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Gyun -Do Pak CURL SNU / LOCEAN Paris 6

2013. 3. 5. Atmospheric Influences to interannual -to-decadal Winter SST Variability in the North-Western Pacific : East Asia Winter Monsoon and Western Pacific Index. Gyun -Do Pak CURL SNU / LOCEAN Paris 6. Supervisor: Kyung-Il Chang (CURL SNU)

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Gyun -Do Pak CURL SNU / LOCEAN Paris 6

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  1. 2013. 3. 5. Atmospheric Influences to interannual-to-decadal Winter SST Variability in the North-Western Pacific: East Asia Winter Monsoon and Western Pacific Index Gyun-Do Pak CURL SNU / LOCEAN Paris 6 Supervisor: Kyung-Il Chang (CURL SNU) Young-Hyang Park (LOCEAN Paris 6) Frederic Vivier (LOCEAN Paris 6)

  2. Review Kwon et al. (2010) Kwon et al. (2010, review paper) - PDO related wind stress curl - Making anomaly (MLD, SSH, ..) - Propagating westward by Rossby wave - Arriving KOE region (takes 3~4 years from center of Pacific) Actually there is also 1 year lag correlation between WSC and KOE SST Park et al. (2012) Park et al. (2012) - Revealed reason of 1 year lag - Difference between 70-80s and 90-00s - Limited to winter (JFM for SST and DJF for WSC) 5yr 4yr 1yr 3yr Extend for all season with CSEOF?

  3. No merit of CSEOF for SST in this region Blue: Normal EOF PCT Black: CSEOF PCT We’ll just study for winter SST with classical methods

  4. Data ERSST v3b Data period: 1854 ~ 2012 (Monthly) Analysis period: 1970 ~ 2012 Base period: 1970 ~ 1989 (Same with Park et al. 2012) Climate Indices East Asian Winter Monsoon (SLPSH – SLPES): Park et al. (2012) Western Pacific (2ndmode of North Pacific 700mb Height) Pacific North-America (1stmode of North Pacific SLP) Arctic Oscillation (1stmodeof North Hemisphere 1000mb Height) Multivariate ENSO Index (ENSO index with SLP, SST, U, V, Cl, Ta) Atmospheric Variables 2000 DJF : 1999 Dec, 2000 Jan, 2000 Feb 2000 OND : 1999 Oct, 1999 Nov, 1999 Dec Trenberth SLP (SLP only) NCEP1 (Long term coverage, not used now)

  5. Correlations between winter (DJF) indices Families EAWM ~ SH ~ ES ~ WP PNA ~ AL ~ MEI ~ AO

  6. Correlation between SST and SH SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  7. Correlation between SST and AL SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  8. Correlation between SST and ES SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  9. Correlation between SST and EAWM SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  10. Correlation between SST and WP SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  11. Correlation between SST and PNA SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  12. Correlation between SST and AO SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  13. Correlation between SST and MEI SST leads OND NDJ DJF JFM FMA OND NDJ Index leads DJF JFM FMA

  14. Correlation between SST and Climate indices 1. Atmosphere (indices) drives SST generally, while SST drives atmosphere very limitedly. 2. Correlation is high when atmosphere leads 1 or 2 month(s). But the results (spatial distribution, …) somewhat depends on the timing of SST or indices. 3. Correlation between JFM SST and NDJ or DJF indices are the highest. 4. High correlation region for JFM SST vs DJF indices 1) EAWM: Near Korea, East of Japan 2) WP: Similar with EAWM, but concentrated to southern part 3) PNA: Southern part of East Sea, Far East of Japan 4) AO: East Sea, East of Japan 5) MEI: South of Korea, South-eastern and East part of the domain

  15. Multi-variable Linear Regression : Reconstructed time series : Climate indices series : SST time series (X1) (X2) (X3) (X4) (X5) Spatial distribution of correlation between ERSST and reconstructed SST series from climate indices (EAWM, WP, PNA, AO, MEI) and contributions of each index to reconstructed SST (contours) with significant limit of partial regression coefficient (dark gray shadings) (See appendix I for calculating significance limit)

  16. APPENDIX I: Multiple and Partial regression coefficient Target variable (SST) Predictor variable (Climate index) Multiple regression correlation coefficient Partial regression coefficient (squared) where Significant test for partial regression coefficient Length of time series Number of predictors => The partial regression coefficient can be considered different from zero

  17. Multi-variable Regression 1. Reconstructed SST from EAWM, WP, PNA, AO, and MEI is well correlated with original SST except near the Kamchatka and KOE region (East of Japan, 40N). 2. EAWM explains the SST variability significantly south of 40N, especially Yellow Sea and east coast of Japan except south of Korea and Japan (30N). 3. WP explains the SST variability well except Yellow Sea and 40N latitudinal band. 4. PNA covers southern part of East Sea and eastern and southern part of the domain. 5. AO covers only little part of the East Sea (northern part). 6. MEI cannot cover any place except south east part and near Thailand (Not our interesting region) => Major influences are from EAWM, WP, and PNA

