Science Objectives:
Improving ocean modeling and assimilation.
Motivations and background:
Wind stress is a major forcing of ocean circulation and the associated property transports
(heat, freshwater, nutrients, carbon, etc.). Even though wind stress observations from
satellite scatteormeters have revolutionized the diagnostic analysis, state estimation, and
forecast of the ocean; several major challenges remain, especially in terms of the temporal
sampling, spatial resolution, high wind, and continuity of consistent climate data record for
wind stress.
Temporal sampling requirement: 4 times daily or better
Ocean modeling and assimilation often reanalysis wind instead of scatterometer wind as forcing
in part because of the lack of sub-daily sampling from scatterometer observations. Sub-daily
wind are important both in the tropics and at mid-latitudes.
Tropics: sub-daily wind important to diurnal coupling that affect the mean state, seasonal
cycle, intraseasonal and interannual variability of the ocean-atmosphere system
Many coupled modeling studies have shown that diurnal ocean-atmosphere coupling in the
tropics has significant rectification both on the background state and on the variability. The
rectification effects are associated with changes in both the ocean and atmosphere, including SST
and mixed-layer depth, ocean surface wind and ocean current, and convection in the atmosphere.
Effects of diurnal coupling on the background state include those on the mean state and that on
climatological seasonal cycle while those on the variability include the impacts on the MaddenJulian Oscillations (MJO) and El Nino-Southern Oscillation (ENSO).
Existing coupled climate models are typically characterized by a bias in the representation of the
state of the tropical Pacific Ocean and atmosphere such as the common cold bias in the tropical
Pacific. Coupled model studies (e.g., Danabasloglu et al. 2006, Bernie et al. 2008, and Ham et al.
2010) showed that the inclusion of diurnal ocean-atmosphere coupling significantly reduced the
tropical bias (e.g., Figure 1). Bernie et al. (2008) showed that the inclusion of diurnal coupling
also has affected the seasonal cycle of the coupled model (Figure 2). Coupled models often overestimate the magnitude of ENSO. The inclusion of diurnal coupling is found to reduce the
magnitude of ENSO significantly, i.e., closer to the observed magnitude (e.g., Danabasoglu et al.
2006 and Ham et al. 2010) (Figure 3). Bernie et al (2008) and Kinghaman et al. (2011) found
that the introduction of diurnal coupling to the coupled models affected MJO simulation; the
magnitude of MJO with diurnal coupling became closer to the observation. Sub-daily
measurements of ocean surface wind stress are required to evaluate and improve the structure of
diurnal coupling prescribed or simulated in coupled models (i.e., in terms of the spatial
distribution and temporal phasing of OVW, SST, and heat flux associated with diurnal
variability).
Mid-latitudes: sub-daily wind excite inertial oscillation in the ocean and play an important
in vertical mixing (at mid- to high-latitude regions, the inertial periods are less than one
day)
Ocean surface wind generates inertial oscillations in the ocean. The latter provide a major
mechanism of vertical mixing, which redistribute heat and other properties (e.g., nutrients and
carbon) in the water column. Therefore, wind-generated inertial oscillations are important to
climate variability and biogeochemistry. At mid- to high-latitude regions, the inertial periods are
less than one day. Lee and Liu (2005) and Lee et al. (2008) contrasted the responses of an ocean
model to twice-daily and daily wind forcing and showed that the simulation with daily wind is
associated with a warm bias in mid-latitude SSST especially during summertime (Figure 4). The
experiment with twice-daily wind alleviated this problem. This is because the period of inertial
oscillation at mid-latitude is about 12 hours. The twice-daily wind is able to generate more
intense inertial oscillation and stronger vertical mixing, which helps minimize the warm SST
bias by increasing the mixing with colder subsurface waters. Atmospheric reanalysis products
provide 4 times daily output of ocean surface wind estimates, which is one of the main reasons
that they often used to force ocean models. However, Lee and Liu (2005) showed that the
NCEP/NCAR reanalysis significantly under-estimated the magnitude of sub-daily wind in midlatitudes
Spatial sampling requirement: 1-5 km required to resolve the
structure and variability of coastal wind, esp. in upwelling regions
The spatial scales of ocean OVW over coastal oceans are often significantly smaller than those
over the open ocean. Current OVW measurements from scatterometers are inadequate to capture
these scales and get close enough to the coasts (Figure 5). Ocean modeling and assimilation in
coastal regions require resolving wind variability/structure at the scales of approximately 1-5 km.
This is especially the case off the west coasts of different continents where coastal wind stress
and wind stress curl drive upwelling that affects the physics and biogeochemistry. Off the west
coast of the United States in the California Current system, wind stress can changes dramatically
within a scale of 10 km. Therefore, 1-5 km resolution would significantly improve coastal ocean
modeling and assimilation.
