1. Impacts of High Resolution SST Fields on Objective Analysis of Wind Fields
Mark A. Bourassa
Center for Ocean-Atmospheric Prediction Studies & Department of Earth, Ocean, and Atmospheric Science, The Florida State University, USA
With contributions from Paul Hughes and Ryan Maue, and with
examples form Darren Jackson, Gary Wick, Brent Roberts, Carol Anne Clayson,
Dudley Chelton, and Sarah Gille

2. Broad Goal Narrower Goal Specific Goal
Understand the SST, winds, fluxes and related variables associated with two-way air/sea interaction on short and long time scales
Understand the response and subsequent feedbacks in the atmospheric and oceanic boundary-layers
Narrower Goal
Demonstrate that fine resolution in space and time are important for many applications related to surface turbulent fluxes.
Specific Goal
Use high resolution (in space and time) SSTs to improve the quality of gridded ocean surface vector wind products in areas without satellite observations of winds.
Mark A. Bourassa

3. Many Air/Sea Interaction Processes - Winds and SSTs are related to many of these processes -
Graphic adapted from CBLAST
SOLAR RADIATION
Wind waves
Swell waves
SST
There are many air/sea interaction processes, most of which are modified by surface waves. Short-term processes include the turbulent transfer of momentum and energy, transfer of radiative energy and gasses, currents, and surface waves.
Mark A. Bourassa

4. Flux and Wind Accuracies Desired for Various Applications
50 Wm-2
100 years
10 Wm-2
5 Wm-2
Climate Change
10 years
1 Wm-2
Meridional Heat Transport
0.1 Wm-2
Ice Sheet Evolution
Annual Ice Mass Budget
0.01 Nm-2
ENSO
1 year
Annual Ocean Heat Flux
Unknown
Coastal Upwelling
Monsoon & Seasonal Prediction
Open Ocean Upwelling
Upper Ocean Heat Content & NH Hurricane Activity
1 month
Stress for CO2 Fluxes
Dense Water Formation
Mesoscale and shorter scale physical-biological Interaction
Storm Track
Ocean Eddies and Fronts
Atm. Rossby Wave Breaking
1 week
Polynyas
Ice Breakup
Shelf Processes
1 day
Inertial Forcing
Conv. Clouds & Precip
NWP High Impact Weather
1 hour
10m m km km km 103km km 105km
Mark A. Bourassa

5. Submonthly Contribution to Average LHF
L is determined through a bulk formula.
Where the overbar indicates a monthly average
There is considerable controversy about that accuracy of this averaging _
If we assume density variations are not important, this equation becomes
A more accurate approach is to calculate the flux at each time step then average these fluxes:
If we apply Reynolds averaging this equation becomes
Following examples of monthly biases are based on ECMWF reanalysis.
Plots bias from using monthly averaged flux input data
They do not include wave information
Mark A. Bourassa

6. Bias in Latent Heat Flux (Wm-2)
January
February
March
Bias in Monthly Latent Heat Flux
(1) latent heat flux determined from 6 hourly data and (2) latent heat flux determined from monthly averaged input
Monthly climatology computed for
Figures show: (1) minus (2)
Probably under-estimated for the Southern Ocean
April
May
June
July
August
September
October
November
December
Bias in Latent Heat Flux (Wm-2)
Mark A. Bourassa
Thanks to Paul Hughes and Ryan Maue

7. Schematic showing the observed relationship of wind stress curl (hashed) and divergence (stippled) to flow across an SST front (thick isotherm). (From Maloney and Chelton (2006)).
SST effects on wind stress divergence and curl. From Chelton et al (2004). Shown are binned scatter plots of spatial high-pass filtered fields of the wind stress divergence from QuikSCAT as a function of the downwind SST gradient (top row) from AMSR-E and the wind stress curl as a function of the crosswind SST gradient (bottom row) for four geographical regions: the Southern Ocean (60°S to 30°S, 0° to 360°E), the eastern tropical Pacific (5°S to 3°N, 150°W to 100°W), the Kuroshio Extension (32°N to 47°N, 142°E to 170°W), and the Gulf Stream (35°N to 55°N, 60°W to 30°W).
Mark A. Bourassa

8. SST Considerations on Winds and Fluxes
The surface humidity is largely a function of the SST
SST modifies the ocean and atmospheric stratification
Transports more or less heat out of the boundary-layer
Mixes more or less momentum down from above
Changing the surface vector winds and fluxes
Modifies the horizontal gradient in air temperature
Changes the surface wind vector and fluxes
Any changes the surface wind speed changes the surface roughness
Further modifies the surface vector winds and fluxes
We are working towards studying these problems
University of Washington Planetary Boundary-Layer Model
Upgraded flux code to be more consistent with satellite observations
Response of SST to winds can be added through POSH and/or a more detailed oceanic boundary-layer
Mark A. Bourassa

9. Example Retrievals of 10m Air Temperature
Multiple linear Regression technique
Pretty good for most conditions
Issues for very low temperature and very high temperatures
Mark A. Bourassa
NASA SSTST 9

