Magdalena D. Anguelova

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Oceanic whitecaps as progenitors of sea-spray
aerosol Measurements, variability and
parameterizations
AeroCenter Seminar Goddard Space Flight Center,
NASA 5 October, 2010

• Magdalena D. Anguelova
• Michael H. Bettenhausen
• William F. Johnston
• Peter
  W. Gaiser

Remote Sensing Division, Naval Research
Laboratory Washington, DC
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Outline

• Sea-spray aerosol in climate models
• Whitecaps measurements
• Remote sensing of whitecaps
• Whitecap database
• Whitecap variability
• Whitecaps in sea spray source function

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Motivation

• Sea-spray aerosols
• Direct effect cooling
• Indirect effect
• Dominate the activation of CCN
• Compete with SO42- aerosols
• Halogen chemistry
• Reactive Cl and Br
• Tropospheric O3
• Sink of S
• Sea spray
• Heat exchange
• Tropical storm intensification

• Whitecaps
• Gas exchange
• Ocean albedo roughness
• Geophysical retrievals
• Surface wind
• Ocean color
• Salinity

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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).
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Possible improvements

• For the size distribution
• Recognize the effect of organics
• Extend the size range, large and small ends
• Introduce ambient factors
• For the scaling factor
• Less uncertainty in measuring W
• Introduce ambient factors

Keene et al de Leeuw et al
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Outline

• Sea-spray aerosol in climate models
• Whitecaps measurements
• Remote sensing of whitecaps
• Whitecap database
• Whitecap variability
• Whitecaps in sea spray source function

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Sea foam definition

• Remote-sensing definition
• Skin depth
• At microwave frequencies
• a few mm to a few cm
• Radiometers detect only the surface foam layers

• Oceanographic definition
• Whitecaps on the surface
• Bubble plumes below.

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Photographic measurements

• Intensity threshold
• A and B stages in oblique view
• High uncertainty
• Up to 30
• Higher

Stramska and Petelski, 2003
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Patchy representation

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Range of conditions
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Natural variability
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Objective

• Model the high variability of foam fraction

U wind speed (U10 or u) ?T atmospheric
stability ( Tair Tsea) X wind fetch d
wind duration Ucur water currents Ts sea
surface temperature S salinity Ck
concentration, type (k) of surface active
materials
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Framework
Whitecap variability

• Improve existing or develop new models
• Investigate correlations
• Extensive database W various factors
• Measurements W various factors
• Existing W measurements
• Photographs/video images
• Insufficient for extensive database
• Alternative approach From satellites to get
• Global coverage
• Wide range of meteo environ conditions

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Outline

• Sea-spray aerosol in climate models
• Whitecaps measurements
• Remote sensing of whitecaps
• Whitecap database
• Whitecap variability
• Whitecaps in sea spray source function

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Whitecaps signature
High Reflectivity
High Emissivity
Reflectivity Emissivity
Vis
IR
mW
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Remote sensing of sea foam

• Microwave region
• Advantages
• Transparent (almost) atmosphere
• ...4 problem ...at 5 GHz,..., 90 problem at
  IR (Swift, 1990)
• Tractable atmospheric correction
• Clouds penetration
• Drawback
• Low resolution
• Smoother geophysical variability
• Trade-off in obtaining more data

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Models

• Rough sea surface model
• 2-scale
• Wave spectrum
• Durden/Vesecky/Yueh
• Tuned for roughness only
• Using WindSat code (v. 1.9.6)
• Foam emissivity model
• RT model
• Layer with vertically non-uniform properties
• Distribution of thicknesses

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Data

• Independent sources
• TB from WindSat
• V, L from SSM/I or TMI
• U10 and ? from QuikSCAT or GDAS
• Ts from GDAS
• S 34 psu
• Trade-off Sampling issues
• GDAS (6-hr analyses)
• Only 4 full swaths
• Large time differences
• QuikSCAT
• Chunks of swaths
• Asc/desc passes opposite

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Estimates of W

• Improvements over the feasibility study
  (Anguelova and Webster, 2006)
• More physical models
• Independence of the variables

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Validation

• Insufficient in situ values
• Data collection
• Slow and expensive
• Sporadic and non-systematic
• Limited range of conditions
• Fewer in situ-satellite matches in time and space
• Different principles of measurement
• Visible photography vs microwave radiometry

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Various validation approaches
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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?

