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WORLD OCEAN ATLAS 2018 Product Documentation Ocean Climate Laboratory NCEI / NESDIS / NOAA NOAA National Centers for Environmental Information

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This document describes the World Ocean Atlas 2018 (WOA18) statistical and objectively analyzed field data files. This description includes the types of statistical fields available, the oceanographic variables analyzed, and at which standard depth levels, time spans, time periods and grid resolutions they were analyzed. This description also includes the naming convention for the files, as well as the structure and format for the files. For a description of the data used, and the procedures for calculating WOA statistical fields, see https://www.nodc.noaa.gov/OC5/woa18/pubwoa18.html
Content may be subject to copyright.
WORLD OCEAN ATLAS 2018
Product Documentation
Ocean Climate Laboratory
NCEI / NESDIS / NOAA
Silver Spring, MD
July 2019
2
For updates on the data, documentation, and additional
information about the WOA18 please refer to:
https://www.nodc.noaa.gov/OC5/woa18
Please cite this document as:
Garcia H.E., T.P. Boyer, O.K. Baranova, R.A. Locarnini, A.V. Mishonov, A. Grodsky, C.R.
Paver, K.W. Weathers, I.V. Smolyar, J.R. Reagan, D. Seidov, M.M. Zweng (2019). World
Ocean Atlas 2018: Product Documentation. A. Mishonov, Technical Editor.
This document is available on line at:
https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18documentation.pdf
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
SSMC 3, 4th floor
1315 East-West Highway
Silver Spring, MD 20910-3282
Telephone: (301) 713-3277
E-mail: NCEI.Info@noaa.gov
WEB: https://www.ncei.noaa.gov/
3
Table of Contents
To Sydney (Syd) Levitus ................................................................................................................ 4
Acknowledgments........................................................................................................................... 5
World Ocean Atlas 2018 (WOA18) Product Documentation ........................................................ 6
Available grid resolution...................................................................................................... 7
Available time spans and time periods ................................................................................ 7
Available fields .................................................................................................................... 8
Available oceanographic variables. ..................................................................................... 9
Data formats ....................................................................................................................... 11
File naming convention...................................................................................................... 12
Utilities ............................................................................................................................... 12
A. Installing gzip for the first time ...................................................................................... 12
B. Decompressing data from WOA ..................................................................................... 13
Appendix 1. Basins defined for objective analysis and the shallowest standard depth level for
which each basin is defined. ..................................................................................................... 14
Appendix 2. Sample from csv file format ................................................................................. 15
Appendix 3. Sample from netCDF file format ......................................................................... 17
List of Tables
Table 1. Time Spans for World Ocean Atlas 2018 ......................................................................... 7
Table 2. Available objectively analyzed and statistical fields ........................................................ 8
Table 3. Depths associated with each standard level number. The maximum depth of the WOA18
is 5500 m (Table 4). ...................................................................................................................... 10
Table 4. Depth ranges and standard depth levels numbers for annual, seasonal, and monthly
statistics of each available oceanographic variable. ..................................................................... 11
4
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
5
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.
6
World Ocean Atlas 2018 (WOA18) Product Documentation
Summary: This document describes the World Ocean Atlas 2018 (WOA18) statistical and
objectively analyzed field data files. This description includes the types of statistical fields
available, the oceanographic variables analyzed, and at which standard depth levels, time spans,
time periods and grid resolutions they were analyzed. This description also includes the naming
convention for the files, as well as the structure and format for the files. For a description of the
data used, and the procedures for calculating WOA statistical fields, see
https://www.nodc.noaa.gov/OC5/woa18/pubwoa18.html
The World Ocean Atlas 2018 (WOA18) release – July 2019 updates previous versions of
the World Ocean Atlas to include approximately 3 million new oceanographic casts added to the
World Ocean Database (WOD) since previous release as well as renewed and updated quality
control. The WOA18 temperature and salinity fields are being released as preliminary in order to
take advantage of community-wide quality assurance and comments. After release of the previous
version of the WOA in 2013, we received a number of communications pointing out suspect
features in the released fields. We investigated these features, provided explanations, and when
necessary made new quality flagging decisions, resulting in a version 2 of WOA13. We would
ask that if users of the atlas find any suspect features in WOA18, they please contact us
(OCLhelp@noaa.gov) with an explanation of the problem. We will investigate and make any
necessary quality decisions before preparing a final WOA18 version.
Grey-shaded cells in Table 4 indicate variables which have not yet been released. The
following publications are released concurrently with the final version of WOA18. These
publications are available at the WOA18 web: https://www.nodc.noaa.gov/OC5/woa18/
Locarnini, R.A., A.V. Mishonov, O.K. Baranova, T.P. Boyer, M.M. Zweng, H.E. Garcia, J.R.
Reagan, D. Seidov, K.W. Weathers, C. R.Paver, I.V. Smolyar (2019). World Ocean Atlas
2018, Volume 1: Temperature. A. Mishonov Technical Editor, NOAA Atlas NESDIS 81.
Zweng, M.M, J.R. Reagan, D. Seidov, T.P. Boyer, R.A. Locarnini, H.E. Garcia, A.V. Mishonov,
O.K. Baranova, K.W. Weathers, C.R. Paver, I.V. Smolyar (2019). World Ocean Atlas 2018,
Volume 2: Salinity. A. Mishonov Technical Editor, NOAA Atlas NESDIS 82.
Garcia H.E., K.W. Weathers, C.R. Paver, I.V. Smolyar, T.P. Boyer, R.A. Locarnini, M.M. Zweng,
A.V. Mishonov, O.K. Baranova, J.R. Reagan (2019a). World Ocean Atlas 2018, Volume 3:
Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation. A. Mishonov
Technical Editor, NOAA Atlas NESDIS 83.
Garcia H.E., K.W. Weathers, C.R. Paver, I.V. Smolyar, T.P. Boyer, R.A. Locarnini, M.M. Zweng,
A.V. Mishonov, O.K. Baranova, J.R. Reagan (2019b). World Ocean Atlas 2018, Volume 4:
Dissolved Inorganic Nutrients (phosphate, nitrate, silicate). A. Mishonov Technical Editor,
NOAA Atlas NESDIS 84.
7
Locarnini, R.A., M.M. Zweng, A.V. Mishonov, O.K. Baranova, J.R. Reagan, D. Seidov, T.P.
Boyer, H.E. Garcia, C.R. Paver, K.W. Weathers, I.V. Smolyar (2019b). World Ocean Atlas
2018, Volume 5: In situ Density. A. Mishonov Technical Editor, NOAA Atlas NESDIS 85.
Reagan, J.R., M.M. Zweng, R.A. Locarnini, D. Seidov, A.V. Mishonov, O.K. Baranova, T.P.
