Determination of vertical thermal structure from sea surface temperature

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Author
Fan, Chenwu
Liu, W. Timothy
Chu, Peter C.
Date
2000-07Metadata
Show full item recordAbstract
A recently developed parametric model by P. C. Chu et al. is used in this paper for determining subsurface
thermal structure from satellite sea surface temperature observations. Based on a layered structure of temperature
fields (mixed layer, thermocline, and lower layers), the parametric model transforms a vertical profile into several
parameters: sea surface temperature (SST), mixed layer depth (MLD), thermocline bottom depth (TBD), thermocline
temperature gradient (TTG), and deep layer stratification (DLS). These parameters vary on different
timescales: SST and MLD on scales of minutes to hours, TBD and TTG on months to seasons, and DLS on an
even longer timescale. If the long timescale parameters such as TBD, TTD, and DLS are known (or given by
climatological values), the degree of freedom of a vertical profile fitted by the model reduces to one: SST. When
SST is observed, one may invert MLD, and, in turn, the vertical temperature profile with the known long
timescale parameters: TBD, TTG, and DLS.
The U.S. Navy’s Master Oceanographic Observation Data Set (MOODS) for the South China Sea in May
1932–94 (10 153 profiles) was used for the study. Among them, there are 40 data points collocating and
coappearing (same week) with the weekly daytime NASA multichannel SST data in 1986–94. The 40 MOODS
profiles were treated as a test dataset. The MOODS dataset excluding the test data is the training dataset,
consisting of 10 113 profiles. The training dataset was processed into a dataset consisting of SST, MLD, TBD,
TTG, and DLS using the parametric model. SST from the test dataset was used for the inversion based on the
known information on TBD, TTG, and DLS. The 40 inverted profiles agreed quite well with the corresponding
observed profiles. The rms error is 0.728C, and the correlation between the inverted and observed profiles is
0.79. This is much better than the simple method of estimating subsurface temperature anomaly from SST
anomaly by correlating the two in the training dataset. The possibility of using this method globally is also
discussed.
Description
Journal of Atmospheric and Oceanic Technology, American Meteorological Society, 17, 971-979.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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