Transcript Sea Spray Generation Functions Environmental Processes

AeroCenter Seminar
Goddard Space Flight Center, NASA
5 October, 2010
Oceanic whitecaps
as progenitors of sea-spray aerosol:
Measurements, variability and parameterizations
Magdalena D. Anguelova
Michael H. Bettenhausen
William F. Johnston
Peter W. Gaiser
Remote Sensing Division, Naval Research Laboratory
Washington, DC
Outline
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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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5 October
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Motivation
Sea-spray aerosols
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Direct effect – cooling
Indirect effect
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Dominate the activation of CCN
Compete with SO42- aerosols
Whitecaps
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Halogen chemistry
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Gas exchange
Ocean albedo & roughness
Geophysical retrievals
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Reactive Cl and Br
Tropospheric O3
Sink of S
Surface wind
Ocean color
Salinity
Photo courtesy of C. Fairall
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Sea spray
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Heat exchange
Tropical storm intensification
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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).
dF r, a, b,...
 f(U )  f( r )
dr
Scaling factor Size distribution
6
f (U )  W (U10 )  3.8 10 U
d f r80 
f (r ) 
d log 10 r80
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From photographic
measurements
(Monahan and O’Muircheartaigh, 1980)
From measurements using
various methods
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Possible improvements
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Keene et al
de Leeuw et al
For the size distribution
Recognize the effect of organics
 Extend the size range, large and small ends
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1 μm  r80  25 μm
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0.1 μm  r80  250 μm
Introduce ambient factors
f (r )  f (r , a, b...)
For the scaling factor
Less uncertainty in measuring W
 Introduce ambient factors W (U )  f (U , a, b...)
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Whitecaps and sea-spray aerosols
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Outline
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Sea-spray aerosol in climate models
Whitecaps measurements
Remote sensing of whitecaps
Whitecap database
Whitecap variability
Whitecaps in sea spray source function
AeroCenter Seminar
5 October
Whitecaps and sea-spray aerosols
Anguelova et al., NRL
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Sea foam definition
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Oceanographic definition:
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Whitecaps on the surface;
Bubble plumes below.
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Remote-sensing definition
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Skin depth
At microwave frequencies:
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Whitecaps and sea-spray aerosols
a few mm to a few cm;
Radiometers detect only the
surface foam layers
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Photographic measurements
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Intensity threshold;
A and B stages in oblique view
High uncertainty:
Up to 30%;
 Higher
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Stramska and Petelski, 2003
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Patchy representation
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60
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30
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0
-30
-60
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-90
-180
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-60
0
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120
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Range of conditions
W (U10 )
W (U10 , T )
W (U10 , Ts )
477 points
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5 October
307 points
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Natural variability
Various whitecap coverage parametrizations
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Whitecap coverage,W (%)
10
1
0.1
0.01
0.001
0.0001
0
5
10
15
20
25
30
Wind speed, U 10 (m s-1 )
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Monahan '71
Wilheit '79
M&O'M '80 RBF
M&O'M '80 OLS
Bondur&Sharkov'82 A
Bondur&Sharkov'82 B
Pandey&Kakar 82
Monahan et al. '83
Spillane et al'86 cold
Spillane et al'86 moder.
Spillane et al'86 warm
M&O'M 86 dT=0 (neutral)
Bortk'87, A+B, cold
Bortk'87, A+B, moder
Bortk'87, A+B, warm
Wu '88
Mon&Woolf'89, A
Monhan'93 visc., A
Monhan'93 visc., B
Asher&Wann'98, A
Hanson&Phillips'99, no <<
Hanson&Phillips'99, all meas
Asher et al.'02
Reising et al. '02, A
Wentz '02 Hpol
Wentz '02 Vpol
Stram&Petel'03 tot
Stram&Petel'03 dev.
Stram&Petel'03 undev.
Villarino et al '03, stable
Villarino et al '03, unst.