Boyer, H.E. Garcia, K.W. Weathers, C.R. Paver, I.V. Smolyar (2019). World Ocean Atlas
2018, Volume 6: Ocean Conductivity. A. Mishonov Technical Editor, NOAA Atlas NESDIS
86.
Boyer, T.P., O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky, R.A. Locarnini, A.V.
Mishonov, C.R. Paver, J.R. Reagan, D. Seidov, I.V. Smolyar, K.W. Weathers, M.M. Zweng
(2019). World Ocean Database 2018. A. Mishonov, Technical Editor, NOAA Atlas NESDIS
87.
Available grid resolution
The World Ocean Atlas 2018 has objectively analyzed climatological mean fields on both a
quarter- and on a one-degree longitude/latitude grids. Statistical fields used in quality control (but
not objectively analyzed climatological means) are available on a five-degree longitude/latitude
grid.
Available time spans and time periods
Time span refers to the years represented in the climatological mean and statistical fields. Time
period refers to the divisions of a calendar year. The time periods are annual, seasonal (by three-
month periods; Winter = January, February, and March; Spring, Summer, and Fall are the
sequentially following three-month periods), and monthly. Time spans are mostly decadal (10
year) spans, but also include ‘all’, denoting all data used regardless of year, and ‘decav’, an average
of all available (year specific) time spans. An objective analysis for a specific time period is
considered to be representative of that time period for the given time span. Table 1 lists all time
spans that are part of WOA18.
Table 1. Time Spans for World Ocean Atlas 2018
Time Span Abbreviation Comment
1955 – 1964
5564
1965 – 1974
6574
1975 – 1984
7584
1985 – 1994
8594
1995 – 2004
95A4
2005 – 2017
A5B7
1981 – 2010
decav
All available
years
all
8
Available fields
Table 2 presents the list of statistical fields and the grid resolutions at which the fields are
available. Quarter-degree fields represent the world as 1440x720 quarter-degree longitude /
latitude boxes. One-degree fields represent the world as 360x180 one-degree longitude / latitude
boxes. Five-degree fields divide the world into 72x36 five-degree longitude / latitude boxes. Five-
degree statistical fields are the fields used for standard deviation window checks to filter the data;
data that pass these statistical checks are then used to calculate the quarter-degree and one-degree
climatology fields.
Table 2. Available objectively analyzed and statistical fields
Field Name
Quarter-
degree field
calculated
One-degree
field
calculated
Five-degree
field
calculated
Field Type
Code (for
file names)
Objectively analyzed climatology an
Statistical mean mn
Number of observations dd
Seasonal or monthly climatology
minus annual climatology ma
Standard deviation from statistical
mean sd
Standard error of the statistical
mean se
Statistical mean minus objectively
analyzed climatology oa
Number of mean values within
radius of influence gp
Short description of the statistical fields in WOA18
Objectively analyzed climatologies are the resulting mean fields for an oceanographic
variable at standard depth levels for the World Ocean.
The statistical mean is the average of all depth interpolated data values that pass quality
control checks at each standard depth level for each variable in each quarter-degree, one-
degree, or five-degree square which contain at least one measurement for the given
oceanographic variable.
The number of observations of each variable in each quarter-degree, one-degree, or five-
degree square of the World Ocean at each standard depth level that pass quality control
checks.
The standard deviation about the statistical mean of each variable in each quarter-degree,
9
one-degree, or five-degree square at each standard depth level that pass quality control
checks.
The standard error of the mean of each variable in each quarter-degree, one-degree, or
five-degree square at each standard depth level that pass quality control checks.
The seasonal or monthly climatology minus the annual climatology at each quarter-
degree or one-degree square at each standard depth.
The statistical mean minus the climatological mean at each quarter-degree or one-degree
square at each standard depth. This value is used as an estimate of interpolation and
smoothing error.
The number of one-degree squares within the smallest radius of influence around each
quarter-degree or one-degree square that contain a statistical mean value.
In addition to the statistical fields found in
https://www.nodc.noaa.gov/OC5/WOA18/woadata18.html, there are two types of mask files
(ending in suffix .msk). These files contain information used to calculate the statistical fields.
The landsea_XX.msk contains the standard depth level number at which the bottom of the
ocean is first encountered at each quarter-degree or one-degree square for the entire world.
Land will have a value of 1, corresponding to the surface. Values of standard depth levels
are listed in Table 3.
The basin_XX.msk contains the basin code number defined for each grid square at each
standard depth from the surface to 5500m. Each basin is identified by a code number that
ranges from 1 to 58. The basin code number in a given quarter-degree and one-degree
square may change with increased depth level. Appendix 1 lists the geographic basin
names, the code number associated with each basin, and the standard depth level at which
the given basin is first encountered.
XX in the above mask names is either 01 (one-degree) or 04 (quarter-degree), depending on the
resolution used to generate the land-sea and basin masks. These mask files are found at
https://data.nodc.noaa.gov/woa/WOA18/MASKS/.
Available oceanographic variables.
The statistical fields were calculated for six oceanographic variables: temperature, salinity,
dissolved oxygen, nitrate, phosphate, and silicate. Due to the irregularity in data in spatial and
temporal distribution at various depths for different variables, not all variables were analyzed at
all depths for all averaging periods (annual, individual seasons and months). Table 4 lists the
depth limits for each variable for each averaging period.
Temperature and Salinity fields are available on one-degree and quarter-degree grids as
follow:
One-degree annual, seasonal, and monthly fields are available for 5564, 6574, 7584,
8594, 95A4, A5B7, and decav’ time spans;
Quarter-degree annual and seasonal fields are available for 5564, 6574, 7584, 8594,
95A4, A5B7, and decav’ time spans;
Quarter-degree monthly fields are ONLY available for A5B7 and decav’ time spans.
10
One-degree and quarter-degree grids Temperature and Salinity fields are NOT available for
the ‘all’ time span.
Oxygen, Nitrate, Phosphate, and Silicate fields are available ONLY for one-degree grid and
for the ‘all’ time span.
Five-degree grid statistics are available only for ‘all’ time span.
Table 3. Depths associated with each standard level number. The maximum depth of the
WOA18 is 5500 m (Table 4).
