objectives; • Work and results: • Whitecap coverage estimation; • Whitecap coverage database; • Generation of sea-salt aerosols; • Conclusions.

  18. Whitecap coverage estimation Vis UV IR mW Whitecaps signature TB Reflectivity Emissivity High Emissivity High Reflectivity

  19. Whitecap coverage estimation • Emissivity of foam-free ocean is low. e – es – er W = ef – es – er Method concept e  as W  • Emissivity of foam-covered ocean is high. • Ocean emissivity is composite e = (es + er)(1-W ) + W ef

  20. Whitecap coverage estimation TB, V, L SSM/I Radiative transfer equation AVHRR NOAA Ts, S Fresnel formula, Debye equation Ts, S Fresnel formula, empirical relation SSM/I AVHRR U10, Ts Empirical relation The task: calculate emissivities • Composite emissivity e: • Specular emissivity es: • Foam emissivity ef : • Roughness correction Der:

  21. Whitecap coverage estimation Valid estimation of W e < es + er W < 0 2 – 10 %

  22. Whitecap coverage estimation Error of W

  23. Whitecap coverage estimation Method accuracy Count Relative error, W/W (%)

  24. Whitecap coverage estimation Whitecap coverage 27 March 1998

  25. Whitecap coverage estimation Validation with in situ data

  26. Whitecap coverage estimation Validation with in situ data • Magnitude; • Trend: • Suppression at high winds; • Enhancement at moderate winds. • Variability!

  27. Outline • Background and objectives; • Work and results: • Whitecap coverage estimation; • Whitecap coverage database; • Generation of sea-salt aerosols; • Conclusions.

  28. Whitecap coverage database Database • Content: • Daily and monthly estimates of W and W for the entire 1998; • Collocated measurements of U10, Ts, S; Use: • Investigate spatial and temporal characteristics of global whitecap coverage; • Evaluate whitecap contribution to climate processes. • Parameterize effects of additional factors on whitecaps;

  29. Same magnitude; Different spatial features: More uniform; 3% instead of 1%. Whitecap coverage database • W U103 Spatial distribution March 1998

  30. Whitecap coverage database Wind speed,U10 (m s-1) Sea surface temperature,Ts(oC) Effects of additional factors March 1998 • Wind fetch and duration; • Surface-active material.

  31. Whitecap coverage database W(U10, Ts) W(U10) W(U10, T, Ts , S, f , d , C) Parameterization of W W(U10, T, Ts , S, f , d , C) Realism

  32. Whitecap coverage database W(U10) = aUb W(U10) = aebU 10 10 W(U10 ,Ts) = a(Ts) Ub(T) W(U10 ,Ts) = a(Ts) eb(T)U s 10 s 10 Parameterization approach • Regression analysis: • Best fit of a function to data; • Least-square method for regression coefficients. • Implementation steps:

  33. Whitecap coverage database Data binning and averaging

  34. Whitecap coverage database Regression application

  35. Whitecap coverage database New method W(U10) New method W(U10) Exp law model Results for W(U10 ,Ts) a(Ts), b(Ts) W(U10 ,Ts) = a(Ts) eb(T )U s 10

  36. Outline • Background and objectives; • Work and results: • Whitecap coverage estimation; • Whitecap coverage database; • Generation of sea-salt aerosols; • Conclusions.

  37. Generation of sea-salt aerosols Improved generation function? r0 1.6 m 0.1 Measurements (Monahan and O’Muircheartaigh, 1986) W(U10, T, Ts , S, f , d , C) W(U10)

  38. Generation of sea-salt aerosols Modified generation function   m Andreas, 2001   m Monahan et al., 1986   m Future work Assimilating new method estimates

  39. Current function (Andreas, 2001) Measurements (Smith et al., 1993) Generation of sea-salt aerosols New method Wind formula Function performance

  40. Generation of sea-salt aerosols Ts in places with U10 = 10 m s-1 Ts in places with U10 = 15 m s-1 Sea surface temperature, TsoC Sea-salt aerosol loading • Magnitude; • Weak wind dependence;

  41. Generation of sea-salt aerosols Haywood et al., 1999 Model - Experiment Spatial distribution of sea-salt 1105410571051106 Number flux, dF/dr0 (# m-1 m-2 s-1) Direct effect: 15 W m-2

  42. Outline • Background and objectives; • Work and results: • Whitecap coverage estimation; • Whitecap coverage database; • Generation of sea-salt aerosols; • Conclusions.

  43. Conclusions Whitecap coverage estimation • A new method developed for estimating whitecap coverage from satellite data; • Advantages of the new method: • Objective stand-alone algorithm; • Global scale estimates; • Various meteorological conditions; • New method accuracy: • Comparable or better than that of in situ photos; • Rel. error < 30% for half of the estimates; • Improvements of the method possible.

  44. Conclusions Whitecap coverage database • Extensive database of W withW and collocated measurements of U10, Ts, S built; • Global whitecap coverage: • Average 3%; • More uniform latitudinal distribution; • Strong effect of Ts; • Parameterization of Ts effect: • Exponential-law model performs best; • W(U10 ,Ts) is better predictor than W(U10); • Not all variability of W predicted;

  45. Conclusions Generation of sea-salt aerosols • Modified generation function: • Accounts for additional factors; • Applicability extended to 0.4 m; • Consistent performance; • Sea-salt aerosols loading: • Realistic; • Spatial distribution consistent with other models; • Climate effects evaluated.

  46. Acknowledgement • Dr. Ferris Webster • Dr. Yan, Dr. Church, Dr. Andreas • Doug White and colleagues • Faculties of CMS • Doris, Debby, and Peggy • Dorothy and Ellen • Students! • Mom, dad, brother • Andrey and Constantin

  47. ????? ?????

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