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NOAA Atlas NESDIS 83

WORLD ATLAS 2018 Volume 3: Dissolved , Apparent Oxygen Utilization, and Dissolved Oxygen Saturation

Silver Spring, MD July 2019

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This document should be cited as:

Garcia H. E., K.W. Weathers, C.R. Paver, I. Smolyar, T.P. Boyer, R.A. Locarnini, M.M. Zweng, A.V. Mishonov, O.K. Baranova, D. Seidov, and J.R. Reagan (2019). 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation. A. Mishonov Technical Editor. NOAA Atlas NESDIS 83, 38pp.

This document is available on-line at https://www.nodc.noaa.gov/OC5/woa18/pubwoa18.html

NOAA Atlas NESDIS 83

WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation

Hernan E. Garcia, Katharine W. Weathers, Chris R. Paver, Igor Smolyar, Timothy P. Boyer, Ricardo A. Locarnini, Melissa M. Zweng, Alexey V. Mishonov, Olga K. Baranova, Dan Seidov, James R. Reagan

Technical Editor: Alexey Mishonov

National Centers for Environmental Information Silver Spring, Maryland July 2019

U.S. DEPARTMENT OF COMMERCE Wilbur L. Ross, Secretary

National Oceanic and Atmospheric Administration Neil Jacobs, Assistant Secretary of Commerce for Environmental Observation and Prediction, Acting Under Secretary of Commerce for and Atmosphere

National Environmental Satellite, Data, and Information Service Stephen Volz, Assistant Administrator

To Sydney (Syd) Levitus

Syd exemplifies the craft of careful, systematic inquiry of the large- scale distributions and low-frequency variability from seasonal-to-decadal scales of ocean properties. He was one of the first to recognize the importance and benefits of creating objectively analyzed climatological fields of measured ocean variables including , , oxygen, nutrients, and derived fields such as depth. Upon publishing Climatological Atlas of the World Ocean in 1982, he distributed this work without restriction, an act not common at the time. This seminal atlas moved the oceanographic diagnostic research from using hand-drawn maps to using objectively analyzed fields of ocean variables. With his NODC Ocean Climate Laboratory (OCL) colleagues, and unprecedented cooperation from the U.S. and international ocean scientific and data management communities, he created the World Ocean Database (WOD); the world’s largest collection of ocean profile data that are available internationally without restriction. The World Ocean Atlas (WOA) series represents the gridded objective analyses of the WOD and these fields have also been made available without restriction. The WOD and WOA series are used so frequently that they have become known generically as the “Levitus ”. These databases and products enable systematic studies of ocean variability in its climatological context that were not previously possible. His foresight in creating WOD and WOA has been demonstrated by their widespread use over the years. Syd has made major contributions to the scientific and ocean data management communities. He has also increased public understanding of the role of the oceans in climate. He retired in 2013 after 39 years of distinguished civil service. He distilled the notion of the synergy between rigorous data management and science; there are no shortcuts. All of us at the Ocean Climate Laboratory would like to dedicate this atlas to Syd, his legacy, vision, and mentorship.

The OCL team members

ii

Table of Contents Table of Contents ...... iii List of Figures ...... iv List of Tables ...... iv List of Acronyms ...... iv Preface ...... vi Acknowledgments ...... vii ABSTRACT ...... 1 1. INTRODUCTION ...... 1 2. DATA AND DATA DISTRIBUTION ...... 3 2.1. DATA SOURCES ...... 3 2.2. DATA QUALITY CONTROL ...... 4 2.2.1. Duplicate elimination ...... 5 2.2.2. Range and gradient checks ...... 5 2.2.3. Statistical checks ...... 5 2.2.4. Subjective flagging of data ...... 6 2.2.5. Representativeness of the data ...... 6 3. DATA PROCESSING PROCEDURES ...... 8 3.1. VERTICAL INTERPOLATION TO STANDARD LEVELS ...... 8 3.2. METHODS OF ANALYSIS ...... 8 3.2.1. Overview ...... 8 3.2.2. Derivation of Barnes (1964) weight function ...... 10 3.2.3. Derivation of Barnes (1964) response function ...... 11 3.2.4. Choice of response function ...... 11 3.2.5. First-guess field determination ...... 12 3.3. CHOICE OF OBJECTIVE ANALYSIS PROCEDURES ...... 13 3.4. CHOICE OF SPATIAL GRID ...... 14 4. RESULTS ...... 14 4.1. COMPUTATION OF ANNUAL AND SEASONAL FIELDS ...... 15 4.2. AVAILABLE OBJECTIVE AND STATISTICAL FIELDS ...... 15 4.3. OBTAINING WOA18 FIELDS ON-LINE...... 16 5. SUMMARY ...... 16 6. FUTURE WORK ...... 17 7. REFERENCES ...... 18

iii List of Figures FIGURE 1. RESPONSE FUNCTION OF THE WOA18, WOA13, WOA05, WOA01, WOA98, WOA94, AND LEVITUS (1982) OBJECTIVE ANALYSIS SCHEMES...... 28 FIGURE 2. SCHEME USED IN COMPUTING ANNUAL, SEASONAL, AND MONTHLY OBJECTIVELY ANALYZED MEANS FOR S DISSOLVED OXYGEN (O2), APPARENT OXYGEN UTILIZATION (AOU), AND OXYGEN SATURATION ( O 2 )...... 29

List of Tables

TABLE 1. DESCRIPTIONS OF FOR DISSOLVED OXYGEN (O2), APPARENT OXYGEN UTILIZATION (AOU), S AND OXYGEN SATURATION ( O 2 ) IN WOA18. THE CLIMATOLOGIES HAVE BEEN CALCULATED BASED ON BOTTLE DATA (OSD) FROM WOD18. THE STANDARD DEPTH LEVELS ARE SHOWN IN TABLE 2...... 23 TABLE 2. ACCEPTABLE DISTANCES (M) FOR DEFINING INTERIOR (A) AND EXTERIOR (B) VALUES USED IN THE REINIGER-ROSS SCHEME FOR INTERPOLATING OBSERVED LEVEL DATA TO STANDARD LEVELS...... 23 TABLE 3. RESPONSE FUNCTION OF THE OBJECTIVE ANALYSIS SCHEME AS A FUNCTION OF WAVELENGTH FOR WOA18 AND EARLIER ANALYSES. RESPONSE FUNCTION IS NORMALIZED TO 1.0...... 25 TABLE 4. BASINS DEFINED FOR OBJECTIVE ANALYSIS AND THE SHALLOWEST STANDARD DEPTH LEVEL FOR WHICH EACH BASIN IS DEFINED...... 26 TABLE 5. STATISTICAL FIELDS CALCULATED AS PART OF WOA18 (“√“DENOTES FIELD WAS CALCULATED AND IS PUBLICLY AVAILABLE)...... 27 TABLE 6. NOMINAL DEPTH AVERAGE O2 (µMOL/KG) DIFFERENCES (± 1 STANDARD DEVIATION) OF THE GLODAPV2 MINUS WOA18 FOR 1-DEGREE OBJECTIVELY ANALIZED FIELDS (60°N-60°S)...... 27

List of Acronyms

Acronym Expanded Term APB Autonomous Pinniped Bathythermograph BAMS Bulletin of the American Meteorological Society CSV Comma-Separated Value CTD Conductivity Temperature Depth DBT Drifting Bathythermograph DOC Department of Commerce DOE Department of Energy DRB Drifting Buoy ENSO El Niño-Southern Oscillation ERL Research Laboratory ETOPO2 Earth Topography 2 arc minute EVR Extended Vertical Resolution GIS Geographic Information System GLD Glider GMT Greenwich Mean Time, or Generic Mapping Tools GODAR Global Ocean Data Archaeology and Rescue GTSPP Global Temperature-Salinity Profile Program IAPSO International Association for the Physical Sciences of the Oceans IOC International Oceanographic Commission IODE International Oceanographic Data Exchange IRI International Research Institute for Climate and Society JAMSTEC Japan Agency for Marine-Earth Science and Technology JPOTS Joint Panel on Oceanographic Tables and Standards LDEO Lamont-Doherty Earth Observatory

