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Seasonal Variability of Salinity in the Karimata Strait, Indonesia : in Situ and Satellite Observations

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Seasonal Variability of Salinity in the Karimata Strait, Indonesia : in Situ and Satellite Observations SEASONAL VARIABILITY OF SALINITY IN THE KARIMATA STRAIT, INDONESIA : IN SITU AND SATELLITE OBSERVATIONS R. Dwi Susanto, Ph.D. AOSC, University of Maryland, USA, and Bandung Institute of Technology, Bandung PACIFIC In collaboration with: A. Setiawan, B. Sulistyo, T. R. Adi, T. Agustiadi, M. Trenggono, Triyono, A. Kuswardani Ministry for Marine Affairs and Fisheries 2200 miles INDIAN Australia OSST Meeting Arlington, VA, Sept 18-20, 2017 Motivation: ü To test the limit of the satellite salinity performances by comparing different satellite SSS products with in situ observation at challenging region such as Indonesian seas. ü Pacifc – Indian Ocean Exchange via Indonesian Seas known as Indonesian throughfow (ITF) ITF plays important role in global ocean circulation and climate. It is affected by local and remote forcing: tides, MJO, monsoon, ENSO and IOD Hence, salinity variability is expected to vary from tidal to interannual time scales ü it is desirable to not only quantify the ITF and its variability, but also monitor it for a longer period of time. Yet, a sustainable in situ observation is expensive and challenging. Therefore, a proxy for ITF transport and freshwater fux is desirable. ü Along the route within the Indonesian seas, the water undergoes strong tidal mixing, air-sea interaction, and other oceanic/atmospheric forcing processes associated with MJO, monsoon, ENSO and IOD. Hence, understanding freshwater fux and salinity variability within the Indonesian seas is very important global ocean circulation and climate. 3-year Animation; Sep2011 – Aug2014 V3.0 Aquarius Salinity Courtesy NASA Science Visualization Studio, GSFC ? Trajectories satellite-track drift buoys from the Global Drifter Program (8/1988-6/2007) courtesy of Drifter Data Assembly Center at NOAA/AOML. Susanto et al., 2010 Pacific to Indian Ocean Exchange: Indonesian Throughflow Susanto et al., 2016 Susanto et al., 2016 South China – Indonesian Seas Transport/Exchange (SITE) SITE PI’s USA: Dwi Susanto P.R. China: Guohong Fang (FIO) Indonesia • Indroyono Soesilo • Sugiarta Wirasantosa • Budi Sulistyo Questions: ü How does SCS/Java exchange affect air-sea interaction and ocean circulation within internal Indonesian Seas and the South China Sea? ü How does this SCS/Java exchange affect the dynamics of the primary ITF? Sustained in situ observations are logistically challenging and expensive. It is desirable to use satellite derived sea surface salinity as a proxy • Ideally, in situ observation should be done at the surface • However because of : ü busy shipping lanes ü heavy fishing activity, ü avoid vandalism ü shallow water (~40 m) ü well mixed water stratification during boreal winter and summer, Salinity and temperature measurements conducted at the bottom Trawl Resistance Bottom Mount ADCP& CTD 0° K ALIMANT KARIMATA STRAIT 1°S AN BANGKA 2°S Diameter: 2m 3°S BE 4°S SUMATERA 5°S 105°E 106°E 107°E 108°E 109°E 1 ADCP (Velocity, T, P), CTD, Modem, GPS-Iridium beacon, Dissolved Oxygen, Acoustic Releases. Summary NW monsoon NE monsoon ü Even though the annual means of South China Sea throughflow & Sunda Strait flow are small, but their large seasonal variability play an important role and may control the thermocline intensified and seasonal flow in the Makassar Strait : enhancing southward flow ITF during Southeast monsoon and reducing ITF during northwest monsoon (boreal winter) Note: 2004-2009 Makassar throughflow -15.5Sv (JFM) and -9.6 Sv (OND) ü SITE Transport -2.7 Sv during northwest monsoon to + 1.2 Sv during northeast monsoon. ü Coastally trapped Kelvin waves from Indian Ocean probably enter the Sunda Strait If main ITF is a cup of Coffee, the SCS and Sunda Strait throughflow is the Creamer, May be it is small but it is important!!! Susanto et al., 2013 Amplitude of MSf (14.77 days) based on SST Ray and Susanto, 2016 0 5 10 BJ1 Feb2008 BJ8 Nov2008 15 Geo Jul2009 Salinity 20 BJ8 