Comparisons of sea surface height variability observed by pressure-recording inverted echo sounders and satellite altimetry in the Kuroshio Extension

Transcription

1 J Oceanogr () 68:4 46 DOI.7/s87--8-x ORIGINAL ARTICLE Comparisons of sea surface height variability observed by pressure-recording inverted echo sounders and satellite altimetry in the Kuroshio Extension Jae-Hun Park D. Randolph Watts Kathleen A. Donohue Karen L. Tracey Received: 9 November / Revised: 5 February / Accepted: March / Published online: 5 May Ó The Oceanographic Society of Japan and Springer J.-H. Park (&) Korea Ocean Research and Development Institute, Ansan , Korea jhpark@kordi.re.kr D. R. Watts K. A. Donohue K. L. Tracey Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 88-97, USA Abstract Satellite-measured along-track and gridded sea surface height (SSH) anomaly products from AVISO are compared with in situ SSH anomaly measurements from an array of 43 pressure-recording inverted echo sounders (PIESs) in the Kuroshio Extension. PIESs measure bottom pressure (P bot ) and round-trip acoustic travel time from the sea floor to the sea surface (s). The P bot and s measurements are used to estimate, respectively, the mass-loading and steric height variations in SSH anomaly. All comparisons are made after accurate removal of tidal components from all data. Overall good correlations are found between along-track and PIES-derived SSH anomalies with mean correlation coefficient of.97. Comparisons between the two measurements reveal that the mass-loading component estimated from P bot is relatively small in this geographical region. It improves regression coefficients about 5 % and decreases mean root-mean-squared (rms) differences from 7.8 to 6.4 cm. The AVISO up-to-date gridded product, which merges all available satellite measurements of Jason-, Envisat, Geosat Follow-On, and TOPEX/Poseidon interlaced, shows better correlations and smaller rms differences than the AVISO reference gridded product, which merges only Jason- and Envisat. Especially, the up-todate gridded product reveals 6.8 cm rms improvement on average at sites away from Jason- ground tracks. Gridded products exhibit low correlation (.75.9) with PIESderived SSH in a subregion where the SSH fluctuations have relatively high energy at periods shorter than days. Keywords Sea surface height Pressure-recording inverted echo sounder (PIES) Satellite altimetry Kuroshio Extension Acoustic echo time Ocean bottom pressure Introduction The Kuroshio is a western boundary current flowing northeastward along the continental shelf-slope of the East China Sea. After leaving the southeast coast of Japan, it forms a freely meandering jet known as the Kuroshio Extension. The Kuroshio Extension separates the cold northern subpolar waters from the warm southern subtropical waters, and provides intense eddy kinetic energy to mix the cold and warm waters. The Kuroshio Extension System Study (KESS) was designed to investigate the dynamic and thermodynamic processes controlling cross-frontal exchanges of heat, salt, momentum, and potential vorticity (Donohue et al. 8, ). As part of the KESS field program, an array of 46 pressure-sensor-equipped inverted echo sounders (PIESs) were deployed for about years during 4 6. The array spanning a 6 km 9 6 km region centered on the first quasi-stationary meander crest and trough of the Kuroshio jet (Fig. ). The PIES measured bottom pressure (P bot ) as well as acoustic round-trip travel time (s) from the sea floor to the sea surface, which can be converted to vertical density profiles (e.g., Meinen ; Watts et al. b; Park et al. 5; Donohue et al. ). The KESS observations have been used to investigate mesoscale barotropic and baroclinic variabilities (Donohue et al. ; 3

2 4 J.-H. Park et al. 4 o N 36 o N 3 o N 8 o N JAPAN Greene et al. 9; Howe et al. 9; Jayne et al. 9), comparisons with GRACE satellite gravity (Park et al. 8), and deep near-inertial wave distribution (Park et al. ) in the Kuroshio Extension region. Satellite altimeters, such as TOPEX/Poseidon (T/P) and follow-on Jason- and Jason-, measure the sea surface height (SSH) changes, which include both steric (baroclinic) and mass-loading (barotropic) components. The former, associated with density changes, is considered to dominate the SSH variability in the low-frequency bands, while the latter contributes more to the high-frequency bands (e.g., Fukumori et al. 998). Under this assumption, oceanographers use the satellite altimetric measurements as a proxy for baroclinic ocean variability at seasonal-tointerannual time scales. Yet, the satellite altimetry measurements can be aliased in the low-frequency bands by the high-frequency barotropic components because of their coarse temporal samplings (Traon and Dibarboure ). Quantitative comparisons between satellite and in situ SSH measurements in the open ocean are few because of the difficulty to obtain simultaneous observations of both steric and mass-loading components. Some studies compare the XBT-derived SSH anomaly along sections with the satellitemeasured SSH anomaly to evaluate the satellite altimetry.5 N A B B B3 B4 B5 C C C3 C4 C5 C6 D D D3 D4 D5 D6 E E E3 E4 E5 E6 E7 F F F3 F4 F5 F6 G G G3 G4 G5 G6 H H3 H4 H5 H6 I S S 4 o E 44 o E 48 o E 5 o E Fig. Array of Kuroshio Extension System Study (KESS). Solid triangles indicate PIES. Open triangles (D3, G6, and S) indicate PIES sites at which the data record is shorter than year. Line contours indicate mean absolute dynamic topography measured by satellite altimetry during the KESS observation period. Dotted lines indicate satellite altimetry tracks. Bottom topography is gray-color contoured with every 5-m interval (SRTM3_PLUS global topography data obtained from web site srtm3_plus.html) m (e.g., Gilson et al. 998; McCarthy et al. ). Guinehut et al. (6) compare the SSH anomaly from historical hydrocast data with altimeter SSH anomaly measurements to describe the global SSH and ocean circulation variations in terms of steric and mass-loading contributions. Yet, all those previous comparison studies simply assume that the mass-loading component is the difference between in situderived SSH anomaly and satellite-measured SSH anomaly because of lack of data to test this. The PIES measures both steric and mass-loading components of SSH variability simultaneously from s and P bot, respectively. Baker-Yeboah et al. () compared satellite-measured SSH anomaly along a Jason- ground track with coincident 7-month-long PIES-derived SSH anomaly at sites in the eastern South Atlantic. They showed that PIES-derived SSH has 7.3 and 9.4 cm rootmean-squared (rms) differences on average from AVISO and PODAAC products, respectively. In the Kuroshio Extension region, Teague et al. (995) compared SSH anomaly along a T/P ground track with coincident PIESderived SSH anomaly at 6 sites across the Kuroshio near 35 N, 43 E, and showed a good agreement between them with cm rms difference. Yet, their PIES data spanned only 8 days along a single T/P ground track, they used only baroclinic SSH, and the measurements and data processing were in an early stage with larger errors than now. The KESS experiment provides a unique opportunity to compare the satellite altimetry measurements with in situ SSH measurements in a wide region, since the KESS array was designed in conjunction with Jason- ground tracks as shown in Fig.. In this study, we compare both along-track and merged SSH anomaly products from AVISO with coincident SSH anomaly measurements from the PIES array. Using spatial distributions of correlation and regression coefficients between them, we investigate quantitatively the impact of steric and mass-loading components on the SSH variability in satellite altimetry measurements. We also investigate the impact of relatively coarse spatio-temporal samplings of the satellite altimeters on aliasing in AVISO gridded products. We estimate rms differences between the in situ and satellite altimeter measurements and discuss their error budgets. Data and methods An array of 46 PIESs, spaced about 88 km apart to cover the Kuroshio Extension region centered near 35 N, 46 E, was deployed from May 4 to June 6 (Fig. ). The hourly records of both s and P bot were obtained by hourly averaging -min burst samples. The s has an accuracy of.5 ms, and the PIES Paroscientific quartz P bot sensor has a stated accuracy of ±. % and. mbar resolution 3

