Progress in Oceanography 216 (2023) 103055

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Progress in Oceanography
journal homepage: www.elsevier.com/locate/pocean

A survey of coastal conditions around the continental US using ahigh-resolution ocean reanalysis
Michael A. Alexander a, *, James D. Scott a, b, Michael G. Jacox a, c, d, Clara Deser e,
Dillon J. Amaya a, Antonietta Capotondi a, b, Adam S. Phillips ea

NOAA Physical Sciences Laboratory, Boulder, CO, USA
CIRES, University of Colorado, Boulder, CO USANOAA Southwest Fisheries Science Center, Monterey, CA, USAd
University of California Santa Cruz, Santa Cruz, CA, USAe
National Center for Atmospheric Research, Boulder, CO, USAbc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Coastal oceanography
US continental shelves
Ocean reanalysis
Temperature salinity
Mixed layer depth

Obtaining a long-term perspective on the evolution of the ocean is hindered by a lack of sub-surface observations.
Ocean reanalyses, which merge fields from an ocean model with observations using complex data assimilation
techniques, provide a dynamically consistent estimate of the ocean in time and space. Recently developed ocean
reanalyses, several with resolutions finer than 10 km, provide a three-dimensional view of the ocean, including
on the continental shelf. Here, we use monthly fields from the 1/12◦ Global Ocean Reanalysis and Simulations(GLORYS) over the years 1993–2019 to provide a broad survey of temperature, salinity, and mixed layer depth
over the shelf (depth < 400 m) around the continental United States. The analyses reveal noteworthy aspects of
US coastal oceanography, including: i) how bathymetry shapes the seasonal cycle of bottom water temperature(BWT) and the role of the mixed layer in linking the BWT and sea surface temperature (SST) anomalies; ii) the
persistence, trends and distributions of SST and BWT anomalies, which strongly influence extremes such as
marine heatwaves; iii) the influence of the outflow from the Mississippi and Columbia rivers on the adjacentocean; and iv) how the mean and anomalous vertical structure of temperature and salinity vary between thenortheast, southeast, Gulf of Mexico and California Current Large Marine Ecosystems. In addition to doc­
umenting coastal ocean conditions, the broader goal of the survey is to encourage more detailed studies using
high resolution ocean reanalyses such as GLORYS.

1. Introduction

contributed $117 billion to gross domestic product, and supported 1.8
million jobs in the U.S. in 2019 (National Marine Fisheries Service,2022), where the majority of the catch occurred on the shelf.
Temperature, salinity and mixed layer depth are key components ofocean physics that also strongly regulate the biogeochemistry andecology of the coastal ocean. Temperature influences physiologicalprocesses in marine organisms (Fry, 1971; Rivkin and Legendre, 2001;
Deutsch et al., 2015). Surface temperatures modulate air-sea in­teractions and are often an indicator and/or driver of marine ecosystem
fluctuations (Mueter et al., 2009; Drinkwater et al., 2010; Ottersen et al.,2010), including fish distributions (e.g., Nye et al., 2009; Block et al.,2011; Pinsky et al., 2013), fish recruitment (e.g., Planque and Fredou,
1999; Hunt et al., 2011; Kristiansen et al., 2011) and biodiversity(Edwards and Richardson, 2004; Tittensor et al., 2010). Bottom water

The coastal ocean, ranging from the shore to the outer edge of thecontinental shelf, is influenced by the depth and shape of the seafloor
and by a wide array of physical processes. For example, ocean conditions
around the continental United States are strongly influenced by: theproximity of the Gulf Stream and Labrador current, combined with anarray of bays, banks and basins along the east coast, the wide West
Florida Shelf (WFS); outflow from the Mississippi River in the Gulf of
Mexico (GOMEX) and wind-driven upwelling over the steep and narrowshelf on the west coast. In addition, the continental margins provide
critical habitat for marine ecosystems and for economic activity
including tourism, aquaculture and fishing. Commercial and recrea­
tional saltwater fishing generated over $255 billion in sales impacts,* Corresponding author at: NOAA/Physical Sciences Laboratory, 325 Broadway, Boulder, CO 80305, USA.
E-mail address: michael.alexander@noaa.gov (M.A. Alexander).
https://doi.org/10.1016/j.pocean.2023.103055
Received 29 December 2022; Received in revised form 22 May 2023; Accepted 26 May 2023
Available online 2 June 2023
0079-6611/Published by Elsevier Ltd.

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temperature is critical to benthic organisms, including bivalves (Barber
and Blake, 2006) and demersal fish species (Harvey, 2009; Younes et al.,2020). Salinity, including shelf bottom waters, can also influence marine
species (Hedger et al., 2004; Miller et al., 2016). Salinity is also an
important characteristic of water masses (e.g., Tomczak, 1999; Liu andTanhua, 2021), it influences the uptake of CO2 by the ocean, and has
been used to estimate alkalinity and other biogeochemical variables (Caiet al., 2010; Salisbury and Jönsson, 2018; Carter et al., 2021). The dis­tribution of both temperature and salinity controls the density profileand thus static stability and vertical mixing. The mixed layer depth(MLD) also varies due to the surface buoyancy and wind forcing and by
dynamic ocean processes including upwelling, eddies and fronts (FoxKemper et al., 2022). The MLD affects the availability of nutrients for
phytoplankton and marine plants and the time phytoplankton remain in
the euphotic zone in light limited regions. Long term changes in tem­
perature, salinity and MLD (Jang et al., 2011; Alexander et al., 2018;
Cheng et al., 2020; Fox-Kemper et al., 2021) as well as sea level rise,ocean acidification, and deoxygenation due to increasing greenhousegasses are already impacting coastal marine ecosystems and thesechanges are likely to become more pronounced in the future (Scavia
et al., 2002; He and Silliman, 2019).
Long-term measurements provided by buoys, surface stations, trawl
surveys and sampling programs such as CalCOFI (Bograd et al., 2003) off
Southern California and the Oleander survey line (Rossby and Gottlieb,
1998) between New Jersey and Bermuda have provided information
about regional coastal conditions, including temperature, salinity and
MLD. Measurements of surface conditions from satellites and the adventof profiling floats and unmanned vehicles have greatly enhanced ourability to observe the ocean (e.g., Legaard and Thomas, 2006; Rudnick,
2016; Roemmich et al., 2019; Erichsen and Middelboe, 2022). Despite
recent advances in monitoring the ocean, in situ observations are still
very limited in space and time, while satellites only measure the ocean
surface and have difficulty measuring some variables in near-shore re­gions. In contrast to sea surface temperature (SST), measurements ofbottom water temperature (BWT) are very limited, as there are few longtime series of near bottom conditions on the shelf, and measurementsfrom trawl surveys are scattered in space and infrequent in time. In
addition, measurements of salinity throughout the water column aremuch more limited than those for temperature. Estimating MLD depends
on collocated vertical temperature and salinity profiles, which can beobtained from ARGO floats since around 2004 but are still relatively
sparse, especially in coastal areas.
Atmospheric reanalyses were first developed more than 25 years ago(Kalnay et al., 1996) to provide a physically-consistent and continuousrecord of conditions throughout the atmospheric column. They arebased on prediction systems that assimilate observations and merge
them with a first guess field provided by the model to initialize a fore­cast. In a reanalysis, this procedure is repeated at set intervals togenerate a continuous record in time and space, using the same modelthroughout the process. Using a similar method, ocean reanalyses weredeveloped beginning in the late 1990s (Carton et al., 2000). Ocean
reanalyses are driven by surface atmospheric conditions, often provided
by an atmospheric reanalysis, and assimilate in situ observations andmeasurements from satellites. Until very recently, ocean reanalyses had
fairly coarse horizontal resolution (~1◦ ; ~100 km) adequate forresolving large-scale phenomena, such as the El Niño/Southern Oscil­lation, but not mesoscale processes. Improvements in model physics,
data assimilation techniques and the ocean observing system have allcontributed to advance ocean reanalyses (e.g., see Moore et al., 2019,
Storto et al., 2019). With horizontal resolutions of 10 km or finer andmore than 40 vertical levels, ocean reanalyses are now capable ofresolving coastal processes.
Ocean reanalyses can be regional or global. An example of the former
is the reanalysis of the California Current System (Neveu et al., 2016),
which has provided key input in several applications (Becker et al.,2016; Tolimieri et al., 2018; Haltuch et al., 2020), although some biases

