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NOAA Atlas NESDIS 83 WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation NOAA National Centers for Environmental Information NOAA Atlas NESDIS 83

July 2019

Publisher: NOAA Atlas NESDIS 83, 38pp.

Authors:

National Oceanic and Atmospheric Administration

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), and dissolved oxygen saturation ( ) 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. 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.

Basins defined for objective analysis and the shallowest standard depth level for which each basin is defined.

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NOAA Atlas NESDIS 83

WORLD OCEAN ATLAS 2018

Volume 3: Dissolved Oxygen, Apparent Oxygen

Utilization, and Dissolved Oxygen Saturation

Silver Spring, MD

July 2019

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Environmental Satellite, Data, and Information Service

National Centers for Environmental Information

For updates on the data, documentation, and additional

information about the WOA18 please refer to:

http://www.nodc.noaa.gov/OC5/indprod.html

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). World Ocean Atlas 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 National Centers for

Environmental Information

Additional copies of this publication, as well as information

about NCEI data holdings and services, are available upon

request directly from NCEI.

NOAA/NESDIS

National Centers for Environmental Information

SSMC3, 4th floor

1315 East-West Highway

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Telephone: (301) 713-3277

E-mail: NCEI.Info@noaa.gov

WEB: http://www.nodc.noaa.gov/

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 Oceans 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

time 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 temperature, salinity,

oxygen, nutrients, and derived fields

such as mixed layer 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 Climatology”. 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

iii

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

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

DISSOLVED OXYGEN (O2), APPARENT OXYGEN UTILIZATION (AOU), AND OXYGEN SATURATION (

). ......... 29

List of Tables

TABLE 1. DESCRIPTIONS OF CLIMATOLOGIES FOR DISSOLVED OXYGEN (O2), APPARENT OXYGEN UTILIZATION (AOU),

AND OXYGEN SATURATION (

) 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

Earth 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

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

Sea 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

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 Ekman transport, 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

vii

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.

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),

and dissolved oxygen saturation (

) 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. 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.

1. INTRODUCTION

The distribution of dissolved oxygen (O2),

apparent oxygen utilization, and oxygen

saturation in the ocean is affected by both

biochemical and physical processes.

Biochemical processes include sources and

sinks of O2 due to marine production,

respiration, and oxidation of organic matter

(e.g., biological pump). Physical processes

include sources and sinks of O2 caused by

water mass ventilation, air-sea flux exchange,

gas solubility (e.g., thermal pump), and water

mixing. The oceanic O2 inventory is sensitive

to local to global changes driven by the

physical and biological state of the ocean as

well as anthropogenic effects acting on

different time and spatial scales (e.g., Keeling

and Garcia, 2001; Matear and Hirst, 2003;

Stramma et al., 2008; Shaffer et al., 2009;

Riebesell et al., 2009; Hofmann and

Schellnhuber, 2009). Global O2 changes can

be substantial. For example, Schmidtko et al.

(2017) suggested that the global ocean O2

inventory has decreased by about 2% since

1960.

This atlas is part of the World Ocean Atlas

2018 (WOA18) series (Garcia et al. 2019a).

The WOA18 series includes analysis for

dissolved oxygen (this atlas), temperature

(Locarnini et al., 2019) salinity (Zweng et al.,

2019), and dissolved inorganic nutrients

(Garcia et al., 2019b). This atlas presents

annual, seasonal, and monthly climatologies

and related statistical fields for dissolved

oxygen (O2), apparent oxygen utilization

(AOU), and oxygen saturation (

Climatologies in this atlas are defined as

mean oceanographic fields at selected

standard depth levels based on the objective

analysis of historical oceanographic profiles

and select surface-only data. An O2 profile

is defined as a set of measurements of

samples collected at discrete depths taken as

an instrument such as a rosette mounted on a

Conductivity-Temperature-Depth (CTD)

package drops or rises vertically in the water

column to collect selected water samples for

analysis.

This atlas includes an objective analysis of all

scientifically quality-controlled historical O2

measurements available in the World Ocean

Database 2018 (WOD18; Boyer et al., 2018).

We present data analysis procedures and

horizontal maps showing annual, seasonal,

and monthly climatologies and related

statistical fields for O2, Apparent Oxygen

Utilization (AOU), and dissolved oxygen

saturation (

) at selected standard depth

levels between the surface and the ocean

bottom to a maximum depth of 5500 m. The

complete set of maps, statistical and

objectively analyzed data fields, and

documentation are all available on-line.

All climatologies use all available O2 data

collected on or after 1960 to present. Note

that previous WOA dissolved oxygen

climatologies were calculated using all

available O2 data regardless of year of

observation that passed our quality control

steps. The availability of more post-1960 O2

data have enable us to use more higher-

quality data. The annual climatology was

calculated using all data regardless of the

month in which the observation was made.

Seasonal climatologies were calculated using

only data from the defined season (regardless

of year). The seasons are here defined as

follows. Winter is defined as the months of

January, February, and March. Spring is

defined as April, May, and June. Summer is

defined as July, August, and September. Fall

is defined as October, November, and

December. Monthly climatologies were

calculated using data only from the given

month regardless of the day of the month in

which the observation was made.

The O2 data used in this atlas are available

from NOAA National Centers for

Environmental Information (NCEI) and the

World Data Service for Oceanography

(WDS-Oceanography; formerly World Data

Center for Oceanography, Silver Spring).

The National Oceanic and Atmospheric

Administration (NOAA) NCEI formed in

2015 combining the former National

Climatic Data Center (NCDC), National

Geophysical Data Center, and National

Oceanographic Data Center (NODC). Large

volumes of oceanographic data have been

acquired because of the fulfillment of several

data management projects including:

a) the Intergovernmental Oceanographic

Commission (IOC) Global

Oceanographic Data Archaeology and

Rescue (GODAR) project (Levitus et

al., 2005);

b) the IOC World Ocean Database project

(WOD);

c) the IOC Global Temperature Salinity

Profile project (GTSPP) (IOC, 1998).

The dissolved oxygen data used in the

WOA18 have been analyzed in a consistent,

objective manner on a one-degree latitude-

longitude grid at standard depth levels from

the surface to a maximum depth of 5500m.

The procedures for “all-data” climatologies

are identical to those used in the World Ocean

Atlas 2013 (WOA13) series (Garcia et al.,

2013 a, b). Slightly different procedures were

followed in earlier analyses (Levitus, 1982;

World Ocean Atlas 1994 series [WOA94,

Levitus et al., 1994; Levitus and Boyer, 1994

a, b; Conkright et al., 1994]). The present

analysis uses 102 depth levels for annual and

and 57 for seasonal and monthly fields.

Objective analyses shown in this atlas are

constrained by the nature of the historical O2

database (data are non-uniform in space,

time, and data quality), characteristics of the

objective analysis techniques, and the grid

size used. These limitations and

characteristics are discussed below.

Since the publication of WOA13, substantial

amounts of additional historical and modern

bottle O2 data have become available (e.g.,

GO-SHIP). However, even with these

additional data, we are still hampered in a

number of ways by a lack of oceanographic

data. Because of the lack of O2 data, we are

forced to examine the annual cycle by

compositing all data regardless of the year of

observation. In some geographic areas,

quality control is made difficult by the

limited number of O2 data collected in these

areas. Data may exist in an area for only one

season, thus precluding any representative

annual analysis. In some areas there may be a

reasonable spatial distribution of data points

on which to base an analysis, but there may

be only a few (perhaps only one) data values

in each one-degree latitude-longitude square.

