64 resultados para Monitoring in the South Pacific region


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The principal tidal constituents M-2, S-2, K-1 and O-1 in the South China Sea, Gulf of Tonkin and Gulf of Thailand are simulated simultaneously using the numerical scheme of Kwok et al. (1995 Proceedings of the 1st Asian Computational Fluid Dynamics Conference, pp. 16-19). The average differences between the computed and observed harmonic constants are mostly within 5 cm and 10 degrees for amplitudes and phase-lags, respectively. The simulated tidal regimes in the present model are believed to be more accurate than the previous numerical results. Our studies confirm that a clockwise rotating M-2 amphidromic system lies in the southeast of the Gulf of Thailand and an S-2 amphidromic system at the near-shore area of the northeast South China Sea. The linear tidal energy equation developed by Garrett (1975 Deep-Sea Research 22, 23-35) is generalized to the nonlinear case. Based on the numerical results, the energy budgets in the South China Sea and its subareas, namely the Taiwan Strait, the Gulf of Tonkin, the Gulf of Thailand and the remaining area are investigated. The tidal motion in the Taiwan Strait is maintained mainly by the energy fluxes from the East China Sea for both semidiurnal and diurnal species and partially from the Luzon Strait for semidiurnal species. For the other parts of the South China Sea, the tidal motion is mainly maintained by the energy fluxes through the Luzon Strait. The energy inputs from the tide-generating force are negative for semidiurnal species and positive for diurnal species. (C) 1999 Elsevier Science Ltd. All rights reserved.

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The mixed layer depth (MLD) in the upper ocean is an important physical parameter for describing the upper ocean mixed layer. We analyzed several major factors influencing the climatological mixed layer depth (CMLD), and established a numerical simulation in the South China Sea (SCS) using the Regional Ocean Model System (ROMS) with a high-resolution (1/12A degrees x1/12A degrees) grid nesting method and 50 vertical layers. Several ideal numerical experiments were tested by modifying the existing sea surface boundary conditions. Especially, we analyzed the sensitivity of the results simulated for the CMLD with factors of sea surface wind stress (SSWS), sea surface net heat flux (SSNHF), and the difference between evaporation and precipitation (DEP). The result shows that of the three factors that change the depth of the CMLD, SSWS is in the first place, when ignoring the impact of SSWS, CMLD will change by 26% on average, and its effect is always to deepen the CMLD; the next comes SSNHF (13%) for deepening the CMLD in October to January and shallowing the CMLD in February to September; and the DEP comes in the third (only 2%). Moreover, we analyzed the temporal and spatial characteristics of CMLD and compared the simulation result with the ARGO observational data. The results indicate that ROMS is applicable for studying CMLD in the SCS area.

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11-year satellite altimeter sea surface height (SSH) anomaly data from January 1993 to December 2003 are used to present the dominant spatial patterns and temporal variations of the South China Sea (SCS) surface circulation through Empirical Orthogonal Function (EOF) analysis. The first three EOF modes show the obvious seasonal variations of SSH in the SCS. EOF mode one is generally characterized by a basin-wide circulation. Mode two describes the double-cell basin scale circulation structure. The two cells were located off west of the Luzon Island and southeast of Vietnam, respectively. EOF mode three presents the mesoscale eddy structure in the western SCS, which develops into a strong cyclonic eddy rapidly from July to September. EOF mode one and mode three are also embedded with interannual signals, indicating that the SCS surface circulation variation is influenced by El Nino events prominently. The strong El Nino of 1997/98 obviously changed the SCS circulation structure. This study also shows that there existed a series of mesoscale eddies in the western SCS, and their temporal variation indicates intra-seasonal and interannual signals.