Depth
(m)
Level
Depth
(m)
Level
Depth
(m)
Level
Depth
(m)
Level
0
1
475
36
2300
70
5700
104
5
2
500
37
2400
71
5800
105
10
3
550
38
2500
72
5900
106
15
4
600
39
2600
73
6000
107
20
5
650
40
2700
74
6100
108
25
6
700
41
2800
75
6200
109
30
7
750
42
2900
76
6300
110
35
8
800
43
3000
77
6400
111
40
9
850
44
3100
78
6500
112
45
10
900
45
3200
79
6600
113
50
11
950
46
3300
80
6700
114
55
12
1000
47
3400
81
6800
115
60
13
1050
48
3500
82
6900
116
65
14
1100
49
3600
83
7000
117
70
15
1150
50
3700
84
7100
118
75
16
1200
51
3800
85
7200
119
80
17
1250
52
3900
86
7300
120
85
18
1300
53
4000
87
7400
121
90
19
1350
54
4100
88
7500
122
95
20
1400
55
4200
89
7600
123
100
21
1450
56
4300
90
7700
124
125
22
1500
57
4400
91
7800
125
150
23
1550
58
4500
92
7900
126
175
24
1600
59
4600
93
8000
127
200
25
1650
60
4700
94
8100
128
225
26
1700
61
4800
95
8200
129
250
27
1750
62
4900
96
8300
130
275
28
1800
63
5000
97
8400
131
300
29
1850
64
5100
98
8500
132
325
30
1900
65
5200
99
8600
133
350
31
1950
66
5300
100
8700
134
375
32
2000
67
5400
101
8800
135
400
33
2100
68
5500
102
8900
136
425
34
2200
69
5600
103
9000
137
450
35
11
Table 4. Depth ranges and standard depth levels numbers for annual, seasonal, and
monthly statistics of each available oceanographic variable.
Please note that the WOA18 will be released incrementally. Grey-shaded cells in Table 4
indicate variables, which have not yet been released.
One-letter codes are first letter of file names for given variable.
Oceanographic
Variable
(one-letter code)
Depths for
Annual
Climatology
Depths for
Seasonal
Climatology
Depths for
Monthly
Climatology
Temperature (t) 0-5500 meters
(102 levels) 0-5500 meters
(102 levels) 0-1500 meters
(57 levels)
Salinity (s) 0-5500 meters
(102 levels) 0-5500 meters
(102 levels) 0-1500 meters
(57 levels)
Oxygen (o) 0-5500 meters
(102 levels 0-1500 meters
(57 levels) 0-1500 meters
(57 levels)
Nitrate (n) 0-5500 meters
(102 levels) 0-800 meters
(43 levels) 0-800 meters
(43 levels)
Phosphate (p) 0-5500 meters
(102 levels) 0-800 meters
(43 levels) 0-800 meters
(43 levels)
Silicate (i) 0-5500 meters
(102 levels) 0-800 meters
(43 levels) 0-800 meters
(43 levels)
Data formats
WOA18 data files are available in four formats:
Climate and Forecast (CF) compliant Network Common Data Format (NetCDF) ,
Comma-separated value (csv) format,
ArcGIS-compatible shapefiles,
Compact grid format (a legacy WOA ASCII format)
Appendix 2 gives an example of the csv format and Appendix 3 gives an example of the structure
of the netCDF file. The legacy ASCII format files are provided for applications that have been set
up to read this format in previous WOA releases. Usage of this format is not encouraged, as it
does not explicitly give depth, possibly resulting in confusion when reading WOA18 files in
software set up for previous releases of World Ocean Atlas, or vice-versa.
For information regarding to the legacy WOA ASCII format, please see
https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18documentation.pdf. Each csv file contains
all depths for a single statistical field; please note that this differs from the csv files released for
WOA18.
12
File naming convention
All files, regardless of format, are follows the same naming convention:
woa18_[DECA]_[v][tp][ft][gr].[form_end]
Where:
[DECA] represents decade, the time span (years) represented by the objectively analyzed
means and other statistical fields as listed in Table 1:
[v] represents the oceanographic variable using one-letter code as listed in Table 4;
[tp] represents the averaging period, two digit code as follows:
00 – annual statistics, all data used;
01 to 12 – monthly statistics (starting with 01 – January, to 12 – December);
13 to 16 – seasonal statistics:
Season 13 – North Hemisphere winter (January - March);
Season 14 – North Hemisphere spring (April - June);
Season 15 – North Hemisphere summer (July - September);
Season 16 – North Hemisphere autumn (October - December);
[ft] represents field type, describing the calculated statistic represented in the file, as listed
in Table 2
[gr] represents the grid size, two digit code as follows:
04 – quarter-degree grid resolution
01 – one-degree grid resolution
5d – five-degree grid resolution
[form_end] format suffix (filename extension), dependent on format as follows:
csv comma-separated value format
nc netCDF format
dbf, shp, shx – shapefiles (when downloaded will be in a .tar file together)
dat compact grid data format (legacy WOA ASCII format)
Example: woa18_95A4_s02an01.nc is a file containing World Ocean Atlas 2018, February
objectively analyzed salinity on one-degree grid resolution for the years 1995-2004 in netCDF
format.
Utilities
Folder utils contains decompression freeware: gzip124.exe self-extracting DOS executable, and
gzip124.tara compressed file containing source code for UNIX users.
A. Installing gzip for the first time
DOS Users: The file gzip124.exe is a self-extracting DOS executable.
Copy gzip124.exe to your hard drive,
Run gzip124.exe and use the file gzip.exe to uncompress data as described in Section B.
13
UNIX Users:
Copy gzip124.tar to your UNIX system
Run the following command: tar -xvf gzip124.tar
This command will create a directory named gzip-1.2.4 that includes the gzip source code and
documentation about copyrights, compression methods and how to compile and install the gzip
code. Read through the README file and when ready to build the gzip executable, follow
instructions in the INSTALL file.
B. Decompressing data from WOA
To decompress the WOA files, it is recommended to first copy the data files to a hard disk. Use
gzip to decompress selected files or a directory and all subdirectories with one command. The
gzip utility has a limited help menu accessible with the -h option (e.g. gzip -h); additional
information may be found at www.gzip.org.
To decompress a single file:
gzip -nd <filename>
To decompress the contents of a directory and all of its subdirectories:
gzip -ndr <directoryname>
If an older version of gzip is used, the -n option is required in order to preserve the correct file
names.
14
Appendix 1. 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
1 Atlantic Ocean 1*
30
North American Basin
82
2 Pacific Ocean 1*
31
West European Basin
82
3 Indian Ocean 1* 32 Southeast Indian Basin 82
4 Mediterranean Sea 1* 33 Coral Sea 82
5 Baltic Sea 1 34 East Indian Basin 82
6 Black Sea 1 35 Central Indian Basin 82
7 Red Sea 1 36 Southwest Atlantic Basin 82
8 Persian Gulf 1 37 Southeast Atlantic Basin 82
9 Hudson Bay 1 38 Southeast Pacific Basin 82
10 Southern Ocean 1* 39 Guatemala Basin 82
11 Arctic Ocean 1 40 East Caroline Basin 87
12 Sea of Japan 1 41 Marianas Basin 87
13 Kara Sea 22 42 Philippine Sea 87
14 Sulu Sea 25 43 Arabian Sea 87
15 Baffin Bay 37 44 Chile Basin 87
16 East Mediterranean 41 45 Somali Basin 87
17 West Mediterranean 47 46 Mascarene Basin 87
18 Sea of Okhotsk 47 47 Crozet Basin 87
19 Banda Sea 55 48 Guinea Basin 87
20 Caribbean Sea 55
49
Brazil Basin
92
21 Andaman Basin 62
50
Argentine Basin
92
22 North Caribbean 67
51
Tasman Sea
87
23 Gulf of Mexico 67
52
Atlantic Indian Basin
92
24 Beaufort Sea 77 53 Caspian Sea 1
25 South China Sea 77 54 Sulu Sea II 37
26 Barents Sea 77 55 Venezuela Basin 37
27 Celebes Sea 62 56 Bay of Bengal 1*
28 Aleutian Basin 77 57 Java Sea 16
29 Fiji Basin 82 58 East Indian Atlantic Basin 97
1Basins marked with a “*” can interact with adjacent basins in the objective analysis.
