Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5):
Upgrades, Validations, and Intercomparisons
BOYIN HUANG,
a
PETER W. THORNE,
b
VIVA F. BANZON,
a
TIM BOYER,
a
GENNADY CHEPURIN,
a,c
JAY H. LAWRIMORE,
a
MATTHEW J. MENNE,
a
THOMAS M. SMITH,
d,e
RUSSELL S. VOSE,
a
AND HUAI-MIN ZHANG
a
a
NOAA/NCEI, Asheville, North Carolina
b
Irish Climate Analysis and Research Units, Department of Geography, Maynooth University, Maynooth, Ireland
c
Department of Atmospheric and Ocean Science, University of Maryland, College Park, College Park, Maryland
d
NOAA/STAR/SCSB, College Park, Maryland
e
CICS/ESSIC, University of Maryland, College Park, College Park, Maryland
(Manuscript received 22 November 2016, in final form 26 June 2017)
ABSTRACT
The monthly global 28328 Extended Reconstructed Sea Surface Temperature (ERSST) has been revised
and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0
(R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from
HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been
substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better
representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy
measurements, while the global long-term trend remains about the same. Progressive experiments have been
undertaken to highlight the effects of each change in data source and analysis technique upon the final
product. The reconstructed SST is systematically decreased by 0.0778C, as the reference data source is
switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are
decreased by 0.18–0.28C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from
remaining innovations are mostly important at small space and time scales, primarily having an impact where
and when input observations are sparse. Cross validations and verifications with independent modern ob-
servations show that the updates incorporated in ERSSTv5 have improved the representation of spatial
variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of
SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and
short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.
1. Introduction
Sea surface temperature (SST) is an essential climate
variable (Bojinski et al. 2014) and one of the most impor-
tant indicators of Earth’s climate (EPA 2014). Historic SST
data are widely used in climate simulations, assessments,
and monitoring activities (IPCC 2013; Xue et al. 2016).
Several SST datasets have been developed by independent
groups and are availab le to the public, with several of these
updated monthly or more frequently. Some analyses only
use in situ observations , prominent examples being the
Extended Reconstructed SST (ERSST; Smith et al. 1996;
Huang et al. 2015a), Hadley Centre SST, version 3
(HadSST3; Kennedy et al. 2011a,b), and Japan Meteoro-
logical Agency Centennial Observation-Based Estimates
of SSTs (COBE-SST; Ishii et al. 2005) and COBE-SST,
version 2 (COBE-SST2; Hirahara et al. 2014). Others use
both in situ and satellite observations, examples including
the National Centers for Environmental Prediction
(NCEP) Weekly Optimum Interpolation SST (WOISST)
(Reynolds et al. 2002), National Centers for Environmen-
tal Information (NCEI) Daily Optimum Interpolation SST
(DOISST; Reynolds et al. 2007), Hadley Centre Sea Ice
and Sea Surface Temperature dataset (HadISST; Rayner
et al. 2003), and Kaplan SST (Kaplan et al. 1998). Reliable
SST retrievals from satellites start in the early 1980s while
Corresponding author: Boyin Huang, boyin.huang@noaa.gov
Denotes content that is immediately available upon publica-
tion as open access.
15 O
CTOBER 2017 H U A N G E T A L . 8179
DOI: 10.1175/JCLI-D-16-0836.1
Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyrig ht information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).
in situ observations are available much earlier but have
changed substantively through time in both their
methods of measurement and where the measurements
are taken. Therefore, the problem of creating a de-
pendable estimate of the true historical variability and
change is a substantial challenge (Kent et al. 2017).
Several intercomparison studies and assessments have
indicated that the global-scale features and long-term
trends are broadly consistent among SST products (e.g.,
Yasunaka and Hanawa 2011; IPCC 2013; Kennedy 2014).