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Objective
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Model the high variability of foam fraction
W U 
W U , T , X , d ,U cur , Ts , S , C 
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: W U10 , T , X , d ,U cur , Ts , S , C 
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Improve existing or develop new models
Investigate correlations
Extensive database: W + various factors
Measurements: W + various factors
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Existing W measurements
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Photographs/video images
Insufficient for extensive database
Alternative approach: From satellites to get
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Global coverage
Wide range of meteo & environ conditions
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Outline
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Sea-spray aerosol in climate models
Whitecaps measurements
Remote sensing of whitecaps
Whitecap database
Whitecap variability
Whitecaps in sea spray source function
AeroCenter Seminar
5 October
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Whitecaps signature
High Reflectivity Reflectivity
Emissivity
Vis
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IR
Whitecaps and sea-spray aerosols
High
Emissivity
mW
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Remote sensing of sea foam
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Microwave region
Advantages
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TB  e Ts
Transparent (almost) atmosphere
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e  1  W  er  W e f
“...4% problem ...at 5 GHz,..., 90% problem at IR” (Swift, 1990)
Tractable atmospheric correction
Clouds penetration
Drawback
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Low resolution
Smoother geophysical variability
Trade-off in obtaining more data
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Rough sea surface model
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2-scale
Wave spectrum
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e  1  W  er  W e f
Durden/Vesecky/Yueh
Tuned for roughness only
Using WindSat code (v. 1.9.6)
© P.R.Hemington
z=0
Foam emissivity model
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Air, ε0=1
RT model
Layer with vertically non-uniform
properties
Distribution of thicknesses
Foam, ε (z)
Courtesy of Prof. Cilliers
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www.pbase.com/petehem/
Models
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Water, ε
z = -d
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Data
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Independent sources
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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
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GDAS (6-hr analyses)
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Only 4 full swaths
Large time differences
QuikSCAT
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Chunks of swaths
Asc/desc passes opposite
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Sample count
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Estimates of W
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Improvements over the feasibility study (Anguelova and
Webster, 2006):
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More physical models
Independence of the variables
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Validation
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Insufficient in situ values
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Data collection
Slow and expensive
 Sporadic and non-systematic
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Limited range of conditions
 Fewer in situ-satellite matches in time and space
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Different principles of measurement
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Visible photography vs microwave radiometry
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Various validation approaches
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Further to do
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Models
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Higher resolution
Improved wave spectrum in 2-scale model
Validation
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More points for direct validation
Indirect validation in terms of other variables
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CO2 fluxes from ship cruises
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AOD from AERONET
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and COARE CO2 parameterization
and AOD from microphysical aerosol model
Uncertainty characterization
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Currently uncertainty minimization
Evaluate the remaining using GOCART?
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5 October
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Anguelova et al., NRL
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Outline
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Sea-spray aerosol in climate models
Whitecaps measurements
Remote sensing of whitecaps
Whitecap database
Whitecap variability
Whitecaps in sea spray source function
AeroCenter Seminar
5 October
Whitecaps and sea-spray aerosols
Anguelova et al., NRL
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Whitecaps data base
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All available orbits for
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Low resolution (50×70 km2)
Time period
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Entire 2006
Months of 2003, 2007 and 2008
Gridding data
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With 0.5 x 0.5 grid box
Any other N x N possible
Time periods:
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Daily;
Monthly
Weekly (7 days)
3-days
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Other factors besides W
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6 additional variables
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Wind speed (U10)
Wind direction ()
Sea surface temperature (Ts )
Air temperature @ 2 m (Ta )
Wave field
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Significant wave height (Hs)
Mean wave period (Tp)
Mar 2006
Various sources
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Other satellites (QuikSCAT)
Models
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GDAS
NWW3
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Derived environmental factors
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Mar 2006
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Atmospheric stability
proxy T  Ta  Ts 
Fetch X  g H s U10 2
Fetch, X (km)
Stability, T (C)
Mar 2006
T > 0 | Stable | Reduced mixing
T < 0 | Unstable | Increased mixing
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Further to do
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Wave field data from satellites
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Matched buoy data
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Independent
Regional features
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Outline
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Sea-spray aerosol in climate models
Whitecaps measurements
Remote sensing of whitecaps
Whitecap database
Whitecap variability
Whitecaps in sea spray source function
AeroCenter Seminar
5 October
Whitecaps and sea-spray aerosols
Anguelova et al., NRL
28 of 44
Geographic characteristics of W
March, 2007
0.5 x 0.5
Wind speed formula
W  U103
Satellite, 10.7 GHz, H pol.
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Seasonal variations of W
37H
Dec-Jan-Feb
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Seasonal variations of W
37H
Mar-Apr-May
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Seasonal variations of W
37H
Jun-Jul-Aug
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Seasonal variations of W
37H
Sep-Oct-Nov
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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
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Sea-spray aerosol in climate models
Whitecaps measurements
Remote sensing of whitecaps
Whitecap database
Whitecap variability
Whitecaps in sea-spray source function
AeroCenter Seminar
5 October
Whitecaps and sea-spray aerosols
Anguelova et al., NRL
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Use W estimates directly
Annual whitecap coverage (1998)
Annual sea spray flux (1998)
Number
flux, dF
(s-1 m-2) W
Whitecap
coverage,
Whitecap coverage, W
(Anguelova and Webster, 2006)
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Sea-salt flux
Annual sea-salt flux (1998)
2105
4105
6105
Haywood et al., Science, 1999
8105
Number flux, dF (s-1 m-2 )
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5 October
Solar irradiance at TOA
(W/m2): GCM – ERBE
NO aerosols;
Max difference over the
oceans.
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Develop parameterization(s)
W(U)
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W(U, T, X, d, Ucur, Ts, S, C )
Relative importance of the variables
Investigate with
Correlation analysis
 Principal component analysis
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Correlation maps
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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
W vs T
W vs U10
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W vs X
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Factors contributing to W variance
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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
Wind
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Fetch
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SST
Stability Wave height Wave period
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New sea-spray source function
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Include wave-field characteristics and one
more factor W U , H 
s
W U , H s , Ts 
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Choose a size distribution
Include organics (O’Dowd et al)
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5 October
Whitecaps and sea-spray aerosols
Anguelova et al., NRL
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