15
Appendix 2. Sample from csv file format
File=woa18_5564_t00an01.csv (showing only the first 30 lines of the file)
#WOA18 one-degreeANNUAL temperature Climatological mean
#COMMA SEPARATED LATITUDE, LONGITUDE, AND VALUES AT DEPTHS
(M):0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,125,150,175,200,225,250,275,300,3
25,350,375,400,425,450,475,500,550,600,650,700,750,800,850,900,950,1000,1050,1100,1150,1200,125
0,1300,1350,1400,1450,1500,1550,1600,1650,1700,1750,1800,1850,1900,1950,2000,2100,2200,2300,2
400,2500,2600,2700,2800,2900,3000,3100,3200,3300,3400,3500,3600,3700,3800,3900,4000,4100,4200
,4300,4400,4500,4600,4700,4800,4900,5000,5100,5200,5300,5400,5500
-77.500,-178.500,-1.202,-1.247,-1.291,-1.303,-1.304,-1.308,-1.360,-1.405,-1.452,-1.504,-1.537,-1.569,-
1.599,-1.613,-1.630,-1.629,-1.638,-1.634,-1.642,-1.644,-1.646,-1.648,-1.673,-1.725,-1.735,-1.778,-1.842,-
1.851,-1.912,-1.991,-2.035,-2.056,-2.063,-2.098,-2.087,-2.082,-2.087,-2.005,-1.960
-77.500,-177.500,-1.215,-1.260,-1.315,-1.327,-1.322,-1.314,-1.349,-1.388,-1.433,-1.487,-1.521,-1.552,-
1.588,-1.607,-1.627,-1.630,-1.644,-1.644,-1.653,-1.655,-1.657,-1.660,-1.685,-1.742,-1.750,-1.797,-1.843,-
1.863,-1.915,-1.990,-2.034,-2.054,-2.062,-2.097,-2.086,-2.080,-2.086,-2.011,-1.969
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1.605,-1.622,-1.639,-1.641,-1.654,-1.654,-1.663,-1.666,-1.667,-1.675,-1.702,-1.755,-1.765,-1.817,-1.877,-
1.873,-1.913,-1.984,-2.028,-2.053,-2.058,-2.093,-2.083,-2.078,-2.084,-2.015,-1.978,-0.895
-77.500,-175.500,-1.292,-1.308,-1.362,-1.377,-1.372,-1.359,-1.397,-1.431,-1.489,-1.538,-1.575,-1.598,-
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1.886,-1.915,-1.978,-2.018,-2.041,-2.052,-2.084,-2.073,-2.067,-2.081,-2.009,-1.986
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1.636,-1.658,-1.675,-1.676,-1.687,-1.692,-1.698,-1.703,-1.711,-1.723,-1.737,-1.784,-1.792,-1.862,-1.894,-
1.895,-1.912,-1.970,-2.002,-2.021,-2.036,-2.067,-2.057,-2.059,-2.070,-2.006
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1.643,-1.654,-1.666,-1.669,-1.683,-1.698,-1.713,-1.730,-1.760,-1.780,-1.812,-1.862,-1.854,-1.920,-1.922,-
1.896,-1.910,-1.956,-1.967,-1.949,-1.961,-1.964,-1.969,-2.012,-2.035,-1.976,-2.013
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1.870,-1.880,-1.910,-1.916,-1.886,-1.883,-1.861,-1.880,-1.929,-1.978,-1.934,-2.005
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1.627,-1.647,-1.668,-1.670,-1.684,-1.700,-1.717,-1.733,-1.755,-1.796,-1.824,-1.857,-1.856,-1.924,-1.936,-
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1.844,-1.837,-1.846,-1.847,-1.821,-1.812
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16
1.612,-1.628,-1.647,-1.651,-1.665,-1.679,-1.692,-1.706,-1.728,-1.753,-1.804,-1.854,-1.833,-1.869,-1.892,-
1.826,-1.810,-1.742,-1.702,-1.670,-1.629,-1.656,-1.706,-1.782,-1.825
-77.500,-163.500,-1.494,-1.547,-1.623,-1.622,-1.634,-1.649,-1.670,-1.685,-1.666,-1.623,-1.610,-1.607,-
1.623,-1.639,-1.658,-1.663,-1.678,-1.693,-1.709,-1.727,-1.763,-1.795,-1.837,-1.856,-1.836,-1.884,-1.917,-
1.848,-1.802,-1.758,-1.731,-1.705,-1.676,-1.686,-1.697,-1.734,-1.744,-1.762,-1.891
-77.500,-162.500,-1.485,-1.553,-1.630,-1.628,-1.637,-1.647,-1.668,-1.682,-1.670,-1.617,-1.599,-1.596,-
1.610,-1.623,-1.567,-1.553,-1.537,-1.556,-1.624,-1.676,-1.726,-1.768,-1.805,-1.831,-1.819,-1.858,-1.851,-
1.798,-1.742,-1.683,-1.635,-1.628,-1.611,-1.623,-1.649,-1.687,-1.687,-1.735,-1.812,-
1.825,0.933,1.108,1.099
-77.500,-161.500,-1.512,-1.564,-1.636,-1.633,-1.640,-1.647,-1.669,-1.684,-1.702,-1.648,-1.617,-1.606,-
1.616,-1.626,-1.641,-1.640,-1.619,-1.605,-1.612,-1.632,-1.656,-1.724,-1.772,-1.812,-1.788,-1.811,-1.832,-
1.755,-1.711,-1.681,-1.640,-1.638,-1.627,-1.619,-1.622,-1.649,-1.659,-1.722,-1.849,-1.907
-77.500,-160.500,-1.491,-1.538,-1.617,-1.639,-1.647,-1.649,-1.672,-1.684,-1.701,-1.719,-1.664,-1.637,-
1.638,-1.635,-1.596,-1.585,-1.549,-1.554,-1.586,-1.641,-1.682,-1.732,-1.762,-1.794,-1.779,-1.798,-1.803,-
1.727,-1.674,-1.631,-1.577,-1.601,-1.592,-1.577,-1.574,-1.608,-1.618,-1.713,-1.778,-
1.878,0.889,1.086,1.092
-77.500,-159.500,-1.502,-1.550,-1.624,-1.645,-1.648,-1.649,-1.619,-1.608,-1.610,-1.603,-1.620,-1.606,-
1.608,-1.610,-1.621,-1.622,-1.632,-1.648,-1.655,-1.680,-1.688,-1.729,-1.750,-1.793,-1.786,-1.805,-1.808,-
1.699,-1.635,-1.576,-1.517
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1.585,-1.592,-1.569,-1.602,-1.649,-1.700,-1.777,-1.851,-1.896,-1.902,-1.908,-1.904,-1.897,-1.857,-1.915,-
1.942,-1.904,-1.841,-1.808
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1.645,-1.718,-1.752,-1.779,-1.804,-1.824,-1.856,-1.886,-1.914,-1.922,-1.942,-1.940,-1.935,-1.897,-1.952,-
1.948,-1.860,-1.772,-1.717,-1.679,-1.656,-1.597