3 Comparisons of sea surface height variability 43 (Inverted Echo Sounder User s Manual 6, available at The following procedures are applied. First, both s and P bot records were despiked. Then, eight major tidal constituents were determined and subtracted from each hourly P bot record using the response analysis method (Munk and Cartwright 966). Pressure drift was also removed using an exponential temporal drift plus a linear trend, determined by a least-squares fit as follows. Most PIESs (43 out of 46) were also equipped with RCM- current meter sensors moored *5 m above the bottom (CPIES). The array of near-bottom current measurements determined a streamfunction that was evaluated at each CPIES site (see Watts et al. (a) for details). P bot records converted to streamfunction were required to match the current-meterderived estimates, which guided the choice of exponential and linear drift parameters for P bot dedrifting (Donohue et al. ). Finally, s and P bot records are sampled at noon and midnight (UTC) every day after applying a 3-day lowpass Butterworth filter. Measurement duration at each site differed because of instrument issues. We exclude three instrument records from the analyses, D3, G6, and S, indicated by open triangles in Fig., because of their noisy measurements or short record lengths (\ year). This study uses 43 datasets of twice-daily s and P bot, collected longer than a year. The reader is referred to Kennelly et al. (8) for a detailed description of the data processing. The SSH anomaly is determined from PIES measurements of s and P bot, as the sum of two contributions (Baker-Yeboah et al. 9). The first, usually largest, contribution to SSH is from density variations, dominantly in the upper water column. It has been given different names in the literature, steric height, geopotential height /=g ¼ð=gÞ Z p ref ð=qþdp ; ðþ integrated relative to a deep reference (p ref ) like 4, dbar, and baroclinic height. Here, g (9.8 m s - )is gravity. Dynamic height / is determined from s via an empirical lookup table tailored to the region of study as described below. In this paper, we will call //g = g bc the upper baroclinic height contribution to SSH. The second, usually smaller, contribution to SSH is from the massloading, which we call P bot /(q b g) = g ref the reference height, where q b (,46 kg m -3 ) is typical deep sea water density at 4, dbar from the historical hydrocasts. The s measurements provide proxy estimates of /. To convert s to /, we reference all the PIES instruments, ranging from 5,3 to 6, dbar, to the 4,-dbar level using the following procedure based on all available historical hydrocasts. First, we remove the effects of seasonal warming and cooling from the available historical hydrocasts and s (see Donohue et al. for details). Next, we calculate the acoustic travel time between 4, dbar and the sea surface (s 4 ) and between the instrument pressure level and the sea surface (s P ). A linear relationship between s 4 and s P is determined by least squares. Finally, this linear relationship is used to convert measured s time series to s 4 time series at each PIES site. The reference level for / estimation should be as deep as practical, so as not to miss any baroclinic variability through the water column. Available historical hydrocasts reaching below 4, dbar are relatively scarce in the KESS region. A test using available hydrocasts reaching to layers deeper than 4, dbar reveals that the baroclinic variance in / below 4, dbar is less than % of the total through the water column (not shown). Thus, we chose 4, dbar as the reference level for / estimation (/ 4 ). A curve-fitting procedure is used to look up / 4 as a function of s 4.The/ 4 look-up curve is obtained using hydrocasts deeper than 4, dbar as shown in Fig.. Note that / 4 here is already deseasoned since as noted earlier we deseasoned all hydrocasts. After obtaining / 4, the total steric component of the SSH is calculated as g bc ¼ / 4 þ / seasonal ; ðþ g where / seasonal is the seasonal component of / obtained from deseasoning procedure (Donohue et al. ). The / seasonal is added back since the AVISO products include φ 4 /g (m) rms error = 3.4 cm for τ 4 < 5.65 s rms error = 5.7 cm for τ s Total rms error = 4.6 cm τ 4 (sec) Fig. Scatter plot for s from 4, dbar to surface, s 4, versus steric height component of SSH referred to 4, dbar, / 4 /g. Thick gray curve spline-curve fitted to dots, and gray dashed curves ±rms error (4.6 cm) 9. The fitted curve is used to determine g bc = / 4 /g from s measurements. Light gray lines ±rms error (3.4 cm) 9 for s 4 \ 5.65 s, and ± rms error (5.7 cm) 9 fors 4 C 5.65 s. The arrow at s 4 = 5.76 s points out two scatter points (asterisks)for which the temperature and salinity profiles are shown in Fig. 8 3