This atlas is divided into sections. We begin

by describing the data sources and data

distribution (Section 2). Then we describe

the general data processing procedures

(Section 3), the results (Section 4), summary

(Section 5), and future work (Section 6).

Global horizontal maps for O2, AOU, and

at each individual depth levels for each

composite time period are available on-line.

2. DATA AND DATA DISTRIBUTION

Data sources and quality control procedures

are briefly described below. For further

information on the data sources used in

WOA18 refer to the World Ocean Database

2018 (WOD18, Boyer et al., 2019). The

quality control procedures used in

preparation of these analyses are described

by Garcia et al. (2019a).

2.1. Data sources

Historical oceanographic data used in this

atlas were obtained from the NCEI/WDS-

Oceanography archives and include all data

gathered as a result of the GODAR and WOD

projects. All of the quality-controlled O2

(expressed in units of micro-mole per

kilogram, µmol kg-1) data used in this atlas

were typically obtained by means of

chemical O2 analysis of serial (discrete)

water column samples. The O2 values were

analyzed following various modifications of

the Winkler titration method (Winkler, 1888)

using visual, amperometric, or photometric

end-detections (e.g., Carpenter, 1965;

Culberson and Huang, 1987; Knapp et al.,

1990; Culberson et al., 1991; Dickson, 1994).

We refer to the discrete water sample dataset

in WOD18 as Ocean Station Data (OSD).

Garcia et al., (2019c) describes the data in the

OSD dataset. Typically, each profile in the

OSD dataset consists of 1 to up to 36 discrete

O2 observations collected at various depths

between the surface and the bottom using

Nansen or Niskin bottle water samplers.

We note WOD18 contains O2 data obtained

by electronic sensors mounted on the

Conductivity-Temperature-Depth (CTD)

rosette frame such as optical O2 electronic

sensors and from other observing systems

(e.g., ARGO). While the number of O2

measurements made by profiling floats in

open ocean waters have now surpassed the

number of O2 Winkler titrations, WOA18

used O2 data believed to be obtained by

chemical Winkler titration methods only.

While optode and other O2 sensor drifts and

calibration issues have been greatly reduced

in the past few years (e.g., Bittig and

Kortzinger, 2015; Bushinsky et al., 2016;

Johnson et al., 2015), we feel that work is still

needed to understand data calibrations and

drifts between the different O2 sensors being

used. We have begun working on an

internally consistent database of sensor-

based O2 measurements obtained by

chemical and sensor-based methods. Our

preliminary results look promising. We

anticipate releasing preliminary fields in the

near future.

In this work, we concentrate on O2 data

obtained by chemical Winkler titration

methods from discreet samples from WOD

OSD. We note that most (>75%) of the Bottle

O2 data in the WOD18 OSD dataset were

collected on or after 1970 when more or less

standard O2 Winkler analysis methods began

to be used (e.g., Carpenter whole bottle

method). AOU (µmol kg-1) and

(percent,

%) are derived (calculated) variables for an

O2 measurement only when in situ

temperature and salinity were also measured

at the same geographic location, time, and

depth (pressure). Section 2.2 describes the

calculation of

and AOU.

To understand the procedures for taking

individual oceanographic observations and

constructing climatological fields, definition

of the terms “standard depth level data” and

“observed depth level data” are necessary.

We refer to the actual measured value of an

oceanographic variable in situ (Latin for “in

place”) as an “observation”, and to the depth

at which such a measurement was made as

the “observed level depth”. We refer to such

data as “observed level data”. Before the

development of oceanographic

instrumentation that measure at high

frequencies along the vertical profile,

oceanographers often attempted to make

measurements at selected “standard levels” in

the water column. Sverdrup et al. (1942)

presented the suggestions of the International

Association of Physical Oceanography

(IAPSO) as to which depths oceanographic

measurements should be made or

interpolated to for analysis. Historically the

World Ocean Atlas used a modified version

of the IAPSO standard depths. However,

with the increased global coverage of high

depth resolution instrumentation, such as

profiling floats, WOA has extended the

standard depth levels from 33 to 102. The

standard depth levels include the original

depth levels presented up to WOA09, but

have tripled the resolution in the upper 100

meters, more than doubled the depth

resolution of the upper 1000 meters, and

almost three and a half times the resolution

for overall depth levels. For many purposes,

including preparation of the present

climatologies, observed level data are

interpolated to standard depth levels if

observations did not occur at the desired

standard depths (see section 3.1 for details).

The levels at which the O2, AOU, and

climatologies were calculated are given in

Table 1. Table 2 shows the depths of each

standard depth level. Section 3.1 discusses

the vertical interpolation procedures used in

our work.

2.2. Data quality control

Performing quality control of the O2 data is a

major task, the difficulty of which is directly

related to lack of data and metadata (for some

areas) upon which to base statistical checks.

Consequently certain empirical criteria were

applied (see sections 2.2.1 through 2.2.4),

and as part of the last processing step,

subjective judgment was used (see sections

2.2.5 and 2.2.6). Individual data, and in some

cases entire profiles or all profiles for

individual cruises, have been flagged and not

used further because these data produced

features that were judged to be non-

representative or questionable. As part of our

work, we have made available WOD18

which contains both observed levels profile

data and standard depth level profile data

with various quality control flags applied.

The flags mark individual measurements or

entire profiles which were not used in the

next step of the procedure, either

interpolation to standard depth levels for

observed level data or calculation of

statistical means in the case of standard depth

level data. Our knowledge of the variability

of the world ocean in the instrumental record

now includes a greater appreciation and

understanding of the ubiquity of eddies,

rings, and lenses in some parts of the world

ocean as well as interannual and interdecadal

variability of water mass properties

associated with modal variability of the

atmosphere such as the North Atlantic

Oscillation, Pacific Decadal Oscillation

(PDO), and El Niño Southern Ocean

Oscillation (ENSO). Therefore, we have

simply flagged data, not eliminating them

from the WOD18. Thus, individual

investigators can make their own decision

regarding the representativeness of the O2

data. Investigators studying the distribution

of features such as eddies will be interested

in those data that we may regard as

unrepresentative or questionable for the

preparation of the analyses shown in this

atlas.

2.2.1. Duplicate elimination

Because O2 data are received from many

sources, sometimes the same data set is

received at NCEI/WDS-Oceanography more

than once but with slightly different time

and/or position and/or data values, and hence

are not easily identified as duplicate stations.

Therefore, to eliminate the repetitive O2 data

values our databases were checked for the

presence of exact and near exact replicates

using eight different criteria. The first checks

involve identifying stations with exact

position/date/time and data values; the next

checks involve offsets in position/date/time.

Profiles identified as duplicates in the checks

with a large offset were individually verified

to ensure they were indeed duplicate profiles.

All replicate profiles were eliminated at the

first step of our processing except one profile.

2.2.2. Range and gradient checks

Range checking (i.e., checking whether an

O2 value is within preset minimum and

maximum values as a function of depth and

ocean region) was performed on all O2 values

as a first quality control check to flag from

further use values that were grossly outside

expected oceanic ranges. Range checks were

prepared for individual regions of the world

ocean. Garcia et al. (2018) and Boyer and

Levitus (1994) detail the quality control

procedures. Tables showing the O2 ranges

selected for each basin and depth can be

found in Garcia et al. (2019a).