Differences among the available products can largely be
reconciled by the quantified uncertainties associated with
those SST analyses (e.g., Huang et al. 2016a; Kennedy
et al. 2011b). However, the differences are often larger
than the recognized single-dataset uncertainties in some
regions (e.g., the tropical Pacific) and over shorter time
scales (e.g., the past two decades) (Huang et al. 2013,
2016b). These interdataset differences mostly result from
how the in situ SST data biases are corrected (Huang et al.
2015a,b; Kent et al. 2017) and may also result from quality
control and gap-filling choices when and where observa-
tions are sparse, particularly in early record periods.
Recent studies based on ERSST, version 4 (ERSSTv4;
Huang et al. 2015a), indicated that the global SST has
been warming in the most recent decade as fast as in the
past 50 years (Huang et al. 2016b), which called into
question the existence of a recent global warming ‘‘hia-
tus’’ reported in the IPCC Fifth Assessment Report
(IPCC 2013; Karl et al. 2015). These changes from the
precedingERSSTv3bweremostlyassociatedwiththe
bias correction of ship SST observations using the most
recent Hadley Centre Nighttime Marine Air Tempera-
ture, version 2 (HadNMAT2; Kent et al. 2013), and how
buoy SSTs were handled. These conclusions were further
confirmed by satellite observations from the Advanced
Very High Resolution Radiometer (AVHRR; Huang
et al. 2016b) and, more recently, independently using
other independent high-quality observations (e.g., Argo
floats; Hausfather et al. 2017).
Dataset construction is not a one-time operation. Any
given dataset version represents a snapshot of the then-
current best knowledge of the data issues and technical
and methodological capabilities available to address
them. Knowledge, data availability, and technical capa-
bilities evolve with time, and hence periodic reassess-
ments and updates are warranted. Several suggestions
regarding new data sources, current limitations, and so
forth have been forthcoming from users. In addition, the
work of Huang et al. (2016a) on uncertainty quantifica-
tion led to potential innovations and improvements that
warranted further investigation.
A number of analyses have been undertaken since the
ERSSTv4 release in early 2014 to constantly question
the assumptions underlying the algorithm and seek im-
proved estimates of the true SST state through time
globally, regionally, and locally. The innovations pur-
sued in ERSSTv5 and their rationale can be considered
to fall into the following four broad classes.
a. Choice of reference observation
A global averaged offset (0.128C; 1990–2010) was
found between ship and buoy observations (Rayner
et al. 2003; Kennedy et al. 2011b; Huang et al. 2015a;
Hirahara et al. 2014). This offset could apply to either
ship or buoy observations as far as the SST anomalies
(SSTAs) are concerned. In ERSSTv4, the offset was
applied to the buoy observations after the 1980s; the
buoy offsetting avoided applying a bias correction to all
pre-1980s data that are dominated by ship observations
(Huang et al. 2015a). On the other hand, it is now re-
alized that applying an offset to ship observations (in-
stead of buoys) has the advantage of making timely
updates simpler as the present-day data (at least by
volume) primarily are from buoy measurements, and in
doing so we are also no longer dependent upon third-
party-provided NMAT data (from Met Office in
ERSSTv4). In addition, buoy-observing techniques have
been shown to be more homogenous and the buoy data
are more accurate than ship data (Reynolds and Smith
1994; Reynolds et al. 2002).
b. Spatial variability of SSTA
Since the public release of ERSSTv4, both internal and
external users have suggested that SSTAs appear to be too
smooth over much of the globaloceans.Thiscan seriously
affect its utility for regional and local scale studies. For
example, it tends to damp the magnitude of El NiñoandLa
Niña events relative to similar SST products. This damping
results from the strong spatial filters applied to the training
data when the base functions [empirical orthogonal tele-
connections (EOTs); van den Dool et al. 2000] were cal-
culated. In addition, the EOTs in ERSSTv4 were damped
in the high latitudes, and there were no EOTs in the Arctic
at all. These made it difficult to reconstruct the SSTs in
partially ice-covered oceans, particularly in recent decades,
when more SST observations have become available be-
cause of decreased sea ice coverage.