4 44 J.-H. Park et al. the seasonal signals. The rms errors caused by this s-to-/ conversion are discussed in Sect. 4. The sum of g bc and g ref provides us in situ measurements of the total SSH (g tot = g bc? g ref ). The effect of free surface displacement (g) on the s data, caused by both mass-loading and inverted barometer (IB) response (g IB ), is corrected using P bot and atmospheric sea surface pressure (P atm ) data from the NCEP/NCAR reanalysis, respectively. The NCEP/NCAR P atm data have in space and in 6 h time resolution. They are spatio-temporally interpolated to obtain P atm time series corresponding to s time series at each PIES site. From the P atm time series, we remove the global mean P atm computed every 6 h by averaging P atm values over regions where the water depths are deeper than, m to obtain P atmib. The path length of sound propagation varies with g, which changes s. The g is obtained using the hydrostatic approximation as g ¼ g ref g IB ¼ P bot q b g P atmib q s g ; ð3þ where denotes mean-removed fluctuations, and q s (,4 kg m -3 ) typical surface water density from historical hydrocasts in the KESS region. The acoustic round-trip travel time associated with the g fluctuation (s g ) is obtained by s g ¼ g ref c b g IB c s ¼ g P bot P atmib c b q b c s q s ; ð4þ where c b and c s are sound speeds near the seafloor and the sea surface, respectively. The s g variance is less than 5 % of the s variance at all PIES sites. The estimated s g is subtracted from s measurements before being converted to /, and hence, this study uses the barotropic- and IB-component-corrected s records, which retain s variations associated with only steric variations. Three AVISO products are used for the comparisons. One is a mono-mission delayed-time along-track sea level anomaly product of Jason- with a 9.9-day interval (hereafter Mono-SLA), produced and distributed by AVISO as part of the Ssalto ground processing segment (AVISO DT CorSSH and DT SLA Product Handbook 5). Another is a Reference delayed-time gridded product with /4-deg. and 7-day resolutions (hereafter Ref- MSLA), generated with a maximum of two satellites sampling from Jason- and Envisat, the follow-on satellites, respectively, of T/P and ERS-. The Ref-MSLA provides homogeneous quality maps with time. The third is the Up-to-date gridded product using up to 4 satellites [Jason-, Envisat, Geosat Follow-On (GFO), and T/P interlaced] available at a given mapping time (hereafter Upd-MSLA). The Upd-MSLA maps provides the same resolution as the Ref-MSLA maps but with improved quality. Because the number of satellites varies with time the Upd-MSLA maps are non-homogeneous (Ssalto/Duacs User Handbook 6). The T/P satellite was moved in September to a new orbit midway between its original ground tracks to give way to Jason-. The T/P mission ended in October 5. An important issue related to the accuracy of the satellite altimetric SSH anomaly mapping is the spatial coverage of satellite ground tracks. Thus, comparisons of both the Ref- and Upd-MSLA products with our data can examine how much the gridded maps are improved by including T/P interlaced and GFO. High-frequency wind and pressure forcings produce high-frequency ocean response that can alias coarse-sampled (e.g., 9.9 days for Jason-) satellite altimetry measurements (e.g., Park and Watts 6). To reduce this problem, AVISO applies Dynamic Atmospheric Correction (DAC) operationally. DAC is based on high-frequency (periods shorter than days) output from the MOGD high-resolution barotropic model forced by both P atm and wind. It also includes a low-frequency (periods longer than days) IB correction developed by Collecte Localisation Satellites (CLS). AVISO provides DAC at resolution of /4 deg. and 6 h as an auxiliary product in their website ( By adding DAC and P atm, the DAC-derived pressure (P DAC ) is calculated for periods shorter than days. As DAC is applied to all AVISO products used for this study, P DAC is removed from PIES-observed P bot at each site before calculating g ref. This correction reduces P bot variances by about 8 % on average over 43 PIES sites. 3 Sea surface height comparisons 3. Comparison with Mono-SLA Mono-SLA along Jason- ground tracks (dotted lines in Fig. ) are compared with in situ SSH anomalies at 5 coincident PIES sites. The twice-daily PIES time series of g bc and g ref are interpolated to 9.9-day intervals coinciding with the times Jason- passed over each site. Mono- SLA has a 7-km spatial resolution along a track. To create comparable time series of the Mono-SLA product at each PIES site, a point closest to each PIES site on the satellite track is determined, and then along-track linear interpolation of Mono-SLA to that point is done. Distances from PIES sites to interpolated ground points are all less than km. Figure 3 compares time series pairs of Mono-SLA (red lines) and g bc anomaly (black lines) at each PIES site along the central Jason- track in the KESS domain. The local time average for each time series is removed to produce the 3

5 Comparisons of sea surface height variability 45 Fig. 3 Time series comparisons of Mono-SLA (red lines) with PIES-derived steric height g bc (black lines), and g tot (g bc? g ref )(blue lines) at9 PIES sites located along the central Jason- ground track in the KESS domain. Correlation coefficient (Cr) and regression coefficient (Re) are shown for g bc only comparisons in black type and g tot comparisons in blue type. An offset of.75 m is added to g tot comparisons N.75 offset Cr=.94, Re=.93 Cr=.96, Re=.97 B4 Cr=.94, Re=.93 Cr=.96, Re=.97 A.75 offset Cr=.96, Re=.95 Cr=.97, Re=.97 C4 Cr=.86, Re=.8 Cr=.9, Re=.9 D4 E4 Cr=.96, Re=.8 Cr=.98, Re=.88 Cr=.98, Re=.96 Cr=.98, Re=. F3 G Cr=.98, Re=.89 Cr=.99, Re=.96 Cr=.97, Re=.9 Cr=.98, Re=.98 H I Cr=.98, Re=.89 Cr=.99, Re=.96 Cr=.95, Re=.75 Cr=.96, Re=.76 S Cr=.94, Re=.95 Cr=.95, Re= anomalies. These two time series agree very well with each other. Correlation coefficients are larger than.94 everywhere except C4, where it drops to.86. Regression coefficients between g bc anomalies and Mono-SLA range from.75 to.96. The lowest regression coefficient is at I. This indicates that the steric component only underestimates the g tot. Figure 3 also compares time series pairs of Mono-SLA and g tot anomaly (blue lines) at the same sites. We added.75 offset to distinguish these comparisons from the g bc -only comparisons. When the reference height g ref is added to the steric height g bc, correlation coefficients increase on average *., while regression coefficients, ranging from.76 to., increase on average *.5. This indicates that adding g ref improves g tot amplitudes to agree even better with Mono-SLA amplitudes. The upper two rows of panels in Fig. 4 show the spatial distribution of correlation and regression coefficients at 5 PIES sites between Mono-SLA and g bc anomalies, and between Mono-SLA and g tot anomalies. The bottom two panels show histograms of regression coefficients for both comparisons. All correlation coefficients between Mono- SLA and g bc anomalies are larger than.9 except C4 (Fig. 4a). The number of sites with correlation coefficients larger than.98 is and mean correlation coefficient of 5 sites is.96. Correlation coefficients improve slightly 3

