ABSTRAK. Variasi Salinitas Permukaan Laut di Laut Cina Selatan yang di Peroleh dari Data Aquarius dan SMAP

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1 ABSTRAK Variasi Salinitas Permukaan Laut di Laut Cina Selatan yang di Peroleh dari Data Aquarius dan SMAP Salinitas Permukaan Laut (SPL) mengambil peranan penting dalam studi dan pengertian siklus air secara global dan juga memiliki peranan yang penting terhadap interaksi udara dan laut. Sekarang ini, observasi SPL menggunakan data satelit semakin diminati untuk dikaji. Ketersedian data SPL dari Aquarius dan SMAP memungkinkan penelitain ini dilakukan di Laut Cina Selatan. Perbedaan SPL antara Aquarius dan SMAP adalah negative, dengan root mean square errors (RMSEs) pada bulan April dan Mei 2015 adalah 0.33 dan 0.31, secara berurutan. Variasi SPL di daerah penelitan ini di analisis menggunakan persamaan sederhana pada mixed layer salinitas. Perbedaan SPL terendah pada musim panas dan tertinggi pada musim dingin, kira-kira sekitar dan psu perbulan, secara berurutan. Faktor utama yang memperngaruhi SPL di Laut Cina Selatan adalah percampuran dengan iar tawar (fluks and residual) di kenal dengan intstruksi kurushio. Flux air tawar akan mengontrol pada fase salinitas terendah dan instruksi kurushio mengontrol pada fase salinitas tinggi. Kata kunci: Aquarius, SMAP, Laut Cina Selatan, Salinitas Permukaan Laut (SPL) viii

2 ABSTRACT Sea Surface Salinity Variation in South China Sea Derived by Aquarius and SMAP Sea Surface Salinity (SSS) plays an important role on being a significant factor for studying and understanding global water cycle and air-sea interactions. Nowadays, satellite observations of SSS come to be more interested and significant for the global SSS scientific studies and researches. The available Aquarius and SMAP SSS data are used together to investigate the variation of SSS in South China Sea (SCS). The evaluation of differences between Aquarius and SMAP SSS data is negative, with the root mean square errors (RMSEs) of the validation between both data in April and May 2015 at 0.33 and 0.31, respectively. Variation of SSS in SCS is evaluated by using the simplified equation of the mixed layer salinity budget, the lowest SSS variation is during summer and the highest is during winter, at about and psu per month, respectively. The factors affecting SSS variation in SCS are mainly considered as freshwater flux and residual (considered as Kuroshio intrusion). The freshwater flux significantly controls low salinity phase and the effect of Kuroshio intrusion will control the high salinity phase. Keywords: Aquarius, SMAP, South China Sea, Sea Surface Salinity ix

3 SUMMARY Sea Surface Salinity Variation in South China Sea Derived by Aquarius and SMAP by Thanarporn Khanpanya, Kakuji Ogawara, Keji Imaoka SSS is significantly important for studying and understanding global water cycle and air-sea interactions. Nowadays, satellite observations of SSS come to be more interested and significant for the large-scale SSS scientific studies and research, such as SMOS, Aquarius, and SMAP SSS. The aims of this research are to investigate SSS variation and the factors which have an impact on SSS variation. The study area of this research is the South China Sea, which is one of a semienclosed sea that represents the large marine ecosystem. This research has used Open Grid Analysis and Display System (OpenGrADS), MATLAB R2013a, and Microsoft Office Excel as the analyzed software. Evaluation of differences between Aquarius version 4.0 and SMAP SSS version 2.0 data shows that Aquarius SSS is smaller than SMAP SSS. Also, the regression tests are established and they provide different correlations between both data, so the correlations will not be applied in this research. SSS seasonal variation is mostly affected by the monsoon winds, and SSS typically reaches the highest in around March and April and reaches the lowest in October. Also, the SSS variations calculated from the simplified equation of the mixed layer salinity budget show that SSS reaches the lowest during summer and reaches the highest during winter. There are several factors can affect the change of SSS in SCS, as considered from the simplified equation of the mixed layer salinity budget, the factors can include freshwater flux (E-P), horizontal advection by surface flow, and residual (including vertical mixing, river runoff, and Kuroshio intrusion). The freshwater flux mainly controls the low salinity phase, because SCS is located in the tropical area which is dominantly occupied by high precipitation over years. The high salinity phase will be controlled by the sum of unknown factors which considered as residual. The residual is considered as Kuroshio intrusion because of the consistency of the maximum residual and the westward flow. x

