PUBLICATIONS. Journal of Geophysical Research: Oceans. Development of a global gridded Argo data set with Barnes successive corrections

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1 PUBLICATIONS Journal of Geophysical Research: Oceans RESEARCH ARTICLE Key Points: A new gridded Argo data set, BOA-Argo, is produced using refined Barnes successive corrections The new data set is comparable or slightly better than other gridded Argo data sets produced using OI or variational analysis The BOA-Argo is able to retain more mesoscale features than other gridded Argo data sets Correspondence to: W. Zhou, zhouwei@scsio.ac.cn; F. Xu, fxu@tsinghua.edu.cn Citation: Li, H., F. Xu, W. Zhou, D. Wang, J. S. Wright, Z. Liu, and Y. Lin (2017), Development of a global gridded Argo data set with Barnes successive corrections, J. Geophys. Res. Oceans, 122, doi:. Received 9 SEP 2016 Accepted 5 JAN 2017 Accepted article online 10 JAN 2017 Development of a global gridded Argo data set with Barnes successive corrections Hong Li 1,2, Fanghua Xu 3, Wei Zhou 4, Dongxiao Wang 4, Jonathon S. Wright 3, Zenghong Liu 1, and Yanluan Lin 3 1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China, 2 Zhe Jiang Institute of Hydraulics and Estuary, Hangzhou, China, 3 Ministry of Education Key Laboratory for Earth System Modeling and Department of Earth System Science, Tsinghua University, Beijing, China, 4 State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China Abstract A new 11 year ( ) monthly 18 gridded Argo temperature and salinity data set with 49 vertical levels from the surface to 1950 m depth (named BOA-Argo) is generated for use in ocean research and modeling studies. The data set is produced based on refined Barnes successive corrections by adopting flexible response functions based on a series of error analyses to minimize errors induced by nonuniform spatial distribution of Argo observations. These response functions allow BOA-Argo to capture a greater portion of mesoscale and large-scale signals while compressing small-sale and high-frequency noise relative to the most recent version of the World Ocean Atlas (WOA). BOA-Argo data set is evaluated against other gridded data sets, such as WOA13, Roemmich-Argo, Jamestec-Argo, EN4-Argo, and IPRC-Argo in terms of climatology, independent observations, mixed-layer depth, and so on. Generally, BOA-Argo compares well with other Argo gridded data sets. The RMSEs and correlation coefficients of compared variables from BOA-Argo agree most with those from the Roemmich-Argo. In particular, more mesoscale features are retained in BOA-Argo than others as compared to satellite sea surface heights. These results indicate that the BOA-Argo data set is a useful and promising adding to the current Argo data sets. The proposed refined Barnes method is computationally simple and efficient, so that the BOA-Argo data set can be easily updated to keep pace with tremendous daily increases in the volume of Argo temperature and salinity data. VC American Geophysical Union. All Rights Reserved. 1. Introduction The Array for Real-time Geostrophic Oceanography (Argo) program was originally proposed by scientists from the United States and Japan at the end of the twentieth century. More than 3000 Lagrangian freedrifting profiling floats were deployed in the global oceans at a spacing of approximately 300 km ( ). The purpose of this Argo global observation network is to provide continuous, high-resolution temperature and salinity (T/S) observations within the upper 2000 m of the ocean globally. Argo data are provided at near real-time, within a few hours after collection. Over 1 million Argo temperature-salinity-pressure profiles had been obtained by November 2012, with more than 100,000 additional profiles every year. The data coverage and volume exceeds all previous traditional observations; however, like those previous observations, temperature and salinity profiles from Argo floats are not evenly distributed in space and time, and thus confines its applications. The Argo floats, measuring near real-time global ocean temperature and salinity profiles in the upper 2000 m, facilitate the production of near real-time global gridded ocean T/S data. Many Argo member states have developed their own individual monthly gridded Argo data (e.g., Jamestec-Argo [Hosoda et al., 2008], Roemmich-Argo [Roemmich and Gilson, 2009], EN4-Argo [Good et al., 2013], and IPRC-Argo ( soest.hawaii.edu/projects/argo/data/documentation/gridded-var.pdf)). These Argo gridded data sets are mostly based on optimum interpolation (OI) or more sophisticated variational analysis methods [Troupin et al., 2010]. As pointed out by Locarnini et al. [2006], optimum interpolation is based on the second-order statistics, which tends to induce large errors when data are relatively sparse. The advantage of Barnes HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 1