iv Acronym Expanded Term MAST Marine Science and Technology MBT Mechanical Bathythermograph MEDAR Mediterranean Data Archeology and Rescue MRB Moored Buoy NAO North Atlantic Oscillation NASA National Aeronautics and Space Administration NATO North Atlantic Treaty Organization NCEI National Centers for Environmental Information NESDIS National Environmental Satellite, Data, and Information Service NOAA National Oceanic and Atmospheric Administration NODC National Ocean Data Center OCL Ocean Climate Laboratory ODV Ocean Data View PFL Profiling Float PIRATA Prediction and Research Moored Array in the Tropical Atlantic PSS Practical Salinity Scale RAMA Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction RDML Rear Admiral SST Surface Temperature SUR Surface TAO/TRITON Tropical Atmosphere Ocean moored buoy array TSK Tsurumi-Seiki Company UNESCO United Nations Educational, Scientific and Cultural Organization UOR Undulating Oceanographic Recorder USA United States of America USN United States Navy WDS World Data Service WOA World Ocean Atlas WOD World Ocean Database XBT Expendable Bathythermograph XCTD Expendable Conductivity Temperature Depth

v Preface

The World Ocean Atlas 2018 (WOA18) is the latest in a line of oceanographic analyses of subsurface ocean variables at standard depths extending back to the groundbreaking Climatological Atlas of the World Ocean (Levitus, 1982). The WOA has been published semi- regularly since 1994, with versions in 1998, 2001, 2005, 2009, 2013, and now 2018. Previous iterations of the WOA have proven to be of great utility to the oceanographic, climate research, geophysical, and operational environmental forecasting communities. The oceanographic variable analyses are used as boundary and/or initial conditions in numerical ocean circulation models and atmosphere-ocean models, for verification of numerical simulations of the ocean, as a form of "sea truth" for satellite measurements such as altimetric observations of sea surface height, for computation of nutrient fluxes by , and for planning oceanographic expeditions among others.

WOA18 includes analyses on a one-degree grid for all variables and on a quarter-degree grid for temperature and salinity. Since WOA13, the ocean variable analyses are produces on 102 depth levels from the surface to 5,500 m (previously 33 levels within the same depth limits). Ocean data and analyses of data at higher vertical resolution than previously available are needed to document the variability of the ocean, including improving diagnostics, understanding, and modeling of the physics of the ocean.

In the acknowledgment section of this publication, we have expressed our view that creation of global ocean profile and plankton databases and analyses are only possible through the cooperation of scientists, data managers, and scientific administrators throughout the international scientific community.

A pre-release version of WOA18 was made available in September, 2018. The final version of WOA18 was released in July, 2019. In the interim community feedback and our own work has led to changes in the temperature atlas in particular. Animal mounted pinniped temperature profiles have been added as a data source improving coverage in some high latitude areas. A different Expendable Bathythermograph (XBT) correction (Cheng et al., 2014) has been employed. These changes are detailed below. Also, the XBTs were doubly corrected in the pre- release version. The Levitus correction was applied after another correction had been applied (Cheng et al., 2014). This error led to an ocean which was less than 0.1°C cooler in the pre-release WOA18 as compared to the final WOA18 for the most affected decades (1975-84, 1985-94, 1995- 2004) in the upper 400m with smaller differences below. The 1981-2010 climate normal for temperature is slightly cooler (< 0.05°C) in the final WOA18 than in the pre-release WOA18 due to inadvertent double-weighting of the 2001-2010 decade in the pre-release version.

Ocean Climate Laboratory Team National Centers for Environmental Information Silver Spring, MD July 2019

vi Acknowledgments

This work was made possible by a grant from the NOAA Climate and Global Change Program, which enabled the establishment of a research group at the National Oceanographic Data Center (now the National Centers for Environmental Information – NCEI). The purpose of this group is to prepare research quality oceanographic databases, as well as to compute objective analyses of, and diagnostic studies based on, these databases. Support is now from base funds and from the NOAA Climate Program Office. The data on which this atlas is based are in World Ocean Database 2018 and are distributed on- line by NCEI. Many data were acquired as a result of the IOC/IODE Global Oceanographic Data Archaeology and Rescue (GODAR) project, and the IOC/IODE World Ocean Database project (WOD). The WOD is a composite of publicly available ocean profile data, both historical and recent. We acknowledge the scientists, technicians, and programmers who have collected and processed data, those individuals who have submitted data to national and regional data centers as well as the managers and staff at the various data centers. We are working on a more substantive and formalized way to acknowledge all those who have collected and contributed to oceanographic measurements, which were used to calculate the fields in the WOA. Until we have such a system in place, we direct the reader’s attention to lists of primary investigators, institutions, and projects, which contributed data (codes can be used to locate data in the World Ocean Database). We also thank our colleagues at the NCEI. Their efforts have made this and similar works possible.

We dedicate this work to Carla Coleman who always contributed with a smile and was taken from us too soon.

vii WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation

ABSTRACT

This atlas consists of a description of data analysis procedures and horizontal maps of climatological distribution fields of dissolved oxygen (O2), apparent oxygen utilization (AOU), S and dissolved oxygen saturation ( O 2 ) at selected standard depth levels of the world ocean on a one-degree latitude-longitude grid. The aim is to illustrate large-scale characteristics of the distribution of dissolved oxygen. The oceanographic data fields used to generate these climatological maps were computed by objective analysis of scientifically quality-controlled historical dissolved oxygen data in the World Ocean Database 2018 collected on or after 1960. Distribution concentration maps are presented for climatological composite periods (annual, seasonal, monthly, seasonal and monthly difference fields from the annual mean field, and the number of observations) at 102 standard depths. We also provide estimates of the basin-scale uncertainty of the WOA18 O2 objectively analyzed annual fields.

This atlas is part of the World Ocean Atlas 1. INTRODUCTION 2018 (WOA18) series (Garcia et al. 2019a). The distribution of dissolved oxygen (O2), The WOA18 series includes analysis for apparent oxygen utilization, and oxygen dissolved oxygen (this atlas), temperature saturation in the ocean is affected by both (Locarnini et al., 2019) salinity (Zweng et al., biochemical and physical processes. 2019), and dissolved inorganic nutrients Biochemical processes include sources and (Garcia et al., 2019b). This atlas presents sinks of O2 due to marine production, annual, seasonal, and monthly climatologies , and oxidation of organic matter and related statistical fields for dissolved (e.g., ). Physical processes oxygen (O2), apparent oxygen utilization include sources and sinks of O2 caused by (AOU), and oxygen saturation ( OS ). mass ventilation, air-sea flux exchange, 2 Climatologies in this atlas are defined as gas solubility (e.g., thermal pump), and water mean oceanographic fields at selected mixing. The oceanic O2 inventory is sensitive standard depth levels based on the objective to local to global changes driven by the analysis of historical oceanographic profiles physical and biological state of the ocean as and select surface-only data. An O2 profile is well as anthropogenic effects acting on defined as a set of measurements of samples different time and spatial scales (e.g., Keeling collected at discrete depths taken as an and Garcia, 2001; Matear and Hirst, 2003; instrument such as a rosette mounted on a Stramma et al., 2008; Shaffer et al., 2009; Conductivity-Temperature-Depth (CTD) Riebesell et al., 2009; Hofmann and package drops or rises vertically in the water Schellnhuber, 2009). Global O2 changes can column to collect selected water samples for be substantial. For example, Schmidtko et al. analysis. (2017) suggested that the global ocean O2 inventory has decreased by about 2% since This atlas includes an objective analysis of all 1960. scientifically quality-controlled historical O2