A check as to whether excessive vertical

gradients occur in the data as a function of

depth has been performed for O2 data in

WOD18 both in terms of positive and

negative concentration gradients. See Garcia

et al. (2019a) for limits for excessive

gradients for O2. We flagged and not used

values that exceeded these gradients.

2.2.3. Statistical checks

Statistical checks were performed as follows.

All data for O2 (irrespective of year), at each

standard depth level, were averaged within

five-degree latitude-longitude squares to

produce a record of the number of

observations, mean, and standard deviation in

each square. Statistics were computed for the

annual, seasonal, and monthly compositing

periods. Below 50 m depth, if data were

more than three standard deviations from the

mean, the data were flagged and withheld

from further use in objective analyses. Above

50 m depth, a five-standard-deviation

criterion was used in five-degree squares that

contained any land area. In selected five-

degree squares that are close to land areas, a

four-standard-deviation check was used. In

all other squares a three-standard-deviation

criterion was used for the 0-50 m depth layer.

For standard depth levels situated directly

above the bottom, a four-standard-deviation

criterion was used.

The reason for the weaker standard deviation

criterion in coastal and near-coastal regions is

the exceptionally large range of values in the

coastal five-degree square statistics for O2.

Frequency distributions of O2 values in some

coastal regions are observed to be skewed or

bimodal. Thus to avoid flagging possibly

good data in environments expected to have

large variability, the standard deviation

criteria were broadened.

The total number of measurements in each

profile, as well as the total number of O2

observations exceeding the standard

deviation criterion, were recorded. If more

than two observations in a profile were found

to exceed the standard deviation criterion,

then the entire profile was flagged. This

check was imposed after tests indicated that

surface data from particular casts (which

upon inspection appeared to be questionable)

were being flagged but deeper data were not.

Other situations were found where

questionable data from the deeper portion of

a cast were flagged, while near-surface data

from the same cast were not flagged because

of larger natural variability in surface layers.

One reason for this was the decrease of the

number of observations with depth and the

resulting change in sample statistics. The

standard-deviation check was applied twice

to the O2 data set for each compositing

period.

In summary, first the five-degree square

statistics were computed, and the data

flagging procedure described above was used

to provide a preliminary data set. Next, new

five-degree-square statistics were computed

from this preliminary data set and used with

the same statistical check to produce a new,

“clean” data set. The reason for applying the

statistical check twice was to flag (and

withhold from further use), in the first round,

any grossly erroneous or non-representative

data from the data set that would artificially

increase the variances. The second check is

then relatively more effective in identifying

smaller, but questionable or non-

representative, O2 observations.

2.2.4. Subjective flagging of data

The O2 data were averaged by one-degree

squares for input to the objective analyses

program. After initial objective analyses

were computed, the input set of one-degree

means still contained questionable data

contributing to unrealistic distributions,

yielding intense bull's-eyes or spatial

gradients. Examination of these features

indicated that some of them were due to

profiles from particular oceanographic

cruises. In such cases, data from an entire

cruise were flagged and withheld from

further use by setting a flag on each profile

from the cruise. In other cases, we flagged

individual profiles and/or measurements

causing such features.

2.2.5. Representativeness of the data

Another quality control issue is O2 data

spatila and temporal representativeness. The

general paucity of data forces the

compositing of all historical data to produce

“climatological” fields. In a given one-degree

square, there may be data from a month or

season of one particular year, while in the

same or a nearby square there may be data

from an entirely different year. If there is

large interannual variability in a region where

scattered sampling in time has occurred, then

one can expect the analysis to reflect this.

Because the observations are scattered

randomly with respect to time, except for a

few limited areas, the results cannot, in a

strict sense, be considered a true long-term

climatological average.

We present smoothed analyses of historical

means, based (in certain areas) on relatively

few observations. We believe, however, that

useful information about the oceans can be

gained through our procedures and that the

large-scale features are representative of the

real ocean. We believe that, if a hypothetical

global synoptic set of ocean O2 data existed

and one were to smooth these data to the

same degree as we have smoothed the

historical means overall, the large-scale

features would be similar to our results.

Some differences would certainly occur

because of interannual-to-decadal-scale

variability.

The volume of O2 observations diminish with

increasing depth. In the upper ocean, the all-

data O2 annual and seasonal mean

distributions are quite reasonable for defining

large-scale features, but for the monthly

periods, the database is inadequate in some

regions. With respect to the deep ocean, in

some areas the distribution of observations

may be adequate for some diagnostic

computations but inadequate for other

purposes (fit for purpose). If an isolated deep

basin or some region of the deep ocean has

only one observation, then no horizontal

gradient computations can be meaningful or

robust. However, useful information is

provided by the observations in the

computation of other quantities (e.g., a

volumetric mean over a major ocean basin).

2.3 Calculation of AOU and

Apparent Oxygen Utilization (AOU, µmol

kg-1) and dissolved oxygen saturation (

%) were estimated when quality-controlled

in situ O2 (µmol kg-1), temperature (T, °C),

and salinity (S) were all measured at the same

geographic location, time, and depth

(hydrostatic pressure). We note that not all

O2 observations included simultaneous

temperature and salinity measurements (see

section 2.2.4). In some cases, the temperature

and/or salinity values did not pass our

quality-control tests. We decided not to use

potential temperature referenced to the

surface ocean because of these reasons. Thus,

the total number of observations available for

calculating AOU and

is slightly smaller

in number than the available number of O2

observations.

AOU represents one rough estimate of the O2

utilized due to biochemical processes relative

to a preformed value or initial value. As

discussed below, AOU cannot be taken to

represent the True Oxygen Utilization; hence

the word “Apparent”. AOU (µmol kg-1) was

calculated as the difference between the O2

gas solubility (

]O[

) and the measured O2

concentrations and expressed as,

]

O[]O[AOU

−=

in which:

]O[

is the O2 solubility concentration

(µmol kg-1) calculated as a function of in situ

temperature and salinity, and one atmosphere

of total pressure. The effect of hydrostatic

pressure on O2 is relatively insignificant

relative to the long-term precision of the data

(~1 µmol kg-1). The

]

values were

calculated using the equations in Garcia and

Gordon (1992) based on the

]O[

values of

Benson and Krause (1984); and

]O[ 2

is the

measured O2 concentration (µmol kg-1).

Apparent Oxygen Utilization (AOU) is an

approximate estimate of True Oxygen

Utilization (TOU). The calculation of AOU

assumes that the amount of O2 used during

local biochemical processes can be estimated

by the difference in concentration between

the observed O2 and the preformed O2

values. However, AOU is affected by

processes other than biochemical processes

such as water mixing of waters of different

preformed values, departures of

]O[

from

full equilibration with the atmosphere,

bubble gas injection, skin temperature

effects, and other factors (e.g., Broecker and

Peng, 1982; Redfield et al., 1963; Garcia and

Keeling, 2001; Ito, 2004). We assume that

these processes are small in magnitude when

compared to the amplitude of the

climatological seasonal O2 signal on basin-

scales.