6 46 J.-H. Park et al. Fig. 4 Plots of a, b correlation coefficients and c, d regression coefficients. Lower panels (e, f) indicate histograms of site numbers sorted by regression coefficients. Left column panels for Mono-SLA versus g bc only, and right column panels for Mono-SLA versus g tot Mono SLA & η bc only Correlation Coefficients Mono SLA & η tot (a) (b).75.7 Regression Coefficients 4 o N Mono SLA & η only Mono SLA & η bc tot.5 38 o N o N o N o N.75 (c) (d).7 3 o N 4 o E 4 o E 44 o E 46 o E 48 o E 5 o E Number of sites (e) Mono SLA & η bc only Regression Coefficient Mono SLA & η tot (f) when Mono-SLA is compared with g tot anomalies (Fig. 4b). The number of sites with correlation coefficients larger than.98 increases to 5 and mean correlation coefficient increases slightly to.97. Regression coefficients between Mono-SLA and g bc anomalies at 5 PIES sites range from.75 to.98 (Fig. 4c). The histogram (Fig. 4e) shows their dominant distribution near.9 and.95 (6 sites). Mean regression coefficient of 5 sites is.89. Improved regression coefficients are shown between Mono-SLA and g tot anomalies (Fig. 4d, f). Regression coefficients dominantly distribute near.95 and. (7 sites). Correspondingly, mean regression coefficient increases to.95. Table summarizes the comparisons between the PIES and satellite SSH, including those from comparisons with two gridded products in the following sections. Rms differences between Mono-SLA and g bc anomalies range from 3.8 to.8 cm, and their mean is 7.8 cm (Table ). The smallest rms difference is at S and the largest is at D4. Rms differences decrease to the range cm 3

7 Comparisons of sea surface height variability 47 Table Summary of comparisons between satellite-measured SSH anomaly products and PIES-derived SSH anomaly PIES ID Mono-SLA Ref-MSLA Upd-MSLA Cr Re rms Cr Re rms Cr Re rms Nl.96 (.94).97 (.93) 5. (6.).93 (.9). (.97) 6.7 (7.).93 (.9).3 (.97) 6.5 (6.9) A.97 (.96).97 (.95) 5. (5.8).94 (.93).5 (.4) 7.6 (8.).94 (.94).7 (.5) 7.3 (7.7) Bl.93 (.93).9 (.83) 9. (9.).87 (.85).97 (.87).8(.6).87 (.86). (.9).5(.) B.85 (.84).89 (.84).9 (.).88 (.87).98 (.93) 9.6 (9.7) B3.78 (.74).96 (.9). (.).86 (.8).99 (.95) 9. (.) B4.96 (.94).97 (.93) 5. (6.3).93 (.9).5 (.) 7.7 (8.3).93 (.9).3 (.) 7.5 (8.) B5.9 (.9).6 (.5) 9.6 (9.).94 (.94). (.) 7.5 (7.) C.98 (.97).99 (.95) 9.6 (9.6).96 (.95). (.96).3 (.3).96 (.95). (.97).8 (.) C.98 (.97). (.97) 8.6 (9.6).96 (.96).4 (.).3 (.5).96 (.96).3 (.99).6 (.4) C3.84 (.8).76 (.7) 5. (6.3).86 (.83).8 (.76) 3.4 (4.9) C4.9 (.86).9 (.8) 7.5 (8.6).79 (.73).98 (.8) 9.8 (.).8 (.73).97 (.8) 9.7 (.) C5.95 (.9).88 (.8) 7. (9.6).9 (.87).83 (.75) 9.3 (.8).9 (.88).8 (.74) 9.4 (.8) C6.88 (.87).97 (.9) 4.3 (4.).9 (.9).5 (.96).4 (.4) Dl.98 (.96).94 (.89) 5.9 (7.6).94 (.93).99 (.93) 9.7 (.).95 (.94). (.94) 9.4 (9.9) D.95 (.94).96 (.93) 6. (6.4).96 (.95).4 (.) 4.7 (4.9) D4.98 (.96).88 (.8) 8.7(.8).9 (.9).96 (.88) 3. (3.8).93 (.9).95 (.88). (3.4) D5.85 (.8).87 (.73) 6. (6.6).89 (.87).9 (.77) 3.7 (4.4) D6.98 (.97).97 (.89) 7. (8.8).9 (.93). (.) 4. (.).93 (.93). (.) 3. (.3) El.98 (.97).9 (.86) 5.4 (7.).95 (.94).95 (.9) 7.8 (8.7).95 (.94).95 (.89) 8. (8.9) E.94 (.93).95 (.9) 3. (4.3).97 (.96).96 (.9). (.6) E3.95 (.94).99 (.96) 3.3 (3.6).96 (.95). (.96).8 (.5) E4.98 (.98). (.96) 7.5 (8.).95 (.94).6 (.99) 3.4 (3.8).95 (.94).8 (.).8 (3.) E5.85 (.84).9 (.).8 (.).9 (.9).4 (.6) 7. (6.4) E6.79 (.79). (.94) 3. (.4).87 (.86).4 (.98) 8.8 (8.) E7.99 (.98).9 (.86) 6. (8.7).93 (.93).95 (.9).9 (.4).93 (.93).95 (.9).7 (.) Fl.99 (.99).96 (.93) 5.7 (7.9).99 (.99).97 (.93) 5.5 (7.6) F.99 (.98).96 (.94) 6.8 (9.).99 (.98).98 (.96) 6. (8.) F3.99 (.98).96 (.89) 6.8 (8.7).96 (.96).99 (.93). (.).97 (.96). (.94).5 (.5) F4.99 (.98).97 (.98) 5.8 (7.).95 (.95).6 (.6).7 (.8).96 (.96).4 (.5) 9.9 (9.8) F5.93 (.93).95 (.9) 7.6 (7.4).95 (.95).98 (.96) 4.5 (4.5) F6.99 (.99).94 (.89) 6.7 (9.3).96 (.96).94 (.89) 3. (3.9).97 (.96).95 (.89).4 (3.) Gl.99 (.99).98 (.94) 4. (6.8).99 (.98). (.98) 6.6 (8.3).99 (.98). (.98) 6.8 (8.3) G.98 (.97).98 (.9) 6. (7.5).96 (.95).99 (.9) 8. (8.7).96 (.95).99 (.9) 8. (8.6) G3.97 (.95). (.98) 8.9 (.5).98 (.97).8 (.7) 7.4 (8.) G4.98 (.97).97 (.95) 6.8 (7.9).95 (.96).99 (.99).5 (.3).96 (.95). (.99).5 (.5) G5.98 (.98). (.93) 7. (7.).97 (.96). (.94).3 (.).97 (.97).99 (.94) 9.8 (.5) H.99 (.98).96 (.89) 5.5 (7.5).98 (.97). (.94) 6.3 (7.5).98 (.97). (.93) 6. (7.) H3.88 (.85).9 (.85) 7.4 (8.).94 (.9).93 (.88) 5.5 (6.) H4.94 (.9).94 (.9) 4.6 (5.6).95 (.9).93 (.9) 4.3 (5.4) H5.95 (.9).87 (.78) 5. (6.7).94 (.93).98 (.9) 6.4 (6.6).95 (.94).97 (.9) 6. (6.6) H6.93 (.93).79 (.78) 8.4 (8.6).96 (.95).85 (.83) 6.5 (6.9) I.96 (.95).76 (.75) 5.7 (5.9).93 (.94).86 (.84) 5.4 (5.).94 (.94).88 (.85) 5. (5.) S.95 (.94).97 (.95) 3.3 (3.8).94 (.94).99 (.97) 3.7 (3.6).95 (.94). (.98) 3.5 (3.6) Mean.97 (.96).95 (.89) 6.4 (7.8).9 (.9).98 (.93). (.).94 (.93).99 (.94) 9.8 (.) Values are expressed as g tot (g bc ) Cr correlation oefficient, Re regression coefficient, rms rms difference 3

