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World Ocean Database

 

The World Ocean Database (WOD) is the world's largest publicly available collection of subsurface ocean profile data. It is a powerful tool for oceanographic, climatic, and environmental research, and the end result of more than 20 years of coordinated efforts to incorporate data from institutions, agencies, individual researchers, and data recovery initiatives into a single database. WOD data spans from Captain Cook's 1772 voyage to the contemporary Argo period, making it a valuable resource for long term and historical ocean climate analysis. Original versions of the 20,000+ datasets in the WOD are available through the NCEI archives. 
 

WODSelect

Use the WODselect retrieval system to search the WOD by specific parameters (date, geographic area, probe type, etc.) and measured variables. View a dataset distribution map and cast count of your search criteria, and download a custom dataset in WOD native, csv, or netCDF.

Launch WODSelect  2018 Updates

Use the FTP server to access the entire World Ocean Database in WOD native, csv, netCDF, or ArcGIS.

Current Citation

Boyer, Tim P.; Antonov, John I.; Baranova, Olga K.; Coleman, Carla; Garcia, Hernan E.; Grodsky, Alexandra; Johnson, Daphne R.; Locarnini, Ricardo A.; Mishonov, Alexey V.; O'Brien, Todd D.; Paver, Christopher R.; Reagan, James R.; Seidov, Dan; Smolyar, Igor V.; Zweng, Melissa M. (2016). NCEI Standard Product: World Ocean Database (WOD). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset.

Datasets

WOD subsets are accessible through custom interfaces that sort data by geographic coordinates, instrument and year, and a collection of secchi disk and water color data.

Data by Location Data by Year 

Secchi Subset

Download Observations  Extended forel-ule scale

Observation File Columns
  • Accession number (corresponds to original data in archive)
  • WOD cruise designator
  • WOD unique cast number
  • Latitude
  • Longitude
  • Year
  • Month
  • Day
  • Time (GMT, 24 hr format)
  • Secchi disk depth (meters)
  • Water color (extended Forel/Ule scale)
     

Major Releases

The WOD consists of periodic major releases and quarterly updates to those releases. Each major release is associated with a concurrent release of the World Ocean Atlas (WOA), and contains final quality control flags used in the WOA, which includes manual as well as automated steps.

Each quarterly update release includes additional historical and recent data and preliminary quality control. The latest major release is World Ocean Database 2018 (WOD18), which includes more than 15.7 million oceanographic casts made up of 3.56 billion individual profile measurements.

WOD18
Boyer, T.P., O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky, R.A. Locarnini, A.V. Mishonov, C.R. Paver, J.R. Reagan, D. Seidov, I.V. Smolyar, K. Weathers, M.M. Zweng,(2018): World Ocean Database 2018. A.V. Mishonov, Technical Ed., NOAA Atlas NESDIS 87.
WOD13 
Boyer, T.P., J.I. Antonov, O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky, D.R. Johnson, R.A. Locarnini, A.V. Mishonov, T.D. O'Brien, C.R. Paver, J.R. Reagan, D. Seidov, I.V. Smolyar, and M.M. Zweng, 2013: World Ocean Database 2013., S. Levitus, Ed., A. Mishonov, Technical Ed.; NOAA Atlas NESDIS 72, 209 pp., doi:10.7289/V5NZ85MT.
WOD09 
Boyer, T.P., J.I. Antonov , O.K. Baranova, H.E. Garcia, D.R. Johnson, R.A. Locarnini, A.V. Mishonov, T. D. O’Brien, D. Seidov, I.V. Smolyar, M.M. Zweng, 2009. World Ocean Database 2009 S. Levitus, Ed., NOAA Atlas NESDIS 66, 216 pp. https://repository.library.noaa.gov/view/noaa/1195
WOD05 
Boyer, T.P., J.I. Antonov, H.E. Garcia, D.R. Johnson, R.A. Locarnini, A.V. Mishonov, M T. Pitcher, O.K. Baranova, I.V. Smolyar, 2006. World Ocean Database 2005. S. Levitus, Ed., NOAA Atlas NESDIS 60, 190 pp., https://repository.library.noaa.gov/view/noaa/1131

Derived Products

  • World Ocean Atlas: A collection of objectively analyzed, quality controlled temperature, salinity, oxygen, phosphate, silicate, and nitrate means that can be used to create boundary and/or initial conditions for a variety of ocean models, verify numerical simulations of the ocean, and corroborate satellite data.
  • Global Ocean Heat and Salt Content: This dataset incorporates data distribution figures for temperature and salinity observations, anomaly fields for 26 standard depths from the surface to 2000 m, heat content, and steric sea levels (thermosteric, halosteric, total). Fields can be used to monitor the time evolution of essential ocean variables and validate climate model simulations and other observations. 
  • Regional Climatologies: NCEI regional climatologies (RCs) are ocean climate analyses derived from the World Ocean Database (WOD) that provide detailed insight into the state and long-term variability of climatologically, economically, and ecologically important regions that substantially contribute to earth and ocean climate change. To improve scientific foundation and reference for multi-disciplinary studies of these regions, NCEI develops high-resolution, quality-controlled, multidecadal RCs with annual, seasonal, and monthly means for temperature, salinity, and other variables found in the WOD. 