The O2 saturation (

, %) was estimated as

100% times the ratio of

]O[

[













=]O[ ]O[

%100O

The calculated AOU and

values were

analyzed following the same quality control

methods outlined in section 2. Furthermore,

if any of the O2, temperature (Locarnini et

al., 2018), or salinity (Zweng et al., 2018)

values were flagged during the quality

control procedure, then AOU and

values

were flagged also, and not used in the

analysis.

3. DATA PROCESSING PROCEDURES

3.1. Vertical interpolation to standard

levels

Vertical interpolation of observed depth level

data to standard depth levels followed

procedures in Joint Panel on Oceanographic

Tables and Standards (JPOTS) Editorial

Panel (1991). These procedures are in part

based on the work of Reiniger and Ross

(1968). Four observed depth level values

surrounding the standard depth level value

were used, two values from above the

standard level and two values from below the

standard level. The pair of values furthest

from the standard level is termed “exterior”

points and the pair of values closest to the

standard level are termed “interior” points.

Paired parabolas were generated via

Lagrangian interpolation. A reference curve

was fitted to the four data points and used to

define unacceptable interpolations caused by

“overshooting” in the interpolation. When

there were too few data points above or below

the standard level to apply the Reiniger and

Ross technique, we used a three-point

Lagrangian interpolation. If three points were

not available (either two above and one

below or vice-versa), we used linear

interpolation. In the event that an observation

occurred exactly at the depth of a standard

level, then a direct substitution was made.

Table 4 provides the range of acceptable

distances for which observed level data could

be used for interpolation to a standard level.

In WOA18, the number of standard levels

used is 102, allowing for analysis with

greater vertical resolution than the earlier

WOA climatologies. The method for

interpolating data to standard levels remains

the same as in previous analysis.

3.2. Methods of analysis

3.2.1. Overview

An objective analysis scheme of the type

described by Barnes (1964) was used to

produce the fields shown in this atlas. This

scheme had its origins in the work of

Cressman (1959). In World Ocean Atlas

1994 (WOA94), the Barnes (1973) scheme

was used. This required only one

“correction” to the first-guess field at each

grid point in comparison to the successive

correction method of Cressman (1959) and

Barnes (1964). This was to minimize

computing time used in the processing.

Barnes (1994) recommends a return to a

multi-pass analysis when computing time is

not an issue. Based on our own experience

we agree with this assessment. The single

pass analysis, used in WOA94, caused an

artificial front in the Southeastern Pacific

Ocean in a data sparse area (Anne Marie

Treguier, personal communication). The

analysis scheme used in generating WOA98,

WOA01, WOA05, WOA13, WOA13, and

WOA18 analyses uses a three-pass

“correction” which does not result in the

creation of this artificial front.

Inputs to the analysis scheme were one-

degree square means of data values at

standard levels (for time period and variable

being analyzed), and a first-guess value for

each square. For instance, one-degree square

means for our annual analysis were computed

using all available data regardless of date of

observation. For July, we used all historical

July data regardless of year of observation.

Analysis was the same for all standard depth

levels. Each one-degree latitude-longitude

square value was defined as being

representative of its square. The 360x180

gridpoints are located at the intersection of

half-degree lines of latitude and longitude.

An influence radius was then specified. At

those grid points where there was an

observed mean value, the difference between

the mean and the first-guess field was

computed. Next, a correction to the first-

guess value at all gridpoints was computed as

a distance-weighted mean of all gridpoint

difference values that lie within the area

around the gridpoint defined by the influence

radius. Mathematically, the correction factor

derived by Barnes (1964) is given by the

expression:

∑

sss

(1)

in which:

(i,j) - coordinates of a gridpoint in the east-

west and north-south directions

respectively;

Ci,j - the correction factor at gridpoint

coordinates (i,j);

n - the number of observations that fall

within the area around the point i,j

defined by the influence radius;

Qs - the difference between the observed

mean and the first-guess at the Sth point

in the influence area;

seW −

(for r ≤ R; Ws =0 for r > R);

r - distance of the observation from the

gridpoint;

R - influence radius;

E = 4.

The derivation of the weight function, Ws,

will be presented in the following section. At

each gridpoint we computed an analyzed

value Gi,j as the sum of the first-guess, Fi,j ,

and the correction Ci,j. The expression for

this is

jijiji

CFG

,,,

(2)

If there were no data points within the area

defined by the influence radius, then the

correction was zero, the first-guess field was

left unchanged, and the analyzed value was

simply the first-guess value. This correction

procedure was applied at all gridpoints to

produce an analyzed field. The resulting field

was first smoothed with a median filter

(Tukey, 1974; Rabiner et al., 1975) and then

smoothed with a five-point smoother of the

type described by Shuman (1957) (hereafter

referred as five-point Shuman smoother).

The choice of first-guess fields is important

and we discuss our procedures in section

3.2.5.

The analysis scheme is set up so that the

influence radius, and the number of five-

point smoothing passes can be varied with

each iteration. The strategy used is to begin

the analysis with a large influence radius and

decrease it with each iteration. This technique

allows us to analyze progressively smaller

scale phenomena with each iteration.

The analysis scheme is based on the work of

several researchers analyzing meteorological

data. Bergthorsson and Doos (1955)

computed corrections to a first-guess field

using various techniques: one assumed that

the difference between a first-guess value and

an analyzed value at a gridpoint was the same

as the difference between an observation and

a first-guess value at a nearby observing

station. All the observed differences in an

area surrounding the gridpoint were then

averaged and added to the gridpoint first-

guess value to produce an analyzed value.

Cressman (1959) applied a distance-related

weight function to each observation used in

the correction in order to give more weight to

observations that occur closest to the

gridpoint. In addition, Cressman introduced

the method of performing several iterations

of the analysis scheme using the analysis

produced in each iteration as the first-guess

field for the next iteration. He also suggested

starting the analysis with a relatively large

influence radius and decreasing it with

successive iterations so as to analyze smaller

scale phenomena with each pass.

Sasaki (1960) introduced a weight function

that was specifically related to the density of

observations, and Barnes (1964, 1973)

extended the work of Sasaki. The weight

function of Barnes (1964) has been used here.

The objective analysis scheme we used is in

common use by the mesoscale

meteorological community. Several studies

of objective analysis techniques have been

made. Achtemeier (1987) examined the

“concept of varying influence radii for a

successive corrections objective analysis

scheme.” Seaman (1983) compared the

“objective analysis accuracies of statistical

interpolation and successive correction

schemes.” Smith and Leslie (1984)

performed an “error determination of a

successive correction type objective analysis

scheme.” Smith et al. (1986) made “a

comparison of errors in objectively analyzed

fields for uniform and non-uniform station

distribution.”

3.2.2. Derivation of Barnes (1964) weight

function

The principle upon which the Barnes (1964)

weight function is derived is that “the two-

dimensional distribution of an atmospheric

variable can be represented by the summation

of an infinite number of independent

harmonic waves, that is, by a Fourier integral

representation”. If f(x,y) is the variable, then

in polar coordinates (r,θ), a smoothed or

filtered function g(x,y) can be defined:

θη

ryrxfyxg

)

(

)sin,cos(

),(

0 0

∫∫

∞

++=

(3)

in which r is the radial distance from a

gridpoint whose coordinates are (x,y). The

weight function is defined as

−

(4)

which resembles the Gaussian distribution.