2 corrections is that it can capture signals at various scales based on predefined response functions. However, there has been no monthly Argo gridded data set generated by the Barnes method so far, because the classic Barnes method also requires spatial uniformly distributed observations. The classic Barnes method is ideal for intensive and evenly distributed observations. Historical observational data are relatively rich at climatological timescales and therefore fulfill the requirement for uniformly distributed observational data inherent in the Barnes correction method. Levitus [1982] successfully built a global climatology of ocean temperature and salinity using simple Barnes successive corrections. Theoretically, this enables the production of objective analyses via examination of the response function and the calculation of key parameters. In contrast, the Argo monthly data are not evenly distributed and relatively sparse compared with WOA observational data. This poses a significant obstacle to apply the default Barnes correction method directly to the nonuniformly distributed monthly Argo data. In the study, we refine the classic Barnes method by adopting the most suitable parameters and response functions (Appendix A). A series of error analyses are conducted to quantify the most appropriate parameters in the Barnes method, including the iteration number, influence radius, convergence factor, and filtering parameter. New response functions are then derived based on these parameters. Compared to the three iterations required in the classic Barnes method, the new approach only requires two iterations. Due to the flexible response functions used, the refined method is able to retain more mesoscale signals than the classic method. We used the following procedure to produce a new Argo gridded data set (Barnes objective analysis BOA-Argo). First, we applied quality control filters for available Argo profiles and used the geometric method proposed by Chu et al. [1999] to calculate the mixed-layer depth for each Argo profile. We then estimated the corresponding sea surface temperature (SST) and sea surface salinity (SSS) according to the average temperature and salinity of mixed-layer depth. The initial background state was constructed using Cressman successive correction [Cressman, 1959]. The Barnes successive correction was then applied to produce monthly gridded analyses. Key parameters, such as filtering parameters, the radius of influence, the number of iterations, and the convergence factor, are determined using error tests. The root-mean-square errors (RMSEs) of the data are examined to eliminate original Argo data with large errors. Barnes correction is then applied recursively until all RMSEs are smaller than prescribed values. We use this approach to generate monthly mean three-dimensional temperature, salinity, and derived products in the global ocean for January 2004 through December In addition to data analysis applications and process studies, this data set could be used directly for evaluation and validation of ocean simulations. The goals of this study are twofold. First, we developed a refined Barnes successive correction to generate ocean analyses at monthly timescale. Second, the new gridded data set (BOA-Argo) is compared and evaluated against other global data sets. Detailed description of the techniques, including the data quality control and preprocessing, for the production of the new gridded ocean analysis is in section 2. Validation of the BOA-Argo against other gridded Argo data sets and WOA13 is presented in section 3. In section 4, we summarize our results and conclusions. Appendix A contains detailed derivation of the response functions for the Barnes successive correction. 2. Methodology for Generation of BOA-Argo Real-time global ocean temperature and salinity profiles are provided by the China Argo Real-time Data Center ( We neglect Argo data collected before 2004 due to the small number of samples. The Argo network has provided global coverage since 2004 and we use data during to set the initial conditions as described in section 2.2. We therefore generate the monthly mean gridded Argo data using observations collected between 2004 and Generation of BOA-Argo data proceeds in four steps (Figure 1). First, Argo profiles are collected, filtered to remove unreliable data, and merged into boxes. Second, the first guess fields for all variables are constructed using a Cressman successive correction method. Third, gridded data are generated using a refined Barnes successive correction method. Fourth, RMSEs of gridded T/S profiles after the objective analysis relative to merged T/S profiles are calculated. T/S profiles with large T/S RMSE values are removed and HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 2

3 Figure 1. Flowchart describing the production of the BOA-Argo gridded data set, including quality control and preprocessing of Argo data, as well as iterative applications of the objective analysis and RMSE check steps. the third (Barnes successive correction) and fourth (calculation of RMSEs) steps are repeated until all T/S RMSEs in the deep ocean (>1500 m) are small (T RMSEs < 0.068C, S RMSEs < 0.01psu). Temperature and salinity in the deep ocean (>1500 m) vary slowly, so that T/S RMSE profiles at these depths should be small and consistent across months. Additional details are provided below Quality Control and Preprocessing Quality Control Argo data centers, including the China Argo Real-time Data Center, impose a set of automatic quality control (QC) filters on all real-time and delayed-mode Argo observations. These QC filters detect 19 types of errors, including errors related to bad platform identification, misidentification of date or location, oceanland interface, float drift speed, stuck values, sudden spikes, sensor drift and calibration issues, and gradient or density anomalies, among others. Readers should refer to the Argo data management documentation [Argo Data Management, 2013] for details. Despite these QC criteria, the quality of some data is still insufficient for our application. We further improve the quality and suitability of the profiles ingested into BOA-Argo by applying additional filters according to the following nine steps. Before QC processes, we generate a landmask. Horizontal grids (179.58W E, 79.58S 79.58N, every 18) are produced by bilinear interpolation based on the ETOPO5 surface topography data ( Then, grid points with water depth less than 400 m is set as land. Argo profiles in the ocean grids are used in the following processes: HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 3

