Dmitry D. Kaplunenko , Olga O. Trusenkova, Viacheslav B. Lobanov

1. The features of short term variability in the Northern Japan Sea obtained from satellite SST and altimetry data Dmitry D. Kaplunenko, Olga O. Trusenkova, Viacheslav B. Lobanov V.I.Il'ichev Pacific Oceanological Institute, Vladivostok, Russia e-mail: dimkap@poi.dvo.ru

2. Objectives • Assessing of SST and altimetry variability (variance) for Japan Sea for the whole basin as well as Northern part of sea (from 40ºN) • Comparison of results obtained from the different datasets of daily resolution

3. Used Data • DatasetI: • ProjectNew Generation SST (NGSST) ofTohoku University, Japan:http://www.ocean.caos.tohoku.ac.jp/ • NGSST – merging of satellite data from infrared (radiometer AVHRR with NOAA and MODIS/Aqua) and microwave ranges (AMSR-E/Aqua). • Data period: 1 July 2002 yr. - present. • ftp://www.ocean.caos.tohoku.ac.jp/pub/mergedsst_binary/ • There is not essential percent of missed data is present. • DatasetII: • Satellite data of DailySST JMAfor North-Western part of Pacific, http://goos.kishou.go.jp/rrtdb-cgi/access/products/dailysst.cgi • This dataset is based on Merged satellite and in-situ data Global Daily Sea Surface Temperature (MGDSST) belonged toJapan MetorologicalAgency (JMA) • http://goos.kishou.go.jp/rrtdb-cgi/access/products/mgdsst.cgi • Data period: 19 October 1993 yr. – present • http://goos.kishou.go.jp/rrtdb/usr/pub/JMA/dailysst/ • Missed data is absent. • Used period: • 12.10.1993 – 11.08.2006 yr. (DailySST JMA) • 01.07.2002 – 09.07.2006 yr.(DailySST JMA, NGSST) • AVISO altimetry: ftp://ftp.cls.fr/pub/oceano/AVISO/ (1992/10/14-2006/07/07)

4. DatasetI – description (NGSST)

5. DataSet II - description • AVHRR/NOAA( NOAA-16, NOAA-17) – infrared sensor • Global area coverage(GAC) Level-1B • from NOAA/Satellite-Active-Archive(SAA) • HRPT data received in the Meteorological Satellite Center (MSC) • AMSR-E/Aqua --- Microwave sensor • AMSR-E Level2 SST is provided • by the Japan Aerospace Exploration Agency (JAXA). • AMSR-E SST is calculated by the Shibata algorithm. • INSITU • buoy and ship data are collected through GTS

6. Initial procedure of satellites’ dataMGDSST/DailySST JMA SAA JAXA MSC GAC Level1B AVHRR/NOAA Level2 AMSRE/Aqua HRPT AVHRR/NOAA Office of Marine Prediction, JMA • cloud clearing • convert to MCSST • quality check by • AMSR-E • quality check • daily average • 0.25 x 0.25 grid MSC MCSST 0.25 degree grid AVHRR(GAC) AMSR-E AVHRR(MSC) daily SST

7. Altimetry-AVISO

8. Methods of data analysis • Used methods of analysis: • Decompositon on Complex Empirical Orthogonal Functions (CEOF) • Spectral analysis of principal components for timeseries of decomposition • Correlation analysis of initial timeseries • Applying of CEOF: • Decomposition of initialSST-field z(x,y,t) in following form: (1) , (2) • PCnand Sn(x,y) timeandspatialcomponentsof initial field • Subtracting of 1stmodeto get rid of its variability: (3).

9. SST CEOF decomposition ofNGSST • The variabiliry, of 1st CEOF modeis describing 96% SSTvariability - annual signal for highly auto-correlated daily data of SST. • 2ndspatialmode – NorthernandSouthernpart of the Sea in antiphase,subarctic front

10. SST decompositonNGSST with subtraction of 1st CEOF mode • 1st mode exclusion allows to show anomalies not connected with annual signal • Now 1st mode – separation by subarctic front, 2nd – antiphasebetweenNorthernandWesternparts of theSea, 3rd – Central part of Japan Sea Annual Semiannual Intraannual?

11. SST CEOF decomposition ofJMA • Spatial anomalies of DailySST JMA is similar with NGSST for the shorter period • 3rd mode – separation of annual component, which cannot be seen for shorter period for NGSST data – the other oscillations need to be detected with the different methods of separation

12. NGSST for Northern part of Japan Sea • Northern part of the Sea has an own part of variability which is not seen during analysis on the whole basin scale • 1st mode separation east/west, 2nd mode – separation between regions of Liman and Tsushima currents near Tsugaru strait, 3rd - separation of cyclonic eddy

13. Altimetry CEOF decomposition ofAVISO full JS • Variability of SSH is not similar to the SST • Probable presence regions associated with the anomaly Propagation • CEOF 1-3 describes 80% of total variance

14. Altimetry CEOF decomposition ofAVISO North JS • CEOF 1-3 describes 86 % of total variance • SSH variability has specific features in the northern area in compare with the whole basin of Japan Sea

15. Results and conclusions • The spatial and temporal variability for datasets in area of Japan Sea for NGSST and DailySST JMA looks similar. • The CEOF decomposition for the Northern part Japan Sea shows the features of variability which are not seen for the basin scale. • The obtained SST CEOF for Northern part of Japan Sea modes is correspondent to opposite phases: 1st – Northwestern and Northeastern part Japan sea , 2nd – Liman ant Tsushima currents, 3rd – activity of Subarctic cyclonic gyre in the Eastern Japan Basin • The variability of SSH is not similar to the SST but also has some specific features in the northern area in comparison to the whole basin of Japan Sea.

16. Results and conclusions (continue) • The anomaly for the SST inter-annual scale most of oscillations seen on the NGSST and DailySST JMA datasets of JS have been found as standing one. The propagating one have been shown earlier (Park and Chu, 2006) using the 8-days mean data from NOAA Advanced Very-High-Resolution Radiometer (AVHRR) Oceans Pathfinder global equal-angle best SST data (Version 4.1). • The altimetry data weekly averaged is also showed probable presence regions associated with the anomaly propagation • The most reasonable cause of semi-annual time scale variability for explored data is correspondent to monsoon variability of Japan Sea region.

17. Thank you!