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1.466,-1.508,-1.531,-1.530,-1.581,-1.645,-1.738,-1.834,-1.894,-1.915,-1.939,-1.935,-1.941,-1.895,-1.966,-
1.951,-1.882,-1.821,-1.772,-1.706,-1.637,-1.547,-1.518,-1.520,-1.532,-1.454
-77.500,-41.500,-1.671,-1.591,-1.695,-1.692,-1.713,-1.733,-1.752,-1.710,-1.680,-1.665,-1.632,-1.626,-
1.634,-1.647,-1.645,-1.642,-1.637,-1.677,-1.736,-1.815,-1.871,-1.900,-1.925,-1.901,-1.902,-1.839,-1.939,-
1.931,-1.861,-1.808,-1.762,-1.688,-1.620,-1.516,-1.498,-1.506,-1.553,-1.408,-1.744,-1.823
-77.500,-40.500,-1.581,-1.535,-1.657,-1.679,-1.712,-1.736,-1.756,-1.767,-1.774,-1.771,-1.748,-1.744,-
1.754,-1.767,-1.763,-1.759,-1.759,-1.790,-1.817,-1.841,-1.869,-1.880,-1.905,-1.903,-1.909,-1.767,-1.896,-
1.888,-1.826,-1.791,-1.743,-1.667,-1.593,-1.480,-1.452,-1.467,-1.485,-1.373,-1.702,-1.792,-1.879,-1.940,-
1.959,-2.009
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1.663,-1.680,-1.687,-1.645,-1.607,-1.687,-1.760,-1.822,-1.863,-1.881,-1.910,-1.907,-1.906,-1.741,-1.891,-
1.879,-1.835,-1.791,-1.733,-1.649,-1.572,-1.428,-1.402,-1.418,-1.432,-1.354,-1.671,-1.775,-1.833,-1.919,-
1.941,-1.996,-2.012,-2.017,-2.051
-77.500,-38.500,-1.652,-1.691,-1.734,-1.744,-1.750,-1.736,-1.691,-1.667,-1.655,-1.638,-1.635,-1.653,-
1.675,-1.673,-1.694,-1.739,-1.776,-1.835,-1.876,-1.887,-1.894,-1.910,-1.930,-1.924,-1.941,-1.764,-1.906,-
1.890,-1.865,-1.797,-1.735,-1.653,-1.567,-1.423,-1.392,-1.403,-1.415,-1.359,-1.663,-1.762,-1.811,-1.888,-
1.908,-1.985,-2.002,-2.006,-2.040,-1.973,-2.022,-1.200
-77.500,-37.500,-1.442,-1.475,-1.515,-1.533,-1.578,-1.585,-1.602,-1.616,-1.626,-1.622,-1.626,-1.644,-
1.664,-1.668,-1.687,-1.727,-1.761,-1.820,-1.858,-1.882,-1.898,-1.910,-1.933,-1.934,-1.954,-1.789,-1.899,-
1.885,-1.870,-1.794,-1.739,-1.660,-1.575,-1.451,-1.422,-1.423,-1.432,-1.379,-1.662,-1.757,-1.820,-1.895,-
1.928,-1.978,-1.993,-1.995,-2.029,-1.961,-2.005,-1.310
17
Appendix 3. Sample from netCDF file format
netcdf woa18_5564_t00_01 {
dimensions:
nbounds = 2 ;
lat = 180 ;
lon = 360 ;
depth = 102 ;
time = 1 ;
variables:
int crs ;
crs:grid_mapping_name = "latitude_longitude" ;
crs:epsg_code = "EPSG:4326" ;
crs:longitude_of_prime_meridian = 0.f ;
crs:semi_major_axis = 6378137.f ;
crs:inverse_flattening = 298.2572f ;
float lat(lat) ;
lat:standard_name = "latitude" ;
lat:long_name = "latitude" ;
lat:units = "degrees_north" ;
lat:axis = "Y" ;
lat:bounds = "lat_bnds" ;
float lat_bnds(lat, nbounds) ;
lat_bnds:comment = "latitude bounds" ;
float lon(lon) ;
lon:standard_name = "longitude" ;
lon:long_name = "longitude" ;
lon:units = "degrees_east" ;
lon:axis = "X" ;
lon:bounds = "lon_bnds" ;
float lon_bnds(lon, nbounds) ;
lon_bnds:comment = "longitude bounds" ;
float depth(depth) ;
depth:standard_name = "depth" ;
depth:bounds = "depth_bnds" ;
depth:positive = "down" ;
depth:units = "meters" ;
depth:axis = "Z" ;
float depth_bnds(depth, nbounds) ;
depth_bnds:comment = "depth bounds" ;
float time(time) ;
time:standard_name = "time" ;
time:long_name = "time" ;
time:units = "months since 1955-01-01 00:00:00" ;
time:axis = "T" ;
time:climatology = "climatology_bounds" ;
float climatology_bounds(time, nbounds) ;
climatology_bounds:comment = "This variable defines the bounds of the climatological time
period for each time" ;
float t_an(time, depth, lat, lon) ;
t_an:standard_name = "sea_water_temperature" ;
t_an:long_name = "Objectively analyzed mean fields for sea_water_temperature at standard
depth levels." ;
t_an:coordinates = "time lat lon depth" ;
18
t_an:cell_methods = "area: mean depth: mean time: mean within years time: mean over years"
;
t_an:grid_mapping = "crs" ;
t_an:units = "degrees_celsius" ;
t_an:_FillValue = 9.96921e+36f ;
float t_mn(time, depth, lat, lon) ;
t_mn:standard_name = "sea_water_temperature" ;
t_mn:long_name = "Average of all unflagged interpolated values at each standard depth level
for sea_water_temperature in each grid-square which contain at least one measurement." ;
t_mn:coordinates = "time lat lon depth" ;
t_mn:cell_methods = "area: mean depth: mean time: mean within years time: mean over years"
;
t_mn:grid_mapping = "crs" ;
t_mn:units = "degrees_celsius" ;
t_mn:_FillValue = 9.96921e+36f ;
int t_dd(time, depth, lat, lon) ;
t_dd:standard_name = "sea_water_temperature number_of_observations" ;
t_dd:long_name = "The number of observations of sea_water_temperature in each grid-square
at each standard depth level." ;
t_dd:coordinates = "time lat lon depth" ;
t_dd:cell_methods = "area: sum depth: point time: sum" ;
t_dd:grid_mapping = "crs" ;
t_dd:units = "1" ;
t_dd:_FillValue = -32767 ;
float t_sd(time, depth, lat, lon) ;
