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Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality remains to be systematically assessed. To address this, the Polar ORA Intercomparison Project (Polar ORA-IP) has been established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography. Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state, we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to offsetting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the polar marine environment.
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Climate Dynamics (2019) 52:1613–1650
An assessment often ocean reanalyses inthepolar regions
PetteriUotila1 · HuguesGoosse2· KeithHaines3· MatthieuChevallier4· AntoineBarthélemy2· ClémentBricaud5·
JimCarton6· NevenFučkar7,8· GillesGarric5· DoroteaciroIovino9· FrankKauker10· MeriKorhonen11·
VidarS.Lien12· MarikaMarnela11· FrançoisMassonnet2,7· DaviMignac3· K.AndrewPeterson13· RemonSadikni14·
LiShi15· SteenTietsche16· TakahiroToyoda17· JipingXie18· ZhaoruZhang19
Received: 11 October 2017 / Accepted: 17 April 2018 / Published online: 18 May 2018
© The Author(s) 2018
Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality
remains to be systematically assessed. To address this, the Polar ORA Intercomparison Project (Polar ORA-IP) has been
established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were
addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography.
Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state,
we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to off-
setting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing
its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the
polar marine environment.
Keywords Oceanography· Reanalyses· Arctic· Antarctic· Sea-ice
Marika Marnela was formerly at Finnish Meteorological Institute.
Electronic supplementary material The online version of this
article (https :// 2-018-4242-z) contains
supplementary material, which is available to authorized users.
* Petteri Uotila
1 Institute forAtmospheric andEarth System Research
(INAR)/Physics, University ofHelsinki, Helsinki, Finland
2 Earth andLife Institute, Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
3 University ofReading andNational Centre forEarth
Observation, Reading, UK
4 Centre National de Recherches Météorologiques, Météo
France/CNRS UMR3589, Toulouse, France
5 Mercator Océan, Toulouse, France
6 University ofMaryland, CollegePark, USA
7 Barcelona Supercomputing Centre, Barcelona, Spain
8 Environmental Change Institute, University ofOxford,
Oxford, UK
9 Fondazione Centro Euro-Mediterraneo sui Cambiamenti
Climatici, Bologna, Italy
10 Alfred Wegener Institute, Bremerhaven, Germany
11 Finnish Meteorological Institute, Helsinki, Finland
12 Institute ofMarine Research, Bergen, Norway
13 Met Office, Exeter, UK
14 University ofHamburg, Hamburg, Germany
15 Bureau ofMeteorology, Melbourne, Australia
16 European Centre forMedium-Range Weather Forecasts,
Reading, UK
17 Meteorological Research Institute, Japan Meteorological
Agency, Tsukuba, Japan
18 Nansen Environmental andRemote Sensing Center, Bergen,
19 Shanghai Jiao Tong University, Shanghai, China
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1614 P.Uotila et al.
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1 Introduction
For years, atmospheric reanalysis products, which consist of
multidecadal meteorological model simulations with assimi-
lated observations, have become an invaluable resource
for researchers representing a wide range of disciplines.
Recently, similar products—ocean reanalyses (ORAs)—
have been constructed by many research groups. It is likely
that these products will become as valuable as their atmos-
pheric counterparts.
Specifically, an ocean analysis describes an ocean state
valid for a particular time by a set of gridded oceanographic
variables. Typically an ocean analysis is generated by an
analysis system consisting of a hydrodynamical or statisti-
cal model and an observation assimilation framework, for
the purpose of initialising a forecast. During the analysis
generation process, the forecast model background state is
adjusted toward new observations. The amount of adjust-
ment is denoted as the analysis increment, which quantify
the impact of data assimilation in the analysis system (Cul-
lather and Bosilovich 2012).
Ocean and sea ice reanalyses are analyses in the form of
time series, where every analysis is generated using the same
analysis system for all historical observations. Hence, they
combine observations either statistically or with a hydrody-
namical model, to reconstruct historical conditions and their
changes in the ocean.
Global and regional ORA products are increasingly used
in polar research, but their quality remains to be systemati-
cally assessed. To address this, the Polar ORA Intercompari-
son Project (Polar ORA-IP) has been established following
on from the ORA-IP project (Balmaseda etal. 2015; Toyoda
etal. 2017a, b; Chevallier etal. 2017; Tietsche etal. 2015;
Karspeck etal. 2015; Shi etal. 2017; Valdivieso etal. 2017;
Palmer etal. 2017; Masina etal. 2015; Storto etal. 2017).
These ORA-IP studies have looked at various aspects of
global ocean hydrodynamics (steric sea level, air-sea fluxes,
ocean heat and salt content among others). The only ORA-
IP publication with a polar focus has been Chevallier etal.
(2017), who compared the representation of the sea-ice
cover in the Arctic Ocean in 14 global reanalyses. Using a
variety of in-situ and satellite-based observational datasets,
they investigated mean states, trends and interannual vari-
ability in these reanalyses, focusing on sea-ice concentration
(with extent and area), thickness (with volume), velocity and
snow depth over sea ice.
Chevallier etal. (2017) showed consistency with respect
to sea-ice concentration, which is primarily due to the con-
straints in surface temperature imposed by atmospheric forc-
ing, and ocean-ice data assimilation. However, they found
a large spread in sea-ice and snow thicknesses within the
ensemble of ORAs, due to biases in the ocean-ice model
components, and lack of observational constraint. Chevallier
etal. (2017) discussed the possible role of model param-
eters, prescribed atmospheric forcing and data assimilation
on the spread. They concluded that none of the ORAs stands
superior to the others when compared with observed sea-ice
thickness calculated from satellite altimetry data, and that
data assimilation does not seem to improve the simulated
sea-ice thickness. As a result, estimates of Arctic sea-ice
volume by individual ORAs suffer large uncertainties, and
the ORA multi-model ensemble mean (MMM) ice volume
does not provide a more robust estimate. Most of the global
reanalyses used in Chevallier etal. (2017) have now been
updated and their updates are evaluated in the present paper
which allows direct comparisons with their results.
In this study, we aim for a comprehensive evaluation
of ten selected ORA products (C-GLORS025v5, ECDA3,
GECCO2, Glorys2v4, GloSea5-GO5, MOVE-G2i, ORAP5,
SODA3.3.1, TOPAZ4 and UR025.4) in the Arctic and
Southern Oceans (Table1). For these regions the diagnostics
target the following topics: hydrography; ocean heat (OHC),
salt content (OSC); ocean transports; mixed layer depth
(MLD); sea-ice concentration (SIC) and thickness (SIT);
and snow thickness over sea ice. The ORA product biases
against observed reference data and their mutual spread are
quantified, and possible reasons for discrepancies discussed.
The scope of our manuscript is to provide a broad state-
of-the-science overview of ocean reanalyses, plus our best
estimate of what the truth might look like. In this context,
we will check if the MMM is a useful estimate. As we
will repeatedly show, it is a set of fields which is gener-
ally most consistent with observations. This is what many
users require, although it may not be best suited to analysing
dynamical or physical processes, for example.
If a user does not want the MMM, but would prefer a
single ORA output, for instance to understand the dynamics,
this paper does not seek to tell the user which one to use,
but in addition to providing a general evaluation, it is able
to show which are outliers for certain variables, which can
still be very useful.
We pay particular attention to the performance of the
MMM compared to individual products and the identifica-
tion of outliers. Notably, as the ORAs assimilate observa-
tions they are not independent of some of the reference data
they are compared to. Moreover, we investigate links and co-
variability between the diagnostics, such as the Arctic Ocean
heat content and North Atlantic heat transport, and between
the mixed layer depth, oceanic convection, the upper ocean
hydrography, sea ice and snow. In this way, we try to iden-
tify physical mechanisms causing common and individual
ORA biases.
Although a large majority of the existing ORA publi-
cations does not focus on polar regions, the Coordinated
Ocean Reference Experiment (CORE-II; Danabasoglu etal.
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1615An assessment often ocean reanalyses inthepolar regions
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Table 1 List of ten ocean reanalyses used in the study and their central characteristics
Name C-GLORS025v5 ECDA3 GECCO2 GLORYS2v4 GloSea5-GO5 MOVE-G2i ORAP5 SODA3.3.1 TOPAZ4 UR025.4
Institution CMCC GFDL/NOAA Hamburg
UK MetOffice MRI/JMA ECMWF University of
NERSC University of
12–16 km
Vertical resolu-
50 z-levels 50 z-levels 50 z-levels 75 z-levels 75 z-levels 52 z-levels 75 z-levels 50 z-levels 28 z-isopycnal
75 z-levels
1 m 10 m 10 m
1 m
1 m 2.25 m
1 m
10 m min 3 m
1 m
Time period 1980–2015 1961–2012 1948–2014 1992–2015 1993–2012 1980–2012 1979–2012 1980–2015 1991–2016 1989–2010
Initialization Spinup Spinup Cold start Cold start Spinup Spinup Spinup Spinup Cold start Cold start
Source of
forcing data
ERA-Interim Coupled NCEP RA1 ERA-Interim ERA-Interim JRA-55aERA-Interim NASA
ERA-Interim ERA-Interim
Ocean restor-
Large scale bias
correction to
Fully coupled None T, S restor-
ing towards
EN4.1.1 for
z > 2000
m and lat <
S (
= 20
Surface Haney
SSS restoring
(− 33.333
PSU), 3D T/S
= 1 year)
Relaxing (by
to merged
= 5 years)
to mean T
and S (
10 years).
Relaxation to
= 3 months)
Relaxing T/S
to merged
Sea-ice DA
Nudging None (SST) None (SST) Reduced order
Sea-ice DA
Sea-ice DA
Ocean DA
order KF +
3DVAR large
scale bias
correction to
in-situ T, S
Ocean DA
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1616 P.Uotila et al.
1 3
2014) has produced papers (Downes etal. 2015; Farneti
etal. 2015; Wang etal. 2016a, b) which evaluate the polar
performance of a number of state-of-the-science global
ocean models. The main difference between the CORE-II
model configurations and the ORAs is that the latter employ
advanced data assimilation schemes using mostly the same
ocean-ice observations, while CORE-II models only apply
simple surface flux corrections that, for example, nudge
their sea surface salinities toward climatological values.
However the CORE-II protocol requires the participating
modelling groups to use common atmospheric states and
boundary layer parameterisations to drive their multidec-
adal simulations (e.g. Griffies etal. 2009; Danabasoglu etal.
2014), which is not the case for the ORAs. It is interesting to
compare the relative effectiveness of the common CORE-II
framework with the ORA observations in producing consist-
ent results.
Due to these dependencies, comparisons between CORE-
II and ORA results potentially enable us to estimate the role
of different factors affecting the multi-model skill in the
polar oceans. Similarities between CORE-II and the ORA
MMM performance may reveal common issues in model
physics and resolution, while discrepancies may provide
information on the role of data assimilation and atmospheric
Along with CORE-II results, other relevant literature for
the Arctic and Southern Oceans are discussed in the next two
Sects. 2.1 and 2.2, respectively. In Sect.3, we describe our
diagnostic methods and in Sect.4 we represent the analy-
sis results of ten ORAs. These results are then compared
with previous results, including Chevallier etal. (2017) and
CORE-II, in the discussion (Sect.5). Conclusions follow
in Sect.6.