c. Quality screening of in situ data
The bias-corrected first-guess (FG) SSTs from
ERSSTv3b were used in the quality control (QC) pro-
cedures in ERSSTv4. SST observations were discarded
if they deviated from the FG by more than 4.5 times the
SST standard deviation (STD). Since the raw SST ob-
servations are approximately normally distributed near
the average of bias-uncorrected SSTs, the selection of
8180 JOURNAL OF CLIMATE VOLUME 30
the bias-corrected FG resulted in the inclusion of more
warm SSTs, since the SST observations were generally
cold biased before the 1940s (Kennedy et al. 2011b;
Huang et al. 2015a; Hirahara et al. 2014). Huang et al.
(2016a) found a strong effect of this choice on the final
analysis within the ensembles. This shortcoming is ad-
dressed in this ERSSTv5 improvement.
d. New input datasets
The SSTs from the International Comprehensive
Ocean–Atmosphere Data Set (ICOADS), release 2.5
(R2.5; Woodruff et al. 2011), and sea ice concentration
from HadISST were used in ERSSTv4. The ICOADS
SSTs have now been updated to R3.0 (Freeman et al. 2017;
https://doi.org/10.7289/V5CZ3562). The HadISST sea ice
concentration dataset has been updated to version 2
(HadISST2; Titchner and Rayner 2014). The Argo program
of autonomous ocean subsurface profilers, with increased
numbers starting from 1999 (Argo 2000; Roemmich et al.
2001), reached global coverage around 2005 (except mar-
ginal seas, ice-covered areas, and continental shelves). The
number of observations from Argo floats between 0- and
5-m depth (Argo5obs) has rapidly expanded over 2000–06
(Fig. 1a) and has maintained near-global coverage since
2006 (Fig. 1b). Theref ore, Argo observations may improve
coverage, making up for the reduction in ship numbers,
and reporting timeliness. However, it is necessary that
the Argo5obs data are corrected toward the SSTs fr om
drifting buoys at a typical depth of 0.2 m, since the ob-
serving depths of the two different types of instruments
differ. As a result, ERSSTv5 is representative of SST mea-
sured at a nominal depth of 0.2 m.
These four innovations have been implemented in
ERSSTv5 using an eight-step process, one step at a time
(Table 1 ). The improvements were evaluated using a com-
bination of independent observations and cross-validation
testing.
The remainder of the paper is arranged as follows:
The validation datasets and the source datasets used in
ERSSTv5 are described in section 2. The ERSST re-
construction method is briefly described in section 3.
The upgrades and their impacts are assessed in section 4.
Intercomparisons are presented in section 5. A summary
is given in section 6.
2. Datasets
a. Data sources used in ERSSTv5 processing
1) ICOADS R3.0
The objective of ICOADS is to support climate as-
sessment and monitoring, reanalyses, and near-real-
time (NRT) applications, among others. In comparison
to R2.5 (Woodruff et al. 2011), R3.0 (Freeman et al.
2017) includes additional metadata such as assignment
of a unique identifier (UID) to each marine report, new
near-surface oceanographic data elements, and cloud
parameters. Many new input data sources have been
acquired and updated, and improvements were made to
existing data sources. Other improvements include re-
moval of erroneous data. R3.0 is available from both the
National Center for Atmosphere Research (NCAR)
and NCEI.
The in situ observations of R3.0 archived at NCEI
from 1854 to 2015 are used in ERSSTv5, while R2.5 was
used in ERSSTv4. The Global Teleconnections System
(GTS) receipts from NCEP after January 2016 are used
in operational ERSSTv5. NCEP GTS may slightly differ
from NRT ICOADS R3.0 (Freeman et al. 2017), but the
difference should not affect the discussion here that
considered the period of 1854–2015. In comparison with
R2.5, ICOADS R3.0 includes substantially more ship
observations in the 1850s–60s, 1950s–60s, 1990s, and
2000s–10s and includes more buoy observations in the
1980s–2000s (Fig. 1a). Spatial coverage is slightly higher
in ICOADS R3.0 than R2.5 in the 1850s–60s and in the
later 2000s (Figs. 1b and 1c).