4 1. We select Argo data between 808S and 808N, excluding data collected at latitudes poleward of 808N or 808S. About 3.63% Argo profiles are removed. Then, the T/S profiles are interpolated to 48 vertical levels (5 m, m at 10 m intervals, m at 20 m intervals, m at 100 m intervals, 1750 m, and 1950 m) via linear interpolation. 2. We exclusively select temperature and salinity (T/S) profiles measured simultaneously with QC flag 5 1. About 6.66% profiles are removed. 3. We remove profiles with less than 15 vertical layers. Each selected profiles must cover a minimum depth range from 10 to 400 m. We also remove profiles that report vertical pressure reversals. About 8.55% profiles are removed. 4. Potential densities are calculated at all depths for each profile. Profiles for which potential density at a deeper layer is more than 0.03 kg m 23 smaller than potential density at the next shallower layer are removed to prevent the interpolation step from introducing spurious values. About 0.04% profiles are removed. 5. According to Argo Data Management [2013], Argo data in the surface or deep ocean with a spike should be removed. A spike is defined as a temperature difference larger than 1.58C or a salinity difference larger than 0.5 psu between adjacent measurements within water depth shallower than 15 m or deeper than 800 m. About 0.01% profiles are removed. 6. We delete profiles with identical T/S values over continuous 150 m in the vertical. It is induced by some instrument errors. About 0.24% profiles are removed. 7. T/S values observed by Argo floats cover a wide range. We use WOA09 data published by Locarnini et al. [2010] and Antonov et al. [2010] to constrain the range of realistic T/S values in every region within 808S 808N and 1808W 1808E. The monthly climatologies of WOA09 T/S are interpolated to the same 48 vertical levels. We set the temperature range to be [max(min(t) 2 1,-2.5), min(max(t) 1 1,35)], salinity range to be [max(min(s) 2 0.5,15), min(max(s) 1 0.5,40)]. Any interpolated T/S value that falls outside of these limits is removed. The entire profile is removed if the number of values outside of these limits is greater than 24 (half of the profile). No profiles are removed. 8. The global ocean is divided into three basins: the Pacific Ocean, the Atlantic Ocean, and the Indian Ocean. The mean and standard deviation (STD) of T/S at every vertical level are calculated monthly in each basin. The variability of T/S changes with depth, so that appropriate definitions for the upper and lower bounds should also change with depth. We use mean 6 6STD for the surface (5 m), and mean 6 5STD for the deepest layer (1950 m), with a linear change. The exceeding rate acceptable limit linearly decreases from between 5 m and to 1950 m. If T or S at a given depth exceeds the corresponding regionally defined limits, data at this level are ignored. Profiles with valid data at less than 24 levels (half the profile) are removed entirely. About 0.16% profiles are removed. 9. The global ocean between 808S and 808N is then divided into regions [Roemmich and Gilson, 2009]. All available T/S Argo data ( ) are used to estimate the mean and STD at each level. Profiles with interpolated values of either T or S outside of the mean 6 6STD interval at some depth are removed [Roemmich and Gilson, 2009]. Profiles with valid data less than half are removed entirely. About 0.02% profiles are removed. These additional quality control steps yield data on a uniform vertical grid with 48 levels. Figure 2 shows time series of monthly data counts before and after the application of our quality control criteria between January 2004 and December The number of available Argo profiles increased from about 2000 in 2004 to over 11,000 in 2014 with the increasing of global Argo floats. The rejected Argo profiles also increases from about 1000 profiles in 2004 to about 1300 profiles in Though the number of rejected profiles increases, the rejection ratio decreases with the advancement of Argo floats. The quality control criteria described above reduce the overall data counts by approximately 19.31%. Argo profiles that meet these criteria are used as inputs for the Barnes objective analysis and the production of the BOA-Argo gridded data set Preprocessing The procedures outlined in section ensure that Argo T/S data are regularly distributed in the vertical dimension, but these data are still distributed irregularly in the horizontal dimension. Data may be sparse or even absent in regions where few floats were deployed, but clustered in areas where a relatively large number of floats were deployed. This type of data clustering can severely impact the results of objective analyses [e.g., Barnes, 1964, 1973; Smith et al., 1986; Koch et al., 1983]. We therefore merge data points where clustering occurred to make the spatial distribution of profiles more homogeneous. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 4

5 Figure 2. Number of Argo data points before (black) and after (red) data quality control from January 2004 to December The global oceans within 808S 808N are gridded into grid cells. Argo T/S profiles in grid cells containing more than one Argo profile are merged to obtain new T/S values located at the center of the box [Locarnini et al., 2006]. The objective analysis uses these more spatially homogeneous observations to generate analyses for every grid cell Generation of Climatological Initial Conditions: The Cressman Scheme Monthly climatological initial conditions are obtained via Cressman analysis because of its simplicity. Cressman [1959] introduced a successive correction scheme, which iteratively corrects gridded background values (first guesses) by applying a linear combination of corrections between predicted and observed values. Corrections are applied at each grid point using the equation fi n11 5f n X Kn i b51 i 1 w n ib ðf o b 2f n b Þ X Kn i wib n b51 ; (1) where fi n is the analysis field after the nth iteration at grid point i, fb o is the bth observation within the scan radius R n (called the influence region) for the nth iteration, fb n is the estimate after the nth iteration interpolated to the location of the bth observation, Ki n is the number of the observations collected within R n, and wib n is a weighting function. The weighting function wn ib is defined as 8 >< wib n 5 R2 n 2r2 ib ; r 2 R 2 n 1r2 ib < R2 n ib ; >: wib n 50; r2 ib R2 n (2) where r 2 ib is the square of the distance between the observation b and the grid point i. R n is usually taken to be several times the grid distance. Cressman [1959] suggested that R n51 should be set to a relatively large value to ensure that the large-scale signal is retained and that R n should then be gradually decreased with successive iterations to better resolve small-scale signals. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 5

