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Indonesia as a region located in the tropics gets a greater heat distribution than the other hemisphere and has an important role in the phenomenon of atmospheric and ocean interactions in the Indo-Pacific region. The heat exchange between the ocean and the atmosphere affects the dynamics of both. The southern coast of Java is known as the upwelling area which is driven by the variability of Ekman transport and Ekman pumping. The present study aims to investigate the effect of heat flux variability on sea surface temperature variability and chlorophyll-a in surface upwelling areas along the Southern coast of Java which was less observed in the previous study. The study was conducted with a quantitative descriptive approach through climatological spatial and temporal data processing for 10 years from 2007 - 2016. The data used are Shortwave Radiation, Longwave Radiation, Latent Heat Flux, Sensible Heat Flux, Sea Surface Temperature, Chlorophyll-a, Surface Wind, and Mixed Layer Depth. The results show that the Southern coast of Java receives an average heat of 547.8 W/m2 per year. Net Heat Flux fluctuations are dominated by heat intake by Shortwave Radiation and heat release by Latent Heat Flux. Net Heat Flux has a very strong relationship with sea surface temperature with the best correlation of 0.84 and 0.83 at lag+2 and lag+3 months indicating that Net Heat Flux plays an important role in modulating changes in sea surface temperature in the next 2-3 months. A significant increase in chlorophyll-a occurred after the Net Heat Flux was positive or there was ocean heating which caused the shoaling of Mixed Layer Depth, resulting in primary productivity in the east monsoon along with nutrient rich entrainment to the surface by EMT and EPV. [ABSTRACT FROM AUTHOR]

The precision of sea surface temperature (SST) from remote sensing data is essential to recognize SST fluctuations prompted by extreme weather conditions due to global climate warming, such as tropical cyclones (TCs). Since 1981 the active remote sensing of satellite-based SST measurements has been around and proliferating to date in Indonesia. However, there has not been much research on the validation of several remote sensing datasets in Indonesia’s seas that has limited coverage of buoy observations. Moreover, no studies correspond to which data are the most precise in describing SST fluctuations in tropical storms. In this study, six remote sensing/satellite (Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), Regional Australian Multi-Sensor SST Analysis (RAMSSA), Global Australian Multi-Sensor SST Analysis (GAMSA), Microwave Infrared Optimally Interpolated (MWIROI), Multi-scale Ultra-high Resolution (MUR), and K10) data are validated and compared to analyze SST fluctuations in TC as a case study. The validation method uses the Haversine distance formula to reach the highest quality iQuam data with satellite data. The comparison analysis is performed by plotting the SST and wind slop in a TC area. Based on the validation, The OSTIA, RAMSSA, GAMSSA, and MWIROI datasets ranked in the top 4 of the smallest RMSEs with values < 0.5. Moreover, in the SST and wind slop in a TC area, TC affects SST cooling as detected in the MUR and K10 datasets where there is a decrease of > 2 ºC. In the MWIROI, the decline is more noticeable significant > 3 ºC. [ABSTRACT FROM AUTHOR]

Marine heatwave (MHW) is an extreme phenomenon of warm sea surface temperature anomaly that has a destructive impact on the marine ecosystem and organisms. This phenomenon increases every year in duration, frequency, and area due to the global warming. Almost all of the world's oceans have experienced MHW, including in the seas of the southern Java-Nusa Tenggara. The lack of detailed research in this area motivated us to analyze MHW drivers. MHW was identified with the 99th percentile method and duration of = 5 days as a threshold by using blended product of SST data. In 2013, an MHW event was identified in the seas of the southern Java-Nusa Tenggara. This MHW lasts for 37 days, from 8 June to 14 July. The maximum (mean) intensity of sea surface temperature anomaly reaches 2.60°C (1.83°C) above climatology with an average area of 36.53 × 104 km². Locally, we found that probably the positive downward shortwave radiation affected this anomaly. Furthermore, a remote process of the propagation of downwelling Kelvin wave during negative Indian Ocean Dipole may also affect this anomaly. The strong westerly wind along the equatorial Indian ocean forms downwelling Kelvin wave that propagates to the southern Java-Nusa Tenggara increasing SST in this area. [ABSTRACT FROM AUTHOR]

The Red Sea has a special geographical feature because it is situated in the tropical and subtropical zones. The uniqueness of the Red Sea makes a variability of oceanography data (Chlorophyll-a (Chla), current, Salinity, etc.) and meteorological data (Precipitation, Wind Speed, etc.). However, investigations of differences interaction of oceanography and meteorology are less studied in of the Red Sea. Satellites and modeling make it possible to observe oceanographic and meteorological data over a long period of time. This study first demonstrates that there are three major areas along the Red Sea that represent the different Chl-a concentrations during the rainy season. The month of July has the highest Chl-a concentration in the south (1.3 mg/m³) and the lowest Chl-a concentration in the north (0.18 mg/m³). The southern part of the Red Sea has a different generation mechanism from the northern part of the Red Sea in terms of increasing and decreasing Chl-a concentrations. The existence of surface runoff in this area may result in the supply of anthropogenic organic compounds and fresh water to coastal waters. This may increase the supply of nutrients at the peak of the rainy season, and finally increase the concentration of Chl-a in the southern part. In the middle part, the variability of Chl-a is mainly affected by wind speed. Meanwhile, the high salinity in the northern part may limit the growth of phytoplankton and keep the Chl-a concentration low. [ABSTRACT FROM AUTHOR]

Semarang coastal area is mainly composed of alluvium sediment, vulnerable to tidal floods caused by land subsidence and seawater inundation. On the other hand, this capital area is also prone to earthquakes evoked by Kaligarang, Semarang, Ungaran 1, and Ungaran 2 Faults. This research aims to examine the earthquake vulnerability index in the coastal area of Semarang using the HVSR (horizontal to vertical spectral ratio) micro tremor method. The study was conducted by measuring the micro tremor signal as many as 110 points spread over the research location with an almost even distribution. Data collection was carried out around existing roads with a distance of about 1000 m west-east and about 500 m north-south. Data was taken with a 3-component TDS seismograph type 303S with a sampling rate of 20 Hz for 10 minutes. The micro tremor data was processed using Software Excel, Pro, and Geopsy data to obtain the dominant frequency values, amplification factor, seismic vulnerability index, Peak Ground Acceleration, and Ground stress-strain. The data obtained is described as a distribution map along with the parameter values using ArcGIS software. The results show that the frequency dominant, amplification, SVI, PGA and GSS vary from 0.13 Hz to 8.44 Hz, from 0.08 to 7.92, from 0.04 gal to 75.74 gal, from 5.75 µs2/cm to 45.22 µs2/cm, and from 12.3x10-6 - 788x10-6, respectively. The results obtained in the research area that have a vulnerability to earthquakes can be grouped into 3 categories, namely weak, medium and strong categories. Most of the research areas have a vulnerability index to moderate earthquakes, except for the northern and southern parts of West Semarang District, Sayung District, and Genuk District which is in the strong category. Areas with a weak category were found around the eastern part of North Semarang District and Central Semarang District in the northern part, Candisari District, Gayamsari District in the southern part, and Pedurungan District in the southern part. This study was conducted during the rainy season. Therefore, collecting the same data for different season I recommended for further study to investigate the influence of seasonal effects on earthquake-impacted areas. [ABSTRACT FROM AUTHOR]