Chapter 4: FINE STRUCTURE AND MICROSTRUCTURE
circulation and weather prediction (Shinoda et al., 1998) and biophysical
processes (McCreary et al., 2001). Diurnal cycling also has important
implications for air-sea gas exchange (McNeil and Merlivat, 1996; Soloviev
et al., 2001b). Relatively strong dependence of the COB 2 B solubility on
temperature suggests that diurnal warming shifts the partial pressure
difference between atmosphere and the ocean surface towards lower COB 2 B
uptake by the ocean.
These studies also emphasize the necessity for improving theoretical
methods of quantifying the SST variations due to diurnal cycling. The main
reason is that clouds affect the space-based infrared imagery of the sea
surface. The microwave measurement does not depend on clouds, but its
R.M.S. accuracy is not better than 0.5 K (Gentemann et al., 2004), which
may not always be sufficient to resolve the diurnal cycle of SST. Rains also
affect the microwave signal.
An effective approach to resolve this situation is to enhance the remote
sensing results with the diurnal mixed layer model forced with remotely
sensed heat and momentum fluxes (the latter may not depend so critically on
cloudiness as infrared SST methods). Under low wind speed conditions the
diurnal warming, however, is a nonlinear function of heat and momentum
fluxes. Simple regression type parameterizations of the diurnal SST
amplitudes may not be effective in conjunction with remote sensing methods
because, strictly speaking, they require tuning empirical coefficients for each
region and event. An accurate model of the diurnal cycle combined with
remotely sensed data may improve the accuracy of estimating the
temperature difference across the diurnal thermocline globally (including
regions with cloud cover) compared to the use of regression type
parameterizations.
Adequate sampling is another critical factor for realistic simulation of
large diurnal warming episodes because they depend not only on
instantaneous fluxes but also on their history (at least from sunrise). The
fundamental problem is that for polar orbiting satellites, track-repeat times
are too long to resolve the diurnal cycle of SST. A multi-satellite approach
including geostationary satellites can help in solving this problem. In
particular, the International Satellite Cloud Climatology Project (see Section
1.4.4) has demonstrated the possibility of providing short wave radiation
data globally every 3 hours.
A variety of retrieval schemes to derive boundary layer parameters from
polar orbiting satellites of the NOAA and DMSP series have been developed
(Katsaros et al., 1981;
and Luthardt, 1991; Wick et al., 1992;
Bauer and
, 1993; Emery et al., 1994; Chou et al., 1995). These
retrieval methods can be used to estimate surface heat and momentum fluxes
(
et al., 1995;
, 1996; Quilfen et al., 2001).
261
Schl ssel
ü
Schl ssel
ü
Schl ssel
ü
Schl ssel
ü
circulation and weather prediction (Shinoda et al., 1998) and biophysical
processes (McCreary et al., 2001). Diurnal cycling also has important
implications for air-sea gas exchange (McNeil and Merlivat, 1996; Soloviev
et al., 2001b). Relatively strong dependence of the COB 2 B solubility on
temperature suggests that diurnal warming shifts the partial pressure
difference between atmosphere and the ocean surface towards lower COB 2 B
uptake by the ocean.
These studies also emphasize the necessity for improving theoretical
methods of quantifying the SST variations due to diurnal cycling. The main
reason is that clouds affect the space-based infrared imagery of the sea
surface. The microwave measurement does not depend on clouds, but its
R.M.S. accuracy is not better than 0.5 K (Gentemann et al., 2004), which
may not always be sufficient to resolve the diurnal cycle of SST. Rains also
affect the microwave signal.
An effective approach to resolve this situation is to enhance the remote
sensing results with the diurnal mixed layer model forced with remotely
sensed heat and momentum fluxes (the latter may not depend so critically on
cloudiness as infrared SST methods). Under low wind speed conditions the
diurnal warming, however, is a nonlinear function of heat and momentum
fluxes. Simple regression type parameterizations of the diurnal SST
amplitudes may not be effective in conjunction with remote sensing methods
because, strictly speaking, they require tuning empirical coefficients for each
region and event. An accurate model of the diurnal cycle combined with
remotely sensed data may improve the accuracy of estimating the
temperature difference across the diurnal thermocline globally (including
regions with cloud cover) compared to the use of regression type
parameterizations.
Adequate sampling is another critical factor for realistic simulation of
large diurnal warming episodes because they depend not only on
instantaneous fluxes but also on their history (at least from sunrise). The
fundamental problem is that for polar orbiting satellites, track-repeat times
are too long to resolve the diurnal cycle of SST. A multi-satellite approach
including geostationary satellites can help in solving this problem. In
particular, the International Satellite Cloud Climatology Project (see Section
1.4.4) has demonstrated the possibility of providing short wave radiation
data globally every 3 hours.
A variety of retrieval schemes to derive boundary layer parameters from
polar orbiting satellites of the NOAA and DMSP series have been developed
(Katsaros et al., 1981;
and Luthardt, 1991; Wick et al., 1992;
Bauer and
, 1993; Emery et al., 1994; Chou et al., 1995). These
retrieval methods can be used to estimate surface heat and momentum fluxes
(
et al., 1995;
, 1996; Quilfen et al., 2001).
261
Schl ssel
ü
Schl ssel
ü
Schl ssel
ü
Schl ssel
ü