Capture high wind would significantly benefit operational meoscale
ocean nowcasting and forecasting
Mesoscale ocean nowcasting and forecasting effort require scatteromters capture high wind in
order to correctly estimate the state of the ocean circulation. It has important operational
implications, including Naval operation, search and rescue, shipping, and the fishing industry.
Continuity is important to climate-oriented multi-decadal ocean
hindcast and ocean reanalysis
OVW is an essential element of decadal and multi-decadal climate variability such as Pacific
Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) (e.g., Mantua et al.
1997, Delworth and Mann 2000). Climate change signals are also closely tied to changes in
OVW. However, our knowledge of the structure and mechanism of decadal and longer
variability and changes has been limited by the lack of continuous and consistent time series of
global OVW measurements on multi-decadal time scales. This has significantly affected the
fidelity of climate-oriented ocean hindcasts and reanalysis products. Multi-decadal changes
inferred from these products forced by different atmospheric reanalysis products are often
inconsistent among one another because of spurious trends in the forcing derived from the
atmospheric reanalysis products. Future scatterometers observations that provide the capability
to cross-calibrate existing and future scatterometer data would be very important.
References:
Bernie, D.J., E.Guilyardi, G.Madec, J.M. Slingo, S. J. Woolnough, and J. Cole (2008): Impact of
resolving the diurnal cycle in an ocean–atmosphere GCM. Part 2: A diurnally coupled CGCM. Climate
Dyn., 31, 909–925.
Chao, Y., Z. Li, and J. Kindle, et al. 2003: A high-resolution surface vector wind product for
coastal oceans: Blending satellite scatterometer measurements with regional mesoscale
atmospheric model simulations. Geophys. Res. Lett., VOL. 30, NO. 1, 1013,
doi:10.1029/2002GL015729.
Danabasoglu G; Large WG; Tribbia JJ; et al. (2006): Diurnal coupling in the tropical oceans of CCSM3. J.
Clim., 19, 2347-2365. DOI: 10.1175/JCLI3739.1.
Delworth, T.L. and M.E. Mann (2000): Observed and simulated multi-decadal variability in the Northern
Hemisphere. Clim. Dyn., 16, 661-676. doi: 10.1007/s003820000075.
Ham, Y.-G., J.-S. Kug, I.-S. Kang, F.-F. Jin, and A. Timmerman (2010): Impact of diurnal atmosphere–
ocean coupling on tropical climate simulations using a coupled GCM. Climate Dyn.,
34, 905–917.
Klingaman, N.P., S.J. Woolnough, H. Weller et al. (2011): The Impact of Finer-Resolution Air–Sea
Coupling on the Intraseasonal Oscillation of the Indian Monsoon. J. Clim., 24, 2451-2468, doi:
10.1175/2010JCLI3868.1
Lee, T., and W. T. Liu (2005): Effects of high-frequency wind sampling on simulated mixed-layer depth
and upper-ocean temperature. J. Geophys. Res., 110, C05002, doi: 10.1029/2004JC002746.
Lee, T., O. Wang, W.-Q. Tang, and W.T. Liu (2008): Wind stress measurements from the QuikSCATSeaWinds scatterometer tandem mission and the impact on an ocean model. J. Geophys. Res., 113,
C12019, doi:10.1029/2008JC004855.
Mantua, N.J., S.R. Hare, and Y. Zhang et al. (1997): a Pacific interdecadal climate oscillation with
impacts on salmon production. Bill. Amer. Meterol. Soc., 78, 1069-1079. Doi: 10.1175/15200477(1997)078.
Figure 1 Climatological bias in SST (a) and precipitation (b) in the control run of the coupled
GCM without diurnal coupling and the correction of SST (c) and precipitation and wind (d) due
to the inclusion of diurnal coupling in the coupled model. After ham et al. (2010).
Figure 2. Climatological seasonal cycle across the equatorial Pacific: SST from the coupled
model run without diurnal coupling (a), the difference in SST from model runs with and without
diurnal coupling (b), diurnal SST variability the run with diurnal coupling (c), and difference in
zonal wind stress from runs with and without diurnal coupling.
Figure 3 Standard deviation of interannual SST anomalies. (a) OISST data, (b) dialy coupling
model run, and (c) diurnal coupling model run. After Ham et al. (2010).
Figure 4. The difference of SST in August 2003 simulated by an ocean GCM forced by twicedaily and daily wind frocing obtained from the QuikSCAT-SeaWinds scatterometer tandem
mission in 2003. The difference is due to stronger vertical mixing in the run with twice-daily
wind because of more intense inertial oscillation generated by the twice-daily wind. After Lee et
al. (2008).
Figure 5. Daily maps (June 1, 2000) of wind stress curl (in 10-6 s-1 ) off US west coast from (a)
QuikSCAT & (b) US Navy’s COAMPS model (after Chao et al. 2003).