Sep2011 BJ8 Nov2012 25 BJ8 Jul2013 Depth (m) 30 BJ8 Dec2014 BJ8 Jun2015 35 BJ8 May2016 40 45 32 32.25 32.5 32.75 33 33.25 33.5 Salinity (PSU) 0 5 10 BJ1 Feb2008 BJ8 Nov2008 15 Geo Jul2009 20 BJ8 Sep2011 BJ8 Nov2012 25 BJ8 Jul2013 Temperature Depth (m) 30 BJ8 Dec2014 BJ8 Jun2015 35 BJ8 May2016 40 45 Susanto et al., 27 27.5 28 28.5 29 29.5 30 30.5 31 2017 (submitted) Temperature (oC) In situ bottom Temperature vs MURSST Susanto et al., 2017 (submitted) Exhibit seasonal variability Satellite Derived Salinity ü NASA Aquarius/SAC-D, Level 2 JPL-processing ü NASA SMAP (Soil Moisture Active Passive) • Level 3 JPL processing (beta version; Fore et al., 2016) • Level 3 REMSS processing (Meissner and Went, 2016) ü ESA’s SMOS (Soil Moisture Ocean Salinity), Level 3 IFREMER- processing Satellite derived sea surface salinity 10oN 36 5oN 35 0o 34 5oS 33 o (a) 10 S (b) 32 15oS 31 90oE 100oE 110oE 120oE 130oE 140oE 10oN 36 10oN 36 5oN 35 5oN 35 0o 34 0o 34 5oS 33 5oS 33 10oS (c) 32 10oS (d) 32 15oS 31 15oS 31 90oE 100oE 110oE 120oE 130oE 140oE 90oE 100oE 110oE 120oE 130oE 140oE a.! Aquarius L2 b. SMAP L3 by JPL c. SMAP L3 by REMSS d. SMOS by IFREMER Soil Moisture Active Passive (SMAP); Soil Moisture and Ocean Salinity (SMOS) Least square fit harmonic analysis of seasonal and annual signals • In situ temperature and salinity Susanto et al., 2017 (submitted) • Seasonal and annual signals • Residual à interannual -à no apparent of ENSO but seems related to IOD Power Spectrum of in situ Temperature and Salinity and Wind Speed Susanto et al., 2017 (submitted) In Situ [ −1.9oN & 108.5oE ] Aquarius larger area SSS L3 V4 Aquarius SSS L3 V4 SMAP JPL L3 V2.2 35 SMAP REMSS L3 V2.0 SMOS IFREMER V2.0 Surface CTD 34 Bottom CTD 33 32 31 30 Jul11 Jan12 Jul12 Jan13 Jul13 Jan14 Jul14 Jan15 Jul15 Jan16 Jul16 35 34 33 32 31 Jan15 Mar15 May15 Jul15 Sep15 Nov15 Jan16 Mar16 May16 Jul16 Susanto et al., 2017 (submitted) Least-squared fit of the salinity 35 In situ, m = 32.77 PSU Aquarius large, m = 32.44 PSU 34 Aquarius medium, m = 32.34 PSU SMAP JPL, m = 33.16 PSU SMAP REMSS, m = 33.26 PSU SMOS, m = 32.56 PSU 33 Salinity (PSU) 32 31 Jul11 Jan12 Jul12 Jan13 Jul13 Jan14 Jul14 Jan15 Jul15 Jan16 Jul16 Susanto et al., 2017 (submitted) Summary: passed the satellite performance!! ü Freshwater flux from the South China Sea into the Java Sea/ Makassar Strait plays a pivotal role in controlling the stratification of the Indonesian throughflow. Therefore it is desirable to use satellite-based salinity measurement (SSS) as a proxy for freshwater flux. ü In situ observation of bottom salinity and temperature exhibit semi-annual variability associated with monsoonal wind reversals. The temperature leads the salinity by ~54 days. Interannual variability clearly visible, there is relation to IOD, but no apparent direct relation to ENSO. ü Despite challenging location (marginal seas, narrow strait, close to many islands) the SSS from Aquarius, SMAP, and SMOS shows seasonal variability which in-sync with the in situ measurement. Summary: ü In general, seasonal variability of SSS is in-sync with the in situ data; However, • the mean (32.34 PSU) and amplitude of Aquarius SSS is less than that of the in situ data (32.65 PSU). • timing and amplitude of SSS SMAP are match well, but the mean (JPL: 33.19 PSU, REMSS: 33.35 PSU) is smaller than in situ (33.52 PSU). • the mean of SSS SMOS (32.63 PSU) and amplitude are smaller than in situ (32.75), and the L3 data is too smooth. On-going & future plan Extract tidal mixing signature from SSS data 4 2 3 1.5 2 1 1 0.5 IOD NINO34 0 0 −1 −0.5 −2 −1 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 Velocity and Transport in the Karimata Strait 0 5 10 15 20 25 Depth/m 30 35 40 45 Dec 2007 to Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov March 2008 −1.2 −1.1 −1 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 V/m.s−1 -2.7 ± 1.1 Sv –0.30 ± 0.11 PW 1 –0.18 ± 0.07 − 2 .s 3 m 6 0 May 2008 to Sept 2008 −2 −4 1.2 ± 0.6 Sv Transport/10 0.14 ± 0.03 PW Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov 0.12 ± 0.04 Sv Susanto et al., 2013 Least square fit harmonic analysis of semi-annual signal Fitting with semi-annual only .
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