2 Observed andsimulated changes
inthepolar oceans
2.1 The Arctic Ocean
The Arctic sea ice has shown an unprecedented decline
since the mid-1990s, which also has impacted the state of
the Arctic Ocean (Comiso 2012; Polyakov etal. 2013; IPCC
2013; Polyakov etal. 2017). This dramatic change high-
lights the need for more comprehensive environmental data
to assess the state and impacts of the Arctic in transition.
However, even after a number of targeted field expeditions
and improved satellite coverage, the Arctic Ocean observa-
tions remain sparse compared to the northern North Atlantic.
An important reason for this is that with a few exceptions
there are no Argo-buoy deployments north of 70
N to pro-
vide hydrographic observations, as the buoys cannot operate
under perennial sea ice. Furthermore, international research
T temperature data, S salinity, SST sea surface temperature, SSS sea surface salinity, SSH sea surface height, SIC sea-ice concentration, SIT sea-ice thickness, SIV sea-ice velocity, DA data assim-
ilation, KF Kalman filter, EnKF ensemble Kalman filter, OI objective interpolation
a Climatological radiation biases from GEWEX3.0 (StackhouseJr etal. 2011) are corrected
Table 1 (continued)
Name C-GLORS025v5 ECDA3 GECCO2 GLORYS2v4 GloSea5-GO5 MOVE-G2i ORAP5 SODA3.3.1 TOPAZ4 UR025.4
Ocean DA
Reference Storto etal.
Chang etal.
Köhl (2015) Garric etal.
Blockley etal.
(2014, 2015)
Toyoda etal.
Zuo etal.
Tietsche etal.
Carton and
Giese (2008)
Xie etal.
Valdivieso etal.
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1617An assessment often ocean reanalyses inthepolar regions
1 3
teams have had restricted access to the observations from the
Russian Arctic which has further limited the observational
coverage. Climate models appear too conservative in terms
of simulating the observed Arctic sea-ice decline, although
there have been some improvements, while their prediction
accuracy is significantly limited by the relatively large cli-
mate variability (Stroeve etal. 2012; Jahn etal. 2016; Melia
etal. 2015).
Despite the aforementioned limitations, significant pro-
gress in understanding of the physical state and evolution
of the Arctic Ocean has been gained during the last decade.
We briefly list some research efforts closely related to the
development of ocean reanalysis products in the Arctic.
The Arctic Ocean Model Intercomparison Project
(AOMIP) and its successor, the Forum for Arctic Modeling
and Observational Synthesis (FAMOS), have in the last two
decades identified many model shortcomings and come up
with recommendations to reduce the impacts of these short-
comings (Proshutinsky etal. 2016). AOMIP and FAMOS
have covered a wide range of topics from Arctic Ocean ener-
getics to sea-ice dynamics (for example Uotila etal. 2006;
Heimbach etal. 2010; Karcher etal. 2012). The first AOMIP
phase proved that the co-ordinated community approach is
the most effective way to address the degree of uncertainty
of model results. During AOMIP, ocean-ice models with
data assimilation were first introduced to the community (see
for example Kauker etal. 2009). Later, FAMOS has been
a very productive collaborative effort by producing more
than 60 publications including a special issue in the Journal
of Geophysical Research (Proshutinsky etal. 2016). The
AOMIP/FAMOS modelling studies document, in addition
to their scientific results, important ORA developments in
the polar regions from the reanalysis methodological per-
spective. However, a systematic diagnostic analysis of ORA
products in the Arctic is missing from the AOMIP/FAMOS
studies. This is likely due to the relatively late appearance of
ORAs, which have a global scope, in contrast to the regional
AOMIP/FAMOS one, and to the strong process focus of
In addition to the sea-ice changes mentioned above, the
upper Arctic Ocean is freshening and Rabe etal. (2014)
were able to identify a freshwater flux trend of 600 ± 300
from 1992 to 2012. The variability of the Arctic
freshwater content correlates well with the atmospheric forc-
ing and can be closely reproduced by the regional coupled
sea ice-ocean model North Atlantic Arctic Sea Ice Ocean
Model (NAOSIM) simulations (Karcher etal. 2003). Rabe
etal. (2014) suggest a high freshwater export through the
Fram Strait until the mid-1990s, followed by lower export
rates with no trend thereafter, although models may show
large differences in terms of interannual variability of the
liquid freshwater through the Fram Strait (Jahn etal. 2012).
Some more recent studies present results from individual
polar ocean reanalyses and are worth mentioning here. For
example, Xie etal. (2017) analysed multi-decadal ensem-
ble simulations from the regional TOPAZ4 ocean-ice data
assimilation system in the Arctic and found that TOPAZ4
performed better with respect to near-surface ocean vari-
ables compared to subsurface ocean and sea-ice thickness
due to sparse observations. Furthermore, the TOPAZ4 skill
improved as the polar observation network became denser.
Specifically, TOPAZ4 has a too cold and diffuse Atlantic
water (AW) layer in the Arctic leading to a cold bias of 0.3
C at around 400 m, while the Barents Sea is too warm and
saline. Although, the decadal reduction of TOPAZ4 sea-ice
extent is close to the observed, its regional distribution has a
dipole bias—sea-ice concentration is too low close to the ice
edge and too high in the central pack, due to the missing sea-
ice heat capacity of TOPAZ4 sea-ice model. Xie etal. (2017)
also found that the TOPAZ4 sea ice is too thin, on average.
Lien etal. (2016) applied objective statistical methods to
assess the added value of data assimilation in three ocean
models, including TOPAZ4, for hydrography, volume and
heat transports in the Nordic Seas (the Greenland, Iceland,
Norwegian and Seas) and the Barents Sea. They found that
both data assimilation and higher model resolution improved
the model realism. Specifically, high model resolution in
ocean and atmospheric forcing improved the representation
of variables closely related to forcing, such as sea-ice con-
centration and sea surface temperature. Hydrographic data
assimilation had a tendency to reduce hydrographic biases,
but its effect on the liquid ocean transport remained limited
(Zuo etal. 2011). Lien etal. (2016) found that the modelled
heat transports through the Fram Strait to the Arctic Ocean
were within the observational range related to generally real-
istic looking hydrography and currents.
Recently, a set of multidecadal ocean-ice model hindcasts
generated following the CORE-II protocol has provided a
wealth of information on the performance of state-of-the-
science global ocean-ice models in the Arctic Ocean (Dana-
basoglu etal. 2014). The CORE-II atmospheric state, includ-
ing the global warming trend, was used to drive the models
for 60 years from 1948 to 2007. In total, CORE-II models
were run for 300 years, corresponding to 5 consecutive loops
of the 60-year forcing period. Wang etal. (2016a) analysed
the sea-ice extent, sources of solid freshwater and the solid
freshwater content of CORE-II models in the Arctic focus-
sing on the fifth forcing cycle. They found that the models
reproduced observed sea-ice variability more consistently
than the mean state. The CORE-II MMM sea-ice extent was
somewhat smaller than observed, in particular in summer,
which resulted in a stronger than observed seasonal cycle.
The CORE-II MMM overestimated the winter-to-summer
sea-ice retreat rate, related to the negative summer sea-ice
extent bias. Models that overestimated the sea-ice thickness,
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1618 P.Uotila et al.
1 3
underestimated the multidecadal decline of the Arctic sum-
mer sea-ice cover. On average, the models underestimated
the observed sea-ice thinning by a factor of two Wang etal.
(2016a) stated.
In terms of hydrography, Ilicak etal. (2016) found that
while the CORE-II MMM appears to be relatively close to
observations, there is a large inter-model temperature spread
in the Arctic Ocean. Specifically, at intermediate depths,
including the warm AW layer, modelled-to-observed tem-
perature differences were large. The CORE-II MMM had
a too cold AW at 400 m whose signal disappeared quickly
northward away from the Fram Strait, and an overall cold
and fresh bias in the Arctic interior, although its mean fresh-
water transports through the Arctic gateways appear realis-
tic (Wang etal. 2016b). With respect to individual models,
those with too cold intermediate depths have an excessive
cold water transport to the Arctic Ocean through the St.
Anna Trough, while those models with a warm Arctic have
a strong inflow of warm water in the Fram Strait. As with
sea ice, the CORE-II models agree on the ocean decadal
variability, which is dictated by the common atmospheric
forcing, more than they do on the ocean mean state. Fol-
lowing these findings, Ilicak etal. (2016) point out that the
CORE-II ocean-ice models have a too coarse horizontal
resolution, typically 1
in latitude, to realistically represent
the AW inflow, and the deep water formation and currents
originating from the shallow continental shelf regions.
2.2 The Southern Ocean
Over recent decades, the Antarctic sea-ice extent has
remained relatively stable but with large interannual vari-
ability and a small increasing trend that strongly contrasts
with the large decline in the Arctic over the same period
(Parkinson and Cavalieri 2012; Maksym etal. 2012). Over
the Southern Ocean the westerlies have strengthened and
shifted southward, spreading the sea ice northward more
effectively (Marshall 2003; Zhang 2014). Below the sur-
face layer, the temperature has risen while a freshening is
observed in many areas (Gille 2008; Schmidtko etal. 2014;
deLavergne etal. 2014). Simulations performed with cou-
pled climate models are generally not able to adequately
reproduce these trends. In particular, the majority of them
display a decrease in ice extent over the last 30 years in
response to anthropogenic forcing. Part of the discrepancy
may relate to the large internal variability of the Southern
Ocean, but systematic biases are also present in the simu-
lations (Zunz etal. 2013; Turner etal. 2015; Jones etal.
2016). Even the ocean-ice models driven by prescribed
forcing derived from atmospheric reanalyses, such as in
CORE-II experiments, have trouble reproducing the mean
state of the Southern Ocean. For example, CORE-II models
display relatively large biases in the position of the ice edge
all year long and the CORE-II MMM sea-ice extent is lower
than observed, particularly in summer (Farneti etal. 2015;
Downes etal. 2015). Part of these common biases are related
to the common CORE-II atmospheric forcing.
In addition to sea-ice biases, the majority of the CORE-II
models underestimate the MLD in summer while some over-
estimate it in winter, with a clear impact on the characteris-
tics of the intermediate water masses (Downes etal. 2015).
On average, the CORE-II MMM winter mixed layer depth
bias is positive and dominated by models with a deep mixed
layer and more-saline-than-observed upper ocean. Models
with warmer and fresher upper ocean produce shallower-
than-observed winter mixed layers. Downes etal. (2015)
conclude that the uniformly shallow summer mixed layers
are mainly a result of the common atmospheric forcing,
while in winter many other additional factors, such as sea
ice, surface buoyancy fluxes and model parameterisations,
affect the mixed layer depth, and result in varying biases in
individual CORE-II models.