Ocean Profiles

The WOD is made up of ocean profiles, which contain measurements for a single variable (temperature, salinity, etc.) taken from one location at different depths, or a horizontal string of readings taken from the surface. WOD profiles must contain more than a single depth/variable pair. Multiple profiles taken at the same location with the same set of instruments form an oceanographic cast.

Variables
  • Temperature
  • Salinity
  • Oxygen
  • Nutrients
  • Tracers
  • Biological variables (plankton, chlorophyll, etc)

Consult the WOD Introduction for a full set of definitions, and the WOD User’s Manual for a description of fields and codes.

GODAR Project

The Global Oceanographic Data Archaeology and Rescue (GODAR) Project was created to consolidate, digitize, and preserve historic oceanographic data against degradation or loss of availability as part of a larger cooperative international data sharing infrastructure maintained by scientific organizations around the world. GODAR began in 1992 as a joint proposal by the World Data Service (WDS) for Oceanography and NCEI (formerly the National Oceanographic Data Center), and was endorsed in 1993 Intergovernmental Oceanographic Commission. NCEI and the WDS for oceanography continue to manage the project along with the International Oceanographic Data and Information Exchange (IODE). All profile and plankton data acquired by the project are made available to the international scientific community and the public through the WOD. 

Quality Control

Quality control procedures are documented and performed on each cast and the results are included as flags on each measurement. The WOD contains the data on the originally measured depth levels (observed) and also interpolated to standard depth levels to present a more uniform set of iso-surfaces for oceanographic and climate work.

The WOD is made up of more than 20,000 separate archived datasets from the United States and around the world, each of which is available in its original form in the NCEI archives. All datasets are converted to the same standard format, checked for duplication within the WOD, and assigned quality flags based on objective tests. Additional subjective flags are set upon calculation of ocean climatological mean fields which make up the World Ocean Atlas (WOA) series.

Code Tables

Code tables necessary to use the World Ocean Database data.

Code Table Library

Programs

Use these programs to read WOD data, or convert it from WOD format to tabular column or comma separated columns (.csv)

wod_nc.f
Sample FORTRAN program for reading WOD ragged array netCDF files
readFOR.txt
Readme file describes the wodFOR programs
wodFOR.f
Sample FORTRAN program for reading the data
sampFOR.txt
Sample of output from wodFOR.f
readASC.txt
Describes the use of wodASC.f
wodASC.f
Outputs a user selected variable in either tabular or comma separated columns
wodASC.exe
Executable for wodASC.f program
sampASC.txt
Sample output data from wodASC.f
wodSUR.f
Writes surface-only data out in a comma-separated-value (CSV) format
wodSUR.exe
Microsoft compatible executable for wodSUR.f
sampSUR.txt
Sample of output from wodSUR.exe
instructions from WOD to csv
Instructions to convert WOD format to ArcMap readable 'csv' format
csvfromwod.c (β - version)
C program for conversion data from WOD format to ArcMap readable 'csv' format
csvfromwod.exe
Executable for C program
ArcGIS tutorial
Tutorial to convert 'csv' files in to shapefiles and upload it in ArcMap
readC.txt
Readme file describing the wodC program
wodC.c
Sample C program for reading the data
wodC.exe
Executable for C program
wodtodepthmatrix_info.txt
Info file describing the wodtodepthmatrix.c program
wodtodepthmatrix.c
Sample C program for reading the data
Wodtodepthmatrix.exe
Executable for wodtodepthmatrix.exe program (for Windows 64bit system)

 

WOD Masks

Data masks delineating ocean areas, range basins, and 5-degree standard deviation. 

NetCDF Ragged Array Format

The World Ocean Database (WOD) officially archived version is provided in a ragged array netCDF format which follows the Climate-Forecast (CF) conventions.

Ragged array format is optimal for ocean profile data collections, such as WOD, which aggregate together multiple oceanographic casts (collections of profiles taken at the same date/time/location; where a profile is a set of measurements of one ocean variable vs. depth/pressure). Different casts between 2 and 24,000 depth/variable pairs for each profile (from 2 to 24,000 in the WOD), and from 1 to 26 variables with separate profiles in each cast, which makes standard array representation (max_depth_count x max_variable_count x number_of_casts) inefficient for oceanographic casts.

Ragged array form has single dimension arrays for each profile variable which contain all the measurements for the given variable (see CF convention description of ragged array, specific to profile data). Ragged array form has a second array, a counting array (called VAR_row_size where VAR is the variable name), which gives the number of variable measurements for each cast. To get to the variable measurements for cast N, the (N-1) VAR_row_size counts are summed, and the pointer in array VAR is moved to this element position. The next VAR_row_size(N) elements in array VAR are the variable measurements for cast N. Note that variable z (depth) is always present and the indexed variable measurements for a particular cast are always associated with the same index for depth.

