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The Importance Of Sea Temperature Anomalies

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The oceans play an important role in the climate system owing to the interannual and longer timescale variability in sea surface temperature (SST). Hasselmann (1976) proposed that this climate variability could be represented by a stochastic first order auto-regressive process
(AR1-process) and should be considered as the null hypothesis for extra-tropical sea surface temperature anomalies (SSTA). According to this concept, SSTAs quickly responds to the atmospheric heat fluxes at short time period and the heat capacity of the ocean integrates the
SSTA variability on a longer time period. Frankignoul and Hasselmann (1977) have further suggested that the atmospheric forcing for SST anomalies follows a spectrum of white noise with constant …show more content…

Thus the broad structure of the SSTA spectrum is determined by the depth of the ocean ML and atmospheric process.
Attempts to include additional processes, such as Ekman transport, entrainment of sub-mixed layer thermal anomalies (Dommenget and Latif 2002; Deser et al. 2003; Lee et al. 2008), state-dependent noise (Sura et al. 2005) and the re-emergence associated with seasonal change in MLD (Schneider and Cornuelle 2005) has shown to increase the SSTA variance at annual and longer timescales. However, studies have demonstrated that the SST variability at some part of the oceans cannot be represented by a simple AR1-process (Hall and Manabe
1997; Dommenget and Latif 2002, 2008). The inconsistency arises from the exchange of heat energy in the mixed layer and sub-mixed layer in the ocean.
The strong seasonal cycle of the MLD can strengthen the persistence of SSTA from one winter to the following. The timescale in which the subsurface temperature anomalies entrain to the surface (nearly 1 year) is expected to influence the spectral variance of SSTA. Möller et al. (2008) have shown a peak in the annual time period in the power spectrum of midlatitude
SSTA that is associated with the re-emergence. Figure 5.3 illustrates the power spectrum of
SSTA and 90% significance level (shaded), presented in different ways (taken from Moller et al. (2008)). Figure 5.3a express the spectral variance density, while figure 5.3b

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