6 We use a Cressman scheme (equations (1) and (2)) with three iterations to generate an annual mean climatology that serves as a first guess for the seasonal climatology. The scan radiuses R 1, R 2, and R 3 are set to 999, 666, and 333 km, respectively. Four seasonal means are then estimated using the same three radii of influence, with the annual mean as the initial condition. Finally, monthly mean climatologies are generated using the same approach, with the corresponding seasonal mean as the first guess Production of Monthly Data: The Barnes Scheme Monthly data are produced using a refined Barnes successive correction method. This method was originally proposed by Barnes [1964] and then revised by Barnes [1973], and is in many respects similar to the Cressman scheme. The major difference between the Cressman and Barnes approaches is the manner in which the weighting function, wib n, is computed: Barnes [1964, 1973] used a Gaussian function to compute wn ib. This approach allows us to construct a relationship between the weighting and response functions, so that it is clear how many waves of different wavelengths are compressed and retained. Barnes [1964] proposed the weighting function 8 >< wib n 5exp ð24r2 ib R 2 Þ; rib 2 < R2 n n ; (3) >: wib n 50; r2 ib R2 n where rib 2 and R n are defined as in equation (2). However, the efficiency of this approach is suboptimal, often requiring three to four iterations to obtain the final analysis. Barnes [1973] refined the method by proposing the modified weighting function 8 >< wib n 5exp ð2r2 ib ac Þ; r2 ib < R2 n ; (4) >: wib n 50; r2 ib R2 n where a is a filtering constant and c is the convergence factor. This modified weighting function reduces the number of iterations typically required to reach a stable solution. Barnes [1964] noted that the resulting response function for each wavelength approximates the percentage of that wavelength s amplitude resolved by the objective analysis. The parameters a and c are chosen prior to the analysis, so that the spatial scales that can be resolved by the data translate to known response amplitudes. It is therefore possible to compute how much of the amplitude of each wavelength will be retained by the final analysis when this method is used, and thereby ensure that the necessary signals are resolved by the response function. Levitus [1982] used a simple theoretical Barnes successive correction method to build climatologies of global ocean temperature and salinity. In contrast, we conduct a series of error analyses to quantify the most appropriate parameters in the Barnes method. New response functions are then derived to minimize errors induced by nonuniform spatial distribution of Argo observations. We first calculate the response function of the objective analysis. The analysis is smoothed using a 9-point smoothing algorithm [Schuman et al., 1957] twice after each iteration to remove spurious small-scale signals. These procedures are applied to generate monthly gridded data from January 2004 to December Response functions are a critical part of the Barnes objective analysis method, because they are used to theoretically estimate the extent to which the analysis can capture full spectrum wave signals. We have compared the response functions for BOA-Argo (Appendix A) with a series of WOA data sets (Table 1 and Figure 3). Main differences in the response function between WOA and BOA-Argo are the iteration number, influence radius, convergence factor, filtering parameter, and smoothing methods (Appendix A). WOA applies three iterations, and their corresponding influence radiuses are 892, 669, and 446 km [Locarnini et al., 2010; Antonov et al., 2010], respectively. The associated filtering parameters are , , and km 2, and the convergence factors are 1. After every iteration, a one-time medium smoothing and a 5-point smoothing are applied. In contrast, BOA-Argo applied iterations twice, and the corresponding influence radiuses are 555 and 555 km, filtering parameters are and km 2, and the convergence factors are 0.2. After every iteration, a 9-point smoothing is applied twice. Consequently, BOA-Argo can retain larger portions of the mesoscale and large-scale signals for wavelength 5DX (Table 1). The WOA data sets are mainly concerned with large-scale climatological signals in the global ocean. According to Table 1 and Figure 3, all WOA data sets produced after 1998 have the same response functions. The HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 6

7 Table 1. Response Functions for Different Gridded Data Sets as a Function of Wavelength, Normalized to 1.0 Wavelength a WOA98,01,05,09,13 BOA-Argo 360DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX DX 1.36E E-28 a For DX km, the meridional separation at the Equator. response functions used in BOA-Argo compress small-scale (wavelength 3DX, where DX is a grid interval for zonal direction) and high-frequency noises by 1 order of magnitude relative to WOA13 while retaining larger portions of the mesoscale and large-scale signals (wavelength 5DX) (Table 1). For example, BOA-Argo retains 78.1% of information for wavelengths of 10DX, while WOA13 retains 69.8%. BOA-Argo therefore retains more of the variability at meso and large scales than WOA13, while reducing noise at small scales BOA-Argo Quality Assessment We check the quality of the gridded objective analyses using distributions of T/S RMSEs at different depths. RMSEs are calculated by taking differences between gridded objective analyses and gridded merged observations (i.e., the output from the procedures described in section 2.1.2). The temporal coverage of the gridded data extends from January 2004 to December 2014, so that we calculate 132 monthly T/S RMSEs at each depth. T/S values are almost unchanged in the deep ocean (depth > 1500 m), so the magnitude and spread of T/S RMSE should generally decrease with depth. RMSEs that deviate significantly from the mean in deep water are used to identify and remove the original T/S data from the data set. The objective analysis is then repeated until all RMSEs are small. Figure 4 shows the final global averaged T/S RMSEs at different depths. For water depths deeper than 1000 m, the average RMSE of temperatures is approximately C, and the average RMSE of salinity is psu. Above 1000 m, the average RMSEs increase to C for temperature and psu for salinity, probably due to the impacts of surface winds, heat fluxes, freshwater fluxes, and so on. Figure 3. Response functions as a function of wavelength for different objective analysis schemes, including WOA98-13 (red squares) and BOA-Argo (black circles). 3. Comparisons 3.1. Climatological Comparison BOA-Argo is able to capture the spatial patterns of temperature and salinity in the ocean. Figure 5 shows horizontal distributions of climatological T/S from BOA- Argo and differences relative to WOA13 at depths of 10 and 500 m. BOA-Argo generally agrees well with WOA13. The mean of WOA13 data ( gov/oc5/woa13/woa13data.html) is used for comparison. Differences relative to WOA13 at 10 m include C warm differences in the Northeast Pacific, the Northwest Atlantic, HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 7