A trivial example: A file contains five oceanographic casts, each of which has profiles of depth/temperature and depth/salinity, one of which contains a profile of depth/oxygen. Only the fourth cast contains a profile of oxygen. The file has the following:

netcdf wod_example {
dimensions:
casts = 5 ;
z_obs = 25 ;
Temperature_obs = 25 ;
Salinity_obs = 25 ;
Oxygen_obs = 5 ;
: : : : : : : : : :
variables:
float lat(casts) ;
float lon(casts) ;
double time(casts) ;
float z(z_obs) ;
int z_row_size(casts) ;
float Temperature(Temperature_obs) ;
int Temperature_row_size(casts) ;
float Salinity(Salinity_obs) ;
int Salinity_row_size(casts) ;
float Oxygen(Oxygen_obs) ;
int Oxygen_row_size(casts) ;
: : : : : : : : : : : :
z_row_size = 5, 5, 5, 5, 5 ;
Temperature_row_size = 5, 5, 5, 5, 5;
Salinity_row_size = 5, 5, 5, 5, 5;
Oxygen_row_size = _, _, _, 5, _ ;
}

Note that '_' for VAR_row_size is a missing value. Fill value is set to zero (0). To read in the fourth cast (N=4), skip the first 15 elements in variables z, Temperature, and Salinity.(N-1)=3, VAR_row_size(1)+VAR_row_size(2)+VAR_row_size(3)=5+5+5=15 for VAR=z,Temperature,Salinity. For oxygen, Oxygen_row_size(1)=Oxygen_row_size(2)=Oxygen_row_size(3)=0, so read from the first value in array Oxygen (position 0 in the array).

For all variables, VAR_row_size(4)=5, so the next 5 values are read from each VAR array (elements 16-20 in arrays z,Temperature,Salinity; elements 1-5 in array Oxygen). VAR_row_size(N) will always be either equal to z_row_size(N) or equal to 0, the latter only in cases where the particular variable was not measured in cast N. All variables present in a cast will have a one-to-one correspondence with the depth (z) for that cast: z(cast=4,element=1) corresponds to Temperature(cast=4,element=1), Salinity(cast=4,element=1), Oxygen(cast=4,element=1).

In the ragged array representation then, z(16) corresponds to Temperature(16), Salinity(16), Oxygen(1) the separate VAR_row_size must be accounted for. Oceanographic casts are complex. Describing the ocean environment requires multiple profile variables associated with depth (z). But all profile variable elements must be associated not only with depth (z), but with cast specific variables such as latitude, longitude, date/time. Further, other cast specific measurements such as bottom depth, wave height, wind speed, etc. help to contextualize the ocean profile variables to describe the ocean environment. Other information, such as ship name, primary investigator, cruise identifier, etc. are important to identify and assess the ocean profile data. It is important to keep all of this information together for each cast and so for the aggregate oceanographic cast file provided to users. It is also important, even with today's system capacities to minimize file size when possible. This is the reason behind using a ragged array format. It is also important to use accepted standards in order to make sure the data are widely accessible. This is why the CF standard has been followed. Two points of the CF standard for contiguous ragged array netCDF are problematic for the efficient arrangement of oceanographic cast, file size, and inclusion of all necessary variables together and are not followed. The first is that all ocean profile variables do not have the same array size, each ocean profile variable has an array size (VAR_obs) commensurate with the number of measurements of the variable itself (VAR). All variables are still associated with the cast depth through the VAR_row_size counter. The second is that there are arrays of variables both ocean profile and other ocean environment descriptors with different axes. For instance, the ocean profile variables are arranged along the depth axis (and the cast axis) while ocean state variables are arranged only along the cast axis.

MBT/XBT Corrections

The WOD contains 4,756,371 casts taken from Mechanical Bathythermographs (MBTs) and Expendable Bathythermographs (XBTs), data collection instruments used to measure ocean temperature. Measurements collected by both instruments have significant biases that need to be taken into account and corrected before they are usable. 

Mechanical Bathythermograph (MBT)

MBTs measure temperature down to the depth of 250m from the decks of slowly moving ships. The following studies attempt to correct a bias in MBT measurements created by systematic errors in depth (pressure) and temperature sensors.

Publications
Corrections

Gouretski and Reseghetti Updated Corrections

Depth

Zcorrected = Zoriginal * Strech

Where sStretch is provided in table 1 at 1m depth interval.

Temperature

Tcorrected = Toriginal - thermal_bias

Where the tThermal_bias is provided in table 2.

Gouretski and Reseghetti Original Corrections

Depth

Below 40 meters: S = 1 + b + a/Zoriginal, where S is the stretching , Zoriginal is the observed depth.

Above 40 meters: S = c + (Zoriginal - 5) * (d - c)/35, where d = 1 + b + a/40

The values of a, b and c for the years 1941 to 2003 are provided in table 1.

The corrected depth, Zcorrected = Zoriginal * S

Temperature

Tcorrected = Toriginal - Thermal bias

Thermal bias for MBT for the years 1941 to 2003 are presented in table 2.

 

Expendable Bathythermograph (XBT)

Gouretski and Koltermann (2007) shows statistics from Expendable Bathythermograph (XBT) vs. Conductivity-Temperature-Depth (CTD)/reversing thermometer instrument comparisons which reveal a warm bias in XBT temperatures. This bias varies over time and depths, and may be due to both errors in the calculation of depth and in measurement of the temperature. An important deviation from the majority of existing correction schemes is that depth correction varies with depth.

Workshops and Reports
Publications

A number of papers with estimates of corrections have been published or submitted to scientific journals. The corrections proposed in some of these works are provided here to facilitate intercomparison by the scientific community. The corrections proposed by Gouretski and Koltermann (2007) are not included here, as they have been superseded by the corrections proposed by Gouretski and Reseghetti (2010).

