1. Magdalena D. Anguelova Michael H. Bettenhausen Michael H. Bettenhausen William F. Johnston William F. Johnston Peter W. Gaiser Peter W. Gaiser Oceanic whitecaps as progenitors of sea-spray aerosol: Measurements, variability and parameterizations AeroCenter Seminar Goddard Space Flight Center, NASA 5 October, 2010 Remote Sensing Division, Naval Research Laboratory Washington, DC

2. Outline Sea-spray aerosol in climate models Sea-spray aerosol in climate models Whitecaps measurements Whitecaps measurements Remote sensing of whitecaps Remote sensing of whitecaps Whitecap database Whitecap database Whitecap variability Whitecap variability Whitecaps in sea spray source function Whitecaps in sea spray source function AeroCenter Seminar 5 October2 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

3. Motivation Sea-spray aerosols Sea-spray aerosols Direct effect – cooling Direct effect – cooling Indirect effect Indirect effect Dominate the activation of CCN Dominate the activation of CCN Compete with SO 4 2- aerosols Compete with SO 4 2- aerosols Halogen chemistry Halogen chemistry Reactive Cl and Br Reactive Cl and Br Tropospheric O 3 Tropospheric O 3 Sink of S Sink of S Sea spray Sea spray Heat exchange Heat exchange Tropical storm intensification Tropical storm intensification Whitecaps Gas exchange Ocean albedo & roughness Geophysical retrievals Surface wind Ocean color Salinity Photo courtesy of C. Fairall AeroCenter Seminar 5 October3 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

4. Sea spray source function Rate of production of sea spray per unit area per increment of droplet radius, r (s -1 m -2  m -1 ). From photographic measurements (Monahan and O’Muircheartaigh, 1980) From measurements using various methods Size distribution Scaling factor AeroCenter Seminar 5 October4 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

5. Possible improvements For the size distribution For the size distribution Recognize the effect of organics Recognize the effect of organics Extend the size range, large and small ends Extend the size range, large and small ends Introduce ambient factors Introduce ambient factors For the scaling factor For the scaling factor Less uncertainty in measuring W Less uncertainty in measuring W Introduce ambient factors Introduce ambient factors Keene et al de Leeuw et al AeroCenter Seminar 5 October5 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

6. Outline Sea-spray aerosol in climate models Sea-spray aerosol in climate models Whitecaps measurements Whitecaps measurements Remote sensing of whitecaps Remote sensing of whitecaps Whitecap database Whitecap database Whitecap variability Whitecap variability Whitecaps in sea spray source function Whitecaps in sea spray source function AeroCenter Seminar 5 October6 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

7. Sea foam definition Oceanographic definition: Oceanographic definition: Whitecaps on the surface; Whitecaps on the surface; Bubble plumes below. Bubble plumes below. Remote-sensing definition Remote-sensing definition Skin depth Skin depth At microwave frequencies: At microwave frequencies: a few mm to a few cm; a few mm to a few cm; Radiometers detect only the surface foam layers Radiometers detect only the surface foam layers AeroCenter Seminar 5 October7 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

8. Photographic measurements Intensity threshold; Intensity threshold; A and B stages in oblique view A and B stages in oblique view High uncertainty: High uncertainty: Up to 30%; Up to 30%; Higher Higher Stramska and Petelski, 2003 AeroCenter Seminar 5 October8 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

9. Patchy representation                    AeroCenter Seminar 5 October9 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

10. Range of conditions 307 points 477 points AeroCenter Seminar 5 October10 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

11. Natural variability AeroCenter Seminar 5 October11 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

12. Objective Model the high variability of foam fraction Model the high variability of foam fraction U – wind speed (U 10 or u * )  T – atmospheric stability (= T air – T sea ) X – wind fetch d – wind duration U cur – water currents T s – sea surface temperature S – salinity C k – concentration, type (k) of surface active materials AeroCenter Seminar 5 October12 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

13. Framework Improve existing or develop new models Improve existing or develop new models Investigate correlations Investigate correlations Extensive database: W + various factors Extensive database: W + various factors Measurements: W + various factors Measurements: W + various factors Existing W measurements Existing W measurements Photographs/video images Photographs/video images Insufficient for extensive database Insufficient for extensive database Alternative approach: From satellites to get Alternative approach: From satellites to get Global coverage Global coverage Wide range of meteo & environ conditions Wide range of meteo & environ conditions Whitecap variability: AeroCenter Seminar 5 October13 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

14. Outline Sea-spray aerosol in climate models Sea-spray aerosol in climate models Whitecaps measurements Whitecaps measurements Remote sensing of whitecaps Remote sensing of whitecaps Whitecap database Whitecap database Whitecap variability Whitecap variability Whitecaps in sea spray source function Whitecaps in sea spray source function AeroCenter Seminar 5 October14 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