1 measurements available in the World Ocean Climatic Data Center (NCDC), National Database 2018 (WOD18; Boyer et al., 2018). Geophysical Data Center, and National We present data analysis procedures and Oceanographic Data Center (NODC). Large horizontal maps showing annual, seasonal, volumes of oceanographic data have been and monthly climatologies and related acquired because of the fulfillment of several statistical fields for O2, Apparent Oxygen data management projects including: Utilization (AOU), and dissolved oxygen S a) the Intergovernmental Oceanographic saturation ( O 2 ) at selected standard depth Commission (IOC) Global levels between the surface and the ocean Oceanographic Data Archaeology and bottom to a maximum depth of 5500 m. The Rescue (GODAR) project (Levitus et complete set of maps, statistical and al., 2005); objectively analyzed data fields, and b) the IOC World Ocean Database project documentation are all available on-line. (WOD); All climatologies use all available O2 data c) the IOC Global Temperature Salinity collected on or after 1960 to present. Note Profile project (GTSPP) (IOC, 1998). that previous WOA dissolved oxygen climatologies were calculated using all The dissolved oxygen data used in the available O2 data regardless of year of WOA18 have been analyzed in a consistent, observation that passed our quality control objective manner on a one-degree latitude- steps. The availability of more post-1960 O2 longitude grid at standard depth levels from data have enable us to use more higher- the surface to a maximum depth of 5500m. quality data. The annual climatology was The procedures for “all-data” climatologies calculated using all data regardless of the are identical to those used in the World Ocean month in which the observation was made. Atlas 2013 (WOA13) series (Garcia et al., Seasonal climatologies were calculated using 2013 a, b). Slightly different procedures were only data from the defined (regardless followed in earlier analyses (Levitus, 1982; of year). The are here defined as World Ocean Atlas 1994 series [WOA94, follows. Winter is defined as the months of Levitus et al., 1994; Levitus and Boyer, 1994 January, February, and March. Spring is a, b; Conkright et al., 1994]). The present defined as April, May, and June. Summer is analysis uses 102 depth levels for annual and defined as July, August, and September. Fall 57 for seasonal and monthly fields. is defined as October, November, and Objective analyses shown in this atlas are December. Monthly climatologies were constrained by the nature of the historical O2 calculated using data only from the given database (data are non-uniform in space, month regardless of the day of the month in time, and data quality), characteristics of the which the observation was made. objective analysis techniques, and the grid The O2 data used in this atlas are available size used. These limitations and from NOAA National Centers for characteristics are discussed below. Environmental Information (NCEI) and the Since the publication of WOA13, substantial World Data Service for amounts of additional historical and modern (WDS-Oceanography; formerly World Data bottle O2 data have become available (e.g., Center for Oceanography, Silver Spring). GO-SHIP). However, even with these The National Oceanic and Atmospheric additional data, we are still hampered in a Administration (NOAA) NCEI formed in number of ways by a lack of oceanographic 2015 combining the former National data. Because of the lack of O2 data, we are

2 forced to examine the annual cycle by the Winkler titration method (Winkler, 1888) compositing all data regardless of the year of using visual, amperometric, or photometric observation. In some geographic areas, end-detections (e.g., Carpenter, 1965; quality control is made difficult by the Culberson and Huang, 1987; Knapp et al., limited number of O2 data collected in these 1990; Culberson et al., 1991; Dickson, 1994). areas. Data may exist in an area for only one We refer to the discrete water sample dataset season, thus precluding any representative in WOD18 as Ocean Station Data (OSD). annual analysis. In some areas there may be a Garcia et al., (2019c) describes the data in the reasonable spatial distribution of data points OSD dataset. Typically, each profile in the on which to base an analysis, but there may OSD dataset consists of 1 to up to 36 discrete be only a few (perhaps only one) data values O2 observations collected at various depths in each one-degree latitude-longitude square. between the surface and the bottom using This atlas is divided into sections. We begin Nansen or Niskin bottle water samplers. by describing the data sources and data We note WOD18 contains O2 data obtained distribution (Section 2). Then we describe by electronic sensors mounted on the the general data processing procedures Conductivity-Temperature-Depth (CTD) (Section 3), the results (Section 4), summary rosette frame such as optical O2 electronic (Section 5), and future work (Section 6). S sensors and from other observing systems Global horizontal maps for O2, AOU, and O 2 (e.g., ). While the number of O2 at each individual depth levels for each measurements made by profiling floats in composite time period are available on-line. open ocean have now surpassed the number of O2 Winkler titrations, WOA18 used O2 data believed to be obtained by 2. DATA AND DATA DISTRIBUTION chemical Winkler titration methods only. Data sources and quality control procedures While optode and other O2 sensor drifts and are briefly described below. For further calibration issues have been greatly reduced information on the data sources used in in the past few years (e.g., Bittig and WOA18 refer to the World Ocean Database Kortzinger, 2015; Bushinsky et al., 2016; 2018 (WOD18, Boyer et al., 2019). The Johnson et al., 2015), we feel that work is still quality control procedures used in needed to understand data calibrations and preparation of these analyses are described drifts between the different O2 sensors being by Garcia et al. (2019a). used. We have begun working on an internally consistent database of sensor- 2.1. Data sources based O2 measurements obtained by chemical and sensor-based methods. Our Historical oceanographic data used in this preliminary results look promising. We atlas were obtained from the NCEI/WDS- anticipate releasing preliminary fields in the Oceanography archives and include all data near future. gathered as a result of the GODAR and WOD projects. All of the quality-controlled O2 In this work, we concentrate on O2 data (expressed in units of micro-mole per obtained by chemical Winkler titration kilogram, µmol kg-1) data used in this atlas methods from discreet samples from WOD were typically obtained by means of OSD. We note that most (>75%) of the Bottle chemical O2 analysis of serial (discrete) O2 data in the WOD18 OSD dataset were samples. The O2 values were collected on or after 1970 when more or less analyzed following various modifications of standard O2 Winkler analysis methods began

3 to be used (e.g., Carpenter whole bottle interpolated to standard depth levels if -1 S observations did not occur at the desired method). AOU (µmol kg ) and O 2 (percent, %) are derived (calculated) variables for an standard depths (see section 3.1 for details). S O2 measurement only when in situ The levels at which the O2, AOU, and O 2 temperature and salinity were also measured climatologies were calculated are given in at the same geographic location, time, and Table 1. Table 2 shows the depths of each depth (pressure). Section 2.2 describes the standard depth level. Section 3.1 discusses S the vertical interpolation procedures used in calculation of O 2 and AOU. our work. To understand the procedures for taking individual oceanographic observations and 2.2. Data quality control constructing climatological fields, definition Performing quality control of the O2 data is a of the terms “standard depth level data” and major task, the difficulty of which is directly “observed depth level data” are necessary. related to lack of data and metadata (for some We refer to the actual measured value of an areas) upon which to base statistical checks. oceanographic variable in situ (Latin for “in Consequently certain empirical criteria were place”) as an “observation”, and to the depth applied (see sections 2.2.1 through 2.2.4), at which such a measurement was made as and as part of the last processing step, the “observed level depth”. We refer to such subjective judgment was used (see sections data as “observed level data”. Before the 2.2.5 and 2.2.6). Individual data, and in some development of oceanographic cases entire profiles or all profiles for instrumentation that measure at high individual cruises, have been flagged and not frequencies along the vertical profile, used further because these data produced oceanographers often attempted to make features that were judged to be non- measurements at selected “standard levels” in representative or questionable. As part of our the water column. Sverdrup et al. (1942) work, we have made available WOD18 presented the suggestions of the International which contains both observed levels profile Association of data and standard depth level profile data (IAPSO) as to which depths oceanographic with various quality control flags applied. measurements should be made or The flags mark individual measurements or interpolated to for analysis. Historically the entire profiles which were not used in the World Ocean Atlas used a modified version next step of the procedure, either of the IAPSO standard depths. However, interpolation to standard depth levels for with the increased global coverage of high observed level data or calculation of depth resolution instrumentation, such as statistical means in the case of standard depth profiling floats, WOA has extended the level data. Our knowledge of the variability standard depth levels from 33 to 102. The of the world ocean in the instrumental record standard depth levels include the original now includes a greater appreciation and depth levels presented up to WOA09, but understanding of the ubiquity of eddies, have tripled the resolution in the upper 100 rings, and lenses in some parts of the world meters, more than doubled the depth ocean as well as interannual and interdecadal resolution of the upper 1000 meters, and variability of water mass properties almost three and a half the resolution associated with modal variability of the for overall depth levels. For many purposes, atmosphere such as the North Atlantic including preparation of the present Oscillation, Pacific Decadal Oscillation climatologies, observed level data are (PDO), and El Niño Southern Ocean