  • Gouretski, V. V., and K. P. Koltermann, 2007, How much is the ocean really warming? Geophysical Research Letters, L01610, doi:10.1029/2006GL027834

  • Wijffels, Susan E., Josh Willis, Catia M. Domingues, Paul Barker, Neil J. White, Ann Gronell, Ken Ridgway, John A. Church, 2008: Changing Expendable Bathythermograph Fall Rates and Their Impact on Estimates of Thermosteric Sea Level Rise. J. Climate, 21, 56575672. doi: http://dx.doi.org/10.1175/2008JCLI2290.1 Wijffels et al. depth corrections: Table 1 (in situ comparison), Table 2 (in situ-altimeter comparison).

  • Ishii, M. and M. Kimoto, 2009: Reevaluation of Historical Ocean Heat Content Variations With An XBT depth bias Correction. J. Oceanogr. 65, 287299, doi:10.1007/s10872-009-0027-7. Ishii and Kimoto depth corrections. New corrections in conjunction with version 6.12* analysis of ocean temperature and salinity.

  • Levitus, S, J. Antonov, T. Boyer, Global ocean heat content 1955-2007 in light of recently revealed instrumentation problems (Geophys. Res. Lett. , 36, L07608, doi:10.1029/2008GL037155). Levitus et al. temperature corrections; updated temperature corrections: September, 2010; April, 2011; July, 2019

  • Gouretski, V. and F. Reseghetti, 2010, On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database. Deep-Sea Research I, Vol. 57(6), pp. 812-834, doi:10.1016/j.dsr.2010.03.011

  •  Good, S.A, 2011,Depth biases in XBT data diagnosed using Bathymetry data ,Journal of Atmospheric and Oceanic Technology, 28, 287-300, doi: 10.1175/2010JTECHO773.1Good depth corrections

  • Hamon, M., G. Reverdin, P-Y Le Traon, 2012, Empirical correction of XBT data. Journal of Atmospheric and Oceanic Technology, doi:10.1175/JTECH-D-11-00129.1, in press. Hamon et al. depth and temperature corrections

  • Gouretski, V., 2012, Using GEBCO digital bathymetry to infer depth biases in the XBT data, Deep Sea Research-I, 62,40-52. Gouretski depth and temperature corrections

  • Cowley, R., S. Wijffels, L. Cheng, T. Boyer, S. Kizu: Biases in Expendable BathyThermograph data: a new view based on historical side-by-side comparisons, Journal of Atmospheric and Oceanic Technology, 30, 11951225, doi:10.1175/JTECH-D-12-00127.1. XBT pairs database used in study.

  • Lijing Cheng, Jiang Zhu, Rebecca Cowley, Tim Boyer, and Susan Wijffels, 2014: Time, Probe Type, and Temperature Variable Bias Corrections to Historical Expendable Bathythermograph Observations. J. Atmos. Oceanic Technol., 31, 1793-1825, doi:10.1175/JTECH-D-13-00197.1.  Note: original CH14-table1, CH14-table2. Updated CH14-table2 February 15, 2017 (personal communication L. Cheng). Updated CH14-table1, CH14-table2, coefficients for T5 added, June 28, 2017 (personal communication L. Cheng).

Corrections

Gouretski and Reseghetti Updated Corrections

XBT probe types T4/T6, T7/deep blue and T5
Temperature

T(corrected) = T(xbt) - thermal_bias

The values of thermal bias for T7/DB, T4/T6, and T5 are provided in table 1, table 2, and table 3 respectively.

Depth

Z(corrected) = Z(xbt) * stretch_factor

The stretch values for T7/DB and T4/T6 at 1m depth interval is provided in table 4, table 5, and table 6 respectively.

XBT probe type T10
Temperature 

T(corrected) = T(xbt) - thermal_bias

Depth

Z(corrected) = Z(xbt) - depth_bias

Where, depth_bias = -1* (coeff1+coeff2*Z(xbt))

The values of coeff1, coeff2 and thermal_bias for T10 probe are provided in table 7.

Gouretski and Reseghetti Original Corrections

1. All XBT sample depths are re-computed (if necessary) according to the SIPPICAN FRE

2. XBT observed temperature is corrected for thermal bias according to the time (year) of the observation (see thermal_bias correction files)

T-corrected = T-observed - Thermal_Bias

3.Depth correction factor ("stretching") is calculated using the following formula (as in Gouretski and Reseghetti, 2010, but with different numerical values of the coefficients).

nominal_stretching(Z) = b + a/Z - c*Z**2

For numerical values of a, b, and c see stretching parameter files.

4. This "nominal" depth-depending stretching factor is further modified depending on the deviation of the XBT-profile mean temperature (tmean_profile) from the "nominal" mean temperature (tmean_nominal). (Values of tmean_nominal are given in mean_temperature files).