Deeper in the ocean, several CORE-II models have cold
biases associated with positive MLD biases in the regions of
the Antarctic Bottom Water formation. The CORE-II MMM
shows warm and saline biases north of 50
S, but cool and
fresh biases to the south in the upper 2000 m layer. The
fresh bias south of 50
S could be linked to the low levels of
brine rejection from ice to the surface ocean related to low
CORE-II sea-ice extents (Downes etal. 2015). Below 2000
m depth the CORE-II MMM is biased towards a colder and
fresher state than the observational WOA09 climatology.
Inter-ocean exchanges play an important role in global
climate in response to variations of local or remote heat
and freshwater fluxes via the global ocean circulation. This
global ocean transport, coupled to global oceanic thermoha-
line circulation, links the full ocean volume to the climate at
long time scales. The Antarctic Circumpolar Current (ACC)
is the most intense current of the world ocean and by far the
largest conduit for interbasin exchanges.
Farneti etal. (2015) found that the CORE-II MMM Drake
passage transport was relatively high (
150 Sv), due to two
ensemble members, but close to the Climate Model Inter-
comparison Project Phase 3/5 (CMIP3/5) MMM transport.
After excluding these two CORE-II models, the CORE-II
MMM transport became closer to observed estimates of
130 to 150 Sv. However, as discussed in Sect.3.5, CORE-
II and CMIP ensembles underestimate more recent ACC
transport estimates by Donohue etal. (2016) and deVerdiére
and Ollitrault (2016).
The CORE-II mass transport time series in the ACC tends
to increase during 1948–2008, although this increase flat-
tens toward the end of the period. Interestingly, the eddy-
permitting models and models with time-dependent and/
or three-dimensional eddy-induced coefficients show lower
transport trends than the models with a constant or absent
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1619An assessment often ocean reanalyses inthepolar regions
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eddy-induced coefficients. This indicates that models which
more realistically represent mesoscale eddy effects do not
support long-term increases in the ACC transport, as a
response to strengthening westerlies. This ACC insensitivity
to the changing winds can be explained by eddy compensa-
tion effects at high resolution and advanced eddy-parame-
terisation models (Farneti etal. 2015).
These ACC transport trends in CORE-II models are
in turn related to the upper ocean water mass structure
and linked to temperature, salinity and sea-ice trends. As
described by Downes etal. (2015), the CORE-II MMM
shows cooling south of 60
S and warming north of the ACC
S in the upper 2000 m. Furthermore, the CORE-
II MMM shows a general freshening which, along with the
upper ocean temperature trends, can be explained by the
stronger and southward moving westerlies which increase
the ocean surface heat loss and enhance the atmospheric
moisture transport (and therefore the precipitation). Another
factor playing a role in the freshening is the redistribution of
freshwater by sea ice which is often more important in the
Southern Ocean than precipitation (Abernathey etal. 2016;
Haumann etal. 2016). These model-produced trends bear
good a resemblance to those observed.
For some variables such as the sea-ice concentration,
observations with a good spatial coverage are available since
1979 from remote sensing. Despite the uncertainties related
to the calibration of the satellite records (e.g. Eisenman etal.
2014), this provides valuable information on the state of the
system and an essential metric for model validation. The
number of subsurface observations has increased over the
last decades thanks to Argo floats (Argo 2000) and sensors
attached to marine mammals.
Nevertheless, these observations remain relatively
scarce, especially below the sea ice (Schmidtko etal. 2014;
deLavergne etal. 2014; Roemmich etal. 2015; Roquet
2015; Pellichero etal. 2017). The amount of in-situ obser-
vations for sea-ice thickness is also relatively limited (Worby
etal. 2008). Data assimilation is potentially a powerful tool
to obtain estimates for variables that cannot be directly
observed or have a poor spatial and temporal observational
coverage such as the Antarctic sea-ice thickness (Massonnet
etal. 2013), the transport of the subpolar gyres (Duan etal.
2016) and the amount and path of deep water formed close
to Antarctica (van Sebille etal. 2013; Azaneu etal. 2014).
3 Material andmethods
3.1 Ten selected ocean reanalyses
The ORA output data have been collected in a data base
hosted by the Integrated Climate Data Center (ICDC) at
Hamburg University1 and are freely available. Some data
were already present from previous ORA-IP studies, but
many products were updated and a few new ones added
for this study. Ten ORAs were selected to be compared
(Table1), with the most comprehensive temporal overlap
over 1993–2010 consisting of all variables required for the
diagnostics. The remaining ORAs were discarded due to lack
of data either in terms of temporal coverage or variables.
Nine ORAs have a global coverage, while one (TOPAZ4)
is a regional Arctic-North Atlantic product. Of nine global
ORAs, five are of European origin (all using varying ver-
sions of the NEMO ocean), three are American and one is
Japanese. All variables analysed were monthly means cover-
ing the common intercomparison period from 1993 to 2010
with a few exceptions (mentioned in particular subsections
of that diagnostic).
For sea-ice diagnostics, Chevallier etal. (2017) analysed
eleven ORAs of which eight are participating in this study,
while three (GECCO2, SODA3.3.1 and TOPAZ4) were not
previously assessed. Only three ORAs of the other eight
(ECDA3, ORAP5 and UR025.4) have not been upgraded
meanwhile. As the horizontal resolution of ORAs vary we
interpolated all fields onto a common regular 1
tude-longitude grid for intercomparisons.
Several observational data sets were used to estimate
the product-to-observed performance. For the hydro-
graphical analysis, three observational products were used:
EN4.2.0.g10 (1993–2010; Good etal. 2013), World Ocean
Atlas 2013 (WOA13, 1995–2015; Locarnini etal. 2013;
Zweng etal. 2013) and the Sumata Arctic hydrography from
Hiroshi Sumata at the Alfred Wegener Institute, Germany
based on 1980–2015 observations (Sumata etal. 2017).
Notably, the Sumata hydrography is the most comprehensive
and up-to-date of the three observational products containing
Arctic observations from 28 campaigns from 1980–2015. As
for the ORA output, observational data were interpolated
onto the common grid for intercomparisons.
3.2 Sea‑ice concentration andthickness
Sea-ice concentration (SIC, the relative amount of area cov-
ered by ice, compared to some reference area) is the most
well-constrained sea-ice variable although not flawless
(Ivanova etal. 2014). Satellite observations using passive
microwave sensors exist since 1979, available on a daily
basis since 1987 at a horizontal resolution finer than 25 km.
Chevallier etal. (2017) evaluated various aspects related
to sea-ice concentration: the position of the ice edge, sea-
ice concentration in the marginal ice zone (concentrations
from 15 to 90%) and in the pack ice (concentrations > 90%),
1 http://icdc.cen.uni-hambu /reana lysis -ocean /oraip .html.
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1620 P.Uotila et al.
1 3
representation of leads within the pack ice, seasonal cycles
and trends of integrated Arctic sea-ice area and sea-ice
We use these metrics to evaluate seasonal cycles of sea-
ice concentration in both the Arctic and Southern Oceans
in the new set of reanalyses. Due to the inclusion of one
regional Arctic reanalysis that excludes the North Pacific,
the Arctic-integrated extent and area are calculated over a
reduced Arctic domain closed at the Bering Strait. We use
the same observational datasets as in Chevallier etal. (2017)
to assess the realism of ORAs, while taking into account
observational uncertainties. Specifically, these observational
sea-ice concentration products are based on the NASATeam
algorithm of the National Snow and Ice Data Centre
(NSIDC; Cavalieri etal. 1999), from Ifremer/CERSAT using
the ARTIST algorithm, and by EUMETSAT Ocean-Sea Ice
Satellite Application Facilities (OSISAF). Although these
three products have resolutions finer than 25 km, all data are
interpolated onto the common regular grid.
Sea-ice thickness (SIT hereafter) is a key diagnostic for
assessing the performance of ORAs in the polar oceans. An
unrealistic reconstruction of SIT would mean that essential
thermodynamic processes controlling ice growth or melt
are missing, or that the dynamics of the sea-ice pack is not
captured accurately, or both. A major obstacle for the assess-
ment of SIT is the lack of observationally-based data. Unlike
sea-ice concentration no large-scale and time-homogeneous
records of sea-ice thickness are available.
For the Arctic sea-ice thickness, most of our knowledge
relies on collections of datasets from various sources (e.g.
Lindsay 2010). Chevallier etal. (2017) used estimates of
sea-ice thickness from the ICESat instruments, and estimates
of sea-ice volume gathered in Zygmuntowska etal. (2014).
In our study, data from the Ice Thickness Regression Proce-
dure (ITRP) are used to analyze the ORA performance. We
selected two 2-month periods (February/March and Octo-
ber/November) for the comparison because the ICESat data
are available in these months. The ITRP combines upward
looking sonar, airborne electromagnetic, NASA operation
Icebridge, and ICESat remote sensed ice thickness obser-
vations, as explained in detail by Lindsay and Schweiger
(2015). Despite the fact that the ITRP thickness data are a
result of complex data processing, we believe that the ITRP
is the best data set to compare models with. This is due to
the following: it allows to calculate sea-ice thickness devia-
tions per grid cell and to integrate total sea-ice volumes in
the ITRP region. These metrics are calculated for the period
of 2000–2012, with which the ORAs are compared, with the
exception of UR025.4 which ends in 2010.
The most comprehensive database adapted for the purpose
of evaluating the Antarctic SIT of ORAs is ASPeCt (Worby
etal. 2008). This product covers the period 1981–2005 and
comprises about 23,000 individual measurements made
during ship voyages or helicopter campaigns in the Southern
Ocean. Sea-ice thickness was estimated visually by experts
onboard. It is therefore likely (1) that systematic errors are
present: ships tend to circulate in thin ice, hence estima-
tions are probably biased thin, and (2) that random measure-
ment errors are large, due to the rather simplistic method of
measurement (see Worby etal. 2008, for further discussion).
The assessment of ORAs with respect to ASPeCt should
therefore be conservative and made with extreme caution, in
order to not discard ORAs for the wrong reasons.
Unlike the ORA-IP dataset, the ASPeCt data is not grid-
ded and is provided as daily and not monthly values, which
complicates further the assessment. We first binned the
ASPeCt data in space and time by matching each of the
23,000 ASPeCt measurements to the corresponding ORA
grid cell, year and month over 1993–2005. The num-
ber of measurements varies greatly from case to case, but
is generally low: in 57% of the cases (one case means one
given grid cell during one given month of one given year),
less than three measurements are available. We excluded
these cases with too few data from our assessment, to limit
the probability of detecting a mismatch by chance. For all
other cases (four ASPeCt measurements or more in a given
month of a given year in a given grid cell), we tested whether
the ASPeCt measurements and the ORA-IP monthly mean
values could be drawn from the same statistical distribution.
