P5.10
Relationships between clouds and diurnal variability of the sea surface temperature in the tropical Pacific

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Thursday, 2 February 2006
Relationships between clouds and diurnal variability of the sea surface temperature in the tropical Pacific
Exhibit Hall A2 (Georgia World Congress Center)
Carol Anne Clayson, Florida State Univ., Tallahassee, FL

Throughout many regions of the tropics, diurnal warming on the order of 3oC or more can occur, which affects the nighttime cooling the next night; the total effect is due to nonlinear processes associated with mixed layer evolution which depends on the details of stratification below the mixed layer in addition to forcing at the surface. Researchers have examined the effect of diurnal warming during Madden-Julian Oscillation (MJO) variability through positive atmospheric feedbacks, the effect of entrainment cooling on the MJO, and the connection between entrainment cooling in the off-equatorial western Pacific warm pool region and positive feedbacks with El Niño. Recent work has shown considerable variability in the diurnal warming across the Pacific basin from small time scales to the interannual. Using a satellite-derived data set of diurnal warming and nocturnal cooling, and surface heat fluxes in the tropical Pacific for the years of 1996 – 2000, we investigate the variability of the sea surface temperature, the cloud structure, and the mediating surface fluxes.

In this research we perform a spatial and frequency analysis of cloud classification, diurnal warming, nocturnal cooling, and the surface heat fluxes. Analyses of time series at several locations within both the western and eastern tropical Pacific (where diurnal warming values are the strongest in the basin) between these variables are conducted in order to determine relationships between the forcing value of the sea surface temperature and the clouds and the surface fluxes. Relationships between the tendencies of these variables are also computed. Lastly we evaluate these relationships using multivariable wavelet analyses, so that we can determine the frequency and spatial dependence of the relationships, with a focus on variations from the intraseasonal to the interannual.