have been noted (Schroeder et al., 2014; Amaya et al., 2023a). Recently,
fine resolution global reanalyses including the Global Ocean Reanalysis
and Simulations (GLORYS, V12; Lellouche et al., 2021), Blue Link
Reanalysis (BRAN, Chamberlain et al., 2021) and the Hybrid Coordinate
Ocean Model (HYCOM, https://www.hycom.org/reanalysis) have beendeveloped. However, global reanalyses were not specifically developed
to represent regional coastal features, raising the question: how well dothey depict actual conditions? De Souza et al. (2021) examined ocean
conditions in the vicinity of New Zealand and found that the GLORYS
reanalysis performed well, with more realistic variability and relatively
small biases in the water column structure compared to other reanalyses.
However, all simulations had some biases and coastal currents were notwell represented. Russo et al. (2022) found that GLORYS, BRAN andHYCOM simulated southern Africa’s major oceanographic features
reasonably well; GLORYS was deemed to be the most accurate reanalysis
for many quantities but overestimated the MLD.
Recent studies have also been undertaken using reanalyses in US
coastal regions. Analyses of conditions on the northeast US continental
shelf also indicated that monthly SST anomalies from GLORYS werehighly correlated (r > 0.96) with observations (Chen et al., 2021), andthat MLD was represented reasonably well in terms of its seasonal cycle
as well as its interannual variability (r = 0.5–0.8) (Cai et al., 2021).
Castillo-Trujillo et al. (2023) evaluated eight ocean reanalyses on thenortheast US shelf by comparing them to several different data sources
for temperature, salinity and sea surface height. They found that higher
resolution reanalyses (1/10˚or finer) performed better than coarser ones(¼˚or coarser), and GLORYS performed well for most of the metrics
evaluated. (Amaya et al., 2023a&b) found that GLORYS represented
nearshore ocean parameters such as coastal sea level and bottom tem­
perature along the US west coast continental shelf reasonably well. It
also accurately depicted coastally trapped waves that propagate pole­ward along the west coast of North America, as verified against tidegauge data (Amaya et al., 2022). However, GLORYS slightly over­estimated SST variability, and in GLORYS and the other reanalyses
examined, sea surface salinity was less accurate than temperature,
particularly near the outflow of the Columbia River (Amaya et al.,2023a).
Here we take the opportunity afforded by a powerful new resource –a global high-resolution ocean reanalysis – to do a comprehensive
baseline assessment of physical variability over the continental US shelf.
We chose to use the GLORYS reanalysis because it has the highest res­olution of current global reanalyses and its overall high-level of skill
based on multiple assessments. We examine temperature, salinity and
MLD over the shelf (<400 m bottom depth) around the Continental
United States (CONUS), including the mean seasonal cycle, as well as thevariability, as characterized by the persistence, standard deviation,
skewness, kurtosis and histograms of monthly anomalies. Skewness andkurtosis have been shown to be important components of SST variability
over portions of the global oceans (Sura and Sardeshmukh, 2008) andskewed or heavy tailed distributions can complicate evaluating the ef­
fects of global warming on climate extremes (Sardeshmukh et al., 2015).
We examine aspects of the coastal oceanography, including how ba­
thymetry and mixed layer depth affect BWTs and their relationship to
SSTs; the influence of the Mississippi and Columbia river discharge on
the Gulf of Mexico and the Pacific coastal waters, respectively, and thevertical structure of temperature and salinity around the continental US.
2. Data and methods
2.1. GLORYS reanalysis
Here we analyze monthly fields from the GLORYS 1/12˚(~9 km
horizontal resolution) global Mercator Ocean and sea ice reanalysis(Lellouche et al., 2021) from the Copernicus Marine Environmental
Monitoring Service (CMEMS). It has 50 vertical levels, with 22 levels in
the upper 100 m. The near-surface layers have a vertical resolution of 1
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Progress in Oceanography 216 (2023) 103055

m with the layer thickness increasing with depth, reaching 450 m at
5,000 m. The reanalysis uses the Nucleus for European Modeling of the
Ocean (NEMO) platform (Madec et al., 2008), forced at the surface by
the European Center for Medium-Range Weather Forecasts (ECMWF)
ERA-Interim atmospheric reanalysis (Dee et al., 2011). The model as­
similates along-track satellite altimetry for the sea surface height, sat­
ellite sea surface temperature (SST), sea ice concentrations, and in situ
profiles of temperature and salinity, including ARGO floats, from the
Coriolis Ocean database ReAnalysis (CORA) dataset (Szekely et al.,2019). Ocean observations are assimilated by means of a reduced-order
Kalman filter. A bias correction is applied to the surface radiation andprecipitation, while a 3D-VAR scheme is also applied to correct for
slowly-evolving large-scale biases in temperature and salinity. The
reanalysis extends from 1993 to 2019.
We compute MLD values based on the depth at which the density
exceeds that at the surface by 0.125 kg m− 3, using daily temperature andsalinity profiles and then averaging the daily MLD to obtain monthly
values. This criterion was used by Monterey and Levitus (1997), Alex­
ander et al. (2018) and Buckley et al. (2019), but is a larger density
difference than has been used in some analyses (e.g., de Boyer Montégut
et al., 2004; Cai et al., 2021). MLD is also provided as a standard output
with the GLORYS reanalysis, calculated using a density jump corre­
sponding to a temperature change of 0.2℃ with a minimum MLD value
set to 10 m, which resulted in many locations having this fixed value
during summer months. We chose to calculate MLD using the larger
density jump as it is generally deeper than 10 m, is more representativeof the seasonal evolution of the MLD, and reduces large daily fluctua­
tions in MLD.
Results are presented for the coastal waters adjacent to the conti­
nental United States and the Atlantic Maritime provinces in Canada, theentire Gulf of Mexico, the northwest portion of the Caribbean, and the
Gulf of California (Fig. 1). Maps for winter (January-February-March,JFM) and summer (July-August-September, JAS) are presented for thefull domain in the main text, while maps for all seasons for three sub­regions [northeast US (NEUS), southeast US (SEUS)/Gulf of Mexico(GOMEX) and the California Current System (CCS)] are presented in the
supplemental (S). The regional maps provide a more detailed view ofcoastal conditions, especially along the west coast and in other regions