4 Oscillation (ENSO). Therefore, we have A check as to whether excessive vertical simply flagged data, not eliminating them gradients occur in the data as a function of from the WOD18. Thus, individual depth has been performed for O2 data in investigators can make their own decision WOD18 both in terms of positive and regarding the representativeness of the O2 negative concentration gradients. See Garcia data. Investigators studying the distribution et al. (2019a) for limits for excessive of features such as eddies will be interested gradients for O2. We flagged and not used in those data that we may regard as values that exceeded these gradients. unrepresentative or questionable for the preparation of the analyses shown in this 2.2.3. Statistical checks atlas. Statistical checks were performed as follows. All data for O2 (irrespective of year), at each 2.2.1. Duplicate elimination standard depth level, were averaged within Because O2 data are received from many five-degree latitude-longitude squares to sources, sometimes the same data set is produce a record of the number of received at NCEI/WDS-Oceanography more observations, mean, and standard deviation in than once but with slightly different time each square. Statistics were computed for the and/or position and/or data values, and hence annual, seasonal, and monthly compositing are not easily identified as duplicate stations. periods. Below 50 m depth, if data were Therefore, to eliminate the repetitive O2 data more than three standard deviations from the values our databases were checked for the mean, the data were flagged and withheld presence of exact and near exact replicates from further use in objective analyses. Above using eight different criteria. The first checks 50 m depth, a five-standard-deviation involve identifying stations with exact criterion was used in five-degree squares that position/date/time and data values; the next contained any land area. In selected five- checks involve offsets in position/date/time. degree squares that are close to land areas, a Profiles identified as duplicates in the checks four-standard-deviation check was used. In with a large offset were individually verified all other squares a three-standard-deviation to ensure they were indeed duplicate profiles. criterion was used for the 0-50 m depth layer. All replicate profiles were eliminated at the For standard depth levels situated directly first step of our processing except one profile. above the bottom, a four-standard-deviation criterion was used. 2.2.2. Range and gradient checks The reason for the weaker standard deviation Range checking (i.e., checking whether an O2 criterion in coastal and near-coastal regions is value is within preset minimum and the exceptionally large range of values in the maximum values as a function of depth and coastal five-degree square statistics for O2. ocean region) was performed on all O2 values Frequency distributions of O2 values in some as a first quality control check to flag from coastal regions are observed to be skewed or further use values that were grossly outside bimodal. Thus to avoid flagging possibly expected oceanic ranges. Range checks were good data in environments expected to have prepared for individual regions of the world large variability, the standard deviation ocean. Garcia et al. (2018) and Boyer and criteria were broadened. Levitus (1994) detail the quality control The total number of measurements in each procedures. Tables showing the O2 ranges selected for each basin and depth can be profile, as well as the total number of O2 found in Garcia et al. (2019a). observations exceeding the standard

5 deviation criterion, were recorded. If more profiles from particular oceanographic than two observations in a profile were found cruises. In such cases, data from an entire to exceed the standard deviation criterion, cruise were flagged and withheld from then the entire profile was flagged. This further use by setting a flag on each profile check was imposed after tests indicated that from the cruise. In other cases, we flagged surface data from particular casts (which individual profiles and/or measurements upon inspection appeared to be questionable) causing such features. were being flagged but deeper data were not. Other situations were found where 2.2.5. Representativeness of the data questionable data from the deeper portion of Another quality control issue is O2 data a cast were flagged, while near-surface data spatial and temporal representativeness. The from the same cast were not flagged because general paucity of data forces the of larger natural variability in surface layers. compositing of all historical data to produce One reason for this was the decrease of the “climatological” fields. In a given one-degree number of observations with depth and the square, there may be data from a month or resulting change in sample statistics. The season of one particular year, while in the standard-deviation check was applied twice same or a nearby square there may be data to the O2 data set for each compositing from an entirely different year. If there is period. large interannual variability in a region where In summary, first the five-degree square scattered sampling in time has occurred, then statistics were computed, and the data one can expect the analysis to reflect this. flagging procedure described above was used Because the observations are scattered to provide a preliminary data set. Next, new randomly with respect to time, except for a five-degree-square statistics were computed few limited areas, the results cannot, in a from this preliminary data set and used with strict sense, be considered a true long-term the same statistical check to produce a new, climatological average. “clean” data set. The reason for applying the We present smoothed analyses of historical statistical check twice was to flag (and means, based (in certain areas) on relatively withhold from further use), in the first round, few observations. We believe, however, that any grossly erroneous or non-representative useful information about the oceans can be data from the data set that would artificially gained through our procedures and that the increase the variances. The second check is large-scale features are representative of the then relatively more effective in identifying real ocean. We believe that, if a hypothetical smaller, but questionable or non- global synoptic set of ocean O2 data existed representative, O2 observations. and one were to smooth these data to the same degree as we have smoothed the 2.2.4. Subjective flagging of data historical means overall, the large-scale The O2 data were averaged by one-degree features would be similar to our results. squares for input to the objective analyses Some differences would certainly occur program. After initial objective analyses because of interannual-to-decadal-scale were computed, the input set of one-degree variability. means still contained questionable data The volume of O2 observations diminish with contributing to unrealistic distributions, increasing depth. In the upper ocean, the all- yielding intense bull's-eyes or spatial data O2 annual and seasonal mean gradients. Examination of these features distributions are quite reasonable for defining indicated that some of them were due to

6 large-scale features, but for the monthly in which:

periods, the database is inadequate in some * regions. With respect to the deep ocean, in [O 2 ] is the O2 solubility concentration (µmol -1 some areas the distribution of observations kg ) calculated as a function of in situ may be adequate for some diagnostic temperature and salinity, and one atmosphere computations but inadequate for other of total pressure. The effect of hydrostatic purposes (fit for purpose). If an isolated deep pressure on O2 is relatively insignificant basin or some region of the deep ocean has relative to the long-term precision of the data -1 * only one observation, then no horizontal (~1 µmol kg ). The [O 2 ] values were gradient computations can be meaningful or calculated using the equations in Garcia and robust. However, useful information is Gordon (1992) based on the [O* ] values of provided by the observations in the 2 Benson and Krause (1984); and[O 2 ] is the computation of other quantities (e.g., a -1 volumetric mean over a major ocean basin). measured O2 concentration (µmol kg ). Apparent Oxygen Utilization (AOU) is an S 2.3 Calculation of AOU and O 2 approximate estimate of True Oxygen Utilization (TOU). The calculation of AOU Apparent Oxygen Utilization (AOU, µmol 2 -1 S assumes that the amount of O used during kg ) and dissolved oxygen saturation ( O 2 , local biochemical processes can be estimated %) were estimated when quality-controlled by the difference in concentration between -1 in situ O2 (µmol kg ), temperature (T, °C), the observed O2 and the preformed O2 values. and salinity (S) were all measured at the same However, AOU is affected by processes other geographic location, time, and depth than biochemical processes such as water (hydrostatic pressure). We note that not all O2 mixing of waters of different preformed observations included simultaneous values, departures of [O* ] from full temperature and salinity measurements (see 2 equilibration with the atmosphere, bubble section 2.2.4). In some cases, the temperature gas injection, skin temperature effects, and and/or salinity values did not pass our other factors (e.g., Broecker and Peng, 1982; quality-control tests. We decided not to use Redfield et al., 1963; Garcia and Keeling, referenced to the 2001; Ito, 2004). We assume that these surface ocean because of these reasons. Thus, processes are small in magnitude when the total number of observations available for S compared to the amplitude of the calculating AOU and O 2 is slightly smaller climatological seasonal O2 signal on basin- in number than the available number of O2 scales.

observations. S The O2 saturation ( O 2 , %) was estimated as AOU represents one rough estimate of the O2 * utilized due to biochemical processes relative 100% times the ratio of [O 2 ] to[O 2 ] , to a preformed value or initial value. As [O ] discussed below, AOU cannot be taken to S =  2  O 2 100% *  represent the True Oxygen Utilization; hence [O 2 ] the word “Apparent”. AOU (µmol kg-1) was The calculated AOU and OS values were calculated as the difference between the O2 2 * analyzed following the same quality control gas solubility ([O ] ) and the measured O2 2 methods outlined in section 2. Furthermore, concentrations and expressed as, if any of the O2, temperature (Locarnini et al., * 2018), or salinity (Zweng et al., 2018) values AOU = [O 2 ] − [O 2 ]

7 were flagged during the quality control the same as in previous analysis. procedure, then AOU and OS values were 2 3.2. Methods of analysis flagged also, and not used in the analysis. 3.2.1. Overview An objective analysis scheme of the type 3. DATA PROCESSING PROCEDURES described by Barnes (1964) was used to produce the fields shown in this atlas. This 3.1. Vertical interpolation to standard scheme had its origins in the work of levels Cressman (1959). In World Ocean Atlas Vertical interpolation of observed depth level 1994 (WOA94), the Barnes (1973) scheme data to standard depth levels followed was used. This required only one procedures in Joint Panel on Oceanographic “correction” to the first-guess field at each Tables and Standards (JPOTS) Editorial grid point in comparison to the successive Panel (1991). These procedures are in part correction method of Cressman (1959) and based on the work of Reiniger and Ross Barnes (1964). This was to minimize (1968). Four observed depth level values computing time used in the processing. surrounding the standard depth level value Barnes (1994) recommends a return to a were used, two values from above the multi-pass analysis when computing time is standard level and two values from below the not an issue. Based on our own experience standard level. The pair of values furthest we agree with this assessment. The single from the standard level is termed “exterior” pass analysis, used in WOA94, caused an points and the pair of values closest to the artificial front in the Southeastern Pacific standard level are termed “interior” points. Ocean in a data sparse area (Anne Marie Paired parabolas were generated via Treguier, personal communication). The Lagrangian interpolation. A reference curve analysis scheme used in generating WOA98, was fitted to the four data points and used to WOA01, WOA05, WOA13, WOA13, and define unacceptable interpolations caused by WOA18 analyses uses a three-pass “overshooting” in the interpolation. When “correction” which does not result in the there were too few data points above or below creation of this artificial front. the standard level to apply the Reiniger and Inputs to the analysis scheme were one- Ross technique, we used a three-point degree square means of data values at Lagrangian interpolation. If three points were standard levels (for time period and variable not available (either two above and one being analyzed), and a first-guess value for below or vice-versa), we used linear each square. For instance, one-degree square interpolation. In the event that an observation means for our annual analysis were computed occurred exactly at the depth of a standard using all available data regardless of date of level, then a direct substitution was made. observation. For July, we used all historical Table 4 provides the range of acceptable July data regardless of year of observation. distances for which observed level data could be used for interpolation to a standard level. Analysis was the same for all standard depth levels. Each one-degree latitude-longitude In WOA18, the number of standard levels square value was defined as being used is 102, allowing for analysis with representative of its square. The 360x180 greater vertical resolution than the earlier gridpoints are located at the intersection of WOA climatologies. The method for half-degree lines of latitude and longitude. interpolating data to standard levels remains An influence radius was then specified. At