Here mean temperature refers to the mean temperature within the respective layer between the ocean surface and the sample depth (Z) for which the correction is calculated.

delta = tmean_profile(Z) - tmean_nominal(Z)

final_stretching (Z) = nominal_stretching(Z) + delta*0.0015

5. "Observed" (Sippican) XBT sample depth is now finally corrected:

Z_corrected = Z*final_stretching(Z)

V. Gouretski, 31 May 2010, KlimaCampus, Hamburg

 

Thermal Gradient Correction

Step 1: Identify appropriate correction

Corrections are provided for T4/T6 (Sippican), T7/Deep Blue (Sippican), TSK T6 and TSK T7/Deep Blue.
If the manufacturer is not given the deploying country and year of deployment and maximum depth should be used to identify probe type.

Deploying Country

TSK types are applied to: Japan, Taiwan, Korea, Thailand, China. All other countries are designated Sippican.
Table 1 contains the information on earliest to market and depth cutoffs for each probe type.

Probe TypeEarliest Date to Market (dd/mm/yyyy)Nominal Depth (m)Depth Cutoff (m)Approximate Percentage in WOD09 (of Total XBTs)
Sippican T46/14/196546055049.00%
Sippican T64/14/1968460550 
Sippican T76/20/196776095029.70%
Sippican Deep Blue4/20/1981760950 
TSK T67/1/197246055012.40%
TSK T74/1/19787609501.10%
TSK Deep Blue8/1/1997760950 
Other Types (No Corrections Supplied):
Sippican T56/3/1971183025000.80%
Sippican Fast Deep9/25/1991100025000.25%
Sippican T103/24/19722003505.50%
TSK T101/1/1979200350 
TSK T58/1/1971100025000.07%

Probes designated TSK T4 use TSK T6 (TSK does not make T4s). All Sparton XBTs use Sippican corrections.

In the paper, the following additional corrections were applied to other probe types for the Global Ocean Heat Content (GOHC) calculation:

Where a correction was not available for a particular year, T4/T6 corrections were used for T7/DB probes and vice versa for Sippican types. The equivalent Sippican correction was used for TSK types. Sippican T4/T6 corrections were applied to all T10, T11, and unknown types with terminal depth < 550 m. Sippican T7/DB corrections were applied to Sippican Fast Deeps and unknown types with terminal depth ›= 550 m and < than 1005m. Corrections were not applied to T5s or to probes with depths ›= 1005m. XBT data from 1996 to the present with no depth equation information were not included in the GOHC calculation.

Step 2: Convert to Hanawa (1995) fall rates if required.

Step 3: Apply the corrections:

Cowley thermal gradient (TG) corrections:

Z(corrected) = (Z(Hanawa)*(1 - Depth_error_slope)) - Depth_error_offset

T(corrected) = T(original) - Thermal_bias

Where Z(Hanawa) is obtained by applying the Hanawa correction to the observed depth and T(original) is the observed temperature.

Depth error slope is provided in Original Table 2 and 2014 Updated Table 2 for different probe types for the years 1967-2010.

Depth error offset is provided in Original Table 3 and 2014 Updated Table 3 for different probe types for the years 1967-2010.

Thermal bias is provided in Original Table 4 and 2014 Updated Table 4 for different probe types for the years 1967-2010.

Cheng Correction (CH)

Step 1: Identify appropriate correction

Corrections are provided for T4/T6 (Sippican), T7/Deep Blue (Sippican), TSK T6 and TSK T7/Deep Blue.

If the manufacturer is not given the deploying country and year of deployment and maximum depth should be used to identify probe type.

Deploying Country

TSK types are applied to: Japan, Taiwan, Korea, Thailand, China. All other countries are designated Sippican.

Table 1 contains the information on earliest to market and depth cutoffs for each probe type.

Probe TypeEarliest Date to Market (dd/mm/yyyy)Nominal Depth (m)Depth Cutoff (m)Approximate Percentage in WOD09 (of Total XBTs)
Sippican T46/14/196546055049.00%
Sippican T64/14/1968460550 
Sippican T76/20/196776095029.70%
Sippican Deep Blue4/20/1981760950 
TSK T67/1/197246055012.40%
TSK T74/1/19787609501.10%
TSK Deep Blue8/1/1997760950 
Other Types (No Corrections Supplied):
Sippican T56/3/1971183025000.80%
Sippican Fast Deep9/25/1991100025000.25%
Sippican T103/24/19722003505.50%
TSK T101/1/1979200350 
TSK T58/1/1971100025000.07%

 

Probes designated TSK T4 use TSK T6 (TSK does not make T4s). All Sparton XBTs use Sippican corrections.

In the paper, the following additional corrections were applied to other probe types for the Global Ocean Heat Content (GOHC) calculation:

Where a correction was not available for a particular year, T4/T6 corrections were used for T7/DB probes and vice versa for Sippican types. The equivalent Sippican correction was used for TSK types. Sippican T4/T6 corrections were applied to all T10, T11, and unknown types with terminal depth < 550 m. Sippican T7/DB corrections were applied to Sippican Fast Deeps and unknown types with terminal depth ›= 550 m and < than 1005m. Corrections were not applied to T5s or to probes with depths ›= 1005m. XBT data from 1996 to the present with no depth equation information were not included in the GOHC calculation.

Step 2: Convert to Hanawa (1995) fall rates if required.

Step 3: Apply the corrections

Cowley et al Cheng Corrections

Back calculate time from depth, t = q - (q2 - z/bh)0.5

Where q = ah/(2 * bh) and time t is in seconds.