4 As the result shows that the freshwater flux (E-P) has an effect on SSS variation in SCS, then the precipitation is considered as the most dominant factor that affects the change of SSS. Furthermore, SSS variation during El Niño (year 2015) tends to be higher than in the regular period because of the increasing of fresh water flux (E-P) rate and the stronger westward flow in the Luzon Strait. For the future study, the freshwater runoff data should be included in the analysis and make sure about the most appropriate current data source which should be used for analyzing. xi

5 TABLE OF CONTENTS Page PREREQUISITES DEGREE... i AGREEMENT SHEET. ii APPROVED BY COMMITTEES... iii THE DECREE OF EXAMINER COMMITTEE.. iv STATEMENT FREE FROM PLAGIARISM... v ACKNOWLEDGEMENT. vi ABSTRAK... viii ABSTRACT... ix SUMMARY... x TABLE OF CONTENTS... xii LIST OF TABLE... xv LIST OF FIGURE. xvi LIST OF ABBREVIATIONS...xviii CHAPTER I INTRODUCTION Background Research Problems Aims of Research Benefit of Research 3 CHAPTER II LITERATURE REVIEW Ocean Salinity General of Ocean Surface Salinity Ocean Salinity Factors affect to Surface Salinity Importance of Ocean Salinity Remote Sensing Principle of Remote Sensing Passive/Active Remote Sensing Microwave Remote Sensing Ocean Salinity Observation Technology Satellite Measuring Sea Surface Salinity Satellite Instruments Measuring Sea Surface Salinity xii

6 Aquarius Soil Moisture Active Passive (SMAP) South China Sea (SCS) Physiography Climate Hydrology CHAPTER III FRAMEWORK OF RESEARCH. 26 CHAPTER IV RESEARCH METHOD Research Scheme Research Area Data Sources Aquarius SSS SMAP SSS Evaporation Data Precipitation Data Ocean Currents Data Instruments of Research Data Analysis Aquarius Version 4.0 SSS Data Validation with SMAP Version 2.0 Data Analysis of Main Factor Affecting SSS Variation in SCS Consideration of SSS Variation during El Niño 34 CHAPTER V RESULTS AND DISCUSSIONS Validation between Aquarius SSS and SMAP SSS in SCS The Differences between Aquarius SSS and SMAP SSS Data Correction of Aquarius SSS and SMAP SSS Data The Typical SSS Distribution in SCS Evaluation of SSS Variation in SCS Consideration of Factors Affecting SSS Variation in SCS Consideration of the Effect of Freshwater Flux on SSS Variation in SCS Consideration of SSS Variation during El Niño 47 xiii

7 CHAPTER VI CONCLUSIONS AND SUGGESTIONS. 51 REFERENCES.. 55 xiv

8 LIST OF TABLES Table Page 2.1 Specifications of Aquarius Mission Specifications of SMAP Lists of Data Sources Used in the Research Descriptions of Each Variable of Evaporation Data TRMM 3B43 Characteristics Data Format Structure for 3B43 32 xv

9 Figure LIST OF FIGURES Page 2.1 Ocean Surface Salinity in the World Oceans Precipitation and evaporation as a function of latitude The 'Global Conveyor Belt' represents in a simple way how currents move beneath the wind-driven upper ocean Schematic Representations of Remote Sensing Important Stages in Remote Sensing System Schematic Representation of Passive and Active Remote Sensing The East China, South China, and Yellow seas Framework of Research Research Scheme South China Sea Map The differences between Aquarius SSS and SMAP SSS in the SCS Scatter diagrams of monthly Aquarius SSS versus monthly SMAP SSS Plot of 4-year Aquarius SSS, from year 2012 to 2015 and converted SMAP SSS (in Aquarius SSS scale) by using equation in Figure 5.2a and equation 5.2b Maps of monsoon winds in SCS Monthly average distribution of Aquarius SSS during year 2012 to Plot of the seasonal distribution of 3-year Aquarius SSS The average monthly SSS variation map evaluated by using the simplified equation of the mixed layer salinity budget (equation (2)) Plot of 4-year (from year 2012 to 2015) of each term in equation (2) which considered as the factors affect to the change of the SSS in the SCS Plot of 3-year average seasonal zonal flow in the Luzon Strait (119 E-123 E, 18 N-23 N) Plots of 4-year salinity tendency and freshwater flux in the SCS.. 46 xvi

10 5.11 Plot of 3-year average of Aquarius SSS and 2015 SMAP SSS in SCS Plot of 4-year average of surface salt gain/loss due to evaporation minus precipitation and surface salt gain/loss due to evaporation minus precipitation in Maps of global evaporation minus precipitation rate during year 2012 to The plot of zonal flow in the Luzon Strait during the normal year and El Niño xvii