8 Figure 4. RMSEs of (a) temperature and (b) salinity between the gridded BOA-Argo analysis and merged observations after the objective analysis step. Data are from January 2004 to December Thick black lines show the average values of RMSEs for each variable, while thin gray lines show the monthly RMSEs. and the Southwest Atlantic (Figure 5b). Near-surface salinity in BOA-Argo is psu fresh relative to WOA13 in the tropical Pacific and Atlantic. BOA-Argo is psu saltier relative to WOA13 in the Gulf of Mexico, the Gulf Stream, and the Brazil-Malvinas Confluence Zone (Figure 5d). At 500 m, temperature differences relative to WOA13 are mainly identified near the Gulf Stream and Kuroshio extension regions, as well as near the Antarctic Circumpolar Current (Figure 5f). This is also found in the comparison of other Argo gridded data against WOA09 [Chang et al., 2014]. Considered the mesoscale eddy shapes of them, these differences may be induced by active meandering and associated eddy activities in these regions. Other gridded Argo data sets (Roemmich-Argo and IPRC-Argo, Jamestec-Argo, and EN4-Argo) exhibit similar spatial patterns relative to WOA13 at 500 m depth (not shown), which supports our contention that these differences are inherited from the original Argo data and are not unique to BOA-Argo. Salinity differences at 500 m are near zero in most of the ocean basins, but fresh differences of psu are found in the Gulf Stream regions (Figure 5h). Figure 6 shows longitude-depth and latitude-depth cross sections of climatological BOA-Argo temperature in the upper 500 m of the ocean along E (Figure 6a) and 0.58N (Figure 6c). The two cross sections are selected because they are mostly occupied by oceans. Each section features a warm stratified lens of water on top of cold deep water. Along E, a lens of warm water with temperatures greater than 288C extends from 208S to 108N during the austral summer. Vertical temperature gradients in this region maximize between 100 and 250 m depth. Temperature gradually decreases toward the pole, with isotherms outcropping at the surface in middle latitudes. The western Pacific warm pool can be clearly identified in the 0.58N longitude-depth cross section. The depth of the 208C isotherm is nearly 200 m from the Maritime Continent to approximately 1658E, at this point it decreases toward the east (Figure 6c). This structure is closely related to the distributions of trade winds and currents in the tropics. Figure 6 also shows temperature differences along the same cross sections relative to the WOA climatology (Figures 6b and 6d). BOA-Argo is C colder within the m layer between 108N and 108S except at equator (Figure 6b), implying a stronger temperature gradient around the Equatorial Under Current in BOA-Argo. In addition, BOA-Argo is more than 0.58C warmer from surface to 500 m centered at 558S, consistent with a warm HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 8

9 Figure 5. Distribution of (a) annual climatology temperature at 10 m depth from BOA-Argo and (b) its difference relative to the WOA13 annual climatology. (c, d) As for Figures 5a and 5b, but for salinity at 10 m depth. (e, f) As for Figures 5a and 5b, but for temperature at 500 m depth. (g, h) As for Figures 5a and 5b, but for salinity at 500 m depth. The contour interval of temperature is expressed in 0.258C in Figures 5b and 5f. And the contour interval of salinity is expressed in 0.1 psu in Figures 5d and 5h. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 9

10 Figure 6. (a) A latitude-depth cross section of climatological temperature (8C) along the E transect for BOA-Argo, and (b) its difference from the WOA13 annual climatology. (c, d) Same as Figures 6a and 6b, but for a longitude-depth cross section along the 0.58N transect. (e, g) Same as Figures 6b and 6d, but for Roemmich-Argo; (f, h) same as Figures 6b and 6d, but for IPRC-Argo; (i, k) same as Figures 6b and 6d, but for Jamestec-Argo; and (j, l) same as Figures 6b and 6d, but for EN4-Argo. Contour lines for temperature differences are labeled at 0.2, 0.5, 1, and 1.58C. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 10

11 eddy feature in Figures 5b and 5f. The difference is also found in Roemmich-Argo (Figure 6e) and Jamestec- Argo (Figure 6i). Along 0.58N, the main difference between BOA-Argo and WOA13 lies in this Pacific region: temperatures are about 0.58C lower in the western Pacific, and 0.58C higher in the eastern Pacific. Other four gridded Argo data sets exhibit similar longitude-depth and latitude-depth cross-sectional differences relative to WOA13. Figure 7 shows BOA-Argo salinity distributions along the same transects as in Figure 6 (165.58E and 0.58N). As expected, salinity is highest in the subtropical regions (Figure 7a) and waters in the Pacific Ocean are fresher than waters in the Atlantic or Indian Oceans (Figure 7c). Differences relative to the WOA13 climatology along E are less than 0.1 psu at most of ocean basins except at surface of Equator (>0.1 psu) and in the Southern Ocean (608S 658S). The relative large differences south of 608S mainly stem from the algorithm s incapability near the edge of the study domain where Argo profiles are sparse. The salinity changes little along 0.58N within a range of psu. Differences in salinity relative to WOA13 are small (<0.2 psu) along this entire transect, except near 808W, where surface water is fresher in BOA- Argo than in WOA13. Other four gridded Argo data sets exhibit similar salinity difference patterns relative to WOA13 as well Comparisons Against Other Gridded Argo Data Sets at 105 Global Tropical Moored Buoy Array (GTMBA) Stations To further explore the BOA-Argo data set quality, we compare it against GTMBA, ( gov/tao/global/global.html) T/S observation together with two other gridded Argo data sets: Roemmich- Argo and IPRC-Argo. Note that all three gridded Argo data sets are independent of the GTMBA T/S observations. Other gridded Argo data set, such as Argo-EN4, has utilized the tropical array data. The GTMBA data includes Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian, Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) in the Pacific, Prediction and Research Moored Array in the Atlantic (PIRATA). One hundred and five GTMBA stations are selected with data duration longer than 36 months and more than three vertical levels. Figure 8 shows geographical distribution of the 105 GTMBA stations. Seventy stations are located in the tropical Pacific (Nos ; Figures 9a and 9c), 19 are located in the tropical Indian Ocean (Nos. 1 19), and the other 16 are located in the tropical Atlantic (Nos ). Figure 9 shows the correlation coefficients and RMSEs between the three Argo gridded T/S (BOA-Argo, Roemmich-Argo, and IPRC-Argo) with GTMBA T/S from January 2004 to December For temperature, depth-averaged correlation coefficients for all data sets are mostly larger than 0.5 (Figure 9a), and RMSEs are less than 1.58C for Roemmich-Argo and BOA-Argo (Figure 9c). In general, BOA-Argo has smaller RMSEs and higher correlation than IPRC-Argo, but with slightly larger RMSEs and smaller correlation than Roemmich-Argo, except at a few stations (e.g., 1, 6, 21, 102, and 105). For salinity, Nos are in the Indian Ocean, Nos are in the Pacific Ocean, and Nos are in the Atlantic Ocean (Figures 9b and 9d). Depth-averaged correlation coefficients from the three Agro data sets are larger than 0.3 at all stations (Figure 9b), and RMSEs are less than 0.4 psu (Figure 9d). Again, BOA-Argo has smaller RMSEs and higher correlation than IPRC-Argo, but has larger RMSEs and smaller correlation than Roemmich-Argo at most stations. The station-averaged correlation coefficients generally decrease with depth for the three data sets. For BOA-Argo, it is relatively high (>0.74 for temperature, and >0.52 for salinity) for water depth shallower than 100 m (Figures 9e and 9f). Below 300 m, the correlation is about 0.5 for temperature, but decreasing from 0.38 toward 0.04 for salinity (Figures 9e and 9f). Compared with the other two data sets, BOA-Argo has higher correlation than IPRC-Argo, but smaller than Roemmich-Argo. RMSEs for temperature are relative small (<0.58C) near surface (<30 m) and at deeper waters (>300 m), but large (>1.08C) in the subsurface (Figure 9g). RMSEs for salinity are all small (<0.25 psu), with the Roemmich-Argo and BOA-Argo smaller than IPRC-Argo over the entire depth range (Figure 9h). In summary, in terms of correlation coefficient and RMSEs, BOA-Argo performs better than IPRC-Argo, but slightly worse than Roemmich-Argo. It is probably caused by geographical distribution of original Argo profiles. When Argo profiles are dense, such as in the Pacific and Atlantic, Roemmich-Argo based on optimal interpolation tends to minimize analysis errors. In contrast, BOA-Argo retains large-scale signals but compresses signals at small scales (Table 1). Thus, the errors could be larger than Roemmich-Argo at those stations. While when original Argo profiles are not dense enough, the ability of BOA-Argo to retain various signals can obtain comparable or even better analysis than Roemmich-Argo, such as in the Indian Ocean, where Argo profiles are relative sparse in early days. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 11