where the continental shelf is very narrow. The NEUS, SEUS, GOMEX
and CCS comprise the four major large marine ecosystems (LMEs, out­
lined in gray in Fig. 1) around CONUS; LMEs are coherent ocean areas
generally along continental margins whose bathymetry, hydrography,
and ecology are similar (Sherman and Duda, 1999). Regional averages
for all four seasons, including spring (April-May-June, AMJ) and fall
(October-November-December, OND) are shown in the supplemental.
Monthly anomalies (monthly values with the 1993–2019 mean sub­tracted) are used to analyze variability, except for interannual standard
deviations (Figs. 11, 12, 17-20), which are calculated using seasonallyaveraged anomalies.
3. Results
3.1. Mean climate
The GLORYS long-term mean sea surface temperature (SST), bottom
water temperature (BWT; for depths ≤ 400 m) and mixed layer depth(MLD) during winter are shown in Fig. 2. At the surface, cold water(<10℃) on the continental shelf extends southward along the east coastfrom Newfoundland to Cape Hatteras in winter. There is a strong
gradient near the shelf break (as indicated by the 400 m isobath in
Fig. 1), with warmer SSTs over the open ocean. Cool SSTs are foundalong the SEUS and GOMEX coasts, with warmer water offshore. The
Gulf Stream is readily apparent as a narrow band of warm water (>23℃)
off the southeast coast that extends northeastward into the Atlantic.
Along the west coast, SSTs progressively increase from Washington state
to the tip of Baja California, from about 8◦ to 22◦ C.
During winter, the bottom water temperature is low (<10℃) for
nearly all of the northeast coast with a sharp break at Cape Hatteras(Fig. 2b). BWTs are moderate (~15℃) adjacent to the coast and at theshelf break, but warmer at mid-shelf over most of the SEUS coast and thenorthern portion of GOMEX; in contrast, temperatures increase towards
the coast over the southern part of the Gulf. Bottom temperatures in
winter increase southward along the WFS but are colder along the outeredge of the shelf slope. BWTs are cold along the US west coast andmoderate along both coasts of Baja California.
Compared to the open ocean, the MLD in winter is relatively shallow

Fig. 1. GLORYS bathymetric depth (m), LME boundaries (light gray line), 400 m isobath (black contour line). Cross-section locations for Figs. 17-20 (darkgray lines).
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Fig. 2. GLORYS seasonal mean climate (a,d) SST, (b,e) BWT < 400 m, (c,f) MLD for JFM (left) and JAS (right). The 400 m isobath (gray line) is shown in (a,c,d,f);
only bottom water values < 400 m are plotted in all figures.

(<50 m) on nearly all of the US continental shelf (Fig. 2c). The mixed
layer on the shelf is constrained by the bathymetry in some regions andby processes such as limited mechanical mixing, since wind speeds
generally increase offshore. A clear exception is the western andnorthern portion of the Gulf of Maine (GOMA) and a narrow region
around Georges Bank, where a deeper bottom coupled with cold air
originating over the continent increases buoyancy mixing (Brown andBeardsley, 1978; Mountain and Jessen, 1987), resulting in deeper mixed
layers (70–100 m). The MLD is also relatively deep (50–75 m) on theouter portion of the WFS. The MLD is influenced by current systems andis relatively shallow (60–75 m) in the vicinity of the Gulf Stream and thenorthern edge of the loop current in the GOMEX, relative to the adjacent
open ocean.
During summer, the climatological SSTs on the NEUS shelf exhibit amonotonic increase southward along the coast, with a very strong zonal
gradient at the shelf break east of New England and Canadian Maritime
provinces (Fig. 2d). Very warm SSTs (>28℃) occur over the southern
portion of the domain, including the SEUS coast, Gulf Stream region, theentire GOMEX and the Gulf of California. The relatively cool water
adjacent to the US west coast is associated with near-shore upwelling,with a broader region of cool water due to equatorward transport by the
California Current and offshore advection by Ekman transport andwestward propagating eddies (Huyer, 1983; Jacox et al., 2014).
Bottom water temperatures remain cold (<10℃) for most of the

NEUS during summer, although they are relatively warm on Georges
Bank and adjacent to the coast from Cape Cod to Cape Hatteras (Fig. 2e).
BWTs are very warm (>28℃) near the coast in SEUS and GOMEX but
cool substantially near the shelf break, as the nearshore waters warm
substantially from winter while those offshore remain relatively un­changed. BWTs are cold during summer along the US west coast but
warm along most of the eastern side of the Gulf of California.
The MLD is shallow (<40 m) over most of the CONUS shelfthroughout the year and is even shallower (<15 m) over the continental
shelf in summer (Fig. 2f). The MLD is especially shallow in the vicinity ofwhere the Mississippi River enters the GOMEX in both summer andwinter, likely due to the input of fresh, low-density water.
Given the exceptionally narrow shelf along the west coast of North
America (the median CCS shelf width is 34 km), we also present Hov­möller diagrams of conditions in the CCS in Fig. 3, where values on the
shelf (<400 m depth) are first zonally averaged and then shown as afunction of calendar month and latitude. The mean SST on the shelf(contours in Fig. 3a) ranges from approximately 10 to 20℃ over themeridional extent of the domain during Jan-May. Warming occurs in
May north of ~40◦ N reaching ~14℃ during summer. The seasonal cycle
in SST is very muted between 35◦ and 40◦ N, with a slight peak in
September, due to strong upwelling of cold water in spring and summer.
The seasonal cycle is larger farther south, especially south of 30◦ N,
where temperatures exceed 20 ◦ C in September. The BWT remains near4

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Progress in Oceanography 216 (2023) 103055

Fig. 3. The seasonal cycle as a function of latitude for coastal (<400 m) points in the CCS based on monthly averaged values for all coastal longitudes of theclimatology (contours) and standard deviation (shading) for (a) SST, (b) BWT, (c) SSS, (d) BWS, (e) MLD, and (f) correlation between SST and BWT anomalies.

10℃ north of 32◦ N throughout the year, with somewhat higher tem­
peratures during fall and winter south of this latitude (Fig. 3b), off thewest coast of Baja California. The mean MLD on the CCS shelf has values
generally between 20 and 40 m in winter, with a maximum of ~50 m at
46◦ N in January (Fig. 3e). MLD values drop to ~10 m or less in spring
and remain very shallow through summer.
Given the influence of bathymetry on temperature, we show the BWT
climatology as a function of bottom depth on the shelf; results are pre­sented in Fig. 4 as box and whisker plots for JFM, MAM, JAS, OND forgrid points located in four regions: NEUS, SEUS, GOMEX and the CCS. In
the NEUS during winter, the median BWT rises from approximately 5℃
for isobaths < 20 m to 9℃ at ~360 m with a very slight decrease below
that. The spread, as a function of location in the region, is large forbottom depths less than 120 m, where the full range exceeds ~10℃.
In some NEUS locations (particularly in the GOMA), the salinity
gradient enables the water column to remain stable despite an increasein temperature with depth, especially in winter (e.g., Fig. 5a, b andFig. 17). The BWT in the upper 40 m warms in spring and especiallyduring summer, as heating from the surface is mixed to the bottom

where the shelf is shallow (Fig. 4e, i). The seasonal increase in tem­
perature is more modest for bottom depths between 60 and 140 m from
JFM to JAS. The upper ocean cools in fall, such that BWT decreases
monotonically with depth to the 160 m isobath, with temperatures atdeeper bottom depths similar to those in the other seasons.
The median SEUS BWT values during JFM increase from ~18 ◦ C forpoints located near the surface to ~20 ◦ C between 60 and 100 m bottom
depths, and decrease at greater depth, although the warmest values(~22℃) in winter are similar throughout the region (Fig. 4b). Bottom
temperatures exhibit a monotonic decrease with depth in spring andsummer, where BWTs in JAS exceed 28℃ near the surface for somelocations within SEUS. From summer to fall, BWTs cool substantially for
depths less than 40 m but warm slightly between 40 and 80 m. BWTs
decrease with depth below 60 m in OND. The temperature range ishighly skewed in space, with the warmest locations nearly 10 ◦ C warmer
than the median and the coldest locations only ~2 ◦ C colder than the
median. In general, BWTs in the GOMEX are similar to those in the SEUS,
except they tend to be slightly warmer and exhibit a much-reduced
range at most depths (Fig. 4c, g, k, o).
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Progress in Oceanography 216 (2023) 103055