For each case, we claimed the ORA product to be ‘com-
patible’ with ASPeCt if the ORA estimate fell within the
range of all available ASPeCt measurements. In addition
we recorded for each case an ‘error’ equal to the difference
between the reanalysed SIT and the mean value of ASPeCt
measurements, and an “absolute error” equal to the absolute
value of the previous metric. The choice of the threshold of
at least four ASPeCt measurements to conduct the compari-
son does not have an impact on the conclusions (not shown
Note that Chevallier etal. (2017) carried out a thorough
evaluation of the Arctic sea-ice drift in the ORA ensemble,
which is not done here for either the Arctic or Antarctic.
Sea-ice dynamics is primarily wind driven. Most of the rea-
nalyses considered here use the same atmospheric reanalyses
as in the ensemble considered by Chevallier etal. (2017),
and there were no significant updates in the model phys-
ics regarding sea-ice dynamics or rheology. Thus, we can
assume that our sea-ice drift results are consistent with those
of Chevallier etal. (2017). Hence we refer to their findings,
where necessary.
3.3 Snow depth
Current sea-ice models simulate snow on ice in rather
rudimentary ways. Due to its low thermal conductivity and
high albedo, snow is strongly altering the snow-ice energy
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1621An assessment often ocean reanalyses inthepolar regions
1 3
balance. Both thermal conductivity and albedo depend on
the snow density which is kept constant in ORAs (
to 342 kg m
), while observations report a seasonal range
of 250–320 kg m
from September to May (Warren etal.
1999; Chevallier etal. 2017). Most of the models melt all
snow in a grid cell before sea ice is melted at the surface.
Many snow related processes (such as precipitation, wind,
ice drift and deformation, flooding, melting, evaporation
and sublimation) are very uncertain and crudely param-
eterized in models.
Snow depth observations are very sparse in both polar
regions, and in particular in Antarctica. A primary Arc-
tic source is the snow depth climatology of Warren etal.
(1999) which is based on data from drifting stations
established typically on multi-year sea ice with relatively
thick snow cover and collected over the past decades
(1954–1991). Due to this, we keep in mind that the War-
ren climatology is likely overestimating the pan-Arctic
average snow depth.
3.4 Mixed layer depth
The oceanic mixed layer constitutes the interface between
the atmosphere and the interior of the ocean. This layer is
where all dynamic, thermodynamic and biogeochemical
air-sea exchanges take place, and where the world’s deep
water masses acquire their properties (e.g. deBoyerMon-
tégut etal. 2004; Holte and Talley 2009). As the MLD is
a relevant physical index of the vertical mixing intensity
in the upper ocean (Toyoda etal. 2017a), the MLDs simu-
lated by the ORAs are evaluated against two observation-
based products. These are the Monthly Isopycnal and
Mixed-layer Ocean Climatology for the Arctic (MIMOC;
Schmidtko etal. 2013) and a recently published Southern
Ocean mixed layer climatology (Pellichero etal. 2017).
These products are both based on temperature and
salinity profiles from ship observations archived in the
World Ocean Database, as well as from float data from
the Argo international program. In addition, MIMOC
includes data recorded by ice-tethered profilers in the
Arctic Ocean, while Pellichero etal. (2017) use observa-
tions from animal-borne sensor programs in the Southern
Ocean (Roquet etal. 2017). These contemporary sources
provide an unprecedented data coverage of the sea-ice
regions over the entire seasonal cycle. Both climatologies
are constructed using an objective mapping of the MLDs
computed from instantaneous profiles with the Holte and
Talley (2009) algorithm. By contrast, reanalysis MLDs
are obtained from monthly mean temperature and salinity
fields, using a density threshold of 0.03 kg/m
with respect
to the value at 10 m depth.
As noted by deBoyerMontégut et al. (2004), MLDs
computed from monthly, hence smoother, profiles can be
underestimated approximately by 10–20 m compared to
those based on instantaneous profiles. This is mostly the
case in spring when rapid restratification occurs (Toyoda
etal. 2017a), and needs to be kept in mind when carrying out
ORA evaluation. On the other hand, Holte and Talley (2009)
found that their algorithm tends to yield slightly shallower
MLDs in winter than the density threshold method.
Table 2 Sections used for calculating net lateral volume, heat and
freshwater exchange between the Arctic and Sub-Arctic
Section Latitude Longitude
Fram strait N79
Barents sea opening N70
Davis strait N66
Bering strait N66
Fig. 1 Regions used to calculate average temperature and salinity
profiles. In a red colour shows the region of the Eurasian basin and
blue colour the Amerasian basin, while in b blue colour shows the
Antarctic open ocean. Annotations: the Fram Strait (FS), Beaufort
Gyre (BG), Barents Sea (BS), Davis Strait (DS), Greenland Sea (GS),
Norwegian Sea (NS), Amundsen Sea (AS), Ross Sea (RS), Weddell
Sea (WS) and Drake Passage (DP)
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1622 P.Uotila et al.
1 3
3.5 Liquid ocean transports
Lateral oceanic volume (V), heat (Q), and liquid freshwater
transports are calculated through four sections nearly clos-
ing the Arctic (see Table2; Fig.1). The calculated values
represent net transport through the openings, with positive
values towards the Arctic. Heat transport is calculated rela-
tive to T
= − 0.1
C (Aagaard and Greisman 1975). Liq-
uid freshwater transport is calculated relative to S
= 34.8
on the dimensionless practical salinity scale (Aagaard and
Carmack 1989).
Observational ocean transport estimates are obtained
from literature, and thus do not represent a consistent
time span. Furthermore, their calculations required some
assumptions due to discrete spatial sampling of observa-
tions. Hence, the observations do not fully close the Arctic
Ocean transport budget.
Specifically, the oceanic flow through the Fram Strait
constitutes the main volume and heat exchanges between
the Arctic and the Atlantic with a complex re-circulation
structure. The total northward flow is estimated as 7 Sv,
while a total southward flow of
9 Sv yields a net south-
ward transport of
2 Sv (TableS1; Fahrbach etal. 2001).
The heat carried northward along the western coast of Sval-
bard has shown a relatively large inter-annual variability,
between 26 TW (1997/98) and 50 TW (2003/04) (Schauer
and Beszczynska-Möller 2009). The flow through the Bar-
ents Sea Opening (BSO) towards the Arctic has a net volume
flow of 2.3 Sv with about 70 TW heat transport (TableS1;
Smedsrud etal. 2013). However, most of this oceanic heat
is lost to the atmosphere while en route across the shallow
Barents Sea shelf upon reaching the Arctic Ocean (e.g. Gam-
melsrød etal. 2009).
Another connection between the Arctic and the Atlan-
tic is through the complex channels of the Canadian Arctic
Archipelago. However, most of this exchange is channelled
through the Davis Strait in Baffin Bay between Greenland
and Baffin Island. Here, observations show a net southward
volume transport of 1.6 Sv (TableS1; Curry etal. 2014).
The only connection to the Pacific is the shallow Bering
Strait. The volume transport through this passage is esti-
mated to be 0.8 Sv directed northward (TableS1; Roach
etal. 1995). However, there is a considerable seasonal cycle
from 0.4 Sv in winter to about 1.2 Sv in summer (Woodgate
and Aagaard 2005), in addition to a possible positive trend
in the recent decade (Woodgate etal. 2012). The Bering
Strait also represents the only oceanic net freshwater input
to the Arctic. Due to its regional Arctic domain, TOPAZ4
model boundary is located in the Bering Strait where a vol-
ume transport of 0.7 Sv to the Arctic is prescribed. As tem-
perature and salinity are not prescribed, we decided it is
not meaningful to estimate heat and freshwater transports
in the Bering Strait for TOPAZ4. Therefore these TOPAZ4
quantities, and consequently the net Arctic heat and fresh-
water fluxes, were excluded from the MMM.
When calculating the ocean transports from the ORA
results, the Hudson Strait in the Canadian Arctic Archipel-
ago is omitted, as is the part north of the Barents Sea Open-
ing, i.e., the opening between Bear Island and Spitsbergen
Island. These choices make the ORA data more easily com-
pared with the observed transports across the same transects.
Some of the modelled ocean transports are calculated based
on aggregated data which are interpolated in space and aver-
aged in time, excluding short-term variability. Hence, the
ORA data also have some shortcomings with respect to clos-
ing budgets for the Arctic Ocean.
For the Southern Ocean transports, we present in
Sect.4.2.3 the values of volume transports across the three
main transects of the ACC: the Drake Passage; a transect
between South Africa and the Antarctica (Fig.1, called
E”); and a transect between Australia and Antarctica
(called “147
E”). We compare the values estimated from
nine global ORAs to estimates from observations.
During the last three decades, the Drake Passage has
been more closely monitored than the other two transects.
Ganachaud and Wunsch (2000) estimate 140 Sv (± 6 Sv)
using an inverse box model applied to WOCE hydrographic
data. With a similar method, Lumpkin and Speer (2007) give
a mean net transport of 129.7 Sv (± 6.8 Sv). The canonical
value of 134 Sv (± 11.2 Sv), obtained by Cunningham etal.
(2003) after reviewing ISOS data deployed from January
1979 to February 1980 (Whitworth and R. 1985), is however
widely utilized by the physical oceanography community.
More recent estimations with a method combining moorings
and altimeter 1993–2012 measurements (Koenig etal. 2014)
also give a total net transport of 140 Sv (± 10 Sv).
Recent estimations from Donohue etal. (2016), based
on 2007–2011 extensive mooring measurements, and from
deVerdiére and Ollitrault (2016), based on time-mean
Argo float displacements and historical hydrography from
the World Ocean Atlas 2009 are likely to be the most reli-
able ones. Compared to earlier studies, they used methods
that reduce uncertainties in the barotropic flow component
due to more comprehensive monitoring array and by global
mass conserving mean circulation. Donohue etal. (2016)
and deVerdiére and Ollitrault (2016) provide total transport
estimations of 173.3 ± 10.7 and 175 Sv, respectively. These
values are
30% larger than the canonical value often used
as the benchmark for global circulation and climate models.
3.6 Ocean heat andsalt contents
Ocean heat and salt contents are denoted as OHC and OSC,
respectively. They are calculated as vertical integrals from
the reference depth H to the surface
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1623An assessment often ocean reanalyses inthepolar regions
1 3
and S are vertical potential temperature and salinity
profiles at a horizontal ORA grid point.
The freshwater content, a common oceanographic diag-
nostic, is the amount of zero-salinity water required to be
taken from the ocean or sea ice so that its salinity is changed
to the chosen reference salinity and is closely related to OSC
and therefore not presented.
3.7 Hydrography
The Antarctic and Arctic ocean basins used to calculate the
hydrographic average profiles follow the definitions given
in Barthélemy etal. (2015). Arctic Ocean was split into
two—the Eurasian basin and the Amerasian basin, along
two meridians, 135
E and 45
W, which join at the North
Pole (Fig.1). The boundary between the two basins approxi-
mately follows the Lomonosov Ridge from the East Siberian
Shelf to the Lincoln Shelf north of Greenland. The reason
for this division of the Arctic Ocean was to see whether
product performance varies between the two main Arctic
basins, for example in terms of the AW advection.