The shape of the weight function is

determined by the value of K, which relates

to the distribution of data. The determination

of K follows. The weight function has the

property that

0 0













∫ ∫

∞

θη

(5)

This property is desirable because in the

continuous case (3) the application of the

weight function to the distribution f(x,y) will

not change the mean of the distribution.

However, in the discrete case (1), we only

sum the contributions to within the distance

R. This introduces an error in the evaluation

of the filtered function, because the condition

given by (5) does not apply. The error can be

pre-determined and set to a reasonably small

value in the following manner. If one carries

out the integration in (5) with respect to θ, the

remaining integral can be rewritten as























∫∫ ∞

ηη

(6)

Defining the second integral as ε yields

−=













∫−

(7)

Integrating (7), we obtain

−

(7a)

Taking the natural logarithm of both sides of

(7a) leads to an expression for K,

ERK 4/

(7b)

where E ≡ -ln ε

Rewriting (4) using (7b) leads to the form of

weight function used in the evaluation of (1).

Thus, choice of E and the specification of R

determine the shape of the weight function.

Levitus (1982) chose E=4 which corresponds

to a value of ε of approximately 0.02. This

choice implies with respect to (7) the

representation of more than 98 percent of the

influence of any data around the gridpoint in

the area defined by the influence radius R.

This analysis (WOA18) and previous

analyses (WOA94, WOA98, WOA01,

WOA05, WOA13) used E=4.

Barnes (1964) proposed using this scheme in

an iterative fashion similar to Cressman

(1959). Levitus (1982) used a four-iteration

scheme with a variable influence radius for

each pass. WOA94 used a one-iteration

scheme. WOA98, WOA01, WOA05,

WOA13, and WOA18 employed a three-

iteration scheme with a variable influence

radius.

3.2.3. Derivation of Barnes (1964) response

function

It is desirable to know the response of a data

set to the interpolation procedure applied to

it. Following Barnes (1964) and reducing to

one-dimensional case we let

)sin()( xAxf

(8)

in which α = 2π/λ with λ being the

wavelength of a particular Fourier

component, and substitute this function into

equation (3) along with the expression for η

in equation (4). Then

[ ]

)()sin()( xDfxADxg ==

(9)

in which D is the response function for one

application of the analysis and defined as

24 











−













−

πα

eeD

The phase of each Fourier component is not

changed by the interpolation procedure. The

results of an analysis pass are used as the

first-guess for the next analysis pass in an

iterative fashion. The relationship between

the filtered function g(x) and the response

function after N iterations as derived by

Barnes (1964) is

∑

−

DDxfxg

)1()()(

(10)

Equation (10) differs trivially from that given

by Barnes. The difference is due to our first-

guess field being defined as a zonal average,

annual mean, seasonal mean, or monthly

mean, whereas Barnes used the first

application of the analysis as a first-guess.

Barnes (1964) also showed that applying the

analysis scheme in an iterative fashion will

result in convergence of the analyzed field to

the observed data field. However, it is not

desirable to approach the observed data too

closely, because at least seven or eight

gridpoints are needed to represent a Fourier

component.

The response function given in (10) is useful

in two ways: it is informative to know what

Fourier components make up the analyses,

and the computer programs used in

generating the analyses can be checked for

correctness by comparison with (10).

3.2.4. Choice of response function

The distribution of O2 observations (see

appendices) at different depths and for the

different averaging periods, are not regular in

space or time. At one extreme, regions exist

in which every one-degree square contains

data and no interpolation needs to be

performed. At the other extreme are regions

in which few if any data exist. Thus, with

variable data spacing the average separation

distance between gridpoints containing data

is a function of geographical position and

averaging period. However, if we computed

and used a different average separation

distance for each variable at each depth and

each averaging period, we would be

generating analyses in which the wavelengths

of observed phenomena might differ from

one depth level to another and from one

season to another. In WOA94, a fixed

influence radius of 555 kilometers was used

to allow uniformity in the analysis of all

variables. For the present WOA18 analyses

(as well as for WOA13, WOA09, WOA04,

and WOA01), a three-pass analysis, based on

Barnes (1964), with influence radii of 892,

669 and 446 km was used for the 1° analysis.

Inspection of Equation 1 shows that the

difference between the analyzed field and the

first-guess field values at any gridpoint is

proportional to the sum of the weighted-

differences between the observed mean and

first-guess at all gridpoints containing data

within the influence area.

The reason for using the five-point Shuman

smoother and the median smoother is that our

data are not evenly distributed in space. As

the analysis moves from regions containing

data to regions devoid of data, small-scale

discontinuities may develop. The five-point

Shuman and median smoothers are used to

eliminate these discontinuities. The five-

point Shuman smoother does not affect the

phase of the Fourier components that

comprise an analyzed field.

The response function for the analyses

presented in the WOA18 series is given in

Table 4 and in Figure 1. For comparison

purposes, the response function used by

Levitus (1982), WOA94, and others are also

presented. The response function represents

the smoothing inherent in the objective

analysis described above plus the effects of

one application of the five-point Shuman

smoother and one application of a five-point

median smoother. The effect of varying the

amount of smoothing in North Atlantic sea

surface temperature (SST) fields has been

quantified by Levitus (1982) for a particular

case. In a region of strong SST gradient such

as the Gulf Stream, the effect of smoothing

can easily be responsible for differences

between analyses exceeding 1.0°C.

To avoid the problem of the influence region

extending across land or sills to adjacent

basins, the objective analysis routine

employs basin “identifiers” to preclude the

use of data from adjacent basins. Table 5 lists

these basins and the depth at which no

exchange of information between basins is

allowed during the objective analysis of data,

i.e., “depths of mutual exclusion.” Some

regions are nearly, but not completely,

isolated topographically. Because some of

these nearly isolated basins have water mass

properties that are different from surrounding

basins, we have chosen to treat these as

isolated basins as well. Not all such basins

have been identified because of the

complicated structure of the sea floor. In

Table 5, a region marked with an (*) can

interact with adjacent basins except for

special areas such as the Isthmus of Panama.

3.2.5. First-guess field determination

There are gaps in the data coverage and, in

some parts of the world ocean, there exist

adjacent basins whose water mass properties

are individually nearly homogeneous but

have distinct basin-to basin differences.

Spurious features can be created when an

influence area extends over two basins of this

nature (basins are listed in Table 6). Our

choice of first-guess field attempts to

minimize the creation of such features. To

maximize data coverage and best represent

global variability, a set of “time-

indeterminant” climatologies were produced

as a first-guess for each set of decadal

climatologies. The time-indeterminant

climatologies used the first-guess field

procedures developed for earlier versions of

WOA: To provide a first-guess field for the

“all-data” annual analysis at any standard

level, we first zonally averaged the observed

temperature data in each one-degree latitude

belt by individual ocean basins. The annual

analysis was then used as the first-guess for

each seasonal analysis and each seasonal

analysis was used as a first-guess for the

appropriate monthly analysis if computed.

We then reanalyzed the temperature data

using the newly produced analyses as first-

guess fields described as follows and as

shown in Figure 3. A new annual mean was

computed as the mean of the twelve monthly

analyses for the upper 1500 m, and the mean

of the four seasons below 1500 m depth. This

new annual mean was used as the first-guess

field for new seasonal analyses. These new

seasonal analyses in turn were used to

produce new monthly analyses. This

procedure produces slightly smoother means.