8 those grid points where there was an If there were no data points within the area observed mean value, the difference between defined by the influence radius, then the the mean and the first-guess field was correction was zero, the first-guess field was computed. Next, a correction to the first- left unchanged, and the analyzed value was guess value at all gridpoints was computed as simply the first-guess value. This correction a distance-weighted mean of all gridpoint procedure was applied at all gridpoints to difference values that lie within the area produce an analyzed field. The resulting field around the gridpoint defined by the influence was first smoothed with a median filter radius. Mathematically, the correction factor (Tukey, 1974; Rabiner et al., 1975) and then derived by Barnes (1964) is given by the smoothed with a five-point smoother of the expression: type described by Shuman (1957) (hereafter

n referred as five-point Shuman smoother). ∑WsQs The choice of first-guess fields is important s=1 and we discuss our procedures in section Ci, j = (1) n 3.2.5. ∑Ws s=1 The analysis scheme is set up so that the in which: influence radius, and the number of five- point smoothing passes can be varied with (i,j) - coordinates of a gridpoint in the east- each iteration. The strategy used is to begin west and north-south directions the analysis with a large influence radius and respectively; decrease it with each iteration. This technique Ci,j - the correction factor at gridpoint allows us to analyze progressively smaller coordinates (i,j); scale phenomena with each iteration. n - the number of observations that fall The analysis scheme is based on the work of within the area around the point i,j several researchers analyzing meteorological defined by the influence radius; data. Bergthorsson and Doos (1955) computed corrections to a first-guess field Qs - the difference between the observed mean and the first-guess at the Sth point using various techniques: one assumed that in the influence area; the difference between a first-guess value and an analyzed value at a gridpoint was the same Er 2 − as the difference between an observation and R2 Ws = e (for r ≤ R; Ws =0 for r > R); a first-guess value at a nearby observing station. All the observed differences in an r - distance of the observation from the area surrounding the gridpoint were then gridpoint; averaged and added to the gridpoint first- R - influence radius; guess value to produce an analyzed value. Cressman (1959) applied a distance-related E = 4. weight function to each observation used in The derivation of the weight function, Ws, the correction in order to give more weight to will be presented in the following section. At observations that occur closest to the each gridpoint we computed an analyzed gridpoint. In addition, Cressman introduced value Gi,j as the sum of the first-guess, Fi,j , the method of performing several iterations and the correction Ci,j. The expression for of the analysis scheme using the analysis this is produced in each iteration as the first-guess field for the next iteration. He also suggested G = F + C (2) i, j i, j i, j starting the analysis with a relatively large

9 influence radius and decreasing it with weight function is defined as successive iterations so as to analyze smaller r 2 scale phenomena with each pass. − η = e 4K (4) Sasaki (1960) introduced a weight function that was specifically related to the density of which resembles the Gaussian distribution. observations, and Barnes (1964, 1973) The shape of the weight function is extended the work of Sasaki. The weight determined by the value of K, which relates function of Barnes (1964) has been used here. to the distribution of data. The determination The objective analysis scheme we used is in of K follows. The weight function has the common use by the mesoscale property that meteorological community. Several studies 2π ∞ of objective analysis techniques have been 1  r2  ηd dθ =1 (5) made. Achtemeier (1987) examined the 2π ∫ ∫  4K  “concept of varying influence radii for a 0 0   successive corrections objective analysis This property is desirable because in the scheme.” Seaman (1983) compared the continuous case (3) the application of the “objective analysis accuracies of statistical weight function to the distribution f(x,y) will interpolation and successive correction not change the mean of the distribution. schemes.” Smith and Leslie (1984) However, in the discrete case (1), we only performed an “error determination of a sum the contributions to within the distance successive correction type objective analysis R. This introduces an error in the evaluation scheme.” Smith et al. (1986) made “a of the filtered function, because the condition comparison of errors in objectively analyzed given by (5) does not apply. The error can be fields for uniform and non-uniform station pre-determined and set to a reasonably small distribution.” value in the following manner. If one carries out the integration in (5) with respect to θ, the 3.2.2. Derivation of Barnes (1964) weight remaining integral can be rewritten as function R ∞ The principle upon which the Barnes (1964)  r 2   r 2  ηd  + ηd  = 1 (6) weight function is derived is that “the two- ∫  4K  ∫  4K  dimensional distribution of an atmospheric 0   R   variable can be represented by the summation of an infinite number of independent harmonic waves, that is, by a Fourier integral Defining the second integral as ε yields representation”. If f(x,y) is the variable, then 2 R r in polar coordinates (r,θ), a smoothed or −  r2  e 4K d  =1− ε (7) filtered function g(x,y) can be defined: ∫  4K  0   1 2π ∞ g(x, y) = ∫ ∫ηf (x + r cosθ , y + r sinθ ) Integrating (7), we obtain 2π 0 0 (3) R2 r2 − d( )dθ ε = e 4K (7a) 4K Taking the natural logarithm of both sides of in which r is the radial distance from a (7a) leads to an expression for K, gridpoint whose coordinates are (x,y). The

10 2 2 K = R / 4E (7b) αR   πR 2 −  −  where E ≡ -ln ε D = e  4  = e  2λ  Rewriting (4) using (7b) leads to the form of The phase of each Fourier component is not weight function used in the evaluation of (1). changed by the interpolation procedure. The Thus, choice of E and the specification of R results of an analysis pass are used as the determine the shape of the weight function. first-guess for the next analysis pass in an Levitus (1982) chose E=4 which corresponds iterative fashion. The relationship between to a value of ε of approximately 0.02. This the filtered function g(x) and the response choice implies with respect to (7) the function after N iterations as derived by representation of more than 98 percent of the Barnes (1964) is influence of any data around the gridpoint in N the area defined by the influence radius R. n−1 gN (x) = f (x)D∑(1− D) (10) This analysis (WOA18) and previous n=1 analyses (WOA94, WOA98, WOA01, Equation (10) differs trivially from that given WOA05, WOA13) used E=4. by Barnes. The difference is due to our first- Barnes (1964) proposed using this scheme in guess field being defined as a zonal average, an iterative fashion similar to Cressman annual mean, seasonal mean, or monthly (1959). Levitus (1982) used a four-iteration mean, whereas Barnes used the first scheme with a variable influence radius for application of the analysis as a first-guess. each pass. WOA94 used a one-iteration Barnes (1964) also showed that applying the scheme. WOA98, WOA01, WOA05, analysis scheme in an iterative fashion will WOA13, and WOA18 employed a three- result in convergence of the analyzed field to iteration scheme with a variable influence the observed data field. However, it is not radius. desirable to approach the observed data too closely, because at least seven or eight 3.2.3. Derivation of Barnes (1964) response gridpoints are needed to represent a Fourier function component. It is desirable to know the response of a data The response function given in (10) is useful set to the interpolation procedure applied to in two ways: it is informative to know what it. Following Barnes (1964) and reducing to Fourier components make up the analyses, one-dimensional case we let and the computer programs used in f (x) = Asin(αx) (8) generating the analyses can be checked for correctness by comparison with (10). in which α = 2π/λ with λ being the wavelength of a particular Fourier 3.2.4. Choice of response function component, and substitute this function into The distribution of O2 observations (see equation (3) along with the expression for η appendices) at different depths and for the in equation (4). Then different averaging periods, are not regular in g(x) = D[Asin(αx)]= Df (x) (9) space or time. At one extreme, regions exist in which every one-degree square contains in which D is the response function for one data and no interpolation needs to be application of the analysis and defined as performed. At the other extreme are regions in which few if any data exist. Thus, with variable data spacing the average separation