8 48 J.-H. Park et al. when g ref is added to g bc. Correspondingly, the mean rms difference decreases to 6.4 cm, which accounts for 4.5 cm pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rms improvement ( 7:8 6:4 ) by adding g ref to g bc. 3. Comparison with Ref-MSLA This section compares Ref-MSLA with in situ SSH anomalies at 43 PIES sites. Again, the local time average for each Ref-MSLA time series is removed to produce anomalies. The PIES time series of g bc and g ref at each site are subsampled at 7-day intervals to coincide with the Ref-MSLA product times. The satellite maps are spatially interpolated to 43 PIES locations to produce comparable time series. Figure 5 compares time series pairs of Ref-MSLA (red lines) and g bc anomalies (black lines) at each PIES site along the central Jason- track. Overall, these two time series agree well with each other, though correlation coefficients are lower than those in Fig. 3 for the Mono- SLA comparison, which is expected because mapping adds errors. Especially, the lowest correlation coefficient at C4 drops from.86 to.73. Regression coefficients range from.8 to., and are higher than those in Fig. 3. Figure 5 also compares time series pairs of Ref-MSLA and g tot anomalies (blue lines). Like the Mono-SLA comparisons, adding g ref to g bc increases the correlation coefficients slightly on average *., and it also increases regression coefficients on average *.6. The regression Fig. 5 As Fig. 3 except for Ref-MSLA comparisons N.75 offset A.75 offset Cr=.9, Re=.97 Cr=.93, Re=. Cr=.93, Re=.4 Cr=.94, Re=.5 B4 C4 Cr=.9, Re=. Cr=.93, Re=.5 Cr=.73, Re=.8 Cr=.79, Re=.98 D4 E4 Cr=.9, Re=.88 Cr=.9, Re=.96 Cr=.94, Re=.99 Cr=.95, Re=.6 F3 G Cr=.96, Re=.93 Cr=.96, Re=.99 Cr=.95, Re=.9 Cr=.96, Re=.99 H I Cr=.97, Re=.94 Cr=.98, Re=. Cr=.94, Re=.84 Cr=.93, Re=.86 S Cr=.94, Re=.97 Cr=.94, Re=

9 Comparisons of sea surface height variability 49 coefficient at C4 increases appreciably from.8 to.98 when g ref is added. Figure 6 maps spatial distribution of correlation and regression coefficients between Ref-MSLA and g bc anomalies (left column), and Ref-MSLA and g tot anomalies (right column) calculated at 43 PIES sites. The bottom panels show histograms of regression coefficients for both comparisons. Correlation coefficients between Ref-MSLA and g bc anomalies (Fig. 6a) are larger than.9 in the southwestern part of the domain. The southwestern corner near F F G shows the highest correlation coefficients C.98. Correlation coefficients lower than.8 occur at B3, C4, and E6 in the northeastern portion. They improve slightly when g ref is added to make g tot for this comparison (Fig. 6b), and their mean value increases from.9 to.9. Regression coefficients between Ref-MSLA and g bc anomalies range from.7 to. (Fig. 6c). The lowest is at C3 and the highest at B4. The histogram (Fig. 6e) shows their dominant distribution between.9 and. (3 sites). Fig. 6 a, b Correlation coefficients and c, d regression coefficients. Lower panels (e, f) indicate histograms of site numbers sorted according to regression coefficients. Left column panels for Ref-MSLA versus g bc only and right column panels for Ref-MSLA versus g tot Ref MSLA & η bc only Correlation Coefficients Ref MSLA & η tot (a) (b).75.7 Regression Coefficients 4 o N Ref MSLA & η only Ref MSLA & η bc tot.5 38 o N o N o N o N.75 (c) (d).7 3 o N 4 o E 4 o E 44 o E 46 o E 48 o E 5 o E Number of sites Ref MSLA & η only bc 5 5 (e) Regression Coefficient Ref MSLA & η tot 5 5 (f)