These time-indeterminant monthly mean

objectively analyzed temperature fields were

used as the first-guess fields for each

“decadal” monthly climatology. Likewise,

time-indeterminant seasonal and annual

climatologies were used as first-guess fields

for the seasonal and annual decadal

climatologies.

We recognize that fairly large data-void

regions exist, in some cases to such an extent

that a seasonal or monthly analysis in these

regions is not meaningful. Geographic

distribution of observations for the “all-data”

annual periods (see appendices) is excellent

for the upper layers of the ocean. By using an

“all-data” annual mean, first-guess field

regions where data exist for only one season

or month will show no contribution to the

annual cycle. By contrast, if we used a zonal

average for each season or month, then, in

those latitudes where gaps exist, the first-

guess field would be heavily biased by the

few data points that exist. If these were

anomalous data in some way, an entire basin-

wide belt might be affected.

One advantage of producing “global” fields

for a particular compositing period (even

though some regions are data void) is that

such analyses can be modified by

investigators for use in modeling studies.

For the time-indeterminant quarter-degree

first-guess field, the one-degree time-

indeterminant field was also used. Each of

the sixteen quarter-degree boxes enclosed

used the one-degree time-indeterminant

value as a first-guess, thereby projecting the

one-degree climatology onto the quarter-

degree grid. In those areas where there was

no one-degree value due to land or bottom

mask, the statistical mean for the entire basin

at the given depth was used. This first-guess

field was then used to calculate time-

indeterminant quarter-degree field. The time

indeterminant quarter-degree field was then

used for each quarter-degree decadal

climatological mean.

3.3. Choice of objective analysis

procedures

Optimum interpolation (Gandin, 1963) has

been used by some investigators to

objectively analyze oceanographic data. We

recognize the power of this technique but

have not used it to produce analyzed fields.

As described by Gandin (1963), optimum

interpolation is used to analyze synoptic data

using statistics based on historical data. In

particular, second-order statistics such as

correlation functions are used to estimate the

distribution of first order parameters such as

means. We attempt to map most fields in this

atlas based on relatively sparse data sets. By

necessity we must composite all data

regardless of year of observation, to have

enough data to produce a global,

hemispheric, or regional analysis for a

particular month, season, or even yearly.

Because of the paucity of data, we prefer not

to use an analysis scheme that is based on

second order statistics. In addition, as

Gandin has noted, there are two limiting

cases associated with optimum interpolation.

The first is when a data distribution is dense.

In this case, the choice of interpolation

scheme makes little difference. The second

case is when data are sparse. In this case, an

analysis scheme based on second order

statistics is of questionable value. For

additional information on objective analysis

procedures see Thiebaux and Pedder (1987)

and Daley (1991).

3.4. Choice of spatial grid

The analyses that comprise WOA18 have

been computed using the ETOPO2 (Earth

Topography 2 arc minute) land-sea

topography to define ocean depths at each

gridpoint (ETOPO2, 2006). From the

ETOPO2 land mask, a quarter-degree land

mask was created based on ocean bottom

depth and land criteria. If sixteen or more 2-

minute square values out of a possible forty-

nine in a one-quarter-degree box were

defined as land, then the quarter-degree

gridbox was defined to be land. If no more

than two of the 2-minute squares had the

same depth value in a quarter-degree box,

then the average value of the 2-minute ocean

depths in that box was defined to be the depth

of the quarter-degree gridbox. If ten or more

2-minute squares out of the forty-nine had a

common bottom depth, then the depth of the

quarter-degree box was set to the most

common depth value. The same method was

used to go from a quarter-degree to a one-

degree resolution. In the one-degree

resolution case, at least four points out of a

possible sixteen (in a one-degree square) had

to be land in order for the one-degree square

to remain land and three out of sixteen had to

have the same depth for the ocean depth to be

set. These criteria yielded a mask that was

then modified by:

1. Connecting the Isthmus of Panama;

2. Maintaining an opening in the Straits

of Gibraltar and in the English

Channel;

3. Connecting the Kamchatka Peninsula

and the Baja Peninsula to their

respective continents.

The one-degree mask was created from the

quarter-degree mask instead of directly from

ETOPO2 in order to maintain consistency

between the quarter-degree and one-degree

masks.

4. RESULTS

The on-line figures for this atlas include

seven types of horizontal maps representing

annual, seasonal, and monthly spatial

distribution of analyzed data and data

statistics as a function of selected standard

depth levels for dissolved O2, AOU, and O2

saturation over one-degree latitude-longitude

grid:

a) Objectively analyzed climatology fields.

Grid boxes for which there were less than

three values available in the objective

analysis defined by the influence radius

are denoted by a white “+” symbol.

b) Statistical mean one-degree fields. Grid

boxes for which there were less than three

values available in the objective analysis

defined by the influence radius are

denoted by a white “+” symbol.

c) Data distribution fields for the number of

observations in each grid box used in the

objective analysis binned into 1 to 2, 3-5,

6-10, 11-30, 31-50 and greater than 51

observations.

d) Standard deviation fields binned into

several ranges depending on the depth

level. The maximum value of the

standard deviation is shown on the map.

e) Standard error of the mean fields binned

into several ranges depending on the

depth level.

f) Difference between observed and

analyzed fields binned into several ranges

depending on the depth level.

g) Difference between seasonal/monthly

temperature fields and the annual mean

field.

h) The number of mean values within the

radius of influence for each grid box was

also calculated. This is not represented as

stand-alone maps, but the results are used

on a) and b) maps (see above) to mark the

grid boxes with less than three mean

values within the radius of influence.

These calculations are available as data

files.

The maps presented were arranged by

composite time periods (annual, seasonal,

month) for O2, AOU, and

, respectively.

Table 5 describes all available O2, AOU, and

maps and data fields. We note that the

complete set of all climatological maps (in

color), objectively analyzed fields, and

associated statistical fields at all standard

depth levels shown in Table 2, as well as the

complete set of data fields and

documentation, are available on-line.

All of the figures use consistent symbols and

notations for displaying information.

Continents are displayed as light-grey areas.

Coastal and open ocean areas shallower than

the standard depth level being displayed are

shown as solid gray areas. The objectively

analyzed fields include the nominal contour

interval used. In addition, these maps may

include in some cases additional contour

lines displayed as dashed black lines. All of

the maps were computer drafted using

Generic Mapping Tools (GMT, Wessel and

Smith, 1998).

We describe next the computation of annual

and seasonal fields (section 4.1) and available

objective and statistical fields (section 4.2).

4.1. Computation of annual and seasonal

fields

After completion of all of our analyses, we

define a final annual analysis as the average

of our twelve monthly mean fields in the

upper 1500 m of the ocean. Below 1500 m

depth we define an annual analysis as the

mean of the four seasonal analyses. Our final

seasonal analyses are defined as the average

of monthly analyses in the upper 1500 m of

the ocean (see Figure 2). The monthly fields

are not available to 1500 me. We note that the

seasonal field values below about 1000 m

generally approximate the annual field value

with noted exceptions where variability is

generally large. As noted before, the volume

of O2 observations below about 1000 m

depth are not abundant as to construct robust

monthly fields.