Due to the vertically integrated ORA-IP hydrographic
data only waters located over deep parts of the basins are
analysed, analogously to OHC and OSC diagnostics. Spe-
cifically, domain averages are limited by their depth so that
in the Arctic the ocean grid points deeper than 500 m are
included, while in the Antarctic the limit was 1000 m. The
northern limit of the Antarctic basin is chosen as to ensure
that the largest fraction of the area is covered with sea ice in
winter, and therefore represents a polar marine environment.
All ten ORAs and three observational products (Sumata,
WOA13 and EN4.2.0.g10) were interpolated to a common
horizontal latitude–longitude grid, which is identical to
the WOA13 grid, before the calculation of regionally aver-
aged hydrographic profiles. As the ORA database does not
provide land-sea masks of individual ORAs, we assumed the
WOA13 land-sea mask available from the WOA13 website.
First, OHC and OSC for all ORAs were calculated from
five reference depths (H = {100, 300, 700, 1500, 3000 m})
to the surface (
= 0 m). After this, the mean potential tem-
peratures and salinities
within each layer 100
0 m, 300
100 m, 700
300 m, 1500
700 m and 3000
1500 m were calculated from
where X is either temperature or salinity, and
average between levels L and U.
values where L is
deeper than the ocean depth at that particular grid point were
excluded from the further analysis. Finally, level averaged
temperatures and salinities
were temporally and
4 Results
4.1 Arctic mean states
4.1.1 Sea ice andsnow
Ten ORAs show an overall agreement in the location of
the sea-ice edge in the Arctic Ocean and along its margins
(Figs.2, S1 and S2), which can be attributed to sea-ice data
assimilation and the constraint by the atmospheric forcing.
On average, there is a good agreement with respect to the
Fig. 2 Number of ORAs per
grid cell (up to 10) where their
sea-ice concentration is > 15%
in March (left) and in Septem-
ber (right) based on 1993–2010
monthly data. Black line is the
15% climatological ice edge by
NSIDC NASATeam. The num-
ber of reanalyses considered
here is 10. Note that the Bering
Sea and the Sea of Okhotsk
are not a part of the domain of
TOPAZ4, so only 9 reanalyses
have a solution in these areas
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1624 P.Uotila et al.
1 3
sea-ice edge in the Barents Sea, the Greenland Sea and the
Bering Sea. Most reanalyses lack sea ice in the Labrador Sea
and the Sea of Okhotsk, as Chevallier etal. (2017) pointed
out. A few ORAs simulate too much sea ice eastward of the
coasts of the Labrador Sea and the Greenland Sea: these
are the ORAs that do not assimilate sea-ice concentration
(Table1; Figure S1). In summer, a number of ORAs under-
estimate the presence of sea ice east of Greenland, and some
underestimate sea-ice melt near the shelves, in the Kara Sea
and in Baffin Bay.
Figure3 shows the seasonal cycles of Arctic sea-ice
extent and area in ten ORAs. The modeled seasonal cycle
is generally in phase with observations, with a maximum
(minimum) sea-ice area and extent in March (September),
although a few ORAs simulate sea-ice extent minima in
August. SODA3.3.1 overestimates sea-ice extent and area in
all months, so it is excluded from the subsequent Arctic sea-
ice concentration ensemble analysis. The ensemble spread
of ORA sea-ice extent, without SODA3.3.1, is limited over
the year, and is comparable to the estimated observational
uncertainty. This was expected, since most reanalyses assim-
ilate sea-ice concentration. The spread is larger during the
winter months, and all ORAs align well during refreezing
in autumn. A few ORAs exhibit systematic biases compared
to the observations in the winter months, which is consist-
ent with the lack of sea ice in the Labrador Sea, as noted
above. In most ORAs, the simulated August–September sea-
ice extents are within the observational uncertainty. Results
are similar for sea-ice area, although its ensemble spread is
larger in spring and summer than the sea-ice extent spread.
For both sea-ice extent and area, the MMM mean without
SODA3.3.1 is near the upper range of the observational
The significant spread in sea-ice area denotes differences
in the distribution of sea-ice concentration within the ice
cover. As in Chevallier etal. (2017), we investigate the sep-
arate contributions of Marginal Ice Zone (MIZ) and pack
ice in the total area spread. In the observations, the MIZ
Fig. 3 Mean seasonal cycle
(over the period of 1993–2010)
of the Arctic sea-ice extent and
area (upper row), and of the
area covered by Marginal Ice
Zone (MIZ) and pack ice (lower
row), in all ORAs (colour lines)
and in NSIDC, CERSAT and
OSISAF observations (grey
shading). Domain of integra-
tion excludes the ocean area
in the North Pacific south of
Bering Strait. MIZ is defined
as a region where the sea-ice
concentration is less than 90%
and greater than 15%, while the
pack ice is the region where the
sea-ice concentration is higher
than 90%. Units are in 10
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1625An assessment often ocean reanalyses inthepolar regions
1 3
area varies between 1 and 2 million km
from November to
April, peaks in July, and decreases slowly from August to
October (Fig.3). Three observational products give consist-
ent results, although CERSAT has a systematically smaller
MIZ area in June–September. During October–December,
the spread among the observational estimates is the largest,
when NSIDC has a larger MIZ than the others. The pack-
ice area has a seasonal cycle evolving at the same rate as
total sea-ice area, although its annual minimum is reached in
July–August. In the Arctic Ocean, sea ice is predominantly
pack ice, except in summer when the MIZ/pack-ice area ratio
is over 50%.
The ORAs reproduce these seasonal sea-ice extent and
area cycles relatively well. Most ORAs are consistent with
the ice product they assimilate (e.g. C-GLORS025v5 with
NSIDC, GLORYS2v4 with CERSAT; Table1). However,
during winter and early spring, all ORAs simulate MIZ area
lower than observed, and systematically too high pack-ice
area when the assimilated ice product is taken into account
(lower right panel of Fig.3). In summer, the ensemble
spread is larger, and there are a number of ORAs that align
well with observational estimates. But no ORA simulates
more MIZ than observed, and a few ORAs stand out with a
lower-than-observed MIZ peak area: those are the products
without data assimilation (Table1). They tend to simulate
very high sea-ice concentration almost all year long (not
The snow volume in the ORAs varies widely—not only
between the ORAs using different precipitation data sets
but also between the ORAs using ERA-Interim precipita-
tion rates (Fig.4; Table1). As apparent from Figs.4 and
S3, ORAs have a thinner snow cover everywhere in the
Arctic and hence smaller snow volumes than Warren etal.
(1999), which is known to have a thick bias, as explained
earlier (Figs.4, S3). The maximum snow volume in the
Warren climatology occurs between March and April with
values around 3000 km
. The ORA values range between
> 4000 km
(SODA3.3.1) and < 200 km
(UR025.4). By
inspecting the ORA ensemble mean and its standard devia-
tion we can identify three ORAs which deviate most from
the other ORAs: UR025.4 which has almost no snow at all,
SODA3.3.1, driven by the MERRA2 reanalysis and asso-
ciated with a high bias in sea-ice area, which exceeds the
Warren climatology for all months, and TOPAZ4 which fits
very closely to the Warren climatology, despite being driven
by ERA-Interim. The remaining ORA snow volumes range
from about 1000 km
(MOVE-G2i) to 2500 km
The large variation between the ORAs driven by the same
reanalysis (ERA-Interim) is surprising. This might point to
large uncertainties in process parameterisations (related to
for example sea-ice ridging and sublimation) which alter
the snow depth.
All ORAs show a strong decrease of the snow volume
from May to June (Fig.4). This is certainly connected to
the fact that ORAs first have to melt all snow off before
their sea ice starts to melt. Related to this, all ORAs except
SODA3.3.1 and TOPAZ4 have almost no snow on ice
from July to August. Then from September to December
the majority of ORAs (except UR025.4, SODA3.3.1 and
TOPAZ4) show only moderate differences in the snow vol-
ume. Interestingly, differences between the ORA snow vol-
umes grow strongly from January to April.
The mean difference of the sea-ice thickness of the ORAs
relative to the ITRP data for February–March is presented
in Fig.5. Most ORAs underestimate the ice thickness north
of the Canadian Arctic Archipelago, north of Greenland
and the Fram Strait. Especially large deviations are found
for ECDA3, MOVE-G2i, SODA3.3.1, and UR025.4 for
which the deviations can amount to more then 2 m. More
moderate deviations are detected for C-GLORS025v5,
GECCO2, GloSea5-GO5, and TOPAZ4. ORAP5 exhibits
only a minor underestimation while GLORYS2v4 overes-
timates the ice thickness by up to 1 m. In the Beaufort Sea,
some of the ORAs overestimate the ice thickness moder-
ately (C-GLORS025v5, GloSea5-GO5, SODA3.3.1) while
ORAP5 exceeds the observed thickness by up to 1 m and
GLORYS2v4 by up to 2 m. TOPAZ4 and GECCO2 show
no notable deviations in the Beaufort Sea. Most of the ORAs
overestimate the thickness over the Eurasian shelves. GLO-
RYS2v4 strongly overestimates ice thickness over almost the
whole Arctic Ocean. In October–November, the ORA-ITRP
mean differences generally appear similar to the differences
in February–March, but with a tendency towards larger
underestimations of sea-ice thickness (Figure S4).
Fig. 4 Monthly climatology of the Arctic snow volume (km
) of the
ORA-IP models for the period from 1993 to 2010, its ensemble mean
(black solid line—errors bars designate one standard deviation uncer-
tainty) and the snow volume of the Warren climatology (black dashed
line). The snow volume is calculated for the entire Arctic Ocean
excluding regions south of the Fram Strait
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1626 P.Uotila et al.
1 3
In February–March the mean (period 2000 to 2012) ice
volume amounts in ITRP to
15,400 km
(Fig.6a). Cor-
responding ORA ice volumes range between 10,500 and
12,800 km
with the ensemble mean of
14,500 km
Two ORAs are very close to the ITRP value (GECCO2
and GloSea5-GO5), but this is, at least in the case of Glo-
Sea5-GO5, due to compensating regional biases (Fig.5).
In October–November the mean ITRP ice volume is about
12,400 km
, while the ORA range is large, from 5300 to
19,200 km
(Fig.6b). The average ice volume of five ORAs
(C-GLORS025v5, ECDA3, GloSea5-GO5, MOVE-G2i and
UR025.4) stays low—below 8000 km
. Cor respondingly,
the ORA MMM ice volume is much lower than the ITRP
value (about 10,000 km
Figure6c displays the mean sea-ice volume loss between
February–March and October–November calculated in
the ICESat domain (i.e. the difference between Fig.6a, b).