10. Satellite Retrievals of Air Temperature (at 10m) Comparison With The Latest Techniques
Build on collaborations with Jackson and Wick (NOAA & CIRES) and Carol Anne Clayson (FSU)
Jackson and Wick
Roberts et al.
Mark A. Bourassa

11. Satellite Retrievals of Humidity (at 10m) Comparison With The Latest Techniques
Build on collaborations with Jackson and Wick (NOAA & CIRES) and Carol Anne Clayson (FSU)
Jackson and Wick
Roberts et al.
Mark A. Bourassa
NASA SSTST 11

12. Data Two data sets are used in this study:
Reynolds high resolution SSTs, and
MERRA sea level pressures.
An overly smooth wind field is simulated by interpolating the MERRA sea level pressures (0.5 x 0.66) to the grid spacing of the Reynolds GHRSST product (0.25 x 0.25).
A spline fit is used to greatly reduce spurious spatial derivatives.
The magnitude of vector wind components is related to the pressure gradient; therefore, the vector wind is sensitive to spurious changes in the pressure field.
The University of Washington Planetary Boundary Layer model (UWPBL) is used to find a field of vector winds based on the pressure or the pressure and the SSTs.
The time period of examination is February The MERRA data are hourly; however, only the data for 01Z are examined herein. This time step is consistent with that of the Reynolds SST product, which has a daily time step.
Mark A. Bourassa

13. Spectral Density of Wind u-component and Speed February, 2003
Spectral Density (m3s-2)
Spectral Density (m3s-2)
U-component
Speed
The spectral density of wind: u component (left), and speed (right)
Isothermal (black)
latitudes (top to bottom): 55N, 45N, 30N and 20N.
The influence of excessive smoothing is quite evident in the zonal component (left column) for 45 and 55N, at wavelengthes of roughly 100km at 55N, and roughly 200km at 45N. S
55N
55N
Wavelength (km)
Wavelength (km)
45N
45N
30N
30N
20N
20N
Wavelength (km)
Wavelength (km)
Mark A. Bourassa

14. Spectral Density of Wind u-component and Speed February, 2003
Spectral Density (m3s-2)
Spectral Density (m3s-2)
U-component
Speed
The spectral density of wind: u component (left), and speed (right)
Isothermal (black)
GHRSST (red)
latitudes (top to bottom): 55N, 45N, 30N and 20N.
The use of GHRSST data largely restores the spectral characteristics lost to smoothing
55N
55N
Wavelength (km)
Wavelength (km)
45N
45N
30N
30N
20N
20N
Wavelength (km)
Wavelength (km)
Mark A. Bourassa

15. Findings
Two sets of vector wind fields are generated from MERRA pressures:
One set ignores air temperature, and assumes that there is no surface temperature gradient.
The spectral density for the meridional vector component has a strange bump at around 200km wavelength (100km at 55N). If there were substantial errors in interpolation, they are highly unlikely to be manifested at that wavelength. Consequently, these features are very likely found in the MERRA data set.
One set treats air temperature as equal to the SST
The Reynolds GHRSST data are used (with the MERRA pressure data)
Air temperature is assumed to equal to SST
The spectral densities for these wind vector components are shown as the red lines in Fig. 1.
Changes in slope associated with smoothing are largely removed.
The SST data are remarkably effective for adding information to the wind field.
More testing is needed
Desire to understand what physical processes dominate this coupling
Mark A. Bourassa

16. Plans for the Future Include Diurnal variability in the SSTs
Create wind fields using UWPBL model
With diurnal cycle winds due to diurnal cycle in SSTs
Use satellite derived Qair & Tair to
investigate links with atmospheric stability and
calculate changes in surface turbulent fluxes
Examine physical processes responsible for linking changes in winds and SST
Mark A. Bourassa

17. Example of Diurnal Changes in SST
Based on a modified POSH model and MERRA input for 01/10/2006

18. Summary
High resolution SSTs are used to add wind information to an overly smooth vector wind field.
The key diagnostic for this study is the spectra density (spatial) of wind vector components and wind speed.
Wind fields are derived from surface pressures through the UWPBL model
(1) A reanalysis pressure field without SST information
The spatial resolution is shown to be limited by smoothing
(2) A reanalysis pressure field with GHRSST information
Finer scales are better represented (from spectral density perspective)
Knowledge of these scales can also be used to determine the averaging scales used in determining tuning parameters in objective analyses.
Mark A. Bourassa

19. With contributions from Paul Hughes and Ryan Maue, and with
Impacts of High Resolution SST Fields on Objective Analysis of Wind Fields
Mark A. Bourassa
Center for Ocean-Atmospheric Prediction Studies & Department of Earth, Ocean, and Atmospheric Science, The Florida State University, USA
With contributions from Paul Hughes and Ryan Maue, and with
examples form Darren Jackson, Gary Wick, Brent Roberts, and Carol Anne Clayson