4.2. Available objective and statistical

fields

Table 5 lists all objective and statistical fields

calculated as part of WOA18. Climatologies

of oceanographic variables and associated

statistics described in this document, as well

as global figures of same can be obtained on-

line.

The sample standard deviation in a gridbox

was computed using:

)(

−

∑

(11)

in which xn= the nth data value in the grid box,

=mean of all data values in the gridbox, and

N= total number of data values in the gridbox.

The standard error of the mean was computed

by dividing the standard deviation by the

square root of the number of observations in

each gridbox.

In addition to statistical fields, the land/ocean

bottom mask and basin definition mask are

available online. A user could take the

standard depth level data from WOD18 with

flags and these masks, and recreate the

WOA18 fields following the procedures

outlined in this document. Explanations and

data formats for the data files are found under

documentation on the WOA18 webpage.

4.3. Obtaining WOA18 fields on-line

The objective and statistical data fields can be

obtained on-line in different digital formats at

the WOA18 webpage. The WOA18 fields

can be obtained in ASCII format (WOA

native and comma separated value [CSV])

and Network Common Data Form (NetCDF)

through our WOA18 webpage. For users

interested in specific geographic areas, the

World Ocean Atlas Select (WOAselect)

selection tool can be used to designate a

subset geographic area, depth, and

oceanographic variable to view, and

optionally download, climatological means

or related statistics in shapefile format which

is compatible with GIS software such as

ESRI ArcMap. WOA18 includes a digital

collection of "JPEG" images of the objective

and statistical fields. In addition, WOA18

can be obtained in Ocean Data View (ODV)

format. WOA18 will be available through

other on-line locations as well. WOA98,

WOA01, WOA05, WOA09, and WOA13 are

presently served through the IRI/LDEO

Climate Data Library with access to

statistical and objectively analyzed fields in a

variety of digital formats.

5. SUMMARY

In the preceding sections we have described

the results of a project to objectively analyze

all historical quality-controlled O2 data in

WOD18. We desire to build a set of

climatological analyses that are identical in

all respects for all variables in the WOA18

series including relatively data sparse

variables such as nutrients (Garcia et al.,

2018). This provides investigators with a

consistent set of analyses to work with.

One advantage of the analysis techniques

used in this atlas is that we know the amount

of smoothing by objective analyses as given

by the response function in Table 3 and

Figure 1. We believe this to be an important

function for constructing and describing a

climatology of any parameter. Particularly

when computing anomalies from a standard

climatology, it is important that the data field

be smoothed to the same extent as the

climatology, to prevent generation of

spurious anomalies simply through

differences in smoothing. A second reason is

that purely diagnostic computations require a

minimum of seven or eight gridpoints to

represent any Fourier component with

statistical confidence. Higher order

derivatives will require more smoothing.

We have attempted to create objectively

analyzed fields and data sets that can be used

as a “black box.” We emphasize that some

quality control procedures used are

subjective. For those users who wish to make

their own choices, all the data used in our

analyses are available both at standard depth

levels as well as observed depth levels. The

results presented in this atlas show some

features that are suspect and may be due to

non-representative data that were not flagged

by the quality control techniques used.

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.

Some may eventually turn out not to be

artifacts but rather to represent real ocean

features, not yet capable of being described

in a meaningful way due to lack of

observational data. The views, findings, and

any errors in this document are those of the

authors.

To provide an estimate of the quality

(uncertainty) of the WOA18 climatology, we

compared the WOA18 and the Global Ocean

Data Analysis Project version 2

(GLODAPv2, Olsen et al., 2016) gridded O2

fields. GLODAPv2 does not have seasonal or

monthly O2 gridded fields; and thus we could

not compare our results.

The results suggest that the basin-scale ddep

O2 differences between the two annual mean

climatologies are relatively small below

about 500 m depth (60°N-60°S). The global

average difference of WOA18 minus

GLODAPv2 O2 gridded fields is -0.4±4.7

µmol kg-1 below 500 m depth (Table 6). This

is less than or comparable to the estimated

long-term O2 measurement uncertainty (~ ±1

µmol/kg). The data do not show a significant

systematic depth offset at trhese broad spatila

scales.

Above 500 m depth, we note significant

measurable regional differences (>

5µmol/kg). This difference is expected

because of larger high frequency variability

in the upper ocean and because WOA18 is

based on a much representative larger spatial

and monthly data coverage than

GLODAPv2. WOA18 contains all of the O2

data used in the creation of GLODAPv2. As

shown in Table 6, the WOA18 and

GLODAPv2 difference is small.

6. FUTURE WORK

Our analyses will be updated when justified

by additional O2 observations. As more data

are received at NCEI/WDS-Oceanography,

we will also be able to produce improved

higher resolution climatologies for O2, AOU,

and

Merging and integrating O2 data collected by

Winkler with other observing systems will

likely improve the results. The analysis of O2

data collected by profiling Argo floats, CTD,

moorings, and gliders with automated

biochemical sensors including O2 will

provide additional observational constraints

on observed inter-annual to decadal-scale

changes (e.g., Emerson et al., 2002;

Körtzinger et al., 2004; 2005, Garcia et al.,

2005a,b; Garcia et al., 1998; Keeling and

Garcia, 2002; Bindoff and McDougall, 2002;

Deutsch et al., 2005; Stramma et al., 2008,

2012; Shaffer et al., 2009; Riebesell et al.,

2009; Hofmann and Schellnhuber, 2009;

Kwon et al., 2016; Johnson et al., 2015).

Each of these different O2 observing systems

add much additional data coverage and have

different data uncertainties and calibrations

that must be reconciled before combining

into an internally consistent climatology.

As indicated earlier, we are working on

constructing climatological fields combining

O2 data obtained by chemical (Winkler) and

sensors (CTD, BCG-Argo, Gliders,

moorings, etc.). Combining such O2 data

requires detailed work to account for

measurement uncertainties and potential

systematic concentration differences between

different observing systems (calibration).

The availability of such an integrated

climatology could enable workers such as the

Global Ocean O2 Network (GO2NE) and

others to estimate global ocean

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).

As the spatial and temporal coverage of the

data increases, we will be able to create

climatological fields on a ¼° spatial

resolution that would enable better

representation of O2 concentration structure

and variability along boundary currents and

Oxygen Minimum Zones (OMZ).

We are encouraged by the potential

acquisition of much additional high-quality

oceanographic observations through recently

adopted complementary global projects such

as the Global Ocean Observing System

(GOOS) 2030 Strategy and the United

Nations Decade of Ocean Science for

Sustainable Development (2021-2030).

GOOS is sponsored by the Intergovernmental

Oceanographic Commission of UNESCO,

the World Meteorological Organization

(WMO), the United Nations Environment

Programme (UNEP), and the International

Science Council (ISC). Expansion of the

current global ocean observing system will

enable the creation of more robust

climatologies that span shorter climatological

time-periods (e.g., inter-annual to decadal).

Creating WOA18 relies on the unrestricted

and timely open access and use of

oceanographic observations collected

worldwide. One country cannot afford the

observational system needed to monitor the

entire Earth; and thus, open access and use of

observations is essential for formulating

informed science-based societal-relevant

strategies for sustainable ocean use and

respond to environmental challenges. The

developing research-quality climatologies

such as WOA O2 serve as reliable science-

based baselines from which to estimate low

frequency regional to global O2 variability.

