Transcript Slide 1

A Global and Regional Comparison of Air-Sea
Flux Parameterizations
David F. Moroni and Mark A. Bourassa
1. Background and Motivation
4. Conclusions
Air-sea fluxes of energy and momentum are the most important diagnostic for understanding the coupling of the oceanatmosphere system. Turbulent flux parameterization is the preferred method that is used in numerical modeling to more
efficiently process coupled interactions within Ocean Circulation Models (OCM), Global Spectral Models (GSM), and
Numerical Weather Prediction Models (NWP). As a result, there are many parameterizations at the modeler’s disposal,
depending on a wide array of physical assumptions. Each modeler also has a specific oceanic region, temporal range, and/or an
assumed physical state of the ocean/atmosphere suited to his/her interests. It is with this in mind, that each modeler should ask
the following question:
“Which flux parameterization is best suited for my needs?”
The smallest differences in parameterized turbulent flux output are observed during “near-neutral” atmospheric
stratification, where the Bulk Richardson stability range is +/- 0.02. Beyond this “near-neutral” threshold, fluxes begin to diverge
depending upon which stability parameterization is in place. Latent heat flux shows the greatest sensitivity to stability
parameterizations, followed by the sensible heat flux. Large and Pond (1981) and Large et al. (1994) show virtually no sensitivity
to stability parameterizations in any of the wind forcing diagnostics, such as: stress, the cube of friction velocity, and the curl of
the stress; this is due to the fact that friction velocity is computed prior to stability calculations for only these parameterizations.
RMS differences for the IQR of stress, averaged over a specific region (refer to Figure 2), show clearly that the dependencies of
stress upon a given stability parameterization are quite small for any given region; this aligns with the PDF analysis which shows
really good agreement between stability parameterizations for all wind forcing diagnostics. Sea-state dependent turbulent flux
parameterizations consistently compute higher flux magnitudes (as compared to the sea-state independent parameterizations) for
each observed diagnostic; the Gulf Stream analysis illuminates this comparison very well. The Bourassa (2006) parameterization
produces the highest overall flux magnitudes, followed by HEXOS and Taylor and Yelland (2001). Among the sea-state
independent parameterizations, Smith (1988) produces the highest overall flux magnitudes, whereas Large and Pond (1981) and
Large et al. (1994) follow very close together throughout. Differences are higher in winter months, as opposed to summer
months, where episodic forcing is more frequent, resulting in greater divergence between parameterized flux possibilities.
To help answer this question, the modeler needs to collectively understand the physical “make-up” of each
parameterization, the relative biases that are induced by each parameterization, and what conditions pose the greatest threat in
terms of generating a high model bias provided by a given parameterization. This project is intended to address these issues by
performing global, regional, and seasonal comparisons between six turbulent flux parameterizations, with an underlying
analysis of stability by mating each flux parameterization with a set of three atmospheric stability parameterizations.
2. Data
Three datasets are used in calculating fluxes from January 30, 1997 until December 31, 2004:
1. Coordinated Ocean Reference Experiments (CORE) version 1.0 (Large and Yeager, 2004)
 10 m winds, temperature, specific humidity, and sea-level pressure
 Available 4x/day on a T62 Gaussian grid (192x94 lon/lat points)
2. Reynolds Daily OI SST (Reynolds, 2006)
 Interpolated to T62 and 4x/day
3. NOAA Wave Watch III (Tolman, 2002)
 Interpolated to T62 and 4x/day
3. Parameterizations
The six turbulent flux parameterizations are broken down into two types:
1. sea-state independent (wave data is not used)
 Large and Pond (1981) - a neutral drag coefficient parameterization accounting for two
possible wind speed regimes:
 Large et al. (1994) – a neutral drag coefficient parameterization accounting for all wind
2.72
10 C 
 0.142  0.0764 U , for 0  U  very large
speeds:
U
Smith (1988) – a Charnock (1955) based roughness length parameterization:
au
0.11v
CD  [ / ln( z / zo )]2 .
z 

