Results and Discussion Optimization of the Acid Catalyst Concentration for Synthesis of Anti‐Cancer Agent Gamavuton‐0 by Using Mathematical and Statistical Software

77 Figure 4. Verification model between simulation and observation data for nitrate and phosphate a b c Figure 5. Distribution pattern a DP, b DN, and c Chl‐a in the Mahakam Estuary. Acknowledgement The author would like to thank The Ministry of Research, Technology, and igher Education Republic of ndonesia and for financial support. References Buranaprathprat. B, Yanagi. T, Niemann. K, Matsumura. K, and Sojisporn. P. 8 . Surface chlorophyll‐a dynamics in the upper Gulf of Thailand revealed by a coupled hydrodynamic‐ecosystem model. Journal of Oceanography,Vol. , pp. ‐ . Allen, G.P., and Chambers, J.L.C. 8 . Sedimentation in the Modern and Miocene Mahakam delta, Jakarta, ndonesia Petroleum Association, Field Trip Guidebook. Andreas. M, and Gunther. R . Reviewe of three‐dimensional ecological modelling related to the North Sea shelf system: Part : models and their results. Progress in Oceanography, Vol. , pp ‐ . Blumberg, A.F., and G. L. Mellor. 8 A description of a three‐dimensional coastal ocean model”, n: Coastal and Estuarine Sciences , Three‐Dimensional Coastal Ocean Models”, Amer Geophys. Union, Washington D.C., – . 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060 0.065 0.070 Nitrate Simulation results Month mm ol l Observation data 0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 Phosphate Simulation results Month mm ol l Observation data 78 Mandang, ., and Yanagi, T. . Cohesive Sediment Transport in the D hydrodynamic‐ baroclinic circulation model in the Mahakam Estuary, East Kalimantan, ndoensia, Coastal Marine Science, Vol. , No. , pp – . Yanagi, T. . Coastal Oceanography, Terra Scientific Publishing Company, Tokyo Joint Scientific Symposium IJJSS 2016 Chiba, 20‐24 November 2016 79 Aquarius Sea Surface Salinity in the Indonesian Seas Wayan Gede Astawa Karang a,b , Bayu Priyono c , Made Narayana Adibusana a , Takahiro Osawa b a Faculty of Marine Science and Fisheries, University of Udayana, Denpasar, Bali, Indonesia b Center for Remote Sensing and Ocean Sciences CReSOS, University of Udayana, Denpasar, Bali, Indonesia c Marine Research and Observation, Jl. BaruPerancak, Negara‐Jembrana, bali Abstract National Aeronautics and Space Administration NASA recently launched Aquarius satelliteto measure the Sea Surface Salinity SSS fields from space at global and regional scales. These five years ofSSS data were analyzed in the ndonesian Seas. The analysis is focused on whatpatterns of the spatial and temporal distributions of SSS in the ndonesian Seas? ow does SSS change with seasons in the ndonesian Seas, particularly in the ndonesian Throughflow TF channel? As a resultAquarius observations of SSS showed ndonesian Sea varied annual and seasonally. SSS propagating feature was characterized with the seasonal cycle such as Southeast monsoon and Northwest monsoon and estimated SSS concentrations during January to May give fresher lower thanduring June to November due to rainfall effects. The seasonal variability of SSS in the TF channel was also estimated in the Java Sea, Makassar Strait, Celebes and Banda Seas. These results showed that SSS patterns in these seas might be influenced by El‐Nino‐ Southern Oscillation ENSO phenomena and La‐Nina duringNorthwest Monsoon period which indicated by remarkable freshening in the South China Sea then expand to the Java Sea, Makasar Strait and Banda Sea. Keywords Aquarius; Sea Surface Salinity SSS ; Indonesian Seas; ITF; Seasonal Variability; ENSO ‐Monsoon

