We investigate how well the 1975-1992 sea level interannual variability in the tropical Pacific is captured by dynamic height from temperature profiles. For each temperature profile, a surface dynamic height relative to 300 m is estimated, assuming a constant temperature-salinity relationship. After multiplication by a latitudinally varying factor and the removal of a seasonal cycle, the dynamic height deviations fit the tide gauge sea level variability to within the sampling errors, except at a few sites near the equator west of the date line, where surface salinity variability is large. The dynamic height data are assimilated into a wind-forced linear numerical model of the sea level in the tropical Pacific, applying a Kalman filter in a space of reduced dimension. A limited number of empirical orthogonal functions of the unfiltered run (1975-1992) define the reduced space, into which the Kalman Filter covariance evolution calculation is done [Cane et at, 1996]. Experiments indicate that results are better with 32 functions than with a smaller number but are not improved by retaining more functions. The resulting analyzed fields of sea level are compared to withheld dynamic height estimates from moorings, sea level data from tide gauges, and sea level analyses made with the same Kalman filter formalism applied to tide gauge measurements. The comparisons to observations suggest that the temperature profiles were usually sufficient to constrain the monthly analyzed fields to be close to the observed sea level with errors typically less than 3 cm neat the equator. The comparison to tide gauge sea level reveals that this analysis is often more accurate than the analysis of tide gauge sea level data with which it shares many characteristics. Near the equator west of the date line, salinity variations ate large and their neglect in estimating dynamic height has a negative impact on the analysis. The analyzed signal is underestimated in the southwest Pacific and at more than 20 degrees off the equator. The reanalysis of the temperature data done with a primitive equation model at the National Meteorological Center (NMC) [Ji et at, 1995; Enfield and Harris, 1995] does not share this problem. On the other hand, NMC reanalysis (RA4) departs more from the observations elsewhere, although more data were included than in our analysis.
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