12 Figure 7. As in Figure 6, but for salinity expressed in psu. The contour lines for salinity differences are labeled as 0.05, 0.1, 0.2, and 0.3 psu. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 12

13 Figure 8. Locations of 105 Global Tropical Moored Buoy Array (GTMBA) observation stations used for estimating correlation coefficients and RMSEs of T/S from gridded Argo data sets. Each station location is labeled with its location ENSO Performance The El Ni~no-Southern Oscillation (ENSO) varies significantly on decadal timescales in terms of oscillation period, onset time, amplitude, and the propagation of sea-surface temperature anomalies (SSTA) along the equator [Wang et al., 2009]. The warm (El Ni~no) and cold (La Ni~na) phases of ENSO occur approximately every 3 7 years, with profound impacts on environments and socioeconomic conditions worldwide. Here we compare ENSO characteristics captured by BOA-Argo data set to other gridded Argo data sets. To evaluate the temporal evolution and spatial structures of El Ni~no, we compute the time-longitude distribution of m vertically averaged temperature anomalies, the 208C isotherm depth (as a proxy for the depth of the thermocline), and the Nino3.4 index (an index for the intensity of SST anomalies in the central Pacific). Figure 10 shows the spatial structure of anomalies in SST and the depth of the 208C isotherm. Warm anomalies in the eastern Pacific are modulated by the excitation of wind-forced, eastward propagating equatorial Kelvin waves (evident in the seasonal oscillations of the depth of the 208C isotherm). These waves can increase the depth of the thermocline by m and reduce sea surface cooling due to weakening of equatorial upwelling [McPhaden, 2008]. Warm SST anomalies in the eastern Pacific are associated with deeper thermoclines (e.g., , , and ). The atmosphere overlying Nino3.4 region is particularly sensitive to thermal forcing from the ocean surface [McPhaden, 2008]. The temporal variations of the Nino3.4 index calculated using BOA-Argo agree well (r 2 > 0.97) with those calculated using other gridded Argo data sets. We also compare the Nino3.4 index calculated using BOA-Argo with a Nino3.4 index derived from independent observations provided by NOAA/ CPC. The correlation coefficient between the two Nino3.4 indices is 0.94, which is similar to the correlations achieved by other gridded Argo data sets ( ). ENSO events are defined as five consecutive 3 month running mean SST anomalies at or above the 10.58C threshold for El Ni~no events and at or below the 20.58C threshold for La Ni~na events [Leetma, 1989; Using this criterion, BOA-Argo identifies three El Ni~no events ( , , and ) and three La Ni~na events ( , , and ). These events agree with the set of events identified during this period by NOAA/CPC ( Comparisons Against Other Gridded Argo Data Sets for AVISO SLA To investigate the mesoscale signals retained in the Argo gridded data, we compare satellite sea level anomalies (SLA) with dynamic height anomalies (DHA) using T/S from five gridded Argo data sets including BOA-Argo, Roemmich-Argo, IPRC-Argo, Jamestec-Argo, and EN4-Argo. The satellite altimeters measure total sea surface height variations, which include both gravity (mass-loading) and steric (density-related) components [Chang et al., 2010]. Comparisons can be made by using satellite SLA as a proxy for dynamic height, because variations of SLA are dominated by steric effects on regional scales [e.g., Gilson et al., 1998; Roemmich and Gilson, 2009]. Altimetric SLA products produced by SSALTO/DUACS and distributed by AVISO ( are used. Daily AVISO SLA has a horizontal resolution of 1/48 3 1/48, spanning from October 1992 to now. Figures 11a 11e show the global distribution of DHA at 10 m relative to 1500 m (10/1500 m) from five gridded Argo data sets in January 2009, and Figure 11f also shows the monthly averaged AVISO SLA in January It is apparent that the SLA fields are diverse in mesoscale anticyclonic and cyclonic features (negative and positive SLA, respectively). Even though the mesoscale features of DHA from Argo data sets are much less than those of AVISO SLA due to their relative coarse resolution, some mesoscale features are HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 13