t_sd:long_name = "The standard deviation about the statistical mean of sea_water_temperature
in each grid-square at each standard depth level." ;
t_sd:coordinates = "time lat lon depth" ;
t_sd:cell_methods = "area: mean depth: mean time: standard_deviation" ;
t_sd:grid_mapping = "crs" ;
t_sd:units = "degrees_celsius" ;
t_sd:_FillValue = 9.96921e+36f ;
float t_se(time, depth, lat, lon) ;
t_se:standard_name = "sea_water_temperature standard_error" ;
t_se:long_name = "The standard error about the statistical mean of sea_water_temperature in
each grid-square at each standard depth level." ;
t_se:coordinates = "time lat lon depth" ;
t_se:cell_methods = "area: mean depth: mean time: mean" ;
t_se:grid_mapping = "crs" ;
t_se:units = "degrees_celsius" ;
t_se:_FillValue = 9.96921e+36f ;
float t_oa(time, depth, lat, lon) ;
t_oa:standard_name = "sea_water_temperature" ;
t_oa:long_name = "statistical mean value minus the objectively analyzed mean value for
sea_water_temperature." ;
t_oa:coordinates = "time lat lon depth" ;
t_oa:cell_methods = "area: mean depth: mean time: mean with years time: mean over years" ;
t_oa:grid_mapping = "crs" ;
t_oa:units = "degrees_celsius" ;
t_oa:_FillValue = 9.96921e+36f ;
int t_gp(time, depth, lat, lon) ;
t_gp:long_name = "The number of grid-squares within the smallest radius of influence around
each grid-square which contain a statistical mean for sea_water_temperature." ;
t_gp:coordinates = "time lat lon depth" ;
19
t_gp:cell_methods = "area: mean depth: mean time: mean within years time: mean over years"
;
t_gp:grid_mapping = "crs" ;
t_gp:units = "1" ;
t_gp:_FillValue = -32767 ;
// global attributes:
:Conventions = "CF-1.6, ACDD-1.3" ;
:title = "World Ocean Atlas 2018 : sea_water_temperature Annual 1955-1964 1.00 degree" ;
:summary = "PRERELEASE Climatological mean temperature for the global ocean from in situ
profile data" ;
:references = "Locarnini, R. A., A. V. Mishonov, O. K. Baranova, T. P. Boyer, M. M. Zweng, H.
E. Garcia, J. R. Reagan, D. Seidov, K. W. Weathers, C. R. Paver, I. V. Smolyar, 2018: World Ocean Atlas
2018, Volume 1: Temperature. A. V. Mishonov, Technical Ed., NOAA Atlas NESDIS ##" ;
:institution = "National Centers for Environmental Information (NCEI)" ;
:comment = "global climatology as part of the World Ocean Atlas project" ;
:id = "woa18_5564_t00_01.nc" ;
:naming_authority = "gov.noaa.ncei" ;
:sea_name = "World-Wide Distribution" ;
:time_coverage_start = "1955-01-01" ;
:time_coverage_end = "1964-12-31" ;
:time_coverage_duration = "P10Y" ;
:time_coverage_resolution = "P01Y" ;
:geospatial_lat_min = -90.f ;
:geospatial_lat_max = 90.f ;
:geospatial_lon_min = -180.f ;
:geospatial_lon_max = 180.f ;
:geospatial_vertical_min = 0.f ;
:geospatial_vertical_max = 5500.f ;
:geospatial_lat_units = "degrees_north" ;
:geospatial_lat_resolution = "1.00 degrees" ;
:geospatial_lon_units = "degrees_east" ;
:geospatial_lon_resolution = "1.00 degrees" ;
:geospatial_vertical_units = "m" ;
:geospatial_vertical_resolution = "SPECIAL" ;
:geospatial_vertical_positive = "down" ;
:creator_name = "Ocean Climate Laboratory" ;
:creator_email = "NCEI.info@noaa.gov" ;
:creator_url = "http://www.ncei.noaa.gov" ;
:creator_type = "group" ;
:creator_institution = "National Centers for Environmental Information" ;
:project = "World Ocean Atlas Project" ;
:processing_level = "processed" ;
:keywords = "Oceans< Ocean Temperature > Water Temperature" ;
:keywords_vocabulary = "ISO 19115" ;
:standard_name_vocabulary = "CF Standard Name Table v49" ;
:contributor_name = "Ocean Climate Laboratory" ;
:contributor_role = "Calculation of climatologies" ;
:cdm_data_type = "Grid" ;
:publisher_name = "National Centers for Environmental Information (NCEI)" ;
:publisher_institution = "National Centers for Environmental Information" ;
:publisher_type = "institution" ;
:publisher_url = "http://www.ncei.noaa.gov/" ;
:publisher_email = "NCEI.info@noaa.gov" ;
20
:nodc_template_version = "NODC_NetCDF_Grid_Template_v2.0" ;
:license = "These data are openly available to the public. Please acknowledge the use of these
data with the text given in the acknowledgment attribute." ;
:metadata_link = "http://www.nodc.noaa.gov/OC5/WOA18/pr_woa18.html" ;
:date_created = "2018-10-06 " ;
:date_modified = "2018-10-06 " ;
}
... The ITCZ characterized by the maximum of precipitation using satellite precipitation data [the 3B42 version of the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) distributed by the National Aeronautics and Space Administration (NASA, https://disc.gsfc.nasa.gov/)] is located approximately 9°N in the studied area. (Good et al., 2020); and the World Ocean Atlas climatology (WOA18) based on in-situ data (Garcia et al., 2019). ...
