The Atmospheric and Oceanic Processes on Thermal Front Variability over the Java Sea
DOI:
https://doi.org/10.61761/tromes.2.1.p.17-20Keywords:
Java Sea, SST Gradient, Thermal Front, Volume Transport, Wind Stress CurlAbstract
The Java Sea is influenced by various atmospheric and oceanographic factors such as monsoon winds and the inflow of water from adjacent seas, thus leading to the formation of thermal fronts. In this research, the atmospheric process over the thermal front was estimated by using sea surface temperature (SST) gradient and crosswind-SST gradient calculation based on the GLORYS12V1 monthly SST data from Copernicus Marine Service (CMEMS) and the ERA5 monthly surface wind data from European Centre for Medium-Range Weather Forecasts (ECMWF) for 27 years (from 1993 to 2019). Moreover, the oceanic process was determined from the volume transports of three major channels surrounding the Java Sea, which are Karimata Strait, Sunda Strait, and the eastern boundary of the Java Sea. Based on the annual variance analysis, there are four main thermal front areas, such as Northern Java Coast (NJC), Eastern Sumatra Coast (ESC), Western Borneo Coast (WBC), and Eastern Borneo Coast (EBC). The annual variation of the thermal front over the ESC, WBC, and ESC has two peaks in March and October, while the NJC thermal front area has three peaks in March, July, and November. This study results that the thermal front activities over the NJC, WBC, and EBC are significantly correlated to the wind stress curl over those areas, with a coefficient correlation of about 0.62, -0.92, and -0.73, respectively. In addition, the increase in thermal front activity over the NJC area is controlled by negative wind stress curl with no lag time, while the WBC and EBC areas are controlled by positive wind stress curl with a lag time of 1 month (wind stress leads). On the other hand, the thermal front activity over the ESC area is closely related to southward volume transport from the Karimata Strait, with a correlation coefficient of -0.74 and a lag time of 2 months (volume transport leads)
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Copyright (c) 2024 Rahaden Bagas Hatmaja, Rangga Amrullah, Shinta Ayu Kusumaningrum, M. Restu Putra Sugianto
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