10 4 J.-H. Park et al. Mean regression coefficient of 43 sites is.93. Regression coefficients closer to. are shown when g tot anomalies are compared with Ref-MSLA (Fig. 6d, f). Their dominant distribution is between.95 and.5 (33 sites). Correspondingly, the mean regression coefficient increases to.98. Rms differences between Ref-MSLA and g tot anomalies are larger than those between Mono-SLA and g tot anomalies (Table ). They range from 3.7 to 3. cm, and their mean is. cm. We will account for the high rms differences in Sect. 4 below, arising due to a badly aliased altimeter mapped SSH near the Kuroshio where energetic high-frequency SSH fluctuations shorter than the Nyquist frequency of Jason- (\ days) exist. Adding g ref reduces rms differences by about. cm on average. 3.3 Comparison with Upd-MSLA Comparisons between Upd-MSLA products and our data can demonstrate how much the gridded maps are improved, since Upd-MSLA products includes T/P interlaced and GFO additionally in their mapping. Spatial distributions of correlation and regression coefficients between Upd-MSLA and g bc anomalies only, and Upd-MSLA and g tot anomalies at 43 PIES sites are shown in Fig. 7. The bottom panels also show histograms of regression coefficients for both comparisons. When compared to Fig. 6a, the correlation coefficient map between Upd-MSLA and g bc (Fig. 7a) exhibits improvement around the low correlation coefficient region in the northeastern part of the domain. Only one site, C4, shows a correlation coefficient lower than.8. Adding the g ref component improves the correlation coefficients slightly, and the mean correlation coefficient increases from.93 to.94 (Fig. 7b). Regression coefficients between Upd-MSLA and g bc anomalies range from.74 to. (Fig. 7c, e). The lowest is at C4 and the highest is at B4. The histogram shows their dominant distribution between.9 and. (3 sites). Correspondingly, the mean regression coefficient of 43 sites is.94. Improved regression coefficients are shown between Upd-MSLA and g tot anomaly (Fig. 7d, f). They distribute dominantly between.95 and.5 (33 sites). Correspondingly, the mean regression coefficient increases to.99. Rms differences between Upd-MSLA and g tot anomalies are decreased relative to those between Ref-MSLA and g tot anomalies (Table ). They range from 3.6 to 8.8 cm, and their mean is 9.8 cm. Sites showing the smallest and largest rms differences are the same as the Ref-MSLA comparison case. Adding g ref reduces rms differences about.3 cm on average over differences with the steric component g bc alone. 4 Error analyses 4. Error estimates in steric height g bc The conversion of s P to s 4 using the historical hydrocasts after deseasoning introduces an uncertainty in the g bc estimated. The rms error of this uncertainty is.34 ms, which produces an rms error of *.3 cm in g bc estimations from the mean slope Dg bc =Ds 4 3:9 cm/ms in Fig.. The other error source in g bc arises from the conversion of s 4 to / 4 /g (see Fig. ), which produces rms error overall 4.6 cm. Figure further reveals that the data are less scattered at small s 4 (south of the Kuroshio), and more scattered for large s 4 (north of the Kuroshio). The rms error for s 4 \5.65 s and [5.65 s is, respectively, 3.4 and 5.7 cm, as shown in Fig.. Recall that the seasonal signals of s have been removed from s 4 before applying the s 4 -to-/ 4 /g look-up curve. They are converted to / seasonal and added back to / 4 /g for comparisons with AVISO products. The scatter around the fitted seasonal curves, s versus / seasonal, leaves 4.7 cm rms error. Other minor error sources affecting both g bc and g ref are sea state scatter on acoustic pings (.5 ms ^.5 cm), sea state bias (. cm), uncertainty in pressure drift (. cm), and tides (. cm) (see Baker-Yeboah et al., table ). Thus, the range of total rms error in g tot is qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cm :3 þðs 4 rms errorþ þ 4:7 þ :, for s 4 rms error = cm. 4. Errors in g bc due to salinity variation Sound speed (and hence g bc ) is insensitive to salinity, whereas specific volume anomaly (and hence SSH) is sensitive. Salinity variations uncorrelated with s mainly contribute to the increased scatter on the northern side of the Kuroshio in Fig.. Fresh waters brought to the region by the Oyashio Current in the subpolar gyre introduce low salinity anomalies to the region north of the Kuroshio, which increase the SSH uncertainty in g bc there. Figure 8 illustrates the effect of salinity variations on the s 4 - to-/ 4 /g conversion errors using two CTD profiles collected in May, 4 and June 4, 5. The temperature profiles in the left panel of Fig. 8 differ in the upper 8 dbar, yet their vertical variations compensate to produce identical s 4 values of 5.76 s. The salinity profiles in the right panel of Fig. 8 also differ, but contrast with compensating high and low values in the vertical. The salinity profile obtained in May 4 reveals low salinity (*33.75 psu) in the - to 3-dbar layer, which produces larger specific volume anomaly, and hence larger / 4 (6.58 m /s ) than that in June 5 (5.8 m /s ). 3

11 Comparisons of sea surface height variability 4 Fig. 7 As Fig. 6 except for Upd-MSLA comparisons Upd MSLA & η bc only Correlation Coefficients Upd MSLA & η tot (a) (b).75.7 Regression Coefficients 4 o N Upd MSLA & η only Upd MSLA & η bc tot.5 38 o N o N o N o N.75 (c) (d).7 3 o N 4 o E 4 o E 44 o E 46 o E 48 o E 5 o E Number of sites Upd MSLA & η only bc 5 5 (e) Regression Coefficient Upd MSLA & η tot 5 5 (f) Scatter points produced by these two temperature and salinity profiles are shown in Fig. (indicated by asterisks). 4.3 Errors in altimetry SSH The error budget is estimated for the Mono-SLA product, and for the difference from PIES-derived SSH. The Mono- SLA dataset is produced from the Jason- version-b altimeter Geophysical Data Records (GDRs), which are reported to have a global accuracy of ±3.3 cm. This error includes altimeter noise, troposphere and ionosphere effects, sea state bias, and radial orbit errors. In addition, processes associated with inverted barometer and tides corrections generate errors, both of which can be about cm (Ponte and Dorandeu 3; AVISO and PODDAC User Handbook 6). Thus, the total rms error in Mono-SLA product is 3.6 cm pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ( 3:3 þ : þ : ). Errors due to geographical position mismatch between Jason- tracks and PIES sites can be about cm when high spatial gradients of SSH anomaly are encountered near the Kuroshio or mesoscale eddies (Teague et al. 995). Therefore, the predicted rms error range of the 3

12 4 J.-H. Park et al. Fig. 8 Comparisons of a temperature and b salinity profiles with the same s 4 (5.76 s) and different / 4. These two CTDs are indicated as asterisks in Fig. Depth (dbar) 5 5 (a) o N, o E 5//4 τ 4 = 5.76 sec φ 4 = 6.58 m /s 37. o N, o E 6/4/5 5 τ = 5.76 sec 4 φ = 5.8 m /s Temperature ( o C) (b) Salinity (psu) difference between Mono-SLA and PIES-derived g tot is qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cm ðg tot rms errorþ þ 3:6 þ, for g tot rms error = cm determined above in Sect. 4.. This overestimates the mean rms difference of 6.4 cm between them (Table ). 5 Discussion Regression coefficients between Mono-SLA and PIESderived g tot show a biased distribution centered around.95 (Fig. 4f), while the correlations between them are close to one at almost all sites. This implies that both time series vary proportionally to each other, yet the variance of Mono-SLA is larger than that of PIES-derived g tot. The reason of this is the 3-day low-pass filtering applied to the s measurements before converting them to g bc. The merging procedure of satellite measurements can filter out highfrequency SSH fluctuations unlike the along-track product, and hence the distribution of regression coefficients improves to be centered around. (see the bottom panels in Figs. 6, 7). Comparisons between both gridded products and PIESderived g tot reveal relatively low correlation coefficients in the northeastern part of the KESS domain (see Figs. 6b, 7b). To investigate what produces low correlation coefficients there, high- and low-pass filters are applied on the PIESderived g tot time series with frequency cutoff of days, which is the Nyquist frequency of Jason- satellite measurements. Then, the variances of the high- (r h ) and lowpass (r l ) filtered time series are calculated at each PIES site. The variance map for g tot shows three high value regions in the middle of the observation domain, while it shows low variance on the north and south sides of the domain (Fig. 9a). The r l map for low-pass filtered ([ days) g tot is similar to that for g tot (Fig. 9b). The r h map for high-pass filtered (\ days) g tot reveals high values near the Kuroshio (Fig. 9c), which are associated with its frontal waves with typical period of about days (e.g., Kouketsu and Yasuda 8). A map of the ratio between r h and r l (Fig. 9d) reveals the highest values near C4. This site is located within a large region in the northeastern portion of the domain with r h /r l [.5. This region also corresponds to the same region where relatively low correlation coefficients arose between satellitemeasured SSH products and PIES-derived g tot (see Figs. 6b and 7b). Due to the coarse temporal resolution of the satellite altimetry measurements, the gridded products do not resolve the high-frequency fluctuations. As a result the satellite gridded SSH fields are aliased. The rms difference map for Ref-MSLA product from g tot shown in Fig. a reveals high rms difference regions consistent with the high r h regions. Similarly, the rms difference map for Upd-MSLA product and g tot shows a similar pattern, but with decreased values (Fig. b). These provide additional evidence that the gridded products are contaminated by high-frequency fluctuations. In Fig., the mean rms difference between g tot and Ref-MSLA is. cm for 43 PIES sites. The rms differences reduce to 9.8 cm when g tot is compared to Upd-MSLA, which accounts for a.4 cm rms improvement pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ( : 9:8 ). Careful examination of the maps reveals, however, that significant improvements appear in regions incoincident with the Jason- ground tracks. To investigate the impact of additional satellite measurements on the gridded products, mean rms differences are calculated separately at 8 PIES sites located between the Jason- tracks and at 5 PIES sites located along the Jason- tracks for Ref- and Upd-MSLA products. Mean rms differences for along-track sites are, respectively, 9.6 and 9.4 cm for Ref- and Upd-MSLA products, and those 3

