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SCIENCE CHINA Earth Sciences, Volume 59 , Issue 11 : 2213-2222(2016) https://doi.org/10.1007/s11430-016-0048-2

Estimating thermohaline variability of the equatorial Pacific Ocean from satellite altimetry

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  • ReceivedJun 13, 2016
  • AcceptedAug 10, 2016
  • PublishedSep 23, 2016

Abstract


Acknowledgment

The TRITON data were provided by the Japan Agency for Marine Earth Science and Technology. Comments from two anonymous reviewers are greatly appreciated. This research was supported by the National Basic Research Program of China (Grant No. 2012CB417400) and the National Natural Science Foundation of China (Grant Nos. 41576017 & U1406401).


References

[1] Book J W, Wimbush M, Imawaki S, Ichikawa H, Uchida H, Kinoshita H. Kuroshio temporal and spatial variations south of Japan determined from inverted echo sounder measurements. J Geophys Res, 2002, 107: 3121 CrossRef ADS Google Scholar

[2] Delcroix T. Observed surface oceanic and atmospheric variability in the tropical Pacific at seasonal and ENSO timescales: A tentative overview. J Geophys Res, 1998, 103: 18611-18633 CrossRef ADS Google Scholar

[3] Gavart M, De Mey P. Isopycnal EOFs in the Azores current region: A statistical tool for dynamical analysis and data assimilation. J Phys Oceanogr, 1997, 27: 2146-2157 CrossRef Google Scholar

[4] Isern-Fontanet J, Lapeyre G, Klein P, Chapron B, Hecht M W. Three-dimensional reconstruction of oceanic mesoscale currents from surface information. J Geophys Res, 2008, 113: C09005 CrossRef ADS Google Scholar

[5] Kessler W S, McCreary J P. The annual wind-driven rossby wave in the subthermocline equatorial Pacific. J Phys Oceanogr, 1993, 23: 1192-1207 CrossRef Google Scholar

[6] LaCasce J H, Mahadevan A. Estimating subsurface horizontal and vertical velocities from sea-surface temperature. J Mar Res, 2006, 64: 695-721 CrossRef Google Scholar

[7] Liu L, Peng S, Wang J, Huang R X. Retrieving density and velocity fields of the ocean’s interior from surface data. J Geophys Res Oceans, 2014, 119: 8512-8529 CrossRef ADS Google Scholar

[8] Lukas R, Lindstrom E. The mixed layer of the western equatorial Pacific Ocean. J Geophys Res, 1991, 96: 3343-3357 CrossRef ADS Google Scholar

[9] Ma X. Hydrographic diagnostic analysis in the western tropical Pacific based on a streamfunction projection method (in Chinese). Doctoral Dissertation. Beijing: Univ of Chinese Academy Sciences. 2015, Google Scholar

[10] Ma X, Sun C. Water mass characteristics in the western North Pacific based on a streamfunction projection. Sci China Earth Sci, 2015, 58: 2067-2077 CrossRef Google Scholar

[11] Ma X, Sun C. Equatorward shift of annual Rossby waves in the equatorial Pacific Ocean. Chin J Ocean Limnol, 2016, 34: 212-218 CrossRef ADS Google Scholar

[12] Marin F, Kestenare E, Delcroix T, Durand F, Cravatte S, Eldin G, Bourdallé-Badie R. Annual reversal of the equatorial intermediate current in the Pacific: Observations and model diagnostics. J Phys Oceanogr, 2010, 40: 915-933 CrossRef Google Scholar

[13] McPhaden M, Ando K, Bourles B. 2009. The global tropical moored buoy array. Proceedings of OceanObs’09, WPP-306. Google Scholar

[14] Meijers A J S, Bindoff N L, Rintoul S R. Estimating the four-dimensional structure of the Southern Ocean using satellite altimetry. J Atmos Ocean Technol, 2011, 28: 548-568 CrossRef Google Scholar

[15] Meinen C S, Luther D S, Baringer M O. Structure, transport and potential vorticity of the Gulf Stream at 68°W: Revisiting older data sets with new techniques. Deep-Sea Res Part I-Oceanogr Res Pap, 2009, 56: 41-60 CrossRef ADS Google Scholar

[16] Nardelli B B, Santoleri R. Reconstructing synthetic profiles from surface data. J Atmos Ocean Technol, 2004, 21: 693-703 CrossRef Google Scholar

[17] Park J H, Watts D R, Tracey K L, Mitchell D A. A multi-index GEM technique and its application to the southwestern Japan/East Sea. J Atmos Ocean Technol, 2005, 22: 1282-1293 CrossRef ADS Google Scholar

[18] Pérez-Brunius P, Rossby T, Watts D R. Transformation of the warm waters of the North Atlantic from a geostrophic streamfunction perspective. J Phys Oceanogr, 2004, 34: 2238-2256 CrossRef Google Scholar

[19] Riley J J, Lelong M P. Fluid motions in the presence of strong stable stratification. Annu Rev Fluid Mech, 2000, 32: 613-657 CrossRef ADS Google Scholar

[20] Roemmich D, Wunsch C. On combining satellite altimetry with hydrographic data. J Mar Res, 1982, 40: 605-619 Google Scholar