Fig. 4. BWT climatology as a function of bottom depth for coastal grid points < 400 m deep. Grid points are separated by LME: (a,e,i,m) NEUS, (b,f,j,n) SEUS, (c,g,k,o) GOMEX, (d,h,l,p) CCS and season JFM (black), AMJ (green), JAS (red), OND (blue). Each “box and whisker” represents the distribution for a 20 m range of depths.
The ends of the whiskers are the max and min of the distribution and the ends of the boxes show the interquartile range, while the median is the midline of the box.
(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Temperatures range between 8 ◦ C and 20 ◦ C with a median of ~12 ◦ C
for grid points where the bottom is shallower than 20 m in the CCS
during JFM (Fig. 4d). During winter, the median temperature andtemperature range generally decreases slowly with bottom depth to 400
m. This pattern for the CCS generally holds throughout the year,
although the near-surface waters are warmer and the range is larger inthe other seasons, particularly in summer.
Maps of sea surface salinity indicate that the SSS increases equa­
torward along the Atlantic coast, ranging from approximately 32 psu to
36 psu from Maine to Florida during JFM (Fig. 5a). Fresher water ex­
tends over much of the NEUS shelf but is confined close to the coast in
SEUS. High values occur offshore in the Atlantic with slighter lower
values in the Caribbean. Low salinity water (<25 psu) stretches along
the central north coast of the GOMEX due to the large input of fresh­water by the Mississippi River. A smaller plume of low salinity water
also occurs along the narrow shelf off the northwest coast as a result ofoutflow from the Columbia River and the salinity is slightly lower in thevicinity of San Francisco Bay (~37◦ N; Figs. 3c & 5a). The bottom and sea
surface salinity exhibit similar patterns over most of the continental US
shelf, but with higher salinity at depth. One notable feature visible in thebottom salinity but not surface salinity is in GOMA, where saltier
Atlantic water enters the gulf at depth at its southeastern edge via theNortheast Channel.
In general, the summer and winter SSS patterns are similar (Fig. 5),
although the inner half of the GOMA is fresher during JAS and the highsalinity water in the Northeast Channel is even more prominent insummer. In most regions, the bottom water salinity (BWS) is also similar
to its winter counterpart. However, off the northwest coast, the mean
SSS is much lower in summer than in winter and is much fresher than thebottom salinity, due to the low-density freshwater output from theColumbia River, which peaks in late spring-early summer (e.g., Helaire
et al., 2019).
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Progress in Oceanography 216 (2023) 103055

Fig. 5. Seasonal mean climate for (a,c) SSS and (b,d) BWS in JFM (left) and JAS (right). The 400 m isobath (gray line) is shown in (a,c).

The timing of the maximum and minimum SSTs and BWTs at eachlocation are shown in Fig. 6. The warmest SSTs generally occur during
August in the western Atlantic and GOMEX and in near-shore regions in
the northern and southern parts of the domain in the Pacific. The highest
SSTs occur during September in the Caribbean and over much of theeastern Pacific including along the coast of California and Baja

California. The coldest month for SST is March for most of the north andeast portion of the NEUS shelf and February for near shore regions southof Cape Cod. SSTs reach their minimum in February in the Caribbean
and Gulf of Mexico, except for a thin strip adjacent to the northern
GOMEX coast, where the coldest temperatures occur in January. The
month when the coldest SSTs occur is more variable along the west

Fig. 6. Climatological warmest (left) and coldest (right) months in the seasonal cycle for (a,c) SST and (b,d) BWT. The 400 m isobath (gray line) is shown in (a,c).
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Progress in Oceanography 216 (2023) 103055

coast: most locations reach a minimum in February or March but there
are near shore regions where the lowest value occurs in January and abroader area to the west of southern Baja California where it occurs aslate as May.
The month when maximum BWT occurs around the US (Fig. 6b) ismuch more variable than for SST. The BWT maximum occurs during
October or November for most of the NEUS shelf. Surprisingly, themaximum BWT occurs during mid-to-late winter in the deeper portionsof central GOMA. Du et al. (2021) attributed the unusual seasonalvariation of deep-water temperatures in the GOMA to the timescale ofadvection of slope water into the Gulf through the Northeast Channel.
On the SEUS and northern GOMEX shelves, the month with the highest
BWTs increases with distance from the shore, ranging from Jul-Aug nearshore to Oct-Nov offshore at depths of 200–400 m — 2–3 months later
than at the surface. Except for a very narrow strip near the shore (alsosee Fig. S39), the maximum BWTs along the northern 2/3 of the US west
coast occur during winter, prior to the start of the upwelling season.
Farther south, the maximum occurs in late summer and fall.
The minimum BWTs occur during Feb-Apr in shallower regions of
the NEUS shelf and later in deeper areas, especially in GOMA where it
can occur in late spring or summer (Fig. 6d). Along the SEUS, WFS andnorthern GOMEX coasts, the coldest month occurs in the near-shore
region in January, and later in the year offshore – in summer or evenfall on the edge of the shelf. The coldest month on the west coast is
generally in late spring or early summer during the upwelling season.
The months when the deepest and shallowest mixed layer depths
occur are shown in Fig. 7. The deepest mixed layers occur from
November to February on the northeast continental shelf. In the SEUS
and northern GOMEX, the MLD reaches a maximum in October toDecember, while over deeper waters in the Atlantic and Gulf of Mexico it
primarily occurs from January to March. The month with the maximum
MLD is highly variable on the narrow west coast shelf ranging from
October to February, but occurs during January or February in the open
ocean. The shallowest MLDs tend to occur during June or July along theeast coast, with isolated areas on the mid-Atlantic and southeast shelvesoccurring earlier in the year. On the WFS and the northwest portion of
GOMEX, the minimum MLD occurs as early as February adjacent to thecoast but later in the year, often in June and July, towards the open
ocean. The minimum MLD occurs during June through September in thesouthern GOMEX, except for a very small region on the western side of
the Yucatan Peninsula where it occurs early in the year. In the Pacific,
the MLD is shallowest in May or June along portions of the US and west
coast of Mexico but occurs earlier near San Francisco Bay. In contrast,
the minimum MLD occurs during July and August over most of the open
ocean in the east Pacific.
The MLD relative to the underlying bathymetry will likely influence
how closely conditions at the surface and the bottom, such as SST and
BWT, are linked. Thus, we also present the ratio of the MLD to thebottom depth (in percent) during the deepest and shallowest months in
Fig. 7. When the mixed layer is deepest, it reaches at or close to thebottom on the shelf over most of the east coast and GOMEX. The main
exception is portions of the GOMA, where the MLD is much shallower
than the bottom. This may result from the intrusion of dense water at
depth through the Northeast Channel and because much of the gulf isdeeper than 150 m with basins that are more than 200 m deep. The
MLD/bathymetry ratio is highly variable adjacent to the west coast, but
it exceeds 70 % between 40◦ and 45◦ N. For locations where the mixed
layer reaches close to or on the bottom, it can remain there for several
months and thus it is more appropriate to consider an extended periodwith a maximum depth, rather than a single month.
When the MLD is at a minimum, the MLD is generally less than 20 %of the bottom depth on the NEUS shelf except in the vicinity of Cape Cod(Fig. 7d). The minimum MLD/bathymetry ratio increases from less than
10 % at the shelf break to 50–80 % in nearshore regions in SEUS. This
gradient is even stronger along the WFS, ranging from <10 % to >90 %from west to east across the shelf. The MLD relative to the bathymetry
varies over the remainder of GOMEX where the MLD is very shallow
relative to the bottom depth in the northeast and southwest portions of
GOMEX, while it exceeds 50 % of the bottom depth in the northwest gulf
and northwest of the Yucatan Peninsula. The minimum MLD is generally
less than 20 % of the bottom depth along the west coast.