2) ARGO SST ABOVE 5 M
Argo observations were not included in ERSSTv4 but
are included in ERSSTv5. The Argo data used in
ERSSTv5 are from the Global Data Assembly Centre,
France (https://doi.org/10.17882/42182). The Argo pro-
gram’s main purpose was to provide as complete a pic-
ture as possible of the oceans’ subsurface temperature
and salinity structure in the upper 2000 m in order to
track changes in ocean heat and freshwater content
(Riser et al. 2016). Floats are deployed oceanwide. They
normally drift at depths of near 1000 m, and then on
usually 10-day cycles, they typically descend to near
2000-m depth; they ascend from that depth to the sur-
face, measuring temperature and salinity along the way.
Data are transmitted to satellite before the floats de-
scend to drift for another 10 days. In this manner, most
of the ice-free ocean above 2000-m depth outside of
marginal seas has been observed. Moreover the in-
ternational program has a coordinated quality control
and dissemination system ensuring the highest quality
and availability of the Argo observations.
For ERSSTv5, Argo5obs are retrieved from Argo
floats and used as SST observations. Roemmich et al.
(2015) showed that the global mean Argo temperature
anomaly above 5 m tracks closely with SST change. The
number of Argo5obs data receipts has been expanded
over 2000–06 and is nearly equivalent to the number of
ship observations by the end of 2015 (Fig. 1a). The
15 OCTOBER 2017 H U A N G E T A L . 8181
global areal coverage of Argo5obs increases to 30% by
the end of 2015, which is as high as that of buoy obser-
vations (Fig. 1c). Argo observations provide approxi-
mately 5%–10% extra area coverage in addition to ship
and buoy observations after 2000 (Fig. 1b).
Our analysis in section 3 shows that Argo5obs data are
close to buoy observations with an averaged difference
and root-mean-square difference (RMSD) of 0.038C.
Thus they are first used as a validation dataset to assess
the improvements in the progressive experiments listed
in Table 1 and only then included in the ERSSTv5 at the
final stage as described in section 4d(2) and for opera-
tional monthly updates.
3) HADISST2 SEA ICE CONCENTRATION
Sea ice concentrations from HadISST2 are used to
relax reconstructed SSTs in partial ice-covered areas
toward the freezing point (21.88C) at the very final stage
FIG. 1. (a) Number (log10 scale), (b) accumulated areal coverage ratio, and (c) individual
area coverage ratio of monthly ship (black), buoy (red), and Argo5obs (green) observations.
The spatial coverage is a ratio between the area of 28328 boxes containing observations and
the total ocean area. The ICOADS R2.5 (R3.0) is represented by solid (dotted) lines. A
12-month running filter is applied in plotting.
8182 JOURNAL OF CLIMATE VOLUME 30
in section 4d(3). During the development of ERSSTv5,
we attempted to replace the SST relaxation by a method
that used sea ice proxy SST as described in Reynolds
et al. (2002). However, we found that the proxy SSTA
from sea ice concentration is 0.28–0.58C lower than
available in situ SSTA in the Southern Ocean before the
1970s, and therefore the proxy SST is not applied in
ERSSTv5. Further study is needed on sea ice proxy SST
and its dependence on historic sea ice reconstruction
prior to potential implementation.
Monthly 18318 sea ice concentration from HadISST2
over 1870–2015 (Titchner and Rayner 2014) is averaged
by area weighting to 28328 and used in ERSSTv5. The
monthly sea ice concentration before 1870 is set to pe-
riodic monthly values of 1870, since the concentration
in 1870–1900s (1870–1940s) was set to a monthly cli-
matology in HadISST2 in the Northern (Southern)
Hemisphere oceans. In comparison with the sea ice
concentration from HadISST (Rayner et al. 2003) that
was used in ERSSTv4, the sea ice concentration in
HadISST2 is approximately 25% higher before the 1980s
and 5% higher after the 1980s in the Southern Hemi-
sphere (Fig. 2b) and is slightly higher in the Northern
Hemisphere in the 1940s–50s and 1970s–90s (Fig. 2a).