And ah,bh are the fall rate coefficients used (Hanawa : ah = 6.691, bh = 2.25x10-3)

Z(corrected) = a*t - b*t2 - c

Where a,b are provided in Table 2, Table 3, and c which is the depth offset term is provided in Table 4.

T(corrected) = T(original) - Thermal_bias

Thermal _bias for Cheng corrections is provided in Table 5.

CH Correction Method

CH14 Method Overview
  1. Recalculates the depth by using the following fall rate equation: Depth_cor=A*time-B*time^2-Offset. Where elapsed time (time) for each reported depth by using the original drop-rate equation (Depth_original = A0*time-B0*time^2). For Unknown-FRE profiles, Hanawa et al. (1995) (A0=6.691, B0=0.00225) should be applied before applying this depth bias correction, if necessary. Original FRE for T5 (A0=6.828, B0=0.00182), T10 (A0=6.301, B0=0.00216).

  2. Corrects each temperature measurement (Temp_original) by using: Temp_cor = Temp_original - Tbias.

  3. The corrections are made for 9 different XBT groups according to probe types: Sippican-T4/T6, Sippican-T7/DB, Sippican-T10, Sippican-T5 (This group includes both Sippican-T5 and Unknown-probe-type profiles with maximum depth deeper than 900m), TSK-T7, TSK-T4/T6, TSK-T5, Unknown-Deep (DX, this is for all unknown probe type XBTs with maximum depth deeper than 550m), and Unknown-Shallow (SX, this is for all unknown probe type XBTs with maximum depth shallower than 550m).

Details
Calculate fall rate coefficients (A, B, Offset)

Fall rate coefficient (A) is obtained by adding a time-variable part and temperature-variable part to Hanawa1995 fall rate coefficient:

  • A = H95_A+CH14_A_time + CH14_A_temp;
    WhereH95_A=6.691with exception ofT5: H95_A=6.828; T10: H95_A=6.301;
    CH14_A_time
    is presented in CH14_table1 for 9 groups;
    CH14_A_temp is calculated according to the following equation:
    For Deep XBTs including T7, Deep Blue, DX:
    CH14_A_temp= Averaged_Temp_100m * 0.0025
    For Shallow XBTs including T4, T6, T10, T11, SX:
    CH14_A_temp= Averaged_Temp_100m *0.0050
    For T5:
    CH14_A_temp= Averaged_Temp_100m *0.0044
    WhereAveraged_Temp_100m is 0-100maveraged ocean temperature calculated by using this corresponding XBT profile.

Fall rate coefficients (B and Offset) are obtained according to A:

  • For Deep XBTs including T7, Deep Blue, DX:
    B=A*0.0070-0.0440; Offset=A*6.3765-40.293
    For Shallow XBTs including T4, T6, T10, T11, SX:
    B=A*0.0069-0.0435;Offset=A*5.7914-37.285
    For T5:
    B=A*0.0046-0.0293; Offset= A*10.093-66.4506
    For TSK XBTs:
    B=A*0.0034-0.0204;Offset= A*8.3176-55.746
Calculate Thermal bias (Tbias)

Thermal bias Tbias also consists of two parts: time-variable part (Tbias_time) and temperature-variable part (Tbias_temp):
Tbias= Tbias_time + Tbias_temp
Tbias_time is presented in CH14_table2 for 9 groups;
Tbias_temp is calculated as:
For Deep XBTs including T7, Deep Blue, DX:
Tbias_temp =Temp * 0.0014 + 0.0139
For Shallow XBTs including T4, T6, T10, T11, SX:
Tbias_temp = Temp * 0.00167 + 0.0115
For T5:
Tbias_temp = Temp * 0.0026 + 0.0227
Where Temp is each individual temperature measurement in a XBT profile.

Example

In case of a XBT-T7 profile (reported fall rate equation: A0=6.691, B0=0.00225), the elapse time is given by t = 1486.89-sqrt (2210838.568-444.444*d), where d is a reported depth. When d is 500 meters, the elapsed time becomes 76.7 seconds. Assuming that the observation was made at the center of 1970, 0-100m averaged temperature (Averaged_Temp_100m) is 18°C, and temperature at 500m (Temp) is 11°C. The two variables can be easily calculated by using this XBT-T7 profile.

  • Depth Error Correction
    Therefore,CH14_A_temp=18*0.0025=0.0450
    CH14_A_time =- 0.0736 (From CH14-table1)
    Then A=H95_A+CH14_A_time+ CH14_A_temp= 6.691+0.0450-0.0736=6.6624;
    B=A*0.0070-0.0440=0.0026
    Offset= A*6.3765-40.293=2.1898
    Therefore, the depth is recalculated as:
    Depth_cor=6.6624*time-0.0026*time2-2.1898
    The corrected depth (Depth_cor) when elapsed time is 76.7 is:
    Depth_cor=6.6624*76.7-0.0026*76.7*76.7-2.1898=493.5208.
    That is, the reported depth is larger by 6.4792 meters than expected.

  • Thermal Bias Correction
    Tbias_time
    = 0.1016 (From CH14-table2)
    Tbias_temp= Temp * 0.0014 + 0.0139 = 11*0.0014+0.0139=0.0293
    Tbias= Tbias_time+ Tbias_temp= 0.1016 +0.0293=0.1309
    Therefore, the temperature measurement at 500m (Temp_original) is corrected by removing this bias: Temp_cor = Temp_original - 0.1309.