11 distance between gridpoints containing data median smoother. The effect of varying the is a function of geographical position and amount of smoothing in North Atlantic sea averaging period. However, if we computed surface temperature (SST) fields has been and used a different average separation quantified by Levitus (1982) for a particular distance for each variable at each depth and case. In a region of strong SST gradient such each averaging period, we would be as the , the effect of smoothing generating analyses in which the wavelengths can easily be responsible for differences of observed phenomena might differ from between analyses exceeding 1.0°C. one depth level to another and from one To avoid the problem of the influence region season to another. In WOA94, a fixed extending across land or sills to adjacent influence radius of 555 kilometers was used basins, the objective analysis routine to allow uniformity in the analysis of all employs basin “identifiers” to preclude the variables. For the present WOA18 analyses use of data from adjacent basins. Table 5 lists (as well as for WOA13, WOA09, WOA04, these basins and the depth at which no and WOA01), a three-pass analysis, based on exchange of information between basins is Barnes (1964), with influence radii of 892, allowed during the objective analysis of data, 669 and 446 km was used for the 1° analysis. i.e., “depths of mutual exclusion.” Some Inspection of Equation 1 shows that the regions are nearly, but not completely, difference between the analyzed field and the isolated topographically. Because some of first-guess field values at any gridpoint is these nearly isolated basins have water mass proportional to the sum of the weighted- properties that are different from surrounding differences between the observed mean and basins, we have chosen to treat these as first-guess at all gridpoints containing data isolated basins as well. Not all such basins within the influence area. have been identified because of the complicated structure of the sea floor. In The reason for using the five-point Shuman Table 5, a region marked with an (*) can smoother and the median smoother is that our interact with adjacent basins except for data are not evenly distributed in space. As special areas such as the Isthmus of Panama. the analysis moves from regions containing data to regions devoid of data, small-scale 3.2.5. First-guess field determination discontinuities may develop. The five-point Shuman and median smoothers are used to There are gaps in the data coverage and, in eliminate these discontinuities. The five- some parts of the world ocean, there exist point Shuman smoother does not affect the adjacent basins whose water mass properties phase of the Fourier components that are individually nearly homogeneous but comprise an analyzed field. have distinct basin-to basin differences. Spurious features can be created when an The response function for the analyses influence area extends over two basins of this presented in the WOA18 series is given in nature (basins are listed in Table 6). Our Table 4 and in Figure 1. For comparison choice of first-guess field attempts to purposes, the response function used by minimize the creation of such features. To Levitus (1982), WOA94, and others are also maximize data coverage and best represent presented. The response function represents global variability, a set of “time- the smoothing inherent in the objective indeterminant” climatologies were produced analysis described above plus the effects of as a first-guess for each set of decadal one application of the five-point Shuman climatologies. The time-indeterminant smoother and one application of a five-point climatologies used the first-guess field

12 procedures developed for earlier versions of anomalous data in some way, an entire basin- WOA: To provide a first-guess field for the wide belt might be affected. “all-data” annual analysis at any standard One advantage of producing “global” fields level, we first zonally averaged the observed for a particular compositing period (even temperature data in each one-degree latitude though some regions are data void) is that belt by individual ocean basins. The annual such analyses can be modified by analysis was then used as the first-guess for investigators for use in modeling studies. each seasonal analysis and each seasonal analysis was used as a first-guess for the For the time-indeterminant quarter-degree appropriate monthly analysis if computed. first-guess field, the one-degree time- indeterminant field was also used. Each of We then reanalyzed the temperature data the sixteen quarter-degree boxes enclosed using the newly produced analyses as first- used the one-degree time-indeterminant guess fields described as follows and as value as a first-guess, thereby projecting the shown in Figure 3. A new annual mean was one-degree climatology onto the quarter- computed as the mean of the twelve monthly degree grid. In those areas where there was analyses for the upper 1500 m, and the mean no one-degree value due to land or bottom of the four seasons below 1500 m depth. This mask, the statistical mean for the entire basin new annual mean was used as the first-guess at the given depth was used. This first-guess field for new seasonal analyses. These new field was then used to calculate time- seasonal analyses in turn were used to indeterminant quarter-degree field. The time produce new monthly analyses. This indeterminant quarter-degree field was then procedure produces slightly smoother means. used for each quarter-degree decadal These time-indeterminant monthly mean climatological mean. objectively analyzed temperature fields were used as the first-guess fields for each 3.3. Choice of objective analysis “decadal” monthly climatology. Likewise, procedures time-indeterminant seasonal and annual Optimum interpolation (Gandin, 1963) has climatologies were used as first-guess fields been used by some investigators to for the seasonal and annual decadal objectively analyze oceanographic data. We climatologies. recognize the power of this technique but We recognize that fairly large data-void have not used it to produce analyzed fields. regions exist, in some cases to such an extent As described by Gandin (1963), optimum that a seasonal or monthly analysis in these interpolation is used to analyze synoptic data regions is not meaningful. Geographic using statistics based on historical data. In distribution of observations for the “all-data” particular, second-order statistics such as annual periods (see appendices) is excellent correlation functions are used to estimate the for the upper layers of the ocean. By using an distribution of first order parameters such as “all-data” annual mean, first-guess field means. We attempt to map most fields in this regions where data exist for only one season atlas based on relatively sparse data sets. By or month will show no contribution to the necessity we must composite all data annual cycle. By contrast, if we used a zonal regardless of year of observation, to have average for each season or month, then, in enough data to produce a global, those latitudes where gaps exist, the first- hemispheric, or regional analysis for a guess field would be heavily biased by the particular month, season, or even yearly. few data points that exist. If these were Because of the paucity of data, we prefer not to use an analysis scheme that is based on

13 second order statistics. In addition, as 2. Maintaining an opening in the Straits Gandin has noted, there are two limiting of Gibraltar and in the English cases associated with optimum interpolation. Channel; The first is when a data distribution is dense. 3. Connecting the Kamchatka Peninsula In this case, the choice of interpolation and the Baja Peninsula to their scheme makes little difference. The second respective continents. case is when data are sparse. In this case, an The one-degree mask was created from the analysis scheme based on second order quarter-degree mask instead of directly from statistics is of questionable value. For ETOPO2 in order to maintain consistency additional information on objective analysis between the quarter-degree and one-degree procedures see Thiebaux and Pedder (1987) masks. and Daley (1991).

3.4. Choice of spatial grid The analyses that comprise WOA18 have been computed using the ETOPO2 (Earth 4. RESULTS Topography 2 arc minute) land-sea The on-line figures for this atlas include topography to define ocean depths at each seven types of horizontal maps representing gridpoint (ETOPO2, 2006). From the annual, seasonal, and monthly spatial ETOPO2 land mask, a quarter-degree land distribution of analyzed data and data mask was created based on ocean bottom statistics as a function of selected standard depth and land criteria. If sixteen or more 2- depth levels for dissolved O2, AOU, and O2 minute square values out of a possible forty- saturation over one-degree latitude-longitude nine in a one-quarter-degree box were grid: defined as land, then the quarter-degree gridbox was defined to be land. If no more a) Objectively analyzed climatology fields. than two of the 2-minute squares had the Grid boxes for which there were less than same depth value in a quarter-degree box, three values available in the objective then the average value of the 2-minute ocean analysis defined by the influence radius depths in that box was defined to be the depth are denoted by a white “+” symbol. of the quarter-degree gridbox. If ten or more b) Statistical mean one-degree fields. Grid 2-minute squares out of the forty-nine had a boxes for which there were less than three common bottom depth, then the depth of the values available in the objective analysis quarter-degree box was set to the most defined by the influence radius are common depth value. The same method was denoted by a white “+” symbol. used to go from a quarter-degree to a one- degree resolution. In the one-degree c) Data distribution fields for the number of resolution case, at least four points out of a observations in each grid box used in the possible sixteen (in a one-degree square) had objective analysis binned into 1 to 2, 3-5, to be land in order for the one-degree square 6-10, 11-30, 31-50 and greater than 51 to remain land and three out of sixteen had to observations. have the same depth for the ocean depth to be d) Standard deviation fields binned into set. These criteria yielded a mask that was several ranges depending on the depth then modified by: level. The maximum value of the 1. Connecting the Isthmus of Panama; standard deviation is shown on the map. e) Standard error of the mean fields binned