  18. EOF Analysis: Trenberth SLP

  19. EOF Analysis: Trenberth SLP

  20. Check previous study (Yeh and Kim 2010) NPO is an important to warm Yellow Sea SST - DJF HadSST (1950-2008) - EOF 1st PC time series - Regression of SLP to the PC1 - NPO pattern (NPO ~ WP)

  21. EOF Analysis: Trenberth SLP 1. The leading EOF mode is the closest to AO highly correlated with EAWM. 2. Second mode is well correlated to Aleutian Low family (PNA) and MEI. Note that PNA is the leading mode when EOF is applied for North Pacific only. 3. The third mode simultaneously contains WP and EAWM. Note Yeh and Kim (2010) suggested NPO which is well correlated to WP is important to warm the Yellow Sea SST. But we now know variability of Yellow Sea SST is mainly contributed by EAWM (WP doesn’t cover the Yellow Sea).

  22. Regression Trenberth SLP onto Climate indices EAWM WP 1970 ~ 2012 PNA AO

  23. Time dependence of correlation between EAWM and WP Correlation with 9-year moving window (red solid line) between EAWM index and WP index with its 95% confidence limit (dotted line). The blue and black solid lines are for EAWM index and WP index, respectively.

  24. North Pacific Oscillation Meridional oscillation on North Pacific WP: 700 (500) hPageopotential height NPO: Sea Level Pressure From Wikipedia http://www.cpc.ncep.noaa.gov/data/teledoc/wp_map.shtml WP – NOAA NPO – Linkin 2008 using Trenbreth (SLP difference) DJF Very similar but different on 90’s..

  25. Comparison of EAWM, WP, and NPO Blue: NPO and WP Red: NPO and EAWM Correlation between NPO and WP is always higher than significance level (0.58) except 90s (But it is still high value). Correlation between NPO and EAWM is low on 60s and 90s which is correspond to the correlation between EAWM and WP.

  26. Regression of Trenberth SLP onto the Climate indices for each period 1970-2012 1970-1987 1988-2004 2005-2012

  27. SST Response to Climate Indices for each period EAWM WP PNA AO 1970-2012 1970-1987 1988-2004 2005-2012

  28. Importance of AO? Corr(c,AO)=0.31 Lagcorr(c,AO,2)=0.52 (AO leads 2 years)

  29. Relationship between EAWM and WP 1. The whole time (1970 – 2012) regression of SLP for EAWM and WP shows very similar results so that we have to consider the two processes are same. 2. However, motivated from Park et al. (2012), when we divided the analysis period, there are some periods of well correlated (70-80’s (=A1) and recent years (=A3)) EAWM and WP and not (90’s, A2). Note that the correlation is low on 60’s. 3. Actually, the regression results are extremely same on A1 and A3, however they can be distinguished on A2 period. The striking feature is extension of Siberian High on A1 and A3,and its shrinking can be seen on A2. 4. On the other hand, the PNA is extended to westward (even penetrate the coast of Eurasian continent) on A2. 5. Correlations between JFM SST and DJF EAWM are high on A1 and A3, but on A2 the correlation is too low. Only covers small region south of Korea. 6. Correlation between SST and WP is always high. On A2 even it covers strongly in the north of 40N. 7. It is slightly related the correlation between EAWM/WP and AO index with 2 yrlag.

  30. Oyashio Extension Index: Not finished The leading PC of the latitude of the maximum meridional SST gradient Temperature Latitude Longitude Latitude Meridional SST distribution (blue), low-pass filtered SST (red), maximum gradient (black circle) SST gradient (shadings), SST (contours), maximum SST (black dots), and maximum SST after lowpass filter (red dots)

  31. Oyashio Extension Index EOF1 (40%) Year Average position Longitude Mean position of the maximum SST gradient (blue) and spatial loading vector of leading mode (red) Latitude of maximum SST gradient Correlations (1yr low pass filtered) vs PDO = -0.65 (without lag) vs NPO = -0.40 (NPO leads 19 months) Correlations (Winter (DJF) Mean) vs PDO = -0.59 (without lag) vs NPO = -0.46 (NPO leads 2 yrs) vs AL = 0.46 (AL leads 1yr) vs EAWM = -0.53 (EAWM leads 2 yrs) vs WP = 0.47 (WP leads 2yrs)

  32. Summary 1. Atmospheric influences to North-western Pacific SST are mainly contributed by EAWM, WP, PNA. (AO’s influence is weak, MEI influence to region not interesting) 2. EAWM dominates on the region near Korea especially in Yellow Sea. WP have large influences to south of Japan. PNA east part of the domain. 3. EAWM and WP are hard to be separated using EOF mode analysis and even regression of SLP. (Ta, U, V, CSEOF, … not shown but not be separated) 4. Splitting the period, EAWM and WP can be separated when Siberian High shrinks. It may related with the AO (Not sure ^^). Future plans 1. Finish this research - Some more research for mechanisms using atmospheric variables (NCEP?) - Some more work with OEI..? (or it is used for second research) - Writing 2. Second research - Oceanic influences to the SST - Heat budgets using modeled data (MERCATOR)

  33. Many many comments please Thank you

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