Access

Am I allowed to use and reproduce WOD/WOA data/figures in my publications?
The World Ocean Database and World Ocean Atlas are available for public use without restriction. Please let us know about WOD and WOA based publications by sending citations to ncei.info@noaa.gov.
How do you extract header information from historical temperature profiles without processing the data?
With some programming skills, you can adapt WOD data reading programs to retrieve only header information from WOD native ASCII files, or create a subset in WODselect to isolate a geographic distribution graphic.
How do I access Sea Surface Temperature (SST) data?
The Global Ocean Heat and Salt Content product has historical sea surface temperature anomaly data from in situ measurements dating back to 1955. Download the temperature anomaly data, then add the temperature anomalies back to the climatological means (available on the same page) to calculate SSTs (as well as water temperature in the depth) for each year (or season) 1955 to present.
How do I download WOD data in NetCDF?
You can download data in netCDF, as well as CSV and WOD Native through WODselect.
How do I convert WOD ASCII data to a readable format?
  1. Ocean Data View (ODV), a freeware oceanographic profile data display. Detailed instructions are available in the WOD Tutorial beginning on page 7. Note that ODV cannot read surface only data, originator's quality flags, or plankton biomass and taxonomic data in the OSD file.
  2. Use WODselect to download data as CSV or netCDF
How do I download WOD18 data for a specific region?
Use WODselect to search data by geographic coordinates
Can I download the entire WOD database?
To download WOD in native ASCII, use the following command:
wget -N -nH -nd -r -e robots=off --no-parent --force-html https://data.nodc.noaa.gov/woa/WOD/YEARLY/ 
For netCDF format, mark all years in WODselect (1773-2018), and request the data in netCDF. You will receive an email with files attached once your request is processed.
How long are my requested files available on the FTP site?
Files are removed from the FTP site 3 days after they are created.
Can I use the cruise ID number to find cruise attributes?
In WODselect:

  1. Check CRUISE option, select Build a Query.
  2. Enter a cruise number in the search box (example format: US029567) and select Get an Inventory. To search multiple cruises, separate the number with a comma.
  3. Select CRUISE LIST for a list of cruises with links to cruise and accession metadata.
  4. Select a cruise to open a file with data distribution map at the top. Scroll down to access metadata.

Example: US029567. Click the accession# for the link to Archive System.

  • WOD CRUISE REFERENCE US029567
  • COUNTRY UNITED STATES (US)
  • NCEI ACCESSION NUMBER (CTD) 59005
  • SHIP NAME ALPHA HELIX
  • INSTITUTE ALFRED-WEGENER-INSTITUTE (BREMERHAVEN)
  • PROJECT SHELF BASIN INTERACTION PROJECT (SBI)
  • DATE OF FIRST CAST 9/8/2001
  • DATE OF LAST CAST 9/12/2001
  • TOTAL NUMBER OF CASTS 54
Where can I look for references for XBT Bias Depth and Temperature Corrections?
The XBT References Table contains the list of relevant references.
How do I make XBT corrections in a profile?
The XBT correction is in the second header code 54. See the list of the codes and the correction they represent.

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Data Specifics

Why does WOD18 have fewer profiling float casts than WOD09?
A number of floats in the North Atlantic had a pressure offset problem which was not correctable. The Argo program removed these floats from their dataset, and we followed their example.
Read Argo's explanation
How can I calculate the apparent oxygen utilization (AOU) in the deep ocean?
Use the saturation oxygen content calculated using potential temperature with respect to the surface, minus the dissolved oxygen measured at depth.
How is the Surface Only Dataset (SUR) organized?
The WOD Surface-Only Dataset is a single profile cast that contains all the surface-only data from an entire cruise. The individual levels of the surface-only form are distinct surface measurement sets from a given date/position. The cruise will span a time period from days to months rather than the single date/time of a profile cast. For this reason, surface-only data are in a single file rather than split out by year in the YEARLY directory, or by position in the GEOGRAPHIC directory.
Is there a time variable in WOD?

WOD time variables are listed in UTC format, and unspecified times are assumed to be UTC until otherwise proven.

As with all data, time zone information is not always correctly reported. We use quality control measure to correct common errors, including UTC designations that should be labeled as local time.

Does the WOD netCDF file contain information about the chlorophyll measuring method used?
Second Header variables in the netCDF file identify collection and processing methods used for the various parameters. For chlorophyll, we identify a whole suite of methods that range from in situ fluorometer to HPLC. The codes are listed below:

  • 600 Fluorescence
  • 601 Fluorescence in-situ Turner fluorometer (Strickland and Parsons 1972)
  • 602 Fluorescence in-vivo underway (Lorenzen 1966)
  • 603 Fluorometer in-situ CTD
  • 604 Fluorometer (Aiken 1981)
  • 605 Fluorometric chl-a assay acetone extraction
  • 606 Fluorometric chl-a assay methanol extraction
  • 607 Fluorometric chl-a assay acetone extraction; Turner fluorometer (Yentsch and Menzel, 1963, Holm-Hansen et al. 1965)
  • 700 HPLC (High Performance Liquid Chromatography)
  • 701 HPLC (normal phase High Performance Liquid Chromatography)
  • 702 HPLC (reverse phase High Performance Liquid Chromatography)
About chlorophyll CTD profiles

Many of the chlorophyll measurements in CTD datasets are uncalibrated fluorometer readings that are often still in engineering units. These data have to be read very carefully, and can only be evaluated on a cruise by cruise basis. There is a calibration indicator (variable specific second header 14), but this information is rarely included with the data.