13 Comparisons of sea surface height variability 43 Fig. 9 Variance (r ) maps for a g tot, b low-pass ([ days) filtered g tot (r l ), and c high-pass (\ days) filtered g tot (r h ) with different scale (unit: m ). d Ratio map between r h and r l Fig. Rms differences from PIES g tot for a Ref-MSLA product, and b for Upd-MSLA product (unit: cm). Detail values are listed in Table 4 o N 38 o N (a) Ref MSLA (b) Upd MSLA 36 o N 5 34 o N 3 o N 5 3 o N 4 o E 4 o E 44 o E 46 o E 48 o E 5 o E for sites elsewhere are, respectively,.4 and.5 cm. This indicates that Upd-MSLA product accounts for a pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 6.8 cm rms improvement ( :4 :5 ) in regions between the Jason- ground tracks by including the two additional satellites, GFO and T/P interlaced. Despite this improvement, the weekly mapped Upd-MSLA exhibits greater rms difference from PIES g tot than the original pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Mono-SLA by 6.9 cm rms ( 9:4 6:4 ). To investigate the impact of reference height g ref on the correlation and regression coefficients, variance of g ref and ratios of variances between g ref and g bc are calculated. The variance map of g ref reveals high values under the mean 3

14 44 J.-H. Park et al. Fig. a Variance map for the reference height component g ref (unit: m ), and b ratio map of variances between steric and mass-loading components. Line contours superimposed in (a) indicate mean absolute dynamic topography measured by satellite altimetry during the KESS observation period as shown in Fig. 4 o N 38 o N 36 o N 34 o N o N (a) 3 o N 4 o E 4 o E 44 o E 46 o E 48 o E 5 o E.. (b).4 RMS differences (cm) Min. Rms diff. with γ = γ Fig. Rms differences between Mono-SLA and PIES-derived g tot depending on the factor c that multiplies to g ref. Thin curves rms differences at 5 PIES sites located along the Jason- satellite tracks. Thick curve mean rms differences of 5 PIES sites. The mean rms differences show a minimal value when c =.84 trough of the Kuroshio as shown in Fig. a. The ratio map of variance between g ref and g bc shows high values north of the Kuroshio and along the southernmost part of the domain. The dominant steric variations occur near the Kuroshio, and energetic mass-loading variations occur north of the Kuroshio trough. The regions showing high ratios are consistent with the regions showing low regression coefficients calculated using only the steric contribution g bc (see Figs. 6c, 7c). Adding g ref to produce g tot improves regression coefficients at both regions north of the Kuroshio trough and most southern parts of the domain. The PIES-measured g ref can have vertical decay with height above the seabed due to near-bottom trapping, characteristic of topographic Rossby waves (Rhines 97). Note that the bottom intensification implies concomitant deep baroclinicity, so it is not precisely a non-steric mode, although it can exist independently of the dominant upperlayer steric mode. The g ref can be expressed by g ref ¼ = cos hð H=bÞðP bot =q b gþ¼cp bot =q b g; ð5þ where H is the water depth, and b the vertical trapping scale. Here, if the trapping scale b is large enough, which means no bottom trapping, then c becomes unity. To test the evidence of vertical decay on the satellite-measured SSH and PIES-derived g tot comparisons in the KESS region, we multiplied the factor c in the range..8 times g ref (g tot (c) = g bc? cg ref ), and recalculated the rms differences between Mono-SLA and g tot (c). Figure shows a minimal value of rms differences of 6.3 cm on average when c =.84. This result suggests that deep eddies or waves in the KESS region (Greene et al. 9) have somewhat bottom-trapped vertical structures, with pressure anomaly at the surface about 84 % of that at the seafloor on average. More details regarding to the bottom-trapped vertical structure in the KESS region are found in Bishop et al. (). 6 Summary This study compares mono-mission along-track (Mono- SLA) and multi-mission gridded (Ref-MSLA and Upd- MSLA) SSH anomaly products from AVISO with in situ steric and mass-loading components of SSH anomaly measurements from an array of 43 PIESs in the Kuroshio Extension. During the -year period in 4 6, PIESderived total (steric?mass-loading) SSH anomaly agrees well with satellite-measured SSH products from AVISO in this region. First, we summarize the comparisons restricted to 5 PIES sites coincident with Jason- ground tracks. Correlation and regression coefficients between the Mono-SLA and PIES-derived steric component, g bc, are.97 and.9, respectively. When the reference height component, g ref,is added to g bc to give g tot, the correlation and regression coefficients improve to.98 and.96, respectively. Mean 3