While the ITRP seasonal volume loss is about 3000 km
seasonal volume losses in six ORAs exceed or are close
to 5000 km
, indicating too high seasonal sea-ice volume
amplitudes. Accordingly the ORA MMM volume loss is
biased high (4500 km
4.1.2 Mixed layer depth
There is no systematic bias in the representation of winter
MLDs in the Arctic Ocean in the various ORAs (Figs. 7,
S5). UR025.4, ECDA3, GloSea5-GO5 and GLORYS2v4
give the largest MLD overestimations, while MOVE-G2i
and GECCO2 yield the strongest underestimations. The
observed pattern, with MLDs around 40 m in the Amund-
sen and Makarov Basin and with shallower mixed layers in
the Amerasian Basin, is not closely matched by any of the
products. In the Barents Sea and south of Svalbard, all ORAs
simulate deeper mixed layers than in the observation-based
product (Figure S5). The difference between the ensemble
mean and the climatology exceeds 400 m locally.
In summer, all ORAs underestimate MLDs (FigureS6).
These shallow mixed layers are generally not due to the
coarse vertical grid, because the top-level thicknesses
of ocean models are mostly from 1–3 m, only ECDA3,
GECCO2 and SODA3.3.1 have the top-layer thickesses of
10 m (Table1). The ensemble mean bias reaches as much as
20 m under sea ice (Fig. 7), although the reliability of the
climatology might be questioned in the regions just north
of the Canadian Arctic Archipelago and Greenland, where
the ice is very thick and few hydrographic measurements
exist. As a result, the MMM negative bias is of the order of
10 m in the Barents Sea and is smaller in the Greenland and
Norwegian Seas.
Fig. 5 The 2000–2012 mean difference of the ORAs to the ITRP sea-ice thickness (m) in February–March
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1627An assessment often ocean reanalyses inthepolar regions
1 3
4.1.3 Liquid ocean transports
The net oceanic exchange between the Arctic Ocean and the
sub-Arctic seas through the four major openings, the Fram
Strait, Bering Strait, Davis Strait, and BSO, from ORAs and
observations are summarized in TableS1 and illustrated as
bar plots in Figs.8, S7 and S8. Generally, ORA volume
transports are within the observational uncertainty and for
the BSO also heat transports, yet with some notable differ-
ences. All ORAs close the Arctic Ocean volume transport
budget comparably to the observations. All ORAs tend to
be on the low side in terms of net oceanic heat transport
towards the Arctic (Fig.8). This could imply a negative
temperature bias in the ORAs. Indeed, in the Barents Sea,
WOA13 and EN4.2.0.g10 are warmer than the ORA MMM,
but Sumata is slightly cooler than the MMM (Figure S9).
GECCO2 is an exception which also shows excessive heat
transports through the BSO. Most of the ORAs also tend to
be on the low side with respect to heat transport through the
Fram Strait—due to either too cold northward flowing AW
or southward recirculation of too warm AW. However, some
ORAs are within the uncertainty range of the observations.
A caveat to this analysis is our method for computation of
the heat transports. For comparison with observation-based
estimates from literature, we have chosen to compute the
heat transports based on net volume transports through each
separate section and relative to a fixed reference tempera-
ture (T
ref =−0.1
C; Aagaard and Greisman 1975), which
is also the common method used in the literature. However,
this method has some inherent inconsistencies related to
the lack of a closed volume transport budget and the actual
temperature difference between the incoming and outgoing
water masses. A more consistent method for the computa-
tion of ocean heat transports is discussed in Schauer and
Beszczynska-Möller (2009).
The ORAs show a generally good agreement with
observations with respect to freshwater export from the
Arctic, except GLORYS2v4 and MOVE-G2i, which have
Fig. 6 The 2000–2012 mean sea-ice volume (km
) in the ICESat
domain in a February–March and b October–November. c Mean ice
volume loss km
in the ICESat domain between February–March
and October–November (the difference of a and b). The ORAs are
denoted by blue bars, the ITRP by green bars and the ensemble mean
(ENSMEAN) by orange bars. The error bar in ENSMEAN represents
the ORA ensemble spread (standard deviation)
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1628 P.Uotila et al.
1 3
particularly low freshwater exports (TableS1; Figure S8).
The reason for this discrepancy is not clear, but GLO-
RYS2v4 has a positive salinity bias in the Arctic Ocean (see
Sect.4.1.4). On the other hand, C-GLORS025v5 shows a
good agreement with observations in terms of total Arctic
freshwater budget, but it has a different distribution with low
freshwater volumes exported through the Fram Strait and an
enhanced export through the Davis Strait.
The MMM represents an estimate of Arctic—sub-Arctic
exchanges comparable to observed estimates (TableS1;
Figure S8). The MMM freshwater transport through the
Bering and Davis straits are in close agreement with the
observations, while the MMM freshwater transport through
the Fram Strait is on the low side, although the volume trans-
port is comparable to the observations. Overall, the MMM
is generally in closer agreement with the observations than
individual ORA estimates.
In terms of heat transport variability, represented by one
standard deviation based on annual averages, all ORAs
overlap with the observed range of variability in the main
Fig. 7 Arctic mixed layer depth for March (top) and August (bot-
tom), in the MIMOC climatology (OBS; Schmidtko etal. 2013), for
the ORA ensemble mean (MMM) and the bias of the ensemble mean
with respect to the climatology (MMM–OBS). Note the logarithmic
colour scale used for March fields
Fig. 8 Mean 1993–2010 Arctic liquid ocean heat transport through
the major Arctic Ocean openings, and their net sum (total) in TW.
Error bars represent standard deviations of monthly values. See Sup-
plementary TableS1 for values of the error bars and references to the
calculation of error bars in the observations
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1629An assessment often ocean reanalyses inthepolar regions
1 3
gateway to the Arctic, i.e. the BSO (Fig.8). Through the
other openings, several ORAs have means and associated
ranges of variability outside the range of the observed
variability, when using one standard deviation. Although
the MMM is very close to the observations in terms of
heat transport through the BSO, the generally lower than
observed modelled heat transports through the other open-
ings causes the MMM to be on the low side regarding overall
heat transport to the Arctic. Note, however, that the model
and observational periods are overlapping but not equal in
4.1.4 Hydrography
We begin with the integrated heat content in the top 1500
m for all products (Fig.9), with anomalies for each product
shown relative to the ORA MMM. The MMM is warmer in
the Eurasian basin than the Amerasian, and much warmer
in the eastern Nordic Seas than the western, reflecting the
path of the warm AW northward and cold Arctic water exit-
ing the Fram Strait. The observational products, Sumata
and WOA13, are slightly warmer in the Arctic than the
MMM, particularly in the Amerasian basin, but consistently
colder in the Nordic Seas. The third observational product,
EN4.2.0.g10 is slightly cooler in the Arctic than Sumata
and WOA13, with OHC very close to the MMM. In the
Nordic Seas the MMM is clearly biased warm by 2 prod-
ucts, GECCO2 and TOPAZ4. GECCO2 and SODA3.3.1
are warm outliers and ECDA3 a cold outlier in the main
Arctic, but other products are all fairly consistent with each
other and with the MMM. Evidence of the warm Atlantic
boundary current in the south Eurasian basin is very weak
in the MMM and in WOA13 and EN4.2.0.g10, but shows
up clearly in Sumata. Some ORAs also show it more clearly,
such as GloSea5-GO5, MOVE-G2i and ORAP5, while oth-
ers show its absence, such as GLORYS2v4 and TOPAZ4.
This is seen as enhanced ORA spread in the boundary cur-
rent region.
The integrated salt content in the top 1500 m for all prod-
ucts is shown in Fig.10. The MMM shows the strong con-
trast between the fresh Amerasian basin, which is influenced
by low salinity inflows (originating from the Pacific, Arctic
rivers and precipitation) captured in the Beaufort Gyre, and
the more saline Eurasian basin, dominated by Atlantic inflow
from the much more saline Nordic Seas. All observational
products Sumata, WOA13 and EN4.2.0.g10 show a fresher
Beaufort Gyre north of Alaska, with the rest of the Arctic
mainly showing more saline than the MMM. The salinity
spread among ORAs is larger in the Arctic Ocean than in the
Nordic Seas, especially in the Amerasian basin, in contrast
to the OHC spread. However, there is considerable cancella-
tion between individual ORA salinity anomalies, suggesting
Fig. 9 Averaged multi-model
Arctic Ocean Heat Content
(OHC) in the layer of 0–1500
m (top left panel), its spread
(top second left panel) and indi-
vidual product departures from
the multi-model mean
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1630 P.Uotila et al.
1 3
the MMM is a meaningful product. The ORAs most similar
to Sumata are MOVE-G2i, ORAP5 and UR025.4, while oth-
ers have clear spatial anomalies.
Seasonal cycles of OHC and OSC in the 0–100 m layer
in terms of mean temperature and salinity are presented in
Figs.11 and 12, respectively. For the mean temperature, the
observational products show lower values than the MMM.
The observational seasonal cycles (except Sumata) do not
appear smooth (Fig.11a, b, the leftmost panels) perhaps
reflecting sparse observational coverage. The MMM shows
a seasonal cycle amplitude that agrees rather well with
Sumata, although the MMM appears biased warm in the
Eurasian basin. Most individual ORAs also show sensible
looking seasonal cycles of mean temperatures with warm
Fig. 10 Averaged multi-model
Arctic Ocean Salt Content
(OSC) in the layer of 0–1500
m (top left panel), its spread
(top second left panel) and indi-
vidual product departures from
the multi-model mean
Fig. 11 Seasonal cycle of
averaged monthly temperature
from 1993 to 2010 in the layer
of 0–100 m in a the Eurasian
basin and b in the Amerasian
basin. In the leftmost panels
seasonal cycles for the multi-
product mean (MMM) and
three observational data sets
(EN.4.2.0.g10, WOA13 and
Sumata) are shown, while in
the middle and rightmost panels
individual ORA seasonal cycles
are presented
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1631An assessment often ocean reanalyses inthepolar regions
1 3
summer and autumn seasons, although their seasonal ampli-
tudes and annual mean temperatures vary (Fig.11a, b, the
middle and rightmost panels). MOVE-G2i seems to have
its warmest month earlier than other ORAs, while ECDA3
seems to have a particularly high seasonal amplitude.
For the seasonal cycle of mean salinity, the agreement
between the observational products and the MMM is better
in the Eurasian basin than in the Amerasian basin (Fig.12a,
b, the leftmost panels). In Sumata salinities in the 0–100
m are higher in spring, then start to decrease until winter.
Given the evolution of sea ice and the timing of river runoff
to the Arctic Ocean such a seasonal cycle appears sensible.
The two other observational products and the MMM mainly
agree with this, except the EN4.2.0.g10 in the Eurasian
basin, which obtains the highest salinities in August instead
of spring. Individual ORAs seem to agree with Sumata and
WOA13 in seasonal amplitudes, although TOPAZ4 stands
out with its relatively small annual variability.