15. Whitecaps signature High Reflectivity High Emissivity ReflectivityEmissivity VisIRmW AeroCenter Seminar 5 October15 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

16. Remote sensing of sea foam Microwave region Microwave region Advantages Advantages Transparent (almost) atmosphere Transparent (almost) atmosphere “...4% problem...at 5 GHz,..., 90% problem at IR” (Swift, 1990) “...4% problem...at 5 GHz,..., 90% problem at IR” (Swift, 1990) Tractable atmospheric correction Tractable atmospheric correction Clouds penetration Clouds penetration Drawback Drawback Low resolution Low resolution Smoother geophysical variability Smoother geophysical variability Trade-off in obtaining more data Trade-off in obtaining more data AeroCenter Seminar 5 October16 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

17. Models Rough sea surface model Rough sea surface model 2-scale 2-scale Wave spectrum Wave spectrum Durden/Vesecky/Yueh Durden/Vesecky/Yueh Tuned for roughness only Tuned for roughness only Using WindSat code (v. 1.9.6) Using WindSat code (v. 1.9.6) Foam emissivity model Foam emissivity model RT model RT model Layer with vertically non-uniform properties Layer with vertically non-uniform properties Distribution of thicknesses Distribution of thicknesses www.pbase.com/petehem/ © P.R.Hemington z = 0 z = -d Air, ε 0 =1 Water, ε Foam, ε (z) Courtesy of Prof. Cilliers AeroCenter Seminar 5 October17 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

18. Data Independent sources Independent sources T B from WindSat T B from WindSat V, L from SSM/I or TMI V, L from SSM/I or TMI U 10 and from QuikSCAT or GDAS U 10 and  from QuikSCAT or GDAS T s from GDAS T s from GDAS S = 34 psu S = 34 psu Trade-off: Sampling issues Trade-off: Sampling issues GDAS (6-hr analyses) GDAS (6-hr analyses) Only 4 full swaths Only 4 full swaths Large time differences Large time differences QuikSCAT QuikSCAT Chunks of swaths Chunks of swaths Asc/desc passes opposite Asc/desc passes opposite Sample count AeroCenter Seminar 5 October18 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

19. Estimates of W Improvements over the feasibility study (Anguelova and Webster, 2006): More physical models Independence of the variables AeroCenter Seminar 5 October19 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

20. Validation Insufficient in situ values Insufficient in situ values Data collection Data collection Slow and expensive Slow and expensive Sporadic and non-systematic Sporadic and non-systematic Limited range of conditions Limited range of conditions Fewer in situ-satellite matches in time and space Fewer in situ-satellite matches in time and space Different principles of measurement Different principles of measurement Visible photography vs microwave radiometry Visible photography vs microwave radiometry AeroCenter Seminar 5 October20 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

21. Various validation approaches AeroCenter Seminar 5 October21 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

22. Further to do Models Higher resolution Improved wave spectrum in 2-scale model Validation More points for direct validation Indirect validation in terms of other variables CO2 fluxes from ship cruises and COARE CO2 parameterization AOD from AERONET and AOD from microphysical aerosol model Uncertainty characterization Currently uncertainty minimization Evaluate the remaining using GOCART? AeroCenter Seminar 5 October22 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

23. Outline Sea-spray aerosol in climate models Sea-spray aerosol in climate models Whitecaps measurements Whitecaps measurements Remote sensing of whitecaps Remote sensing of whitecaps Whitecap database Whitecap database Whitecap variability Whitecap variability Whitecaps in sea spray source function Whitecaps in sea spray source function AeroCenter Seminar 5 October23 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

24. Whitecaps data base All available orbits for All available orbits for Low resolution (50×70 km 2 ) Low resolution (50×70 km 2 ) Time period Time period Entire 2006 Entire 2006 Months of 2003, 2007 and 2008 Months of 2003, 2007 and 2008 Gridding data Gridding data With 0.5  x 0.5  grid box With 0.5  x 0.5  grid box Any other N  x N  possible Any other N  x N  possible Time periods: Time periods: Daily; Daily; Monthly Monthly Weekly (7 days) Weekly (7 days) 3-days 3-days AeroCenter Seminar 5 October24 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

25. Other factors besides W 6 additional variables 6 additional variables Wind speed ( U 10 ) Wind speed ( U 10 ) Wind direction (  ) Wind direction (  ) Sea surface temperature ( T s ) Sea surface temperature ( T s ) Air temperature @ 2 m ( T a ) Air temperature @ 2 m ( T a ) Wave field Wave field Significant wave height ( H s ) Significant wave height ( H s ) Mean wave period ( T p ) Mean wave period ( T p ) Various sources Various sources Other satellites (QuikSCAT) Other satellites (QuikSCAT) Models Models GDAS GDAS NWW3 NWW3 Mar 2006 AeroCenter Seminar 5 October25 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