Article
Full-text available
Mesoscale dynamics is essential to understanding the physical and biological processes of the coastal ocean regions due to its ability to modulate water properties. However, on the shelf, interactions between eddies, coastal currents, and topography involve complex processes whose observation, understanding, and accurate simulation still pose a major challenge. The purpose of our work is to quantify the mesoscale eddies in the northern Gulf of Guinea, off West Africa (10°W–10°E, 2°N–7°N), and their dynamical interaction with the near-surface ocean particularly in the coastal upwelling that occurs in summer between 2°W and 2°E. We used a regional NEMO model simulation at 1/36° resolution over the 2007–2017 period with daily outputs. A total of 38 cyclonic and 35 anticyclonic eddy trajectories were detected over the 2007–2017 period in July–August–September (JAS), with a mean radius along their trajectories of 95 km and 125 km, respectively. The mean lifetime for cyclones and anticyclones is approximately 1 month with an associated sea-level amplitude between 1 and 2 cm. We then focused on the JAS upwelling period of the year 2016 and found a 73 km radius cyclonic eddy east of Cape Three Points (Ghana) with a lifetime of 1 month which interacted with the coastal upwelling. Indeed, the quasi-stationary eddy dwelled within the coastal upwelling region from mid-July to mid-August 2016. A Lagrangian study shows that the eddy waters come from the coastal upwelling, then mix with warmer offshore waters, and later are transported eastward by the Guinea Current. Using a heat budget analysis, we show that this eddy–coastal upwelling interaction has an impact on sea surface temperature (SST) with a double effect: i) the eddy expands offshore the cold and salty waters (23°C and 35.6) of the coastal upwelling from 14 to 26 July; and ii) from 27 July until its dissipation, the eddy weakens this upwelling by an easterly inflow of warm offshore waters. This study highlights how the eddy–upwelling interaction can modulate the coastal upwelling in the northern Gulf of Guinea.
... To build the training water mass classes for the knn method, we assemble the in-situ observations from the World Ocean Database 2018 'WOD18' [13] and the Global Data Assembly Centers 'GDACs' [14] within the area of interest (Fig. 7). ...
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In the realm of oceanography, understanding the complex dynamics of Earth’s oceans is crucial. One key aspect of this understanding is the classification of water masses based on their physical properties, such as temperature and salinity. Clustering analysis has emerged as a powerful method for automated water masses classification using the temperature-salinity (T-S) diagram. However, it faces limitations in regions with complex mixing processes. To address this, a novel approach using the K Nearest Neighbors (KNN) algorithm based on potential density and potential spicity (σ-π) diagrams was proposed. In this context, we introduce the classwms tool, a userfriendly MATLAB Graphical User Interface (GUI) that combines clustering analysis and KNN classification for efficient water mass classification.
... To build the training water mass classes for the knn method, we assemble the in-situ observations from the World Ocean Database 2018 'WOD18' [13] and the Global Data Assembly Centers 'GDACs' [14] within the area of interest (Fig. 7). ...
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Full-text available
In the realm of oceanography, understanding the complex dynamics of Earth’s oceans is crucial. One key aspect of this understanding is the classification of water masses based on their physical properties, such as temperature and salinity. Clustering analysis has emerged as a powerful method for automated water masses classification using the temperature-salinity (T-S) diagram. However, it faces limitations in regions with complex mixing processes. To address this, a novel approach using the K Nearest Neighbors (KNN) algorithm based on potential density and potential spicity (σ-π) diagrams was proposed. In this context, we introduce the classwms tool, a userfriendly MATLAB Graphical User Interface (GUI) that combines clustering analysis and KNN classification for efficient water mass classification.
... In this study we will primarily use CRU as reference for atmospheric fields, namely, precipitation and near surface temperature (T2), while ERA-I reanalysis is employed to understand possible uncertainties in these reference data. For oceanic fields, the World Ocean Atlas (WOA) observational dataset (Boyer et al., 2019) is used as reference to provide sea surface temperature (SST) and sea surface salinity (SSS) fields. ...
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We have developed a physics ensemble of Weather Research and Forecasting (WRF) model simulations for the Middle East, Mediterranean and North Africa (MEMNA) regions. These simulations use different configurations for the cumulus, microphysics, surface layer, planetary boundary layer, and land surface schemes and are forced by the Community Earth System Model (CESM) General Circulation Model for the historical period 1979–1993. We have also created a complementary ensemble in which the WRF model is fully-coupled to the Regional Ocean Modelling System (ROMS) that simulates the dynamics of the entire Mediterranean Sea. Analysis of our ensembles reveals that the simulated precipitation and near surface temperature (T2) fields in WRF are largely influenced by the cumulus and the land surface schemes during the summer and winter seasons, respectively. The coupling of Weather Research and Forecasting to Regional Ocean Modelling System yields Mediterranean sea surface temperatures that are directly correlated with T2 and have higher spatial resolution than the global model. Meanwhile no significant difference is found between the atmospheric fields from the coupled and uncoupled runs because the Community Earth System Model sea surface temperatures over the Mediterranean, that are used for surface forcing in the uncoupled runs, are already in close agreement with both Regional Ocean Modelling System and observations. We conclude that our high-resolution coupled atmosphere-ocean modelling system is capable of producing climate data of good quality, and we identify those combinations of physics schemes that result in an acceptable level of bias that facilitates their use in future studies.
... They have a spatial resolution of 0.25° × 0.25°, and are estimated by Optimal Interpolation, merging the measurement from multiple altimeter missions available (e.g., Jason-1, Jason-2, Jason-3, Sentinel-3A, and HY-2). Monthly data of MLD and potential temperature are from the World Ocean Atlas (WOA) and have a spatial resolution of 0.25° × 0.25° (Boyer et al., 2019). Eastward current speed (U) and northward current speed (V) of the ocean are provided by the Daily Global Reanalysis Multi-Ensemble Product GREP reanalysis, which has 75 levels vertically (from 0 to 5,500 m) and a horizontal resolution of 0.25° × 0.25°. ...
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Plain Language Summary While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we proposed to achieve this goal by building a model using an efficient and robust machine learning‐based method. The constructed model uses 12 predictors associated with TC characteristics (e.g., intensity, and translation speed) and pre‐storm ocean states (e.g., mixed layer depth). The model performs well in producing the TC‐induced spatial structure and temporal evolution of the cold wake and can capture most of the variance in the observed SST response. We quantified the relative importance of the 12 predictors, and found that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate in deciding the magnitude of the SST response. The results and proposed method have important implications for predicting the response of ocean primary production to the TC wind pump effects.
... Finally, phytoplankton uptake of nutrient increase TA. However, ocean climatology databases (World Ocean Atlas 2018 and Global Ocean Data Analysis Project version 2) showed depletions of NO 3 − in the surface layer of the study area (Garcia et al., 2019;Olsen et al., 2020). ...
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The role of sea ice melting on the air-sea CO2 flux was investigated at two ice camps in the East Siberian Sea of the Arctic Ocean. On average, sea ice samples from the two ice camps had a total alkalinity (TA) of ∼108 and ∼31 μmol kg–1 and a corresponding salinity of 1.39 and 0.36, respectively. A portion (18–23% as an average) of these sea ice TA values was estimated to exist in the sea ice with zero salinity, which indicates the excess TA was likely attributed to chemical (CaCO3 formation and dissolution) and biological processes in the sea ice. The dilution by sea ice melting could increase the oceanic CO2 uptake to 11–12 mmol m–2 d–1 over the next 21 days if the mixed layer depth and sea ice thickness were assumed to be 18.5 and 1.5 m, respectively. This role can be further enhanced by adding TA (including excess TA) from sea ice melting, but a simultaneous release of dissolved inorganic carbon (DIC) counteracts the effect of TA supply. In our study region, the additional impact of sea ice melting with close to unity TA:DIC ratio on air-sea CO2 exchange was not significant.