More information is available on page 19 of the World Ocean Database 2018 Introduction.

What do large negative values at the particular depths mean? Why do these data have !C code ‘0’, for good data?
-999.99 represents missing data in the WOD. We use zero as the flag for missing data in conjunction with the -999.99 value.
How are WOD depth values calculated?
  • Data are submitted with depth values
  • We use UNESCO algorithm for standard ocean to calculate depth for data that includes pressure measurements
  • Data collected from an expendable bathythermograph (XBT) has depths which are calculated from a drop-rate equation and time since drop
  • Pre 1976 depth calculations were derived from wire length and angle

Regardless of the depth calculation method, all profiles are interpolated to the standard World Ocean Atlas standard depths.

Explain the output format for WOD-reading programs

Output generally includes ocean parameters, number of significant digits stored, and QC flags. The parenthetical value states the number of significant digits in the measurement directly to its left. The same is true for second headers. The two bracketed numbers are single digit quality flags; one set by the WOD, and the other by the originator.

Note: The program only prints the first 3 decimal places, but the full value to the given significant figures is stored in the file’s array read.

Learn more about output format in the WOD User Manual, and consult WOD codes for specific flag values.

Which methods are used to measure nutrient data?

There isn’t a definitive set of methods for measuring or calculating nutrient data. From page 45 of the WOD Introduction:

"It is difficult to estimate the precision and reproducibility of the historical chemical data in part because (1) there has not been a generally accepted set of standard international analytical oceanographic methods; (2) there has been a continuous availability over time of new or improved analytical techniques for the sampling and determination of the concentration of dissolved and particulate constituents in seawater; (3) there is the practical difficulty of periodic comparison of the precision and accuracy of oceanographic data collected by oceanographic institutions worldwide. At present, we are not aware of a suitable monitoring program for the systematic comparison of analytical instruments, measurements, and certified reference standards used by international research Institutions or Universities to collect oceanographic observations."

We do include information on the method used for nutrient (and other) data when it is available. Variable description for the second header 6 may be found on WOD codes page, or accessed directly through the methods list.

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Global Ocean Heat and Salt Content

What is the file format for Global Ocean Heat and Salt Content?
Officially archived versions of the fields are stored in Climate-Forecast (CF) compliant netCDF. See section 5, page 8 of World Ocean Atlas 2018 documentation for more details, other available formats.
Why is there an inconsistency between the 5-year and shorter average ocean heat anomalies?
The 5 year period is actually a composite calculated from the mean anomaly in each 1 degree grid square over a 5-year period using all measurements from that 5 year stretch. There’s a discrepancy between the 5 year composite and shorter periods, because the shorter means are based on conservative estimates due to limited data coverage. The 5-year composite mean typically has better data coverage, which allows for a less cautious, more precise estimate.
How is heat content calculated?

Temperature anomalies are calculated at 16 standard depths from 0-700m (or 26 standard depths 0-2000m) by subtracting observed (interpolated) temperatures from the long-term (1955-2018) climatological monthly mean.The mean of all temperature anomalies is calculated at each standard depth for every box on the grid. The temperature anomaly represents the volume of water that makes up the vertical distance from halfway between the next shallower depth and the given standard depth to halfway between the next deeper depth and the given standard depth.

The temperature anomaly is multiplied by the climatological mean density of the one-degree square and the heat capacity of water and the area and volume of the one-degree square for the given standard depth. The heat contents for each volume surrounding a standard depth are summed to calculate full ocean heat content anomaly for each one degree gridbox. The values for each gridbox are summed to calculate global value. [Note this is a global integral, not an average.] The area of each one-degree gridbox is calculated similar to the attached FORTRAN subroutine (easily adaptable to any software language).

The land/sea mask used to decide whether a one-degree gridbox is land or ocean (the one-degree, not quarter-degree) is derived from the ETOPO2 altitude/bathymetry data set. This same land/sea mask is used to determine whether the volume of a given one-degree square extends to the bottom of the integration level (700 m or 2000 m) or to a shallower depth. Because ocean temperature measurements are relatively sparse at the subsurface level, we use an objective analysis technique to calculate a complete set of one-degree temperature anomaly data at each standard depth after the anomalies from existing data are calculated. From there the heat content is calculated. The objective analysis technique is described in a number of publications, including WOA18 Temperature.

Does the dataset include yearly salt content data?
We have pentadal salinity anomalies back to 1955. See Global Heat and Salt Content, but yearly data only spans the Argo time period (i.e., 2005 onward) and available on the salinity anomaly page.
What are the units for 0-700 m heat content?
The value of J/m**2 is multiplied by the grid area over which the heat content is calculated, resulting in final units of joules [J].
What is the halosteric component of sea level rise?
Halosteric change is the change in sea level due to the effects of salinity change on seawater density.

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