Fig. 7. Climatological deepest (left) and shallowest (right) months in the seasonal cycle for (a,c) MLD and (b,d) MLD as a percentage of the total column depth. The
400 m isobath is shown by the gray line.
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3.2. Interannual variability

The modulation of the SST-BWT relationship by the mixed layer
depth and bathymetry is explored further in Fig. 9, which shows themonthly SST-BWT anomaly correlations (top row) as a function of the
MLD/bottom depth ratio using box and whisker diagrams. The corre­
lations at all grid points within the four CONUS LMEs are aggregatedinto 10 bins based on the ratio (%) of the climatological MLD relative tothe total column depth. The median value for the SST-BWT correlations
ranges between ~0.2 in GOMEX and ~0.5 for the NEUS when the MLD
is only 10 % of the bottom depth. Although there is a very wide range ofvalues at the individual LME grid points (as indicated by the dashedlines), the correlations increase monotonically as the MLD/bathymetry
ratio increases in all four LMEs and approach 1.0 (for the upper 75 % of
the grid points within an LME) when the MLD reaches the bottom (100%). Similar to SST-BWT, SSS-BWS correlations increase monotonicallyas the MLD approaches the bottom, with median values exceeding ~0.8
when the MLD is ≥ 70 % of the bottom depth (bottom row of Fig. 9).
These results confirm the importance of the MLD in linking the condi­
tions at the surface and the bottom on the CONUS shelf.
The decorrelation time scales for SSTs, BWTs and their difference
based on monthly anomalies (Fig. 10, left column) are computed using
correlation values at lags of 1 to 12 months following the method
described in Delsole (2001) and Buckley et al. (2019). For exponentiallydecaying values, the method provides an average e-folding time scale,but it can also accommodate oscillatory variability. Typical values for
SST anomalies are on the order of 2–3 months in the western Atlanticand 2–5 months in the eastern Pacific. There are a few regions with little
persistence (≤1 month, the minimum value), including portions of the
Gulf Stream, along the SE coast and over much of the GOMEX. (Ananalysis based on daily values indicates the decay time scale is <30 daysin these regions and <15 days in portions of the Gulf Stream and near theMississippi Delta; Amaya et al., 2023b). Regions with greater SST
persistence, with decorrelation times > 4 months, include portions of
GOMA, and west of Baja California and off the Pacific northwest coast,
where values can exceed 8 months. Bottom temperature anomaly decay
rates generally range from 2 to 5 months but are much longer (>10months) in the central GOMA. SST autocorrelations vary with the

The relationship between surface and bottom temperature anomalies
is illustrated in Fig. 8 (top row) by the local concurrent correlation be­
tween the two during JFM and JAS. The correlations between SST and
BWT are high (>0.8) over most of the continental shelf along the eastand gulf coasts during winter. In most regions, this likely reflects tem­
perature anomalies created at the surface that extend towards the bot­
tom, due to strong wind and buoyancy mixing in winter. In somelocations, higher correlations between the two may reflect dynamic
ocean processes that influence the entire water column. The JFM SSTBWT correlations are lower (<0.5) in the center of the GOMA and on
the outer edge of the continental shelf on the east and GOMEX coasts.
The correlation values are also high along the CCS coastline in JFM but
drop offshore as the shelf deepens rapidly and the MLD is relatively
shallow. The SST-BWT correlations remain high adjacent to the CONUS
coast in summer but are greatly reduced over much of the continental
shelf compared to winter (Fig. 8c, also see Fig. 3f). This is consistent withthe shoaling of the mixed layer in spring and shallow MLDs in summer,
effectively decoupling the surface from the deeper ocean. The surface
and bottom may remain correlated to some degree as temperature
anomalies created over the entire water column persist from winter tosummer associated with the relatively high auto-correlation of ocean
temperature anomalies (see Fig. 10).
The geographical structure of concurrent correlations between sur­face and bottom salinity anomalies is generally similar to that for tem­
perature, with some notable exceptions (Fig. 8, bottom). These include
deeper portions of the shelf along the east coast, where SSS and BWS
anomalies are uncorrelated or even exhibit a small negative correlationin both summer and winter. However, during summer, Georges Bankand other shallow regions in the NEUS exhibit higher correlations for
SSS-BWS than for SST-BWT. In the GOMEX, the surface and bottom
temperature and salinity are uncorrelated south and east of the Mis­sissippi Delta in winter. Surface-bottom water correlations are sub­stantially lower on the west coast for salinity than for temperature,
particularly north of ~42◦ N (also see Fig. S41).

Fig. 8. Correlation of SST and BWT during (a) JFM and (b) JAS. Correlation of SSS and BWS during (c) JFM and (d) JAS.
9

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Fig. 9. Surface-Bottom correlations as a function of the mean mixed layer depth, as a percentage of total column depth for (a,b,c,d) temperature and (e,f,g,h) salinity,
computed using monthly values aggregated over LME regions: (a,e) NEUS, (b,f) SEUS, (c,g) GOMEX and (d,h) CCS. The box and whiskers represent the spatial
distribution of values within the LME in the MLD/bathymetry bin, where each bin represents 10% of the total column depth. The ends of the whiskers are the max
and min of the distribution, the ends of the boxes show the interquartile range, and the median is the midline of the box.

seasons, usually with greater persistence in winter than in summer andenhanced winter-to-winter autocorrelations due to the “reemergencemechanism’’ (Deser et al., 2003; Stock et al., 2015).
In general, the BWT decay rates are longer than those for SST(Fig. 10c); the BWT-SST difference in decay rates is positive at most of
the grid points on the shelf in the domain. The BWT decay rates exceed
those for SST by one month for most nearshore regions and 2–3 months
for mid-shelf areas of the NEUS and GOMEX. The differences are muchlarger in the northern Gulf of California and GOMA, where they exceed
eight months. The greater persistence of GOMA BWT anomalies resultsfrom the water in the deep basins being below the pycnocline, and thus
isolated from high-frequency surface forcing, and from slower dynamic
ocean processes in the Atlantic that influence the water properties thatenter the gulf through the Northeast Channel (Du et al., 2021).
The decorrelation times for SSS anomalies are on the order of 2–5
months off the east coast (Fig. 10d), slightly longer than those for SSTs inmost locations. The persistence is low (<2 months) in the GOMEX, but ishigher in the Caribbean, especially south of Cuba. The decay time of SSS
anomalies varies widely in the Pacific, with rapid decay off the Pacificnorthwest coast, likely associated with fluctuations in Columbia Riveroutflow, with much longer-lived anomalies farther offshore, as occurs
for SST. Patches with higher SSS persistence occur west of Baja Cali­
fornia. BWS decay time scales range from one to six months along theeast coast (Fig. 10e). While BWS anomalies persist slightly longer than
SSS anomalies in the central GOMA, they are less persistent than the
BWT anomalies. BWS decay times are generally less than five months
around the rest of CONUS but greater than seven months in the north­east corner of the WFS, with even greater persistence in the northern
Gulf of California. The BWS-SSS differences (Fig. 10f) exhibit a similar
pattern to those of BWT-SST, although the difference is larger for tem­
perature than salinity in the GOMA and vice versa in the western
GOMEX and Gulf of California.
The temperature, salinity and mixed layer depth variability as
measured by the standard deviation (σ) is computed using seasonallyaveraged anomalies. The SST σ ranges from 0.5 to 1.5 ℃ over most of the

