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| Titel |
EOF analysis of long-term reconstructed AVHRR Pathfinder SST in the South China Sea |
| VerfasserIn |
Hong-Ngu T. Huynh, Aida Alvera-Azcárate, Alexander Barth, Jean-Marie Beckers |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250091493
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| Publikation (Nr.) |
 EGU/EGU2014-5791.pdf |
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| Zusammenfassung |
| Sea surface temperature (SST) is one of the key variables often used to investigate ocean
dynamics, ocean-atmosphere interaction, and climate change. For recent decades, the
AVHRR Pathfinder SST, measured by infrared sensors, has been widely used because of its
high resolution and long time-series. The disadvantage of the AVHRR Pathfinder SST is high
percentage of missing data due to cloud coverage. This becomes more serious in the South
China Sea (SCS) because it is located in the tropical region, frequently covered by clouds. In
this study, we used the Data INterpolating Empirical Orthogonal Functions (DINEOF)
method to reconstruct daily night-time 4 km AVHRR Pathfinder SST spanning
from 1989 to 2009 for the whole SCS. In order to better understand the spatial and
temporal variability of the SCS SST, an EOF analysis of the reconstructed field is
performed in association with surface wind. The first SST mode, accounting for
69% of the variance, presents the cooling (warming) of the basin due to the solar
inclination through seasons, water exchange, topography, and monsoon-induced
cyclonic circulation. The second SST mode, explaining 24.8% of the variance,
shows the advection of cold and warm water from two opposite directions along the
southwest-northeast diagonal of the basin. The second SST mode is affected by the
atmospheric anticyclone (cyclone) located over the Philippine Sea. Comparing
both SST modes with Nino3.0 index, it shows that the interannual variability of the
SCS SST is influenced by the moderate and strong ENSO events with a lag of 5-6
months. Moreover, the analysis of the high-resolution reconstructed dataset reveals
some oceanic features that could not be captured in the previous EOF analyses. |
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