[21] Rodrigues R R, Wimbush M, Watts D R, Rothstein L M, Ollitrault M. South Atlantic mass transports obtained from subsurface float and hydrographic data. J Mar Res, 2010, 68: 819-850 CrossRef Google Scholar

[22] Sun C. The columnar structure in stratified geostrophic flows. Geophys Astro Fluid, 2001, 95: 55-65 CrossRef Google Scholar

[23] Sun C, Watts D R. A circumpolar gravest empirical mode for the Southern Ocean hydrography. J Geophys Res, 2001, 106: 2833-2855 CrossRef ADS Google Scholar

[24] Sun C. Temperature phase tilt in unstable baroclinic waves. J Atmos Sci, 2007, 64: 4520-4522 CrossRef ADS Google Scholar

[25] Sun C. A baroclinic laminar state for rotating stratified flows. J Atmos Sci, 2008, 65: 2740-2747 CrossRef ADS Google Scholar

[26] Sun C. High-order exact solutions for pseudo-plane ideal flows. Phys Fluids, 2016, 28: 083602 CrossRef Google Scholar

[27] Swart S, Speich S, Ansorge I J, Lutjeharms J R E. An altimetry-based gravest empirical mode south of Africa: 1. development and validation. J Geophys Res, 2010, 115: C03002 CrossRef ADS Google Scholar

[28] Wang J, Flierl G R, LaCasce J H, McClean J L, Mahadevan A. Reconstructing the ocean’s interior from surface data. J Phys Oceanogr, 2013, 43: 1611-1626 CrossRef Google Scholar

[29] Watts D R, Sun C, Rintoul S. A two-dimensional gravest empirical mode determined from hydrographic observations in the Subantarctic Front. J Phys Oceanogr, 2001, 31: 2186-2209 CrossRef Google Scholar

[30] Zhang L, Sun C. A geostrophic empirical mode based on altimetric sea surface height. Sci China Earth Sci, 2012, 55: 1193-1205 CrossRef Google Scholar

  • Figure 1

    The mean SSH field from AVISO product (contour lines with 5 cm interval) and the correlation between SSH and Argo-derived surface dynamic height relative to 2000 dbar (color map). Black dots denote the TRITON buoys with a pentagram highlighting the buoy at 0°, 147°E.

  • Figure 2

    Spline fits of TRITON temperature and salinity data at 0°, 147°E (dots) against ADT SSH values. The lower panels are resultant HGEM fields.

  • Figure 3

    Comparison of TRITON temperature and salinity measurements (dots) and HGEM estimates (lines) at 0°, 147°E.

  • Figure 4

    Periodogram calculated at each depth for TRITON temperature measurements at 0°, 147°E (a). (b) Vertically averaged power.

  • Figure 5

    Climatological seasonal cycle of temperature and salinity (annual mean removed) derived from TRITON data at 0°, 147°E.

  • Figure 6

    Lag-correlation between ADT SSH and TRITON temperature at 0°, 147°E, with negative correlation values below 450 dbar (a). Corresponding phase lead by SSH (b). The dashline represents correlation without phase lag.

  • Figure 7

    Normalized temperature data at 200 dbar and 500 dbar from TRITON buoy at 0°, 147°E. The 500 dbar temperature series has been inversed and shifted forward by 176 day to obtain an optimal correlation. For better view both temperature series are 30-day running mean.

  • Figure 8

    EGEM temperature and salinity fields at SSH=110 cm and their seasonal anomalies (lower panels).

  • Figure 9

    EGEM temperature fields at 200 dbar and 500 dbar.

  • Figure 10

    Comparison of TRITON temperature and salinity measurements (dots) and EGEM estimates (lines) at 0°, 147°E

  • Figure 11

    Percentage variance ratio for HGEM (dashline) and EGEM fields (solid line) at 0°, 147°E.

  • Figure 12

    Temperature percentage variance ratio distribution at 500 dbar for HGEM, EGEM and their difference (left panels). The right panels are for salinity.

  • Table 1   Lag correlation between 200 dbar and 500 dbar for TRITON temperature data during 2000–2013

    Buoy

    cor0

    mcor

    lag

    137°E, 8°N

    0.22

    0.22

    0

    137°E, 5°N

    0.71

    0.71

    0

    138°E, 2°N

    −0.03

    −0.53

    128

    138°E, 0

    0.16

    −0.49

    133

    147°E, 5°N

    0.57

    0.57

    0

    147°E, 2°N

    0.04

    −0.58

    119

    147°E, 0

    0.26

    −0.59

    176

    156°E, 8°N

    0.36

    0.36

    0

    156°E, 5°N

    0.43

    0.43

    0

    156°E, 2°N

    0.12

    −0.54

    132

    156°E, 0

    0.17

    −0.56

    165

    156°E, 2°S

    0.01

    −0.50

    142

    156°E, 5°S

    0.37

    0.37

    0

    Here cor0 is correlation without time lag, and mcor is the optimal correlation with time lag in days (positive lag value means 200 dbar leads 500 dbar). The 99% significance level of correlation is 0.04. Difference between cor0 and mcor is considered to be significant when it is larger than 0.1.

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