11 LIST OF ABBREVIATIONS CCDD : Climate Change and Data Detection CMAP : Climate Prediction Center Merged Analysis of Precipitation CONAE : Comisión Nacional de Actividades Espaciales EM : Electromagnetic EMR : Electromagnetic Radiation ESSP : Earth System Science Pathfinder GPCC : Global Precipitation Climatology Centre JPL : Jet Propulsion Laboratory NASA : National Aeronautics and Space Administration NOAA : National Oceanic and Atmospheric Administration NSCS : Northern South China Sea OAFlux : Objectively Analyzed air-sea Fluxes OCO : Office of Climate Observations ORAS4 : Ocean Reanalysis System 4 PPT : Part Per Thousand ( ) PSU : Practical Salinity Unit RMS : Root Mean Square RSS : Remote Sensing Systems SAC-D : Satélite de Aplicaciones Científicas-D SAR : Synthetic Aperture Radar SCS : South China Sea SMAP : Soil Moisture Active Passive SSS : Sea Surface Salinity SSWS : Sea Surface Wind Speed Sv : Sverdrup TRMM : Tropical Rainfall Measuring Mission xviii

12 CHAPTER I INTRODUCTION 1.1 Background The ocean plays an important role in Earth s water cycle as it holds 97% of the total water on the planet; 78% of global precipitation occurs over the ocean, and it is the source of 86% of global evaporation. Nowadays, ocean surface salinity can be used to trace the ocean's role in the Earth's water cycle because there are processes which can affect to salt concentration in the ocean, such as evaporation and freezing of seawater have made the ocean salty. Those same processes are still at work today and are counterbalanced by processes that decrease the salt in the ocean, like the precipitation of rain and snow over the ocean, melting of glaciers and icebergs, and freshwater input from rivers. Then, the result of the changes in the concentration of salt can have large-scale effects on the Earth's water cycle and ocean circulation (Water Cycle). The ocean salinity is one of the factors that drive the world s ocean circulation, as known as thermohaline circulation by causing the sinking and rising of water masses. Furthermore, the salinity is important in the perspective of environmental considerations as an ecological factor of considerable importance and influence the types of organisms in the seawater (Faridi, 2010). The study of the sea surface salinity (SSS) variation in the region of South East Asia especially in South China Sea (SCS) is interesting. Since this region is a 1

13 2 semi-enclosed area and consists of the factors which mainly affect the surface salinity variations, e.g., river inflow mainly from Mekong River and Pearl River. Also, the circulation due to the monsoons in this region and the salt advection can be the additional factors which affect the surface salinity variations. Moreover, the quantification of observing the salinity changes is one key to understand the salinity distribution and its variability (Ren & Riser, 2009). The measurement of SSS had been existed for centuries from ships and buoys, measuring changes in the saltiness of Earth's vast ocean demanded the use of satellite technology. The first attempt to measure SSS from space occurred in 1973 on Skylab at the specific frequency, which would eventually be used by Aquarius. Therefore, we can use Aquarius mission to observe SSS on the global scale. As the Aquarius was stopped operating in June 2015, it caused the lack of SSS data for this research. However, there was the new satellite which can observe the sea surface salinity after the Aquarius operation was stopped, which is Soil Moisture Active Passive (SMAP). Therefore, this research will be related to the observation of SSS distribution and also focus on the quantitative of SSS variation in SCS by using the Aquarius and SMAP. And hope this research will be benefits to a scientific profession and people who are interested in sea salinity distribution around Southeast Asian coastal areas.

14 3 1.2 Research Problems Based on the background of this research, the formulations of the problem are two namely: 1. How does the sea surface salinity regularly distribute in South China Sea? 2. What are the effects on sea surface salinity distribution in South China Sea? 1.3 Aims of Research The aims of this research are: 1. To observe regular sea surface salinity distribution in South China Sea. 2. To identify the effects on sea surface salinity distribution in South China Sea. 1.4 Benefit of Research Study of sea surface salinity can provide the knowledge of water cycle and ocean circulation. Moreover, it can be used as the database of the ocean conditions for studying the ability of distribution of living organisms in the ocean. Therefore, this research can be useful in case of studying the water cycle, ocean circulation, distribution of oceanic living organisms, and also recognition of the effects of surface salinity on the marine ecosystem in the South China Sea.

National Aeronautics and Space Administration. Aquarius/SAC-D. Sea Surface Salinity from Space

National Aeronautics and Space Administration Aquarius/SAC-D Sea Surface Salinity from Space Acknowledgments Aquarius Web site http://aquarius.nasa.gov/ Special thanks to the Aquarius Science Team for