26. Derived environmental factors Mar 2006 Stability,  T () Stability,  T (  C ) Mar 2006 Fetch, X (km)  T > 0 | Stable | Reduced mixing  T < 0 | Unstable | Increased mixing Atmospheric stability proxy Atmospheric stability proxy Fetch Fetch AeroCenter Seminar 5 October26 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

27. Further to do Wave field data from satellites Matched buoy data Independent Regional features AeroCenter Seminar 5 October27 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

28. Outline Sea-spray aerosol in climate models Sea-spray aerosol in climate models Whitecaps measurements Whitecaps measurements Remote sensing of whitecaps Remote sensing of whitecaps Whitecap database Whitecap database Whitecap variability Whitecap variability Whitecaps in sea spray source function Whitecaps in sea spray source function AeroCenter Seminar 5 October28 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

29. Geographic characteristics of W March, 2007 0.5  x 0.5  Wind speed formula Satellite, GHz, H pol. Satellite, 10.7 GHz, H pol. AeroCenter Seminar 5 October29 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

30. Seasonal variations of W 37H Dec-Jan-Feb AeroCenter Seminar 5 October30 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

31. Seasonal variations of W Mar-Apr-May 37H AeroCenter Seminar 5 October31 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

32. Seasonal variations of W Jun-Jul-Aug 37H AeroCenter Seminar 5 October32 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

33. Seasonal variations of W Sep-Oct-Nov 37H AeroCenter Seminar 5 October33 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

34. Spatial and temporal variations Every 5th day in March 2006 AeroCenter Seminar 5 October34 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

35. Spatial and temporal variations Every 5th day in July 2006 AeroCenter Seminar 5 October35 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

36. Spatial and temporal variations Every 5th day in November 2006 AeroCenter Seminar 5 October36 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

37. Outline Sea-spray aerosol in climate models Sea-spray aerosol in climate models Whitecaps measurements Whitecaps measurements Remote sensing of whitecaps Remote sensing of whitecaps Whitecap database Whitecap database Whitecap variability Whitecap variability Whitecaps in sea-spray source function Whitecaps in sea-spray source function AeroCenter Seminar 5 October37 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

38. Use W estimates directly Whitecap coverage, W Number flux, dF (s -1 m -2 ) Annual sea spray flux (1998) Annual whitecap coverage (1998) (Anguelova and Webster, 2006) AeroCenter Seminar 5 October38 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

39. Sea-salt flux Solar irradiance at TOA (W/m 2 ) : GCM – ERBE Solar irradiance at TOA (W/m 2 ) : GCM – ERBE NO aerosols; NO aerosols; Max difference over the oceans. Max difference over the oceans. 2  10 5 4  10 5 6  10 5 8  10 5 2  10 5 4  10 5 6  10 5 8  10 5 Number flux, dF (s -1 m -2 ) Haywood et al., Science, 1999 Annual sea-salt flux (1998) AeroCenter Seminar 5 October39 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

40. Develop parameterization(s) Relative importance of the variables Relative importance of the variables Investigate with Investigate with Correlation analysis Correlation analysis Principal component analysis Principal component analysis W(U)W(U)W(U)W(U) W(U,, T s, S, C ) W(U,  T, X, d, U cur, T s, S, C ) AeroCenter Seminar 5 October40 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

41. Correlation maps W vs  T W vs XW vs U 10 Time series of W and each factor x Time series of W and each factor x x = {U, H s, T s, T p,  } x = {U, H s,  T, T s, X, T p,  } For each 0.5  0.5  grid box For each 0.5  0.5  grid box Find r for each W-x pair Find r for each W-x pair AeroCenter Seminar 5 October41 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

42. Factors contributing to W variance In each correlation map In each correlation map Check stat significance of r for each W-x pair Check stat significance of r for each W-x pair If r is stat significant, get coefficient of determination ( r 2 ) If r is stat significant, get coefficient of determination ( r 2 ) In each grid box take the factor with the max( r 2 ) besides that for U In each grid box take the factor with the max( r 2 ) besides that for U Color-code each contributing factor Color-code each contributing factor AeroCenter Seminar 5 October42 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

43. Contributions to W variance Monthly data, correlations on up to 12 data points AeroCenter Seminar 5 October43 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL

44. New sea-spray source function Include wave-field characteristics and one more factor Include wave-field characteristics and one more factor Choose a size distribution Choose a size distribution Include organics (O’Dowd et al) Include organics (O’Dowd et al) AeroCenter Seminar 5 October44 of 44Whitecaps and sea-spray aerosols Anguelova et al., NRL