Temporal and spatial scales of the Yellow Sea thermal variability

Abstract

Abstract. This paper presents an analysis on the space/time statistical thermal structure in the Yellow Sea from the Navy's Master Observation Oceanography Data Set during 1929-1991. This analysis is for the establishment of an Optimum Thermal Interpolation System of the Yellow Sea (a shallow sea), for the assimilation of observational data into coastal o-coordinate ocean prediction models (e.g., the Princeton Ocean Model), and for the design of an optimum observational network. After quality control the data set consists of 35,658 profiles. Sea surface temperatures at 50% and 80% water depths are presented here as representing the thermal structure of surface, middepth, and nearbottom layers. In the Yellow Sea shelf the temporal and spatial signals fluctuate according to the Asian monsoon. Variation of surface forcing from winter to summer monsoon season causes the change of the thermal structure, including the decorrelation scales. Our computation shows that the seasonal variation of the surface horizontal decorrelation scale is around 90 km from 158 km in winter to 251 km in summer and the seasonal variation of the surface temporal decorrelation scale is around 2.4 days from 14.7 days in winter to 12.3 days in summer. The temporal decorrelation scale increases with depth in both summer (evident) and winter (slight). The near-bottom water (rr -0.8) has the longest temporal scale in summer, which could be directly related to the existence of the Yellow Sea Cold Water throughout the summer in the middle of the Yellow Sea. The temporal and spatial decorrelation scales obtained in this study are useful for running optimum interpolation models and for designing an optimum observational network. The minimum sampling density required to detect thermal variability in the Yellow Sea shelf would be 50-80 km and 4-6 day intervals per temperature measurement with the knowledge that the subsurface features will also be adequately sampled.

Decorrelation scales for Arctic Ocean Hydrography. Part I: Amerasian Basin

“…We assume isotropy and homogeneity of the autocorrelation in the Amerasian Basin, supported by the fact of weak planetary-β effect in polar regions and the homogeneity of the Rossby radius in the Amerasian Basin [Nurser and Bacon, 2014;Zao et al, 2014]. Chu et al, 1997Chu et al, , 2002, i.e.,…”

“…In the mid-latitude and equatorial regions, there are a number of decorrelation scale estimates [e.g., White and Meyers, 1982;Chu et al, 1997Chu et al, , 2002Deser et al, 2003;Martins et al, 2015], and these have been applied for a variety of studies including data assimilation (see the papers mentioned above). On the other hand, while a few studies have examined scales of temperature and salinity variability in the Arctic Ocean [e.g., Timmermans and Winsor, 2013;Marcinko et al, 2015], there has been no assessment of basin-wide decorrelation scales of T/S field to date.…”

“…In the context of dynamical studies, the decorrelation scale is used as a measure of the scale of prevailing phenomena, and used to relate dynamical processes with the observed signals [e.g., Stammer, 1997;Ito et al, 2004;Kim and Kosro, 2013]. In optimal interpolation and objective mapping, the decorrelation scale gives a measure of influential radius of a point measurement; the autocorrelation function, together with the associated decorrelation scale, provides the weight of a point measurement on mean field estimates Chu et al, 1997;Davis, 1998;Wong et al, 2003;Böhme and Send, 2005]. For observation network design, decorrelation scales are one guide to estimate optimal sampling intervals in space and time [S printall and Meyers, 1991;White, 1995;Delcroix et al, 2005].…”

Reply to “Comment on ‘A parametric model for the Yellow Sea thermal variability’ by P. C. Chu et al.”

“…The output parameters included sea surface temperature (SST), mixed-layer depth (MLD), thermocline depth (THD), thermocline temperature difference (TTD), and deep layer stratification. On the basis of the U.S. Navy's Master Oceanographic Observation Data Set (MOODS) that was taken in the Yellow Sea (YS) from 1950 to 1988, we computed the thermal structure functions and spatial decorrelation scales of the water properties in the Yellow Sea [Chu et al, 1997b]. The results shown in our papers were derived from •35,658 profiles, which have provided us with the statistical seasonal variations of the thermal structure in this region.…”

Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin

“…In the context of dynamical studies, the decorrelation scale is used as a measure of the scale of prevailing phenomena and used to relate dynamical processes with the observed signals (e.g., Stammer, 1997;Ito et al, 2004;Kim and Kosro, 2013). In optimal interpolation and objective mapping, the decorrelation scale gives a measure of influential radius of a point measurement; the autocorrelation function, together with the associated decorrelation scale, provides the weight of a point measurement on mean field estimates Chu et al, 1997;Davis, 1998;Wong et al, 2003;Böhme and Send, 2005). For observation network design, decorrelation scales are one guide to estimate optimal sampling intervals in space and time White, 1995;Delcroix et al, 2005).…”