1. Introduction

The global scale observations of oceanic and climate variables will be answered how is the ocean‐atmosphere interaction process occurred and its relationship with the climate change. The space observations of oceanic variables are starting in the sixties with sea surface temperatures SST followed by ocean color in the eighties and the surface wind field in the nineties Menezes at al, . The availability of satellite oceanography data has given great benefit in the last six decades not only for physical processes but also in term of biology and ecosystem of the ocean. owever, one important variable that is still not well understood, due to the lack of routine global observations, is salinity [e.g., Roemmich and Gilson, ; Locarnini et al., ; Antonov et al., ., Menezes at al, ]. Compared to SST and ocean colour measurements, salinity observations are Corresponding author. Tel.: + ‐ 8 E ‐mail address:gedekarangunud.ac.id 80 sparse both temporally and spatially especially in the tropical oceans. .Together with temperature, salinity variations have a major influence on the density of seawater and, therefore, the oceanic circulation. n the recent past, the technological advances have significantly improved the basis of salinity measurements, and gives oceanographers more possibility to investigate contemporary salinity variations for spatial and temporal purposes. The advancement in observing ocean salinity is the launch of two satellite missions designed to retrieve sea surface salinity SSS from spaceborne measurements Kohler at al., . The first mission is the European Space Agency’s ESA SMOS ”Soil Moisture and Ocean Salinity”, Font et al. , which started retrieving SSS in November , followed by the American National Aeronautical Space Agency’s NASA ”AquariusSAC‐ D” Satelite de Aplicaciones Cientificas mission Lagerloef et al., 8 two years later. The latter ended on June , due to an unrecoverable hardware failure. SMOS SSS data are now available on a routine basis, covering the global ocean every days. Aquarius covered the global ocean every days and SSS data are available until June . Significant benefits can be expected from these novel satellite SSS fields for quantitative studies of ocean salinity variations. Recently several study of oceanic and atmospheric process related to the SSS using Aquarius satellite data archive have been done. Lee at al investigated suchthe tropical instability waves in the equatorial Pacific and Atlantic oceans using Aquarius data set. Gierach et al used Aquarius to study the extreme Mississippi river flooding event in the Gulf of Mexico in May–July . The used of Aquarius data were extend for the salinity changes study in the AmazonOrinoco [Grodsky et al., ] and South ndian Ocean: Revealing annual‐period planetary waves Menezes et al., . Most of these studies focus on the Atlantic and ndian Oceans and to the best of our knowledge none has been dedicated to the ndonesian Seas. The ndonesian Seas plays a central role in the climate system. They carried out the Pacific waters in to ndian Ocean trough the ndonesian Throughflow TF Sprintall at al., . The surface layer of the ndonesian Seas is well known controlled by winds with a strong annual signal driven by seasonally reversing monsoon. The aims of present study is to compare SSS annual mean and to observe SSS variability in the ndonesian Seas.

2. Research methods

The study area extends from , ° E – , ° E and , °S – , ° N Figure . n this study, monthly as well as ‐day smoothed fields at x from January to December were used. The Level‐ product used and their metadata were obtained from Physical Oceanography Distributed Active Archive Center, ftp:podaac‐ ftp.jpl.nasa.govallDataaquariusL mappedCAPv . Aquarius flies in a sun‐ synchronous polar orbit and crosses the equator at am descending and pm ascending local time. Aquarius directly retrieves brightness temperatures in approximately km wide swaths with a global coverage of ice‐free ocean every days from an altitude of km and an inclination of 8◦. The Aquarius instrument consists of three L‐band horn antennas sharing a common parabolic reflector. Each horn antenna connects to a separate Dicke radiometer, and the scatterometer is time‐shared sequentially between the three horns, which means that the scatterometer signal is rotated among the three horns and through vertical and horizontal polarization channels.n the present study we calculate the annual and monthly mean SSS for the ndonesian Seas and find the differences. Compute seasonal anomalies of the SSS by removing the annual mean. Calculate the seasonal SSS anomaly for a few different locations in the ndonesian Seas as a part of TF area South China Sea, Java Sea, Banda Sea, Makassar Strait, Selebes Sea, Western Pacific and South‐east ndian Ocean as