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Outline

• Sea-spray aerosol in climate models
• Whitecaps measurements
• Remote sensing of whitecaps
• Whitecap database
• Whitecap variability
• Whitecaps in sea spray source function

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Whitecaps data base

• All available orbits for
• Low resolution (5070 km2)
• Time period
• Entire 2006
• Months of 2003, 2007 and 2008
• Gridding data
• With 0.5? x 0.5? grid box
• Any other N? x N? possible
• Time periods
• Daily
• Monthly
• Weekly (7 days)
• 3-days

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Other factors besides W

• 6 additional variables
• Wind speed (U10)
• Wind direction (?)
• Sea surface temperature (Ts )
• Air temperature _at_ 2 m (Ta )
• Wave field
• Significant wave height (Hs)
• Mean wave period (Tp)
• Various sources
• Other satellites (QuikSCAT)
• Models
• GDAS
• NWW3

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Derived environmental factors

• Atmospheric stability proxy
• Fetch

Fetch, X (km)
Mar 2006
?T gt 0 Stable Reduced mixing ?T lt 0
Unstable Increased mixing
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Further to do

• Wave field data from satellites
• Matched buoy data
• Independent
• Regional features

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Outline

• Sea-spray aerosol in climate models
• Whitecaps measurements
• Remote sensing of whitecaps
• Whitecap database
• Whitecap variability
• Whitecaps in sea spray source function

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Geographic characteristics of W
March, 2007 0.5? x 0.5?
Wind speed formula
Satellite, 10.7 GHz, H pol.
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Seasonal variations of W
37H
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Seasonal variations of W
37H
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Seasonal variations of W
37H
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Seasonal variations of W
37H
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Spatial and temporal variations
Every 5th day in March 2006
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Spatial and temporal variations
Every 5th day in July 2006
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Spatial and temporal variations
Every 5th day in November 2006
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Outline

• Sea-spray aerosol in climate models
• Whitecaps measurements
• Remote sensing of whitecaps
• Whitecap database
• Whitecap variability
• Whitecaps in sea-spray source function

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Use W estimates directly
Annual whitecap coverage (1998)
Annual sea spray flux (1998)
Number flux, dF (s-1 m-2)
Whitecap coverage, W
Whitecap coverage, W
(Anguelova and Webster, 2006)
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Sea-salt flux
Annual sea-salt flux (1998)
Haywood et al., Science, 1999
2?105 4?105 6?105
8?105 Number flux, dF (s-1 m-2 )

• Solar irradiance at TOA (W/m2) GCM ERBE
• NO aerosols
• Max difference over the oceans.

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Develop parameterization(s)

• Relative importance of the variables
• Investigate with
• Correlation analysis
• Principal component analysis

W(U)
W(U, ?T, X, d, Ucur, Ts, S, C )
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Correlation maps

• Time series of W and each factor x
• x U, Hs, ?T, Ts, X, Tp,?
• For each 0.5??0.5? grid box
• Find r for each W-x pair

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Factors contributing to W variance

• In each correlation map
• Check stat significance of r for each W-x pair
• If r is stat significant, get coefficient of
  determination (r2)
• In each grid box take the factor with the max(r2)
  besides that for U
• Color-code each contributing factor

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Contributions to W variance
Monthly data, correlations on up to 12 data points
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New sea-spray source function

• Include wave-field characteristics and one more
  factor
• Choose a size distribution
• Include organics (ODowd et al)

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