14 Figure 9. Vertical average and average (all points) correlation coefficients and RMSEs of temperature and salinity between three gridded Argo data sets and 105 GTMBA observations. sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Vertical average RMSE is estimated using data from January 2004 to December 2014 as X m j51 X n i51 ðs ij gridded data 2Sij taoþ 2 =n, where S denotes temperature or salinity and n is the num- sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ber of months, m is observation temperature or salinity vertical layer. Average RMSE is estimated using data from January 2004 to December 2014 as where S denotes temperature or salinity and n is the number of months, k is observation temperature (105) or salinity station (54) number. m X k j51 X n i51 ðs ij gridded data 2Sij taoþ 2 =n k, retained near the strong western boundary currents and Antarctic Circumpolar Current. Among the five gridded Argo data sets, the BOA-Argo possesses more mesoscale features than others (Figures 11a 11e). To further explore mesoscale features from these data sets, we make comparisons in Northwest (NW) Pacific (208N 408N, 1308E 1708E) and NW Atlantic regions (308N 508N, 708W 308W) (Figure 12). Similar to Roemmich-Argo, the BOA-Argo clearly shows more mesoscale features than the other three data sets. For example, a strong anticyclonic appears around 34.98N, E with O (200 km) radius scales in BOA-Argo in NW Pacific. A similar feature is found in Roemmich-Argo. But the anticyclonic is much weaker in IPRC-Argo, Jamestec-Argo. For EN4-Argo, the anticyclone is weaker and elongated southward. A similar strength anticyclone is centered at 35.28N, E with O (150 km) radius scales in AVISO SLA fields. Another weaker anticyclone (see 20 cm contour) centered at 23.88N, E is found in BOA-Argo (Figure 12a). The anticyclone located at the same position is relatively weak (10 cm contour lines) in Roemmich-Argo (Figure 12b). In HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 14

15 Figure 10. Hovm oller diagrams of anomalies in (a) SST and (b) the depth of the 208C isotherm from January 2004 to December 2014, based on BOA-Argo data between 2.58N and 2.58S. Anomalies are calculated relative to the BOA-Argo annual climatology. (c) Time series of temperature anomalies (8C) in the Nino3.4 region (58S 58N, 1708W 1208W) based on data from the uppermost 100 m for multiple gridded oceanographic data sets. Anomalies are calculated relative to the annual climatology from the corresponding gridded data set. contrast, the anticyclone disappeared in other three data sets (Figures 12c 12e). From AVISO SLA, an anticyclone (20 cm contour) is seen around 248N, E (Figures 12f). In NW Atlantic, there are two strong cyclones, centered at 36.28N, 60.58W, and 38.38N, 50.18W, and one strong anticyclone centered at 40.48N, 61.58W in BOA-Argo. Similar eddies are found in Roemmich-Argo, but with weaker magnitudes. The BOA-Argo is closer to AVISO SLA in terms of eddy strength than Roemmich-Argo. Note that these eddies are absent in other three Argo data sets. Thus, the advantage of BOA-Argo in capturing mesoscale features is manifest. Figure 13 shows the global distribution of temporal correlation coefficients and RMSEs between BOA-Argo DHA and satellite SLA from January 2004 to December The DHA are highly correlated (>0.6) with SLA in lower latitude band (208S 208N). Relative high correlations (>0.4) are found in the eastern basins of North Pacific, North Atlantic, and South Pacific, consistent with historical results [Ishii et al., 2003]. On the other hand, the correlation coefficients are relatively low (<0.4) in the Southern Ocean (Figure 13a). The RMSEs HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 15

16 Figure 11. Geographical distributions of DHA (cm) at 10 m relative to 1500 m from (a) BOA-Argo, (b) Roemmich-Argo, (c) IPRC-Argo, (d) Jamestec-Argo, (e) EN4-Argo, and SLA (cm) from Aviso averaged over January The contour interval is 10 cm. Zero line is omitted. Red and solid lines are positive, and blue and dash lines are negative. The red boxes indicate Northwest Pacific (208N 408N, 1308E 1708E) and Northwest Atlantic (308N 508N, 708W 308W) for DHA and SLA comparison in Figure 12. are mainly less than 6 cm in the Tropics and Eastern Pacific, but exceed 20 cm in the Kuroshio, Gulf Stream, Agulhas current regions, where mesoscale features are dominant Comparisons Against Other Gridded Argo Data Sets for ILD, MLD, and BLD Mixed-layer depth (MLD), Isothermal layer depth (ILD), and barrier layer depth (BLD) variations have important consequences on the upper ocean physics, air-sea interactions, and potential climatic impact [de Boyer Montegut et al., 2007; Mignot et al., 2007]. One significant advantage of Argo profiles is that it can continuously provide information about subsurface ocean. ILD is the depth at which T decreases by 0.28C from the reference depth of 10 m [Montegut et al., 2007]. MLD is where the potential density has increased from that HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 16

17 Figure 12. Comparison of DHA (cm) at 10 m relative to 1500 m from (a) BOA-Argo, (b) Roemmich-Argo, (c) IPRC-Argo, (d) Jamestec-Argo, (e) EN4-Argo, and SLA (cm) from (f) Aviso averaged over January In each pair, the top figure is for the Northwest Pacific, and the bottom figure is for the Northwest Atlantic. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 17

