Modeling whitecaps on global scale

Aishwarya Raman, Science Systems and Applications, Inc., Lanham, MD, United States; NASA Goddard Space Flight Center, GMAO, Greenbelt, MD, United States and Anton Darmenov, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, United States
Whitecaps play an important role in the surface-atmosphere interactions
across the ocean. They are directly linked to the energy dissipation rate,
transfer of heat, momentum, and gas/aerosol exchange in the ocean.
Although the first models of W were
dependent only on wind speeds, a large number of diverse models based on wind
and sea state have been proposed since then. However, it is recognized
that most of the proposed W models have strong systematic
and random errors when compared against observations. This is partly due to
the differences in environmental conditions, measurement techniques, and
partly due to the inability of the proposed models to capture the variability in
W in certain wind/wave regimes. Despite the knowledge of existing biases, W
residual relationships with wind and wave fields remain
uncertain, with residual trends varying between the published studies.
Here, we take advantage of the relatively dense observations
of W from WindSat microwave satellite retrievals in combination with the
University of Miami wave model which was recently incorporated within the
NASA GMAO/GEOS system (GEOS-UMWM). We use Windsat W retrievals to assess and
constrain the previously published W models and understand the relationships
of residuals from models in different wind/wave regimes. We link these
unexplained residual variations to additional factors such as swell index,
drag coefficient etc. Regression of wind/wave fields with all Windsat data points show that W is overestimated
upto ~4% for wave age < 10 and underestimated by upto ~2% as wave age
increases. We attest to this bias by considering two approaches. One is to
perform regression separately for different stages of wave development thereby
understanding the sensitivity of regression coefficients to sea state (EXP1).
Another is to derive coefficients of W models in EXP1 as a function of
additional wind/wave factors such as swell index, drag coefficient, deriving more nonlinear W models (EXP2). EXP2 provides
reduction in Root Mean Squared Error by 0.1-0.3%. Sea surface drag has
a stronger relationship with regression coefficients compared to swell index.
These additional factors provide improved parameterizations in different wind
and wave age regimes, with smaller unexplained/residual variations in W that
has been a major concern in the W community.