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Table 1. Descriptions of climatologies for dissolved oxygen (O2), Apparent Oxygen Utilization

(AOU), and oxygen saturation (

) in WOA18. The climatologies have been calculated based on

bottle data (OSD) from WOD18. The standard depth levels are shown in Table 2.

Oceanographic

Variable

Depths for Annual

Climatology

Depths for Seasonal

Climatology

Depths for Monthly

Climatology

O2, AOU, and

0-5500 m

(102 levels)

0-1500 m

(57 levels)

0-1500 m

(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

Level #

Standard

Depths (m)

A B

Standard

Level #

Standard

Depths (m)

A B

200

1250

200

400

200

1300

200

1000

200

1350

200

1000

200

1400

200

1000

200

1450

200

1000

200

1500

200

1000

200

1550

200

1000

200

1600

200

1000

200

1650

200

1000

200

1700

200

1000

200

1750

200

1000

200

1800

200

1000

200

1850

200

1000

200

1900

200

1000

200

1950

200

1000

200

2000

1000

200

2100

1000

200

2200

1000

200

2300

1000

200

2400

1000

100

200

2500

1000

125

200

2600

1000

150

200

2700

1000

175

200

2800

1000

200

2900

1000

225

200

3000

1000

Standard

Level #

Standard

Depths (m)

A B

Standard

Level #

Standard

Depths (m)

A B

250

100

200

3100

1000

275

100

200

3200

1000

300

100

200

3300

1000

325

100

200

3400

1000

350

100

200

3500

1000

375

100

200

3600

1000

400

100

200

3700

1000

425

100

200

3800

1000

450

100

200

3900

1000

475

100

200

4000

1000

500

100

400

4100

1000

550

100

400

4200

1000

600

100

400

4300

1000

650

100

400

4400

1000

700

100

400

4500

1000

750

100

400

4600

1000

800

100

400

4700

1000

850

100

400

4800

1000

900

200

400

4900

1000

950

200

400

5000

1000

200

400

5100

1000

1050

200

400

5200

1000

1100

200

400

100

5300

1000

1150

200

400

101

5400

1000

1200

200

400

102

5500

1000

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.

Wavelength1 Levitus (1982) WOA94

WOA98, 01, 05,

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.

Table 4. Basins defined for objective analysis and the shallowest standard depth level for which

each basin is defined.

# Basin1

Standard

Depth

Level

# Basin1

Standard

Depth

Level

Atlantic Ocean

North American Basin

Pacific Ocean

West European Basin

Indian Ocean

Southeast Indian Basin

Mediterranean Sea

Coral Sea

Baltic Sea

East Indian Basin

Black Sea

Central Indian Basin

Red Sea

Southwest Atlantic Basin

Persian Gulf

Southeast Atlantic Basin

Hudson Bay

Southeast Pacific Basin

Southern Ocean

Guatemala Basin

Arctic Ocean

East Caroline Basin

Sea of Japan

Marianas Basin

Kara Sea

Philippine Sea

Sulu Sea

Arabian Sea

Baffin Bay

Chile Basin

East Mediterranean

Somali Basin

West Mediterranean

Mascarene Basin

Sea of Okhotsk

Crozet Basin

Banda Sea

Guinea Basin

Caribbean Sea

Brazil Basin

Andaman Basin

Argentine Basin

North Caribbean

Tasman Sea

Gulf of Mexico

Atlantic Indian Basin

Beaufort Sea

Caspian Sea

South China Sea

Sulu Sea II

Barents Sea

Venezuela Basin

Celebes Sea

Bay of Bengal

Aleutian Basin

Java Sea

Fiji Basin

East Indian Atlantic Basin

1Basins marked with a “*” can interact with adjacent basins in the objective analysis.

Table 5. Statistical fields calculated as part of WOA18 (“√“denotes field was calculated and is

publicly available).

Statistical field One-degree Field

Calculated

Five-degree

Statisctics

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

Figure 1. Response function of the WOA18, WOA13, WOA05, WOA01, WOA98, WOA94, and

Levitus (1982) objective analysis schemes.

Wavelength (km)

01000 2000 3000 4000

Amplitude (%)

100

Levitus (1982)

WOA94 (1-degree)

WOA98, WOA01, WOA05, WOA09, WOA13 (1-degree)

WOA13 (1/4-degree)

WOA: 98, 01, 05, 09, 13, & 18 (1-degree)

WOA: 13 & 18 (1/4 degree)

Figure 2. Scheme used in computing annual, seasonal, and monthly objectively analyzed means

for dissolved oxygen (O2), Apparent Oxygen Utilization (AOU), and oxygen saturation (

Zonal Mean

Annual Mean (OA)

Seasonal Mean (OA)

Monthly Mean (OA)

Seasonal Mean

Monthly Mean (OA)

Annual Mean

Seasonal Mean

Annual Mean

Seasonal Mean (OA)

Z ≤ 1500 m

Z > 1500 m

Mean of

3 months

Mean of

12 months

Mean of

3 months

Mean of 4

seasons

Annual Mean

Mean of 4

Seasons

First-guess field used

to calculate mean field

Mean of climatologies

Final mean field

Legend:

OA - Objectively analyzed field

Z - Depth

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The california current system (CCS) supports a wide array of ecosystem services with hypoxia historically occurring in near‐bottom waters. Limited open ocean data coverage hinders the mechanistic understanding of CCS oxygen variability. By comparing three different models with varying horizontal resolutions, we found that dissolved oxygen (DO) anomalies in the CCS are propagated from shallower coastal areas to the deeper open ocean, where they are advected at a density and velocity consistent with basin‐scale circulation. Since DO decreases have been linked to water mass redistribution in the CCS, we conduct a water mass analysis on two of the models and on biogeochemical Argo floats that sampled multiple seasonal cycles. We found that high variability in biogeochemical variables (DO and nutrients) seen in regions of low variability of temperature and salinity could be linked to water mass mixing, as some of the water masses considered had higher gradients in biogeochemical variables compared to physical variables. Additional DO observations are needed, therefore, to further understand circulation changes in the CCS. We suggest that increased DO sampling north of 35˚N and near the shelf break would benefit model initialization and skill assessment, as well as allow for better assessment of the role of equatorial waters in driving DO in the northern CCS.

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Article

Full-text available

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The loss of oxygen from the ocean due to warming is not ubiquitous. In the Atlantic Ocean above 1 km depth, there is oxygen loss at subpolar latitudes, but there has been no oxygen loss or gain in the subtropics over the past six decades. Here we show that the amplification of the hydrological cycle, a response to climate change that results in a ‘salty-get-saltier, fresh-get-fresher’ sea surface salinity pattern, influences ocean ventilation and introduces a spatial pattern in the rate of climate change-driven oxygen loss in an Earth system model. A salinification enhances ventilation of (already salty) mode waters that outcrop in the subtropics and opposes warming-driven oxygen loss, while a freshening reduces ventilation of (already fresh) deep waters that outcrop at subpolar latitudes and accelerates oxygen loss. These results suggest that climate change introduces patterns of oxygenation through surface salinity changes, key to understanding observed and future regional changes.

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NOAA Atlas NESDIS 83 WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation NOAA National Centers for Environmental Information NOAA Atlas NESDIS 83

Abstract and Figures