14 into several ranges depending on the objective and statistical fields (section 4.2). depth level. 4.1. Computation of annual and seasonal f) Difference between observed and fields analyzed fields binned into several ranges depending on the depth level. After completion of all of our analyses, we define a final annual analysis as the average g) Difference between seasonal/monthly of our twelve monthly mean fields in the temperature fields and the annual mean upper 1500 m of the ocean. Below 1500 m field. depth we define an annual analysis as the h) The number of mean values within the mean of the four seasonal analyses. Our final radius of influence for each grid box was seasonal analyses are defined as the average also calculated. This is not represented as of monthly analyses in the upper 1500 m of stand-alone maps, but the results are used the ocean (see Figure 2). The monthly fields on a) and b) maps (see above) to mark the are not available to 1500 me. We note that the grid boxes with less than three mean seasonal field values below about 1000 m values within the radius of influence. generally approximate the annual field value These calculations are available as data with noted exceptions where variability is files. generally large. As noted before, the volume The maps presented were arranged by of O2 observations below about 1000 m depth composite time periods (annual, seasonal, are not abundant as to construct robust S monthly fields. month) for O2, AOU, and O 2 , respectively. Table 5 describes all available O2, AOU, and 4.2. Available objective and statistical S O 2 maps and data fields. We note that the fields complete set of all climatological maps (in Table 5 lists all objective and statistical fields color), objectively analyzed fields, and calculated as part of WOA18. Climatologies associated statistical fields at all standard of oceanographic variables and associated depth levels shown in Table 2, as well as the statistics described in this document, as well complete set of data fields and as global figures of same can be obtained on- documentation, are available on-line. line. All of the figures use consistent symbols and The sample standard deviation in a gridbox notations for displaying information. was computed using: Continents are displayed as light-grey areas. N Coastal and open ocean areas shallower than 2 (x − x) the standard depth level being displayed are ∑ n s = n=1 (11) shown as solid gray areas. The objectively N −1 analyzed fields include the nominal contour th interval used. In addition, these maps may in which xn= the n data value in the grid box, include in some cases additional contour x =mean of all data values in the gridbox, and lines displayed as dashed black lines. All of N= total number of data values in the gridbox. the maps were computer drafted using The standard error of the mean was computed Generic Mapping Tools (GMT, Wessel and by dividing the standard deviation by the Smith, 1998). square root of the number of observations in each gridbox. We describe next the computation of annual and seasonal fields (section 4.1) and available

15 In addition to statistical fields, the land/ocean all respects for all variables in the WOA18 bottom mask and basin definition mask are series including relatively data sparse available online. A user could take the variables such as nutrients (Garcia et al., standard depth level data from WOD18 with 2018). This provides investigators with a flags and these masks, and recreate the consistent set of analyses to work with. WOA18 fields following the procedures outlined in this document. Explanations and One advantage of the analysis techniques data formats for the data files are found under used in this atlas is that we know the amount documentation on the WOA18 webpage. of smoothing by objective analyses as given by the response function in Table 3 and 4.3. Obtaining WOA18 fields on-line Figure 1. We believe this to be an important function for constructing and describing a The objective and statistical data fields can be climatology of any parameter. Particularly obtained on-line in different digital formats at when computing anomalies from a standard the WOA18 webpage. The WOA18 fields climatology, it is important that the data field can be obtained in ASCII format (WOA be smoothed to the same extent as the native and comma separated value [CSV]) climatology, to prevent generation of and Network Common Data Form (NetCDF) spurious anomalies simply through through our WOA18 webpage. For users differences in smoothing. A second reason is interested in specific geographic areas, the that purely diagnostic computations require a World Ocean Atlas Select (WOAselect) minimum of seven or eight gridpoints to selection tool can be used to designate a represent any Fourier component with subset geographic area, depth, and statistical confidence. Higher order oceanographic variable to view, and derivatives will require more smoothing. optionally download, climatological means or related statistics in shapefile format which We have attempted to create objectively is compatible with GIS software such as analyzed fields and data sets that can be used ESRI ArcMap. WOA18 includes a digital as a “black box.” We emphasize that some collection of "JPEG" images of the objective quality control procedures used are and statistical fields. In addition, WOA18 subjective. For those users who wish to make can be obtained in Ocean Data View (ODV) their own choices, all the data used in our format. WOA18 will be available through analyses are available both at standard depth other on-line locations as well. WOA98, levels as well as observed depth levels. The WOA01, WOA05, WOA09, and WOA13 are results presented in this atlas show some presently served through the IRI/LDEO features that are suspect and may be due to Climate Data Library with access to non-representative data that were not flagged statistical and objectively analyzed fields in a by the quality control techniques used. variety of digital formats. Although we have attempted to identify and eliminate as many of these features as possible by flagging the data, which generate these features, some obviously could remain. 5. SUMMARY Some may eventually turn out not to be In the preceding sections we have described artifacts but rather to represent real ocean the results of a project to objectively analyze features, not yet capable of being described all historical quality-controlled O2 data in in a meaningful way due to lack of WOD18. We desire to build a set of observational data. The views, findings, and climatological analyses that are identical in

16 any errors in this document are those of the Winkler with other observing systems will authors. likely improve the results. The analysis of O2 To provide an estimate of the quality data collected by profiling Argo floats, CTD, (uncertainty) of the WOA18 climatology, we moorings, and gliders with automated compared the WOA18 and the Global Ocean biochemical sensors including O2 will Data Analysis Project version 2 provide additional observational constraints on observed inter-annual to decadal-scale (GLODAPv2, Olsen et al., 2016) gridded O2 changes (e.g., Emerson et al., 2002; fields. GLODAPv2 does not have seasonal or Körtzinger et al., 2004; 2005, Garcia et al., monthly O2 gridded fields; and thus we could 2005a,b; Garcia et al., 1998; Keeling and not compare our results. Garcia, 2002; Bindoff and McDougall, 2002; The results suggest that the basin-scale deep Deutsch et al., 2005; Stramma et al., 2008, O2 differences between the two annual mean 2012; Shaffer et al., 2009; Riebesell et al., climatologies are relatively small below 2009; Hofmann and Schellnhuber, 2009; about 500 m depth (60°N-60°S). The global Kwon et al., 2016; Johnson et al., 2015). average difference of WOA18 minus Each of these different O2 observing systems GLODAPv2 O2 gridded fields is -0.4±4.7 add much additional data coverage and have µmol kg-1 below 500 m depth (Table 6). This different data uncertainties and calibrations is less than or comparable to the estimated that must be reconciled before combining long-term O2 measurement uncertainty (~ ±1 into an internally consistent climatology. µmol/kg). The data do not show a significant systematic depth offset at these broad spatial As indicated earlier, we are working on scales. constructing climatological fields combining O2 data obtained by chemical (Winkler) and Above 500 m depth, we note significant sensors (CTD, BCG-Argo, Gliders, measurable regional differences (> moorings, etc.). Combining such O2 data 5µmol/kg). This difference is expected requires detailed work to account for because of larger high frequency variability measurement uncertainties and potential in the upper ocean and because WOA18 is systematic concentration differences between based on a much representative larger spatial different observing systems (calibration). and monthly data coverage than The availability of such an integrated GLODAPv2. WOA18 contains all of the O2 climatology could enable workers such as the data used in the creation of GLODAPv2. As Global Ocean O2 Network (GO2NE) and shown in Table 6, the WOA18 and others to estimate global ocean GLODAPv2 difference is small. deoxygenation variability with less uncertainty because of the greater spatial and temporal coverage of the data (e.g., Breitburg et al., 2018, Schmidtko et al., 2017). 6. FUTURE WORK As the spatial and temporal coverage of the Our analyses will be updated when justified data increases, we will be able to create by additional O2 observations. As more data climatological fields on a ¼° spatial are received at NCEI/WDS-Oceanography, resolution that would enable better we will also be able to produce improved representation of O2 concentration structure higher resolution climatologies for O2, AOU, and variability along boundary currents and S and O 2 . Oxygen Minimum Zones (OMZ).

Merging and integrating O2 data collected by We are encouraged by the potential

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22 Table 1. Descriptions of climatologies for dissolved oxygen (O2), Apparent Oxygen Utilization S (AOU), and oxygen saturation ( O 2 ) in WOA18. The climatologies have been calculated based on bottle data (OSD) from WOD18. The standard depth levels are shown in Table 2. Oceanographic Depths for Annual Depths for Seasonal Depths for Monthly Variable Climatology Climatology Climatology S 0-5500 m 0-1500 m 0-1500 m O2, AOU, and O 2 (102 levels) (57 levels) (57 levels)

Table 2. Acceptable distances (m) for defining interior (A) and exterior (B) values used in the Reiniger-Ross scheme for interpolating observed level data to standard levels.