We now look at the vertical structure of the different
products in the Eurasian (above) and Amerasian (below)
basin-mean temperatures shown in Fig.13, using the layers
defined in Sect.3.7. The observational profiles are shown,
along with the MMM, in the left-most plots. In the upper-
most, 0–100 m layer, Sumata correctly shows the horizon-
tal temperature difference between Eurasian and Amerasian
basins with temperatures about −1.6 and −1.4
C, respec-
tively, reflecting the colder freezing temperatures of the
more saline Eurasian basin and the warmer surface waters
in the Amerasian basin. Opposite to the horizontal in Suma-
ta’s hydrography, WOA13 has the Amerasian Basin slightly
colder than the Eurasian Basin and in EN4.2.0.g10 there is
practically no horizontal variability with both basins having
surface temperature between −1.6 and −1.5
C. The MMM
shows similar temperatures to WOA13.
Figure13b, d show basin-averaged errors and their
standard deviations of temperature anomalies for all ORA
products from Sumata, as probably the most reliable
observational product. Notably the standard deviation of
error is relatively large compared to mean error in top 300
m, but becomes relatively small at deeper layers indicating
more systematic basin scale biases. In the Eurasian basin
Sumata is warmer than the MMM from 100–700 m but
slightly colder from 1500 to 3000 m (Fig.13b). This prob-
ably reflects an AW deficit of the MMM in the both basins.
In the Eurasian basin GloSea5-GO5 matches Sumata
most closely in the Atlantic water layers, 100–700 m and
ECDA3, TOPAZ4 and ORAP5 are cold outliers. In the
700–1500 m layer in the both basins all products except
GECCO2 are inside the range of the observational prod-
ucts. The dominant warm layer in GECCO2 is 700–1500
m, while the dominant warmer layer in SODA3.3.1 is
above 300 m in both basins, and ECDA3 has a cold bias
in all layers below 100 m. In the Amerasian basin Sumata
is clearly warmer than the MMM in all layers below 300
m (Fig.13c, d). This is because the Canadian Basin Deep
Water (CBDW) is known to be warmer (close to −0.5
than the Eurasian Basin Deep Water (EBDW). The MMM
shows a difference between the basins, but not as large as
Sumata or WOA13. In the Amerasian basin, the 300–700
m layer is the AW layer and the underestimation of its
temperature reflects difficulties to correctly capture the
boundary current in ORAs (Fig.13d).
Turning to the vertical structure in salinity, S of each
layer in each basin are shown in (Fig.14). At these cold
Fig. 12 Seasonal cycle of
averaged monthly salinity
from 1993 to 2010 in the layer
of 0–100 m in a the Eurasian
basin and b in the Amerasian
basin. In the leftmost panels
seasonal cycles for the multi-
product mean (MMM) and
three observational data sets
(EN.4.2.0.g10, WOA13 and
Sumata) are shown, while in
the middle and rightmost panels
individual ORA seasonal cycles
are presented
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1632 P.Uotila et al.
1 3
temperatures the salinity controls the potential densities.
Salinities from Sumata, WOA13 and EN4.2.0.g10 agree well
in all layers and in both Amerasian and Eurasian basins. Only
Sumata surface salinity in the Amerasian basin is slightly
lower (
31.3) than in WOA13 (
31.6) and EN4.2.0.g10
31.8) perhaps capturing better the recent freshening
(Proshutinsky etal. 2009). The MMM 0–100 m salinity
is 31.6, similar to WOA13 and slightly more saline than
Sumata. In the Eurasian basin, the MMM 0–100 m salinity
is low (
32.8) compared to the observational products,
33.3, mostly due to ECDA3, with a surface salinity
(Fig.14b). However in the Eurasian AW layers, 100–700
m, the MMM is also fresher (and colder) than Sumata and
WOA13, reflecting the AW deficit. GECCO2 is the fresh,
Fig. 13 Averaged temperature
profiles (a, c) and their depar-
tures (errors) from the Sumata
climatology (b, d) in the
Eurasian (a, b) and Amerasian
(c, d) basins shown in Fig.1.
In a, c, thin black lines show
the non-depth averaged Sumata
temperature profiles. In b, d
horizontal bars indicate mean
errors and black, horizontal
lines their standard deviations
as error ± deviation. In cases
where the mean error or its
standard deviation is too large to
fit inside the panel, their values
are indicated as numbers
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1633An assessment often ocean reanalyses inthepolar regions
1 3
low density outlier (Fig.14b). The individual ORA spread
for 0–100m in the Amerasian basin is small with almost all
ORAs between 31 and 32 (except GLORYS2v4, with > 32).
Below 100 m in the Amerasian basin the MMM salinity is
also fairly consistent with Sumata although GECCO2 and
ECDA3 are fresh low density outliers (Fig.14d). However,
the basin mean salinities hide a lot of spatial variability that
is reflected in the standard deviations.
To give a better view of spatial distributions of the
MMM hydrography biases in the Arctic relative to the
observational climatology (the mean of Sumata, WOA13
and EN4.2.0.g10) as a reference, Figure S10 shows the
temperature and salinity anomalies (MMM–Obs) in each
layer (0–100, 100–300, 300–700, and 700–1500 m). In the
top 100 m the MMM in the Amerasian basin is slightly
too cold and much too saline, while the Eurasian basin
Fig. 14 Averaged salinity pro-
files (a, c) and their departures
(errors) from the Sumata cli-
matology (b, d) in the Eurasian
(a, b) and Amerasian (c, d)
basins shown in Fig.1. In a, c,
thin black lines show the non-
depth averaged Sumata salinity
profiles. In b, d horizontal bars
indicate mean errors and black,
horizontal lines their standard
deviations as error ± deviation.
In cases where the mean error
or its standard deviation is too
large to fit inside the panel, their
values are indicated as numbers
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1634 P.Uotila et al.
1 3
is too warm and a little too fresh. The bias changes just
below (100–300 m) with the Amerasian basin now too
warm, with both fresher and more saline regions, and the
Eurasian basin now too cold and fresh presumably reflect-
ing the lack of AW. Deeper layer salinities (300–1500
m) are slightly too fresh across both basins due mainly
to GECCO2, ECDA3 and TOPAZ4 (Fig.10). However,
deeper temperatures are too cold in the 300–700 m layer,
which looks like an inadequately resolved AW again, and
too warm in the 700–1500 m layer, especially in the Eura-
sian basin, which is largely due to GECCO2 and TOPAZ4
as was seen in Fig.13.
4.2 Antarctic mean states
4.2.1 Sea ice
As for the Arctic Ocean, and for the same reasons (ocean
and/or sea-ice data assimilation, and atmospheric forc-
ing) there is an overall agreement in the position of the
winter sea-ice edge in the Southern Ocean (Fig.15). The
SODA3.3.1 is a major outlier extending ice too far north in
the Pacific and the Indian Ocean sectors, and in all sectors
during summer (Figure S11). This can be traced to extremely
high snow precipitation in the MERRA2 atmospheric forc-
ing product. In particular, thicker snow layer takes longer
to melt than thin snow layer and promotes the formation of
snow-ice, which increases the total ice thickness. Moreover,
sea ice with thick snow on top survives longer because it
retains longer a higher albedo compared to sea ice with thin
snow on top. Almost all other reanalyses simulate a realistic
minimum sea-ice cover and, as in the Arctic, SODA3.3.1
will be removed from multi-model estimates in further sea-
ice concentration analysis.
Figure16 is the counterpart of Fig.3 for the Antarctic
seasonal sea-ice cycle. All systems have a maximum in Sep-
tember, and a minimum in February. ECDA3 is the only
reanalysis that loses all sea ice in summer, and stands out
from November to May, but has a realistic maximum (Figure
S12). The ORA ensemble spread in sea-ice extent is gener-
ally larger in winter than in summer. A possible reason is
that data assimilation and atmospheric forcings provide a
strong constraint on summer sea-ice extent, while there are
more degrees of freedom during winter. Sea-ice area is gen-
erally overestimated during winter, as shown by the MMM,
although driven by two outliers on the high side (SODA3.3.1
and GECCO2), whereas the remaining ORAs lie essentially
within the range of observations.
In the observations, we see that the MIZ/pack-ice ratio
in the Antarctic is much more balanced than in the Arc-
tic. The MIZ area peaks in October–November (after the
annual sea-ice area maximum) while pack-ice area peaks
earlier, in June–September. Most systems that assimilate
sea ice have a seasonal cycle of MIZ area consistent with
that observed. However, pack-ice area in June–October is
higher than observed in most systems, with the exception
of ECDA3, which does not assimilate sea-ice data, and
C-GLORS025v5, which assimilates sea-ice data. Therefore,
sea-ice data assimilation does not seem to be sufficient to
reproduce a correct MIZ/pack-ice ratio.
Figure S13 shows the ORA MMM sea-ice thickness dur-
ing periods for which satellite measurements (Kurtz and
Markus 2012) were taken and can be directly compared with
their Fig.2. General spatial features of the Antarctic ORA
MMM sea-ice thickness distribution agree rather well with
Kurtz and Markus (2012), although having a thin bias. Fig-
ure17 shows the mean ice thickness errors assessed against
the independent ASPeCt data. The results are discussed in
Fig. 15 Number of ORAs per
grid cell (up to 9) where their
sea-ice concentration is >15%
in February (left) and in Sep-
tember (right) based 1993–2010
monthly data. Number of
reanalyses considered here is 9.
Black line is the 15% clima-
tological ice edge by NSIDC
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1635An assessment often ocean reanalyses inthepolar regions
1 3
two steps: first considering each ORA reanalysis individu-
ally, and then considering ORAs as a multi-model ensemble
and using the compatibility index as described in Sect.3.2.
At the individual level, Fig.17 shows that the ORA prod-
ucts perform fairly poorly. Indeed, if the ASPeCt SIT and the
ORA SIT would actually be drawn from the same distribu-
tion, one would expect an incompatibility—in the sense that
we defined it in Sect.3.2—to occur by chance with probabil-
ity 1/2
where N is the number of ASPeCt samples, that is
12.5% or less for N greater or equal than 4, as we designed
it. All ORAs are individually incompatible with ASPeCt at
least more than 39, or 61% compatible, of the time, hence
sampling alone cannot explain this discrepancy. This leaves
two possibilities: either ORAs are truly wrong, or the obser-
vational reference has a systematic error. Given the prior
information that ASPeCt is biased thin (Worby etal. 2008),
we can state with confidence that the already thin ORAs
(ECDA3, MOVE-G2i, UR025.4) are probably inconsistent
with reality. GECCO2 can also be excluded from any real-
istic ensemble, given its large average absolute bias (22.0
cm) compared to the background ASPeCt SIT standard
deviation (36.2 cm), and it is also an outlier in other assess-
ments reported in this study. For the other ORAs, nothing
can be said given the unclear role of the ASPeCt errors in
the assessment.
For the ensemble mean, however, the agreement seems
better. The ORA MMM is biased thick, but again it cannot
be excluded that this is linked to the thin bias of ASPeCt.