CONUS shelf during winter (Fig. 11a). Regions on the CONUS shelf withsomewhat greater variability include sections of the eastern seaboard,
the WFS, northwestern GOMEX and west of southern Baja California;
SST variability is enhanced in the latter region (28◦ –32◦ N) primarily
during fall (see Fig. 3a). Much higher variability associated with the Gulf
Stream occurs on the shelf near Cape Hatteras and east of the shelf off
the NEUS coast, likely due to meridional excursions of the current, as
well as meandering and mesoscale eddy activity. The pattern of the BWT
σ on the shelf (Fig. 11b) is similar to that at the surface during winter.
Some regions exhibit stronger BWT than SST variability, including
portions of the NEUS shelf, the Gulf of California and on the southwestside of Baja Peninsula (also see Fig. 3a&b), but the bottom temperature
variability is slightly lower than at the surface along much of the US west
coast. The MLD σ in JFM (Fig. 11c) is less than 20 m over nearly all of the
CONUS shelf and less than 5 m over much of the east coast and GOMEX.
It is larger beyond the outer edge of the NEUS shelf, where it approaches
50 m.
The SST variability in summer (Fig. 11d) is less than in winter overnearly all of the western Atlantic and GOMEX, except near the center ofGOMA. On the west coast, however, the SST variability is slightly larger
in summer than in winter (also see Fig. 3a and Fig. S44). The summer
BWT variability is stronger than both its winter counterpart and summer
SST variability over much of the NEUS shelf and at mid shelf along theentire length of the WFS. The reverse occurs along the US west coast
where the BWT variability in JAS is low (σ < 0.5℃). MLD exhibits little
variability over the entire CONUS shelf in summer (Fig. 11f).
The surface and bottom water salinity σ in winter and summer are
shown in Fig. 12. The SSS variability off the NEUS coast is enhanced in
GOMA, along the coast of the mid-Atlantic states and just east of the
shelf break in winter. The SSS variability is enhanced in the northern
GOMEX with a strong peak near the Mississippi River delta; it is alsoenhanced in the coastal zone of the Pacific northwest. The area coveredby higher SSS variability is more widespread in summer than winter inboth regions (Figs. 3 and 12). Salinity variability in these regions islikely driven by fluctuations in outflow from the Mississippi and10

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Fig. 10. Decay rate in months computed from autocorrelation at lags of 1 to 12 months (using the method described in Buckley et al., 2019) for (a) SST, (b) BWT, (c)BWT - SST, (d) SSS, (e) BWS, (f) BWS-SSS. The 400 m isobath (gray line) is shown in (a,c).

Columbia rivers, respectively. The BWS σ in both JFM and JAS is similar
to the JFM SSS variability, although the variability is highest at thesurface in winter in GOMA.
To explore the full range of temperature variability, we present theprobability distributions of monthly SST and BWT anomalies for JFM
and JAS at all grid squares within the NEUS, SEUS, GOMEX and CCS
LMEs in Fig. 13. The amplitudes of the SST and BWT anomalies reach
4.0–5.0℃, except for SSTs in summer in the SEUS and GOMEX, where
the anomalies are generally less than |2.0|◦ C. Also shown in Fig. 13 are∑
3higher order metrics: the skewness ( Ni (Xi − X) /((N − 1) × σ 3 ))) and∑4
kurtosis ( Ni (Xi − X) /((N − 1) × σ4 ) − 3), where N is the number of grid
points, Xi the individual grid point value and X the mean of the distri­bution; by subtracting 3 the normal distribution has kurtosis = 0.
Skewness indicates if the distribution is asymmetrical. Kurtosis provides
information about the tails of a distribution: whether it’s light (<0) or
heavy tailed with more outliers (>0), respectively, relative to a Gaussian
distribution. The temperatures in the NEUS LME have little skew during
winter and summer, with slightly more low amplitude anomalies rela­tive to a Gaussian distribution. The temperatures in the SEUS exhibit anegative skew and positive kurtosis in both seasons. In the GOMEX, theSST and BWT distributions have heavy tails (leptokurtic distributions),
and a slight negative skew. Both the SST and BWT anomalies are posi­
tively skewed in the CCS, resulting in more extreme positive

temperature anomalies relative to a Gaussian distribution. The CCS is
the only LME where ENSO has a strong influence on the SST and BWT
distribution: with more positive extremes when La Niña events are
excluded (SST and BWT skewness of 0.65 and 2.02, respectively)
compared to when El Niño events are excluded (SST and BWT skewness
of 0.33 and 0.44). The CCS SST kurtosis values are near zero in winterand one in summer. In contrast, CCS BWTs exhibit exceptionally highkurtosis values (9.3 in JFM and 7.7 in JAS), since BWT anomalies are
generally close to zero but occasionally have large amplitudes at somelocations and/or times. The results demonstrate that the SST and BWT
probability distributions can be significantly different within the same
region. Similar plots for SSS and BWS indicate that salinity exhibits aslightly negative skew but very large kurtosis for the four LMEs in bothseasons (not shown).
Given the unusual nature of the temperature distribution and thenarrowness of the shelf in the CCS, we show the skewness and kurtosis as
a function of depth (for all vertical levels) and latitude along the US west
coast in Fig. 14. Results are presented as the median value of these twoquantities at each latitude in the reanalysis (to account for the variable
number of points on the shelf at different latitudes) during JFM and JAS.
The distribution measures are very different in winter and summer.
During winter large positive values occur for skewness at depths ofapproximately 80–180 m and between 60 and 200 m for kurtosis, where
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Fig. 11. Interannual Standard Deviation of (a,d) SST & (b,e) BWT (◦ C) and (c,f) MLD (m) for (left) JFM and (right) JAS. The 400 m isobath (gray line) is shown in (a,c,d,f).

latitude increases. Kurtosis values are negative at shallower depths. The
temperatures are positively skewed at most latitudes and depths insummer with larger skew within ~40 m of the surface poleward of~35◦ N and between 40 and 100 m from 32◦ -34◦ N. The kurtosis is also
positive with larger magnitudes in these areas but becomes negative atgreater depths over most latitudes.
Longer term changes in temperature, as indicated by the linear
temperature trend over 1993–2018 for SST and BWT in winter andsummer are shown in Fig. 15. The SST trend is positive over most of thedomain especially in summer. The trend is statistically significant (95 %level based on a Mann-Kendall test) over most of the Atlantic in both
winter and summer and in the Gulf of Mexico and near Baja California insummer. The most intense warming, which exceeds 3℃ dec-1 occurs off
the NEUS coast. The intense warming at the surface and at the bottom is
consistent with the observational analyses of Pershing et al. (2015) and
Kavanaugh et al. (2017), respectively. The positive SST trend is mostintense east of the shelf break in winter and over central GOMA. The
increase in the NEUS SSTs is likely due to a northward shift of the Gulf
Stream (Wu et al., 2012; Seidov et al., 2021) as well as warming over theadjacent continent (Hayhoe et al., 2018) and the subsequent eastward
advection of the warm air over the ocean. The NEUS warming is
consistent with results from models forced with an increase in green­house gasses (e.g., Alexander et al., 2018, 2020), although internal

climate variability including fluctuations in the Atlantic Meridional
Overturning Circulation, contribute to multi-decadal variability (Deserand Phillips, 2021). In addition, the climate change signal is likely to be
nonlinear in some regions (Frankignoul et al., 2017, Xu et al., 2022),
although a linear estimate appears to be reasonable over ≤ 30-year
periods (Alexander et al., 2018; Wang et al., 2022).
The SST exhibits a strong negative trend in a narrow strip along theSEUS coast, with weaker cooling along portions of the northern andwestern GOMEX coasts during winter. However, the cooling trend all
but disappears in summer. The SST trends are generally weaker and of
mixed sign in the Pacific in winter, but become more positive in summer.
While in many locations on the CONUS shelf the BWT trend reflects thatat the surface, there are regions where they are quite different. For
example, the bottom temperature in JAS has a negative trend on parts ofthe southeastern side of the GOMA including Georges Bank, and in the
middle of the WFS where the surface exhibits a moderate to strong
positive trend. A strong positive BWT trend also occurs at the shelf breakalong nearly all of the east and GOMEX coasts and in the northern Gulf ofCalifornia. The modest BWT cooling along most of the west coast in JFM
is replaced by weak warming in JAS. The warming BWT trend is sig­
nificant in the GOMA and portions of the GOMEX and the north Gulf ofCalifornia in winter, with more locations exhibiting a significant
warming trend along both coasts and in the GOMEX during summer.
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Fig. 12. Interannual standard deviation of (a,c) SSS and (b,d) BWS (psu) for (left) JFM and (right) JAS. The 400 m isobath (gray line) is shown in (a,c).