Standard Standard Standard Standard A B A B Level # Depths (m) Level # Depths (m) 1 0 50 200 52 1250 200 400 2 5 50 200 53 1300 200 1000 3 10 50 200 54 1350 200 1000 4 15 50 200 55 1400 200 1000 5 20 50 200 56 1450 200 1000 6 25 50 200 57 1500 200 1000 7 30 50 200 58 1550 200 1000 8 35 50 200 59 1600 200 1000 9 40 50 200 60 1650 200 1000 10 45 50 200 61 1700 200 1000 11 50 50 200 62 1750 200 1000 12 55 50 200 63 1800 200 1000 13 60 50 200 64 1850 200 1000 14 65 50 200 65 1900 200 1000 15 70 50 200 66 1950 200 1000 16 75 50 200 67 2000 1000 1000 17 80 50 200 68 2100 1000 1000 18 85 50 200 69 2200 1000 1000 19 90 50 200 70 2300 1000 1000 20 95 50 200 71 2400 1000 1000 21 100 50 200 72 2500 1000 1000 22 125 50 200 73 2600 1000 1000 23 150 50 200 74 2700 1000 1000 24 175 50 200 75 2800 1000 1000 25 200 50 200 76 2900 1000 1000 26 225 50 200 77 3000 1000 1000

23 Standard Standard Standard Standard A B A B Level # Depths (m) Level # Depths (m) 27 250 100 200 78 3100 1000 1000 28 275 100 200 79 3200 1000 1000 29 300 100 200 80 3300 1000 1000 30 325 100 200 81 3400 1000 1000 31 350 100 200 82 3500 1000 1000 32 375 100 200 83 3600 1000 1000 33 400 100 200 84 3700 1000 1000 34 425 100 200 85 3800 1000 1000 35 450 100 200 86 3900 1000 1000 36 475 100 200 87 4000 1000 1000 37 500 100 400 88 4100 1000 1000 38 550 100 400 89 4200 1000 1000 39 600 100 400 90 4300 1000 1000 40 650 100 400 91 4400 1000 1000 41 700 100 400 92 4500 1000 1000 42 750 100 400 93 4600 1000 1000 43 800 100 400 94 4700 1000 1000 44 850 100 400 95 4800 1000 1000 45 900 200 400 96 4900 1000 1000 46 950 200 400 97 5000 1000 1000 47 1000 200 400 98 5100 1000 1000 48 1050 200 400 99 5200 1000 1000 49 1100 200 400 100 5300 1000 1000 50 1150 200 400 101 5400 1000 1000 51 1200 200 400 102 5500 1000 1000

24 Table 3. Response function of the objective analysis scheme as a function of wavelength for WOA18 and earlier analyses. Response function is normalized to 1.0. WOA98, 01, 05, Wavelength1 Levitus (1982) WOA94 09, 13, 18 360ΔX 1.000 0.999 1.000 180ΔX 1.000 0.997 0.999 120ΔX 1.000 0.994 0.999 90ΔX 1.000 0.989 0.998 72ΔX 1.000 0.983 0.997 60ΔX 1.000 0.976 0.995 45ΔX 1.000 0.957 0.992 40ΔX 0.999 0.946 0.990 36ΔX 0.999 0.934 0.987 30ΔX 0.996 0.907 0.981 24ΔX 0.983 0.857 0.969 20ΔX 0.955 0.801 0.952 18ΔX 0.923 0.759 0.937 15ΔX 0.828 0.671 0.898 12ΔX 0.626 0.532 0.813 10ΔX 0.417 0.397 0.698 9ΔX 0.299 0.315 0.611 8ΔX 0.186 0.226 0.500 6ΔX 3.75x10-2 0.059 0.229 5ΔX 1.34x10-2 0.019 0.105 4ΔX 1.32x10-3 2.23x10-3 2.75x10-2 3ΔX 2.51x10-3 1.90x10-4 5.41x10-3 2ΔX 5.61x10-7 5.30x10-7 1.36x10-6 1For ΔX = 111 km, the meridional separation at the Equator.

25 Table 4. Basins defined for objective analysis and the shallowest standard depth level for which each basin is defined. Standard Standard # Basin1 Depth # Basin1 Depth Level Level 1 Atlantic Ocean 1* 30 North American Basin 29 2 Pacific Ocean 1* 31 West European Basin 29 3 Indian Ocean 1* 32 Southeast Indian Basin 29 4 Mediterranean Sea 1* 33 Coral Sea 29 5 Baltic Sea 1 34 East Indian Basin 29 6 Black Sea 1 35 Central Indian Basin 29 7 Red Sea 1 36 Southwest Atlantic Basin 29 8 Persian Gulf 1 37 Southeast Atlantic Basin 29 9 Hudson Bay 1 38 Southeast Pacific Basin 29 10 Southern Ocean 1* 39 Guatemala Basin 29 11 Arctic Ocean 1 40 East Caroline Basin 30 12 Sea of Japan 1 41 Marianas Basin 30 13 Kara Sea 8 42 Philippine Sea 30 14 Sulu Sea 10 43 Arabian Sea 30 15 Baffin Bay 14 44 Chile Basin 30 16 East Mediterranean 16 45 Somali Basin 30 17 West Mediterranean 19 46 Mascarene Basin 30 18 Sea of Okhotsk 19 47 Crozet Basin 30 19 Banda Sea 23 48 Guinea Basin 30 20 Caribbean Sea 23 49 Brazil Basin 31 21 Andaman Basin 25 50 Argentine Basin 31 22 North Caribbean 26 51 Tasman Sea 30 23 Gulf of Mexico 26 52 Atlantic Indian Basin 31 24 Beaufort Sea 28 53 Caspian Sea 1 25 South China Sea 28 54 Sulu Sea II 14 26 Barents Sea 28 55 Venezuela Basin 14 27 Celebes Sea 25 56 Bay of Bengal 1* 28 Aleutian Basin 28 57 Java Sea 6 29 Fiji Basin 29 58 East Indian Atlantic Basin 32 1Basins marked with a “*” can interact with adjacent basins in the objective analysis.

26 Table 5. Statistical fields calculated as part of WOA18 (“√“denotes field was calculated and is publicly available). Five-degree One-degree Field Statistical field Statisctics Calculated calculated Objectively analyzed climatology √

Statistical mean √ √

Number of observations √ √

Seasonal (monthly) climatology minus annual climatology √

Standard deviation from statistical mean √ √

Standard error of the statistical mean √ √

Statistical mean minus objectively analyzed climatology √

Number of mean values within radius of influence √

Table 6. Nominal depth average O2 (µmol/kg) differences (± 1 standard deviation) of the GLODAPv2 minus WOA18 for 1-degree objectively analyzed fields (60°N-60°S).

Depth range (m) Atlantic Pacific Indian Global 0-500 1.1±9.8 1.8±11.5 0.9±10.6 1.4±10.9 500-5500 -0.4±4.4 0.9±4.6 0.1±4.9 0.4±4.7

27

100

80

60

40 Amplitude(%)

Levitus (1982) 20 WOA94 (1-degree) WOA:WOA98, 98, WOA01, 01, 05, 09,WOA05, 13, & WOA09, 18 (1-degree) WOA13 (1-degree) WOA:WOA13 13 (1/4-degree) & 18 (1/4 degree)

0 0 1000 2000 3000 4000 Wavelength (km)

Figure 1. Response function of the WOA18, WOA13, WOA05, WOA01, WOA98, WOA94, and Levitus (1982) objective analysis schemes.

28 Zonal Mean

Annual Mean (OA)

Seasonal Mean (OA)

Z ≤ 1500 m Z > 1500 m Mean of 4 seasons Monthly Mean (OA) Annual Mean Mean of 3 months Seasonal Mean Seasonal Mean (OA)

Mean of 4 Monthly Mean (OA) Seasons

Mean of Mean of Annual Mean 12 months 3 months

Annual Mean Seasonal Mean

Legend: First-guess field used OA - Objectively analyzed field to calculate mean field Z - Depth Mean of climatologies Final mean field

Figure 2. Scheme used in computing annual, seasonal, and monthly objectively analyzed means S for dissolved oxygen (O2), Apparent Oxygen Utilization (AOU), and oxygen saturation ( O 2 ).

29