The interannual variability after the 1960s is larger in
HadISST2 than in HadISST. These differences indicate
that the sea ice concentration in HadISST2 and HadISST
may have large uncertainty in the Arctic before the 1950s
and in the Southern Ocean before the 1970s.
Starting from January 2016, the daily 0.5830.58 sea ice
concentration from NCEP (Grumbine 2014)isaveraged
by area weighting to monthly 28328 ERSST ocean grids
and used for monthly operational ERSSTv5 production.
Then, the NCEP sea ice concentration is adjusted toward
the HadISST2 sea ice concentration, when the sea ice
concentration in NCEP is higher than 0.3. The adjustment
uses the monthly varying averaged offsets between the
two products over 2006–15. The adjustment is estimated
by fir st calculating monthly differences between
HadISST2 and NCEP ice concentration over 2006–15 as a
function of month from January to December and ice
concentration from 0.0 to 1.0 with an increment of 0.1.
The difference is then applied to NCEP ice concentration
on 28328 grids. The procedure is done separately for
Northern and Southern Hemisphere oceans.
The adjusted NCEP sea ice concentration is close to
the HadISST2 sea ice concentration (Fig. 2). However,
the adjustment is not perfect because the concentration
below 0.3 is not adjusted to avoid negative sea ice con-
centrations. This will not impact the SST analysis since
SSTs are only adjusted in regions where ice concentra-
tions are above 0.6 as described in section 4d(3).
4) HADNMAT2
The monthly 58358 HadNMAT2 data in 1880–2010
(Kent et al. 2013) were used in ERSSTv4 to perform the
ship SST bias correction. The bias correction was calcu-
lated at 58358 grid, regridded to 28328, and applied to
ERSSTv4. The bias correction to the ship SSTs in
ERSSTv5 is similar to that in ERSSTv4 except that the
bias correction is adjusted by subtracting 0.0778Ctomatch
the average ship-buoy offset over 1990–2010. The offset is
derived as the difference between buoy-based correction
(20.1188C)andNMAT-basedcorrection(20.0418C) over
1990–2010, which will be discussed further in section 4c(1).
The reasons for using NMAT to correct ship SST obser-
vations are 1) NMAT excludes the potential bias due to
diurnal changes in solar radiation during daytime, 2)
NMAT has a good relationship with SST (Huang et al.
2015a), 3) biases in NMAT observations can be corrected
relatively easily using available metadata (Kent et al.
2013), and 4) NMAT is largely independent of SST ob-
servations. It should be noted that the bias correction for
the transition from buckets to engine room intakes (ERI)
starting in the 1940s using NMAT is different from using
the individual bucket model applied in H adSST3
TABLE 1. The eight progressive experiments (Exp) toward ERSSTv5 starting from ERSSTv4.
Exp Progressive revisions
ERSSTv4
UnadjFG Same as ERSSTv4 except for using unadjusted FG.
NDP Same as UnadjFG except for no high-latitude damping in EOT training.
SMT Same as NDP except for using a smoothing of one-time 6-deg instead of three-time 14-deg running
filter in EOT training.
EOT140 Same as SMT except for including additional 10 EOTs in the Arctic.
ShipBias Same as EOT140 except for using combined ship bias corrections referenced to buoy SSTs and a
linear fit estimation prior to Lowess filtering to better account for the 1940s
transition in ship observing methods.
ICOADS3 Same as ShipBias except for using ICOADS R3.0.
Argo5m Same as ICOADS except for including Argo observations above 5 m (Argo5obs).
ERSSTv5 Same as Argo5m except replacing the HadISST sea ice concentration with the HadISST2 sea ice concentration.
15 O
CTOBER 2017 H U A N G E T A L . 8183