18 Figure 13. Spatial distribution of temporal (a) correlation coefficients and (b) RMSEs between BOA-Argo DHA and AVISO SLA, estimated from January 2004 to December For correlation coefficients, contours of 0.4, 0.6, 0.8, and 0.9 are labeled. For RMSEs, contours of 4, 6, 8,12, 15, 20, and 25 cm are labeled. at 10 m depth by a threshold equivalent to the density difference for the same 0.28C temperature change at constant salinity. BLD is the difference between the two. We compare ILD, MLD, and BLD generated from BOA-Argo gridded T/S to those from other data sets. Figures 14 and 15 show the global distribution of ILD, MLD, and BLD from four Argo data sets in January and July 2009, respectively. Here we take year 2009 as an example instead of climatology because longterm averaging tends to blur mesoscale features. In general, ILD, MLD, and BLD are deep in winter and shallow in summer for both hemispheres. Barrier layers are quasi-permanent in the tropical regions, such as the western Pacific and Atlantic, the eastern Indian, and the Bay of Bengal. BLD can exceed 100 m in the subpolar and polar regions in the winter hemisphere, consistent with previous findings [e.g., Montegut et al., 2007; Liu et al., 2009]. In January, ILD and MLD are deeper in North Atlantic than in the North Pacific. In July, ILD and MLD are extremely deep in the Southern Ocean. Though spatial distributions from the four Argo data sets are similar to each other, BOA-Argo and Roemmich-Argo show more fine structures than the other two. Some unique mesoscale features from BOA-Argo revealed by BLD, such as an anticyclone (centered at 368N, 1258W) in Northeast Pacific, can also be found in the DHA map and AVISO SLA (Figure 11), implying the potential applications of BOA-Argo involved in mesoscale processes. 4. Conclusions We have produced a new gridded Argo temperature and salinity data set based on a refined Barnes successive correction method and associated response functions. This data set can be used for scientific research, HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 18

19 Figure 14. Global distribution of (a d) isotherm layer depth, (e h) mixed-layer depth, and (i l) barrier layer depth in January 2009 for BOA-Argo, Roemmich-Argo, IPRC-Argo, and EN4-Argo from left to right. or operational real-time ocean forecasts. The data set contains monthly mean estimates of global temperature, salinity, and derived products (such as mixed-layer depth, isothermal depth, etc.) from January 2004 to December The horizontal resolution is , with 49 vertical levels between the surface and 1950 m. Figure 15. As in Figure 14, but in July HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 19

20 Figure 16. The spatial pattern of ocean heat content (OHC) trend ( ), m estimated from the BOA-Argo. The contour interval is 5 W m 22. The thick black line indicates zero. Ocean heat content (OHC) from surface to 1950 m estimated from BOA-Argo suggests that OHC gain during (Figure 16) mainly occurs in the extratropical ocean of the Southern Hemisphere. In the North Pacific and North Atlantic, the heat gain and loss almost compensate each other. These findings are broadly consistent with Roemmich et al. [2015], indicating the BOA-Argo data set is a useful and promising adding to the current Argo data sets. BOA-Argo well captures the mesoscale and large-scale patterns of temperature and salinity in the ocean, and climatologies between BOA-Argo and WOA13 are in good agreement. Relative to original merged Argo observations, global mean RMSE of temperatures and salinity is approximately 0.058C and psu below 1000 m. Above 1000 m, RMSEs increase to 0.318C for temperature and 0.04 psu for salinity, probably due to the impacts of surface winds, heat fluxes and freshwater fluxes. Validation of BOA-Argo against other gridded data sets shows that BOA-Argo is consistent with other Argo-based analyses, such as Roemmich-Argo, Jamestec-Argo, EN4-Argo, and IPRC-Argo. The T/S comparisons at GTMBA show that BOA-Argo has higher correlation and lower RMSEs than IPRC-Argo, but slightly lower correlation and higher RMSEs than Roemmich-Argo. BOA-Argo captures the temporal evolutions and spatial structures of El Ni~no and La Ni~na well. Compared with AVISO SLA, the new Argo data set retains some mesoscale features, better than other gridded Argo data sets, because the response functions used in BOA-Argo can well preserve mesoscale and large-scale signals and compress small-scale and high-frequency noises. In addition, the monthly climatological initial conditions of BOA-Argo are generated from original Argo profiles after QC, similar to Roemmich and Gilson [2009] and Chang et al. [2009]. While other Argo gridded data sets (e.g., Jamestec-Argo, EN4-Argo, and IPRC-Argo) apply WOA monthly climatology directly. The monthly initial conditions generated from original Argo observations can better retain signals from original data and eliminate noises from other analyzed fields. It benefits analysis on monthly, seasonal, and even longer timescales since the ocean memory deeply relies on subsurface initial values. The use of original Argo profiles to generate initial conditions is thus helpful to improve performance of gridded Argo data sets. The refined Barnes method can be easily applied to generate global Argo-based distributions of temperature, salinity, and derived products. The ease of implementation enables it to keep pace with the tremendous daily increases in the numbers of Argo temperature and salinity profiles. As more and more Deep Argo floats (>2000 m) have been implemented [Zilberman and Maze, 2015] and plans of Argo toward to ice-coverage ocean [Riser et al., 2016] and marginal seas [Yang et al., 2015] are conducted, it is possible to provide a wider coverage compared to WOA13 both horizontally and vertically. We will continue to work on new techniques for the interpolation of global Argo data set and construct optimal response functions to further improve the BOA-Argo gridded data set and streamline its operational production for near realtime climate monitoring. HONG ET AL. A NEW GLOBAL GRIDDED ARGO DATA SET 20