... The domain selected for this analysis comprised of the Arabian Gulf and Sea of Oman which is divided into three sub-regions: Arabian Gulf, Hormuz (transition area), and Sea of Oman to study the surface and vertical distribution of nutrients, see Fig 1. Records of monthly macronutrients, nitrate (NO 3 ), phosphate (PO 4 ), and micronutrient silicate (SiO 4 ), have been obtained from the global World Ocean Atlas (WOA) 2018: https://www.ncei.noaa.gov/access/ world-ocean-atlas-2018) [30]. The WOA data are extensively used as initial and boundary conditions as well as for model validation in many biogeochemical modelling studies [31][32][33][34][35][36][37][38][39][40] assuring its reliability and accuracy in many regions. ...
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This study demonstrates the vertical and horizontal distribution of nutrients and the seasonal response of nutrients to upwelling in the Arabian Gulf and the Sea of Oman. Thus, monthly data on nitrate, phosphate, and silicate are obtained from the World Ocean Atlas 2018 (WOA), as well as estimates of coastal and curl driven upwelling in both regions. The results of the study indicate that the Sea of Oman's surface and deep waters contained higher concentrations of nutrients than the Arabian Gulf by 80%. In addition, both regions have exhibited a general increase in the vertical distribution of nutrients as the depth increases. Among the aforementioned nutrients, nitrate is found to be a more limiting nutrient for phytoplankton growth than phosphate as the nitrate-to-phosphate ratios (N:P) in surface waters are lower (� 4.6:1) than the Redfield ratio (16:1). As for the upwelling, curl-driven upwelling accounts for more than half of the total upwelling in both regions, and both play an important role in nutrient transport. Thus, nutrients are upwelled from the subsurface to the mixed layer at a rate of 50% in the Oman Sea from 140 m to 20 m during the summer and to 40 m during the winter. Similarly, the Arabian Gulf shows 50% transport for nitrates, but 32% for phosphates, from 20 m to 5-10 m. However, due to the abundance of diatoms at the surface of the Ara-bian Gulf, the surface silicate content is 30% higher than that of the deeper waters.
... The domain selected for this analysis comprised of the Arabian Gulf and Sea of Oman which is divided into three sub-regions: Arabian Gulf, Hormuz (transition area), and Sea of Oman to study the surface and vertical distribution of nutrients, see Fig 1. Records of monthly macronutrients, nitrate (NO 3 ), phosphate (PO 4 ), and micronutrient silicate (SiO 4 ), have been obtained from the global World Ocean Atlas (WOA) 2018: https://www.ncei.noaa.gov/access/ world-ocean-atlas-2018) [30]. The WOA data are extensively used as initial and boundary conditions as well as for model validation in many biogeochemical modelling studies [31][32][33][34][35][36][37][38][39][40] assuring its reliability and accuracy in many regions. ...
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This paper presents the results of measurements of δ18O and δ2H stable isotope values in the waters of the Barents Sea, which were carried out in March–April 2021. Based on the data presented, the ratio of base water content – atlantic (fa), river (fr) and melt water (fi), as well as the volume of water withdrawn for ice formation in the studied water area was assessed. The comparative estimation of base water content, calculated on values “δ18O–salinity” and “δ2H–salinity” is presented and obtained values do not exceed limits of mean value ±sd. However, average values of Atlantic and ice water content calculated using δ2H parameter are higher than those using δ18O, and river water content is lower. The use of δ18O and δ2H parameters in oceanographic studies in the calculation of balance characteristics is preferable to temperature and salinity because of their greater conservativity.
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Ballast water is recognized as a leading pathway for the introduction of aquatic non‐indigenous species which have caused substantial ecological damage globally. Following international regulations, most international ships will install a ballast water management system (BWMS) by 2024 to limit the concentration of aquatic organisms in ballast water discharges; however, these new technologies may not operate as expected at global ports having variable water quality or may periodically malfunction. Using simulations informed by empirical data, we investigated the risk of non‐indigenous species establishment associated with BWMS inoperability and evaluated potential mitigation strategies. Scenarios considered included bypassed or inoperable BWMS achieving no reduction in organisms, and partially functioning BWMS with discharged organism concentrations exceeding permissible limits. These scenarios were contrasted to outcomes with fully functioning BWMS and to voyages where ballast water exchange (BWE) was used to mitigate risk. Partially functioning BWMSs were nonetheless beneficial, reducing organism concentrations in ballast and thus establishment risk. When a BWMS is bypassed or partially functioning, BWE is a useful emergency mitigation measure, reducing establishment risks more than partial BMWS. However, the greatest risk reduction was achieved when partial BWMS and BWE were combined. Voyage‐specific characteristics such as concentration of organisms at uptake and destination port salinity can affect the optimal management strategy for voyages when the BWMS does not achieve compliant discharges. Synthesis and applications. The risk of aquatic invasions and their associated ecological damages can be substantially reduced by using a ballast water management system (BWMS) and/or ballast water exchange (BWE). When a BWMS is inoperable, appropriate mitigation measures should be decided on a trip‐by‐trip basis considering voyage route and reason for BWMS inoperability (when known). BWE is a useful strategy for reducing invasion risk, except when uptake concentrations are very low. Combining BWE and partial BWMS always reduced risk compared with BWE alone, but did not greatly reduce risk when uptake concentrations were high.
  • H E Boyer
  • C R Garcia
  • K W Paver
  • I V Weathers
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Boyer, H.E. Garcia, C.R. Paver, K.W. Weathers, I.V. Smolyar (2019b). World Ocean Atlas 2018, Volume 5: In situ Density. A. Mishonov Technical Editor, NOAA Atlas NESDIS 85.
  • J R Reagan
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Reagan, J.R., M.M. Zweng, R.A. Locarnini, D. Seidov, A.V. Mishonov, O.K. Baranova, T.P. Boyer, H.E. Garcia, K.W. Weathers, C.R. Paver, I.V. Smolyar (2019). World Ocean Atlas 2018, Volume 6: Ocean Conductivity. A. Mishonov Technical Editor, NOAA Atlas NESDIS 86.
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  • A V Mishonov
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  • J R Reagan
  • D Seidov
  • I V Smolyar
  • K W Weathers
  • M M Zweng
Boyer, T.P., O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky, R.A. Locarnini, A.V. Mishonov, C.R. Paver, J.R. Reagan, D. Seidov, I.V. Smolyar, K.W. Weathers, M.M. Zweng (2019). World Ocean Database 2018. A. Mishonov, Technical Editor, NOAA Atlas NESDIS 87.