Fig 13. Probability distributions for all coastal points (<400 m) in each LME for SST (red) and BWT (blue) along with normal distributions (dashed lines) during(top) JFM and (bottom) JAS in (a,e) NEUS, (b,f) SEUS, (c,g) GOMEX and (d,h) CCS. Also shown are the skewness and kurtosis values. The skewness (third moment)
measures the asymmetry. If the skewness > 0 (skewness < 0), the tail of the distribution is longer on the right (left) side. The coefficient of kurtosis (fourth moment)
measures the tails (outliers) of the distribution. The normal distribution has a kurtosis of 3, which is subtracted from the calculated kurtosis. If the calculated kurtosis> 0 (<0), it is heavy (light) tailed with more (less) outliers, than a normal distribution. (For interpretation of the references to colour in this figure legend, the reader
is referred to the web version of this article.)

Only a few points have statistically significant negative trends in either
season.
The salinity trends (Fig. 16) indicate that the SSS in the NEUS region
is becoming more saline in winter. The trend is reduced in summer overmost of the region, although it remains strong in the southwest corner ofthe LME (mid-Atlantic coast) and there is a small area of freshening in
GOMA. SSS exhibits a positive (negative) trend in the eastern (western)
GOMEX in winter. The pattern is similar in summer, although there is a13

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Fig 14. CCS LME median temperature (a,b) skewness and (c,d) kurtosis of monthly anomalies. Includes all longitude points shallower than 400 m at each latitude(from 30◦ N-48◦ N) over all depths in the column during (a,c) JFM and (b,d) JAS. Black contour indicates the median climatological MLD at each latitude.

relatively strong upward trend in the northeastern part of the gulf
located between two areas of freshening. The SSS trends are generally
small in the Pacific, although freshening in winter is replaced by sali­
nification in summer along the northwest Pacific coast. The trend in thisregion is likely associated with changes in Columbia River outflow,which has decreased over the past several decades during summer(Forbes et al., 2019). The bottom water salinity trends generally reflect
those at the surface. The positive salinity trends are statistically signif­
icant over much of the NEUS in winter, although fewer locations have asignificant SSS trend in summer. The SSS and BWS trends are also sig­
nificant over the southeast Caribbean, western Gulf of Mexico, Gulf ofCalifornia and roughly one third of the points in the Pacific.

3.3. Vertical structure
Finally, we explore conditions through the water column by showing
cross sections (locations shown in Fig. 1) of the temperature and salinity
climatology and standard deviation for the NEUS, SEUS, GOMEX and the
CCS for the four seasons. Fig. 17 displays the transect at 42.5◦ N, whichcrosses the complex bathymetry of the southern GOMA, extending
eastward from the shore including the Wilkinson Basin, Georges Basin,
the Northeast Channel and Browns Bank. The mean temperature in­creases with depth and exhibits a weak thermocline located at approx­
imately 100 m depth across GOMA in JFM. A seasonal thermocline
develops at a depth of ~20 m in spring and becomes very intense during
summer before weakening in fall. The mean temperatures have a min­imum value at ~75 m in spring and summer and relatively warm water(~10 ◦ C) remains throughout the year at ~150 m on the eastern side of14

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Fig. 15. Linear trends (f1993-2019) in (a,c) SST, and (b,d) BWT for (left) JFM and (right) JAS. Th 400 m isobath (black line) is shown in (a,c). Significant trends (95%) using Mann-Kendall non-parametric test are indicated by stippling.

Fig. 16. As in Fig. 15 but for linear trends (1993–2019) in (a,c) SSS, and (b,d) BWS.

Georges Basin (66◦ W). The greatest variability occurs below the surface
layer in the eastern portion of the basin, likely associated with fluctua­
tions of warm Atlantic water on Brown’s Banks and entering GOMA
through the Northeast Channel.

The climatological near-surface salinity is relatively fresh in theeastern and western portions of the 42.5◦ N transect with a maximumthat shifts slightly eastward from JFM to OND (Fig. 17 bottom). The
salinity gradient is strong: west of ~70◦ W above 50 m, at all depths in
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Fig. 17. Vertical cross-sections of (a,c,e,g) temperature (◦ C) and (b,d,f,h) salinity (psu) standard deviation (shading) and climatology (contours) along 42.5◦ N in the
NEUS for (a,b) JFM, (c,d) AMJ, (e,f) JAS and (g,h) OND. Location of the cross section is shown as a gray line in Fig. 1.

the eastern portion of the basin, below about 70 m in all seasons, andover the entire water column in summer. Given the temperature profile,salinity maintains the static stability of GOMA, especially below ~70 m
and in the northeast channel. Salinity variability is largest in the easternpart of the transect, particularly at depth in spring and near the surface
in fall. Enhanced variability also occurs in the upper 50 m across muchof GOMA in JFM and between 68◦ -70◦ W in AMJ.
The SEUS cross section (Fig. 18) extends eastward along 32◦ N(starting near Savannah, GA) into the Atlantic. The maximum mean
temperature, associated with the Gulf Stream, occurs in the vicinity of78◦ W from the surface to 100 m depth. The climatological temperature
gradient is strong on either side of the Gulf Stream below about 50 m,
especially on its eastern flank to the shelf break. There is also a strong
positive offshore gradient from the coast that extends from the surface tothe bottom for depths shallower than ~50 m in fall and winter. The
temperature variability is strongest adjacent to the shelf, especially be­
tween 50 and 150 m in JFM and OND. The variability is also somewhat
higher in the thermocline to the east of the Gulf Stream. The salinity
field is fairly uniform offshore, with strong gradients along the shelfespecially in the shallow near-shore region and below ~100 m. The
salinity variability is very strong on the shelf at depths of less than ~30
m and along the shelf break but with lower amplitude. The strong
climatological gradient and variability in the near shore region is likely
associated with freshwater input from rivers entering into the LME(Blanton, 1981), while the strong temperature and salinity gradientalong the shelf break may be influenced by the local bathymetric fea­tures, including the “Charleston Bump”, and the proximity of the Gulf
Stream (Blanton et al., 2003). The variability is also higher to the east of