g
u
3
DN

10
10
10
2
*
o
*
N
2. sea-state dependent (wave data is used)
 HEXOS (Smith et al. 1992, 1996) – similar to Smith (1988), except that roughness length
is a function of waveage: zo  0.48 u*3 / gcp
 Taylor and Yelland (2001) – an alternative to HEXOS, such that roughness length is a
4.5
function of significant wave height and wave slope: zo  1200 H s  H s / LP 
 Bourassa (2006) – sea state contributes the lower wind profile, adding shear to the
horizontal wind at the sea-surface, expressed as orbital velocity: Uorb   H s / Tp .
Each flux parameterization is mated with three parameterizations for atmospheric stability:
1. Assumption of neutral atmospheric stratification above the air-sea interface.
2. “Old” stability parameterizations, incorporating the Businger-Dyer relations (Businger 1966
and Dyer 1967 for unstable stratification; Businger et al. 1971 and Dyer 1974 for stable
stratification).
3. “New” stability parameterizations, incorporating Benoit (1977) for unstable stratification and
Beljaars and Holtslag (1991) for stable stratification.
LP81
Large94
Smith88
HEXOS
TY01
Bourassa06
SW Indian
0
0
0.001035
0.001563
0.00096
0.00152
SW Pacific
0
0
0.001452
0.002064
0.001343
0.002145
Drake's
Passage
0
0
0.000599
0.00088
0.00067
0.000891
Cold
Tongue
0
0
0.000696
0.000913
0.000541
0.001056
Kuroshio
0
0
0.002846
0.003837
0.002895
0.004176
Gulf
Stream
0
0
0.002117
0.003022
0.002191
0.003408
Neutral
New
Old
SW Indian
0.029899
0.030619
0.030692
SW Pacific
0.019858
0.021313
0.021397
Drake's Passage
0.039358
0.039893
0.039962
Cold Tongue
0.010037
0.010541
0.010568
Kuroshio
0.022257
0.025305
0.025499
Gulf Stream
0.025819
0.028347
0.028537
Figure 4: Global probability distribution functions are displayed separately for all six turbulent flux parameterizations and for all five flux diagnostic variables: latent heat flux, sensible heat flux, stress, friction velocity cubed,
and the curl of the stress. Each PDF is individually broken down by a Bulk Richardson range for atmospheric stability, delineated by color. Fluxes derived from each of the three stability parameterizations are delineated by line
type (solid, dashed, and dashed-dotted). PDF diagrams that appear to have only the solid line type (i.e. the neutral assumption), are due to an exact overlap from other stability parameterizations, suggesting that stability
parameterizations produce virtually no difference for that given flux.
Figure 2: Both charts represent the RMS difference of the
IQR for stress, averaged over a specified region. The top
chart holds a given drag coefficient parameterization
constant, while accounting for all stability parameterizations.
The bottom chart holds a given stability parameterization
constant, while accounting for all six drag coefficient
parameterizations.
70.5%
Figure 5: Regional probability distribution functions for the Gulf Stream are displayed separately for each turbulent flux diagnostic: latent heat flux, sensible heat flux, stress, friction velocity cubed, and the curl of the stress. Turbulent
flux output is represented by all six turbulent flux parameterizations, representing only the output from the “New” atmospheric stability parameterizations. The upper diagrams depict the summer season, and the lower plots depict the
winter season. For brevity, intermediate seasons and the output from remaining atmospheric stability parameterizations are left out.
5. Future Work
Figure 1: The standard deviations from the composite mean interquartile
range of latent heat flux, sensible heat flux, and stress are shown
respectively. Boxed regions highlight areas of interest, where probability
distribution functions will be used to determine modeled flux output
differences. The Kuroshio, Gulf Stream, SW Indian Ocean, and SW
Pacific Ocean are chosen based upon their high modeled latent and
sensible heat flux variability. Drake’s Passage is chosen to better
understand the response of heat fluxes to the highly variable stress in the
ACC region. The Equatorial Cold Tongue is a predetermined area of
interest, which appears to show the least modeled flux variability among
the remaining regions.
4.1%
22.3%
3.1% 3.1%
Figure 3: Shows a probability distribution function of the
Bulk Richardson numbers, derived from the CORE and
Reynold’s SST datasets. Colors delineate the Bulk
Richardson stability ranges. Percentages marked within the
diagram show the relative contribution of the total
distribution of stability ranges. Since the y-axis is
logarithmic, the scale of each stability range is exaggerated.
Include comparisons for each remaining region of interest:
Kuroshio, SW Indian Ocean, SW Pacific Ocean, Drake’s
Passage, and the Equatorial Cold Tongue. Include the
COARE version 3.0 algorithm and the polynomial curve fit
to COARE (Kara et al. 2005) in the PDF analysis.
6. Acknowledgements
I would finally like to thank NOAA’s Climate Observation
Division for providing the funding for this project.
7. References
•Beljaars, A. C. M., and A. A. M. Holtslag, 1991: Flux parameterization over land surfaces for atmospheric models, J. Appl. Meteor., 30, 327-341.
•Benoit, R., 1977: On the integral of the surface layer profile-gradient functions. J. Appl. Meteor., 16, 859-560.
•Bourassa, M. A. 2006, Satellite-based observations of surface turbulent stress during severe weather, Atmosphere - Ocean Interactions, Vol. 2., ed., W. Perrie, Wessex Institute of Technology
Press, 35 – 52 pp.
•Businger, J. A., 1966: Transfer of momentum and heat in the planetary boundary layer. Proc. Symp. Arctic Heat Budget and Atmospheric Circulation, the RAND Corporations, 305-331.
•______, J. C. Wyngaard, Y. Izumi, and E. F. Bradley, 1971: Flux profile relationships in the atmospheric surface layer. J. Atmos. Sci., 28, 181-189.
•Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639-640.
•Dyer, A. J., 1967: The turbulent transport of heat and water vapour in an unstable atmosphere. Quart. J. Roy. Meteor. Soc., 93, 501-508.
•Dyer, A. J., 1974: A review of flux-profile relationships. Bound.-Layer Meteor., 7, 363-372.
•Large, W. G., and S. Pond, 1981: Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr., 11, 324—336.
•______, J. C. McWilliams, S. C. Doney, 1994: Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32. 363-403.
•______, and S. G. Yeager, 2004: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR Technical Note, NCAR/TN-460†STR, 105pp.
•Reynolds, R. W., 2006: Personal communication.
•Smith, S. D., 1988: Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J. Geophys. Res., 93, No. C12, 15,467-15,472.
•Taylor, P. K., M. J. Yelland, 2001: The Dependence of sea surface roughness on the height and steepness of the waves. J. Phys. Oceanogr., 31, 572-590.
•Tolman, H. L., 2002: Validation of WAVEWATCH III version 1.15 for a global domain. Technical Note. Environmental Modeling Center, Ocean Modeling Branch, NOAA.