15 Comparisons of sea surface height variability 45 rms differences between these two time series are 7.8 and 6.4 cm for g bc alone and g tot comparisons, respectively. The mass-loading component accounts for 4.5 cm of rms improvement. The improvement of regression coefficients and rms differences indicates that adding g ref produces g tot amplitudes that are closer to the satellite-measured SSH amplitudes. Second, we summarize the comparisons at all 43 PIES sites with two versions of AVISO gridded products, Ref- MSLA and Upd-MSLA. The mean correlation and regression coefficients between the Ref-MSLA and PIES-derived g bc are, respectively,.9 and.93. When g ref is added to the g bc, the correlation and regression coefficients improves to.9 and.98. Mean rms differences are. and. cm for g bc alone and g tot comparisons, respectively. Comparisons of Upd-MSLA with g bc alone and g tot reveals relatively small improvements of the mean correlation and regression coefficients and mean rms differences for 43 PIES sites (Table ). Yet, 8 PIES sites away from the Jason- ground tracks show considerable improvements of rms differences, which accounts for a mean 6.8 cm rms improvement. Comparisons of PIES g tot with Ref- and Upd-MSLA products reveal a low correlation region (.75.9) on the northeastern side of the KESS domain, where the g ref contribution has peak variance and accounts for about % of g bc variance. Our twice-daily PIES-derived g tot time series show that the region has relatively energetic variations at periods shorter than days, the Nyquist frequency of Jason-. The high-frequency SSH variations in that subregion decrease the correlation and increase the rms error between the AVISO 7-day SSH anomaly maps and the PIES-derived g tot. Acknowledgments We are grateful to Andrew D. Greene for producing the initial version of GEM fields that were used for the PIES steric height calculations, and to Amy L. Cutting who assisted in early stages on a summer undergraduate research experience (REU), both under NSF support. This work was supported by NSF grants OCE-8 and OCE-8546, and J.H.P. was also supported by KORDI grants PE9873 and PE9874. References AVISO and PODDAC User Handbook: IGDR and GDR Jason Products (6) SMM-MU-M5-OP-384-CN. Tech. rep. AVISO DT CorSSH and DT SLA Product Handbook (5) CLS- DOS-NT Tech. rep. Baker-Yeboah S, Watts DR, Byrne DA (9) Measurements of sea surface height variability in the Eastern South Atlantic from pressure sensor-equipped inverted echo sounders: baroclinic and barotropic components. J Atmos Ocean Technol 6: Baker-Yeboah S, Watts DR, Byrne DA, Witter DL () Sea surface height variability in the eastern South Atlantic from satellite and in situ measurements: a comparative study. J Geophys Res (in press) Bishop SP, Watts DR, Park J-H, Hogg NG () Evidence of bottom-trapped currents in the Kuroshio Extension region. J Phys Oceanogr 4:3 38 Donohue KA, Watts DR, Tracey K, Greene AD, Kennelly M () Mapping circulation in the Kuroshio Extension with an array of current and pressure recording inverted echo sounders. J Atmos Ocean Technol 7:57 57 Donohue KA et al (8) Program studies the Kuroshio Extension. EOS Trans AGU 89(7):6 6 Fukumori I, Raghunath R, Fu L-L (998) The nature of global largescale sea-level variability in relation to atmospheric forcing: a modeling study. J Geophys Res 3: Gilson J, Roemmich D, Cornuelle B (998) Relationship of TOPEX/ Poseidon altimetric height to steric height and circulation in the North Pacific. J Geophys Res 3(C), Greene A, Watts DR, Sturyn G (9) Deep cyclogenesis by synoptic eddies interacting with a seamount. J Mar Res 67:35 3 Guinehut S, Traon P-YL, Larnicol G (6) What can we learn from global altimetry/hydrography comparisons? Geophys Res Lett 33:L64. doi:.9/5gl555 Howe PJ, Dononue KA, Watts DR (9) Stream-coordinate structure and variability of the Kuroshio Extension. Deep Sea Res I 56:93 6 Jayne SR et al (9) The Kuroshio Extension and its recirculation gyres. Deep Sea Res I 56:88 99 Kennelly M, Donohue KA, Greene A, Tracey KL, Watts DR (8) Inverted echo sounder data report: Kuroshio Extension System Study (KESS), April 4 to July 6. GSO Tech. Rep. 8-, Grad. Sch. of Oceanogr., Univ. of R. I., Narragansett. KESSdatareport.pdf Kouketsu S, Yasuda I (8) Unstable frontal waves along the Kuroshio Extension with low-potential vorticity intermediate oyashio water. J Phys Oceanogr 38:38 3 McCarthy MC, Talley LD, Roemmich D () Seasonal to interannual variability from expendable bathythermograph and TOPEX/Poseidon altimeter data in the South Pacific subtropical gyre. J Geophys Res 5(C8), Meinen CS () Structure of the North Atlantic Current in streamcoordinates and the circulation in the Newfoundland Basin. Deep Sea Res I 48: Munk WH, Cartwright DE (966) Tidal spectroscopy and prediction. Philos Trans R Soc Lond A 59: Park J-H, Donohue KA, Watts DR, Rainville L () Distribution of deep near-inertial waves observed in the Kuroshio Extension. J Oceanogr 66:79 77 Park J-H, Watts DR (6) Near 5-day nonisostatic response of the Atlantic Ocean to atmospheric surface pressure deduced from sub-surface and bottom pressure measurements. Geophys Res Lett 33: L6. doi:.9/5gl634 Park J-H, Watts DR, Donohue KA, Jayne SR (8) A comparison of in situ bottom pressure array measurements with GRACE estimates in the Kuroshio Extension. Geophys Res Lett 35: L76. doi:.9/8gl34778 Park J-H, Watts DR, Tracey KL, Mitchell DA (5) A multi-index GEM technique and its application to the southwestern Japan/ East sea. J Atmos Ocean Technol :8 93 Ponte RM, Dorandeu J (3) Uncertainties in ECMWF surface pressure fields over the ocean in relation to sea level analysis and modeling. J Atmos Ocean Technol :3 37 Rhines, PB (97) Edge-, bottom-, and Rossby waves in a rotating stratified fluid. Geophys Fluid Dyn :73 3 SSALTO/DUACS User Handbook: (M)SLA and (M)ADT Near-Real Time and Delayed Time Products (6) CLS-DOS-NT Tech. rep. 3

16 46 J.-H. Park et al. Teague WJ, Hallock ZR, Jacobs GA, Mitchell JL (995) Kuroshio sea surface height fluctuations observed simultaneously with inverted echo sounders and TOPEX/Poseidon. J Geophys Res (C): Traon PYL, Dibarboure G () Velocity mapping capabilities of present and future altimeter missions: the role of high-frequency signals. J Atmos Ocean Technol 9:77 87 Watts DR, Qian X, Tracey KL (a) On mapping abyssal current and pressure fields under the meandering Gulf Stream. J Atmos Ocean Technol 8:5 67 Watts DR, Sun C, Rintoul S (b) A two-dimensional gravest empirical mode determined from hydrographic observations in the Subantarctic Front. J Phys Oceanogr 3:86 9 3