Fig. 18. As in Fig. 17 but for vertical cross-sections of temperature and salinity along 32◦ N in the SEUS.
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the Gulf Stream above ~75 m in AMJ and JAS.
The GOMEX cross section (Fig. 19) extends along 26◦ N from south
Florida to the central Gulf of Mexico. In contrast to the previous crosssections, the climatological temperature and variability vary smoothly.
The mean temperatures are colder on the WFS in winter and increase
offshore to 85◦ W, where the temperatures are higher throughout thewater column than at other longitudes at the same depth. This depres­sion in the isotherms decreases from spring to summer and is slightly
inverted in fall. The vertical temperature gradient is strong and fairly
regular over most of the water column below about 100 m in JFM, 30 m
in AMJ and JAS and 50 m in OND. The largest temperature variability
occurs offshore at ~89◦ W at depths of 125–250 m in JFM. As the seasons
progress, the maximum σ shifts closer to shore and is quite extensive in
OND where it reaches ~3 ◦ C between 100 and 300 m at ~87◦ W. The
strong variability is likely associated with fluctuations in the Loop
Current, in contrast, there is little variability on the shelf. The verticaltemperature gradient remains strong but the variability decreases when
the GOMEX transect is located further north (not shown).
The mean salinity profile is fairly uniform across the GOMEX tran­
sect, with a slight maximum around 150 m that is located in the vicinityof 86◦ W in winter and spring but closer to the continental slope in fall(Fig. 19). One center of salinity variability is located above 30 m and isconfined close to the shelf except in JAS, when it extends over the fulltransect. A second region of enhanced variability occurs in all seasons
below 150 m west of ~86◦ W.
The west coast transect (Fig. 20) extends offshore from northern
California along 40◦ N. The upper thermocline is fairly diffuse in JFM
and slopes upward from ~100 m at 130◦ W to towards the surface at thecoast. It strengthens through spring into summer and nearly reaches thesurface as upwelling of fairly uniform cold water acts to compress theisotherms. The greatest temperature variability occurs offshore within
the thermocline, with the maximum occurring in fall.
The climatological halocline west of California extends between 50
and 200 m over most of the transect and like temperature, it is com­pressed and slopes more strongly towards the surface near the coast(Fig. 20). The salinity σ is greatest near the surface and the shore in AMJ,
with elevated variability in the same area in JFM and located near thesurface but further offshore in JAS. These regions of higher variability
appear to be associated with fluctuations in the outflow from theColumbia River (see Figs. 12, S46, S47) and the California Current,

which transports water southward along the west coast. Areas of higher
variability also occur within the halocline.
4. Discussion and conclusions
While coastal ocean conditions have been studied using satellite andin situ observations as well as regional ocean models, recently developed
fine-scale global ocean reanalyses enable a broader, observationallyconstrained, three-dimensional view of conditions on continental
shelves. Here, we used the 1/12◦ (~9 km) Global Ocean Reanalysis andSimulations (GLORYS) to examine temperature and salinity (with afocus on surface and bottom waters) and mixed layer depth on the shelf(depth < 400 m) around the continental United States. We examined theclimatological seasonal cycle, standard deviation and broader measuresof variability, including the persistence, trend, skewness, kurtosis andprobability distributions over the period 1993–2019.
A key aspect we examined was the relationship between surface andbottom water conditions on the highly variable US continental shelf andits modulation by the mixed layer depth relative to bathymetric features.
In general, the SST and BWT are more tightly coupled at shallower
depths during winter when the mixed layer is deep and turbulence canmix the surface and near-bottom waters, as suggested by Schaeffer andRoughan (2017) using in situ observations at two locations along the east
coast of Australia and by Amaya et al. (2023b) who used the GLORYS
reanalysis to investigate bottom water heat waves around North
America. Our analyses indicated that this is an important factor inlinking SST and BWT anomalies over the US continental shelf, where the
SST-BWT relationship is influenced by a number of factors including thebathymetry, the MLD seasonal cycle and other dynamical ocean pro­
cesses. The Gulf of Maine, with shallow banks, deep basins and aconnection to the North Atlantic at depth, illustrates the potential
complexity in the relationship between surface and bottom conditions.
For example, while the maximum SST occurs in August over the entire
gulf, the month with the warmest BWT ranges from September on
Georges Bank to as late as March and April in the deep basins in theeastern part of GOMA. The SST and BWT are well correlated (>0.8) inshallower regions but weakly correlated (<0.5) in the deeper portions of
GOMA, which are below the mixed layer even in winter. The correla­tions drop significantly at relatively shallow depths in summer but
remain relatively unchanged in deeper layers. Sheltered from higher

Fig. 19. As in Fig. 17 but for vertical cross-sections of temperature and salinity along 26◦ N in the GOMEX.
17

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Progress in Oceanography 216 (2023) 103055

Fig. 20. As in Fig. 17 but for vertical cross-sections of temperature and salinity along 40◦ N in the CCS.

frequency surface forcing, BWT anomalies last longer than SST anom­alies by one to two months in most locations, and up to six months in the
inner portion of GOMA.
The surface-bottom salinity anomaly correlation pattern is similar tothat for temperature over most of the continental US shelf, but the
GOMA SSS and BWS are nearly uncorrelated during summer. In addition
to local mixing, inflow of warmer/saltier water from the Atlantic into
the GOMA at depth can result in the decoupling between conditions at
the surface and bottom. Other processes that can separate conditions on
the bottom from the surface include freshwater outflow from rivers,
especially the Mississippi and Columbia, upwelling, especially along thewest coast in summer, interactions with currents, such as the Loop
Current on the West Florida Shelf (He and Weisberg, 2003), and verticaldisplacement of the pycnocline where it intersects the shelf, as is often
the case along the US west coast. The SSS decorrelation time ranges fromabout 1 to 10 months over the full domain and is slightly longer thanthose for SSTs over most of the western Atlantic. SSS anomalies decay
rapidly in the northern GOMEX and off the Pacific northwest coast,
likely associated with variability in the discharge from the Mississippi
and Columbia rivers, respectively. The BWS-SSS values are generally
similar to those of BWT-SST.
The monthly SST σ ranges from 0.5 to 1.5 ℃ over most of the CONUS
shelf during winter, with somewhat greater variability in parts of theeast coast, the WFS, northwestern GOMEX and west of southern Baja
California. The BWT and SST variability patterns are similar during
winter, when some regions exhibit stronger BWT than SST variability,
including portions of the NEUS shelf, and on either side of Baja Penin­sula. The BWT variability in summer is stronger than both its wintercounterpart and summer SST variability over much of the NEUS shelfand at mid shelf along the entire length of the WFS.
The SST anomalies in SEUS and GOMEX, and especially the BWT in
the CCS LME, exhibit strong kurtosis, indicating more low amplitude
anomalies but a few very large anomalies (heavy tails) relative to a
Gaussian distribution. In addition, the kurtosis over the depth of thewater column in the CCS changes between winter and summer, where itis negative at depths shallower than ~80 m and positive between
approximately 80–200 m in winter and nearly the reverse in summer.
Both the SST and BWT CCS anomalies are positively skewed due to more
extreme positive temperature anomalies than negative in the CCS. The
processes responsible for the large departures from a normal distribution

require further study, as they have strong influence on extreme tem­
peratures including marine heat waves.
We have conducted a broad survey of ocean conditions around themargins of the continental United States with a goal of encouraging
more detailed analyses using high-resolution ocean reanalyses, such as
GLORYS. While studies evaluating recent ocean reanalyses are being
conducted, better understanding of how well they represent conditions
and processes should lead to improvements in the data, models andassimilation systems used to create them. These advances may beespecially critical in coastal regions, as most global reanalysis systems
have been designed for the open ocean.
CRediT authorship contribution statement
Michael A. Alexander: Conceptualization, Methodology, Writing –original draft, Writing – review & editing, Supervision, Project admin­
istration, Funding acquisition. James D. Scott: Methodology, Software,
Formal analysis, Investigation, Data curation, Visualization, Writing –review & editing. Michael G. Jacox: Conceptualization, Methodology,
Writing – review & editing, Funding acquisition. Clara Deser:
Conceptualization, Methodology, Writing – review & editing, Funding
acquisition. Dillon J. Amaya: . Antonietta Capotondi: Conceptuali­zation, Methodology, Writing – review & editing. Adam S. Phillips:
Writing – review & editing, Visualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The data is publicly available from CMEMS at https://doi.org/
10.48670/moi-00021
Acknowledgements
We thank the NOAA Climate Program Office, including theModeling, Analysis, Predictions and Projections (MAPP) Program for18

M.A. Alexander et al.

Progress in Oceanography 216 (2023) 103055

funding this project. CD and AP received support through NOAA grant#NA20OAR4310378.
Data Availability.
All of the GLORYS version 12 output provided by the Copernicus
Marine Environmental Monitoring Service (CMEMS) is available at:https://resources.marine.copernicus.eu/products.

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Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.pocean.2023.103055.
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