As in other parts of this study, the ensemble mean performs
very well compared to the individual products, perhaps
thanks to the compensation of random errors present in each
product. We also considered the ensemble of nine ORA SITs
and compared it to the ASPeCt data. This approach, unlike
the ORA MMM, does not average SIT, it checks that the
observations lie within the ensemble. We found that the
ORA ensemble is only inconsistent with ASPeCt (meaning,
the two ensembles do not overlap one another) 7.8% of the
time, less than the significance level of 12.5%.
Fig. 16 Mean seasonal cycle
(over the period of 1993–2010)
of the Antarctic sea-ice extent,
area (upper row) and of the
area covered by Marginal Ice
Zone (MIZ) and pack ice (lower
row), in nine global ORAs,
the multi-model mean (colour
lines) and in NSIDC, CERSAT
and OSISAF observations
(grey shading). MIZ is defined
as a region where the sea-ice
concentration is less than 90%
and greater than 15%, while the
pack ice is the region where the
sea-ice concentration is higher
than 90%. Units are in 10
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1636 P.Uotila et al.
1 3
4.2.2 Mixed layer depth
In the Southern Ocean, summer MLDs are underesti-
mated south of the ACC in all ORAs except UR025.4 and
MOVE-G2i (Figs.18, S14). The band of mixed layers
reaching between 60 and 80 m within the ACC is well
represented in C-GLORS025v5 and UR025.4, but the
ORA MMM is biased low compared to observations, as
in the Arctic. In winter, the ocean destabilizes to depths
down to several hundred meters in a narrow band on the
equatorward edge of the ACC (e.g. Sallée etal. 2013).
While the observed pattern is well captured by the MMM,
most ORAs strongly overestimate these deep MLDs (Fig-
ureS15). Observations show that mixed layers also reach
depths close to 200 m on the continental shelves of the
Ross and Weddell Seas and along the coast of East Ant-
arctica. Reanalyses tend to underestimate MLDs around
East Antarctica, whereas an ORA ensemble mean positive
bias of more than 200 m is seen in the Ross and Weddell
Seas. Pellichero etal. (2017) note however that the Ross
Sea sector is the least well observed region so the climatol-
ogy must be used with caution in this area. Of individual
ORAs, MOVE-G2i and, to a lesser extent, GLORYS2v4
and GECCO2, show signs of open ocean deep convection
in the Weddell Sea (Figure S15).
Fig. 17 Mean error (thin coloured horizontal lines) and mean abso-
lute error (coloured rectangles) of nine ORA-IP reanalyses as well
as their ensemble mean of the Antarctic sea-ice thickness. For each
reanalysis, an compatibility index (in %, see Sect.3.2 for details) is
also provided: this index records the percentage of cases where the
reanalysis was found to be consistent with the reference ASPeCt
data set (see text for details). This index is further broken down in
cases where the incompatibility comes from a thin bias and a thick
bias with respect to ASPeCt (first and second number in parentheses).
The total number of cases (n) on which the assessment is done is also
Fig. 18 Antarctic mixed
layer depth for January (top)
and August (bottom), in an
observation-based climatology
(OBS; Pellichero etal. 2017),
for the ensemble mean of the
ORAs (MMM) and bias of the
ensemble mean with respect to
the climatology (MMM–OBS)
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1637An assessment often ocean reanalyses inthepolar regions
1 3
4.2.3 Liquid ocean transports
Among the ORAs, ORAP5.0 and GloSea5-GO5 simulate
the weakest transport along the ACC, with SODA3.3.1
being weak on the “30
E” section (see Fig.19). GECCO2
and ECDA3 have the strongest transports. The ensemble
mean of ORAs for the Drake Passage is 152 Sv (±19.2 Sv),
149 Sv (±16.8 Sv) for “30
E” and 169 Sv (±17 Sv) for
E. Upper bounds of the ORA MMM transport estimate
in the Drake Passage match the mean of the most realistic
observed estimates (Donohue etal. 2016; deVerdiére and
Ollitrault 2016). However, the ORA MMM mean value
remains less than the mean minus one standard deviation
bound estimate by Donohue etal. (2016). Only GECCO2,
Fig. 19 Antarctic Circumpolar Current (ACC) liquid ocean volume
transport through the Drake Passage (left), the “30
E” section (mid-
dle) and the “147
E” section (right). Units are in Sv (10
Standard deviation, measuring the interannual variability, is given for
each of the nine ORA products together with the standard deviation
among these nine members for the ensemble mean (ENS). Estima-
tions from different observed measurements are also given, see text
for details
Fig. 20 Averaged multi-model
Antarctic Ocean Heat Content
(OHC) in the layer of 0–1500
m (top left panel), its spread
(top second left panel) and indi-
vidual product departures from
the multi-model mean
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1638 P.Uotila et al.
1 3
Move-G2i and ECDA3 have their mean values within this
range. The amplitude of the seasonal cycle in all ORAs
is weak compared to the absolute value (see Figure S16,
right panel). All the ORAs exhibit insignificant trends in
the three transects (see Figure S16, left panel for the Drake
We also analyzed surface ocean currents. The Ocean
Surface Current Analyses Real-time (OSCAR) pro-
vides surface currents averaged over the top 30 m on
a 1/3
grid every 5 days (Dohan and Maximenko 2010;
Bonjean and Lagerloef 2002). The pattern comparison
of different ORAs and the ORA MMM with OSCAR
dataset in the Southern Ocean indicate a good match
at monthly frequency (not shown) which is reflected
in well contained mean errors of zonal and meridional
components, south of 50
S, on the order of 1 cm s
less (Figure S17).
4.2.4 Hydrography
The OHC in the top 1500 m for all products is first stud-
ied (Fig.20). The MMM is warmer along the Antarctic
continent but generally colder to the north than observa-
tional products EN4.2.0.g10 and WOA13. WOA13 appears
Fig. 21 Averaged multi-model
Antarctic Ocean Salt Content
(OSC) in the layer of 0–1500
m (top left panel), its spread
(top second left panel) and indi-
vidual product departures from
the multi-model mean
Fig. 22 Seasonal cycle of averaged monthly temperature from 1993
to 2010 in the layer of 0–100 m in the Southern Ocean. In the left-
most panels seasonal cycles for the multi-product mean (MMM) and
two observational data sets (EN.4.2.0.g10, WOA13) are shown, while
in the middle and rightmost panels individual ORA seasonal cycles
are presented
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1639An assessment often ocean reanalyses inthepolar regions
1 3
warmer than EN4.2.0.g10 with larger differences to the
MMM. ECDA3 and GECCO2 have largest anomalies from
the MMM. The individual ORA OHC anomalies are gen-
erally larger than those of the two observational products,
especially for example ECDA3 and GECCO2.
In terms of OSC in the top 1500 m, shown in Fig.21,
EN4.2.0.g10 and WOA13 closely agree. The MMM OSC
indicates somewhat fresher water around Antarctica (pos-
itive values in the EN4.2.0.g10 and WOA13 difference
plots) than the two observational products on average,
despite negative values immediately adjacent to Antarc-
tica. Again Fig.21 shows that observational products
(EN4.2.0.g10 and WOA13) have smaller anomalies with
respect to the MMM than most ORAs. GECCO2 is still
by far the biggest outlier, while GLORYS2v4, GloSea5-
GO5 and ORAP5 all broadly show opposite anomalies
to GECCO2. These anomaly patterns also largely agree
with anomalies in the observational products EN4.2.g10
and WOA13. Because the MMM is relatively close to the
observations despite the large departures of individual
ORAs, there is clear evidence that the MMM is averaging
out individual ORA biases.
Seasonal cycles of mean temperature in the 0–100 m layer
for the Antarctic region are presented in Fig.22. Observa-
tional products and the ORAs generally agree well, being
warmest in summer (February–March) and coldest in winter
(September) with comparable amplitudes. MOVE-G2i has
a somewhat anomalous seasonal cycle of mean temperature
with maximum in January and minimum in July. SODA3.3.1
has the lowest seasonal amplitude of upper ocean tempera-
ture, associated with its excessive sea-ice cover.
Seasonal cycles of mean salinity in the 0–100 m layer
for the Antarctic region are presented in Fig.23. Obser-
vational products and the MMM generally agree well
Fig. 23 Seasonal cycle of averaged monthly salinity from 1993 to
2010 in the layer of 0–100 m in the Southern Ocean. In the leftmost
panels seasonal cycles for the multi-product mean (MMM) and two
observational data sets (EN.4.2.0.g10, WOA13) are shown, while in
the middle and rightmost panels individual ORA seasonal cycles are
Fig. 24 Averaged temperature
profiles (a) and their depar-
tures (errors) from the WOA13
climatology (b) in the Antarctic
region shown in Fig.1. In a,
thin, dashed blue line shows the
non-depth averaged WOA13
temperature profiles. In b hori-
zontal bars indicate mean errors
and black, horizontal lines their
standard deviations as error ±
deviation. In cases where the
mean error or its standard devia-
tion is too large to fit inside the
panel, their values are indicated
as numbers
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1640 P.Uotila et al.
1 3
with increasing monthly mean salinities during the freeze
up period (autumn–winter) which then decrease towards
the summer during the ice and snow melt season. The
MMM agrees somewhat better with WOA13 than with
EN4.2.0.g10. Although the annual mean salinities of indi-
vidual ORAs vary to some extent, their seasonal cycles
of upper ocean salinity agree and have similar shapes to
the MMM. C-GLORS025v5 has a relatively higher sea-
sonal amplitude with a steep decline of salinity in spring.
MOVE-G2i, on the other hand, has a relatively small sea-
sonal amplitude.
The vertical profiles of temperature from the reanaly-
sis products and observational datasets that are averaged
over the Antarctic region (Fig.1) are evaluated against
the WOA13 temperature profiles in Fig.24. As noted in
Sect.3.7, the water column is divided into five layers: 0–100,
100–300, 300–700, 700–1500 and 1500–3000 m, and mean
temperatures for these layers were computed and compared
between the separate datasets and WOA13.
In Fig.24a the EN4.2.0.g10 and the MMM temperatures
are in fairly good agreement with the WOA13 temperature
below 700 m. Between 300 and 700 m, where the water
column is the warmest due to the presence of the Upper
Circumpolar Deep Water (UCDW), the EN4.2.0.g10 tem-
perature is almost identical to the MMM, while the MMM
temperature is higher than WOA13 by
C. The largest
temperature differences between the MMM and observa-
tional datasets occur between 100 and 300 m, where the
MMM has a warm bias of 0.4 and 0.2
C relative to WOA13
and EN4.2.0.g10, respectively. This possibly indicates a
larger fraction of winter water in the observations. In the
0–100 m layer, where the water is the coldest, the MMM
is also warmer than observed values, but the deviations are
not as large as in the layer below. In the 0–100 m layer the
WOA13 and EN4.2.0.g10 temperatures are colder than the
MMM temperature by 0.25 and 0.1
C, respectively.
Figure24b shows the vertical profiles of basin-wide
mean and standard deviation of temperature differences
between individual ORAs, MMM and EN4.2.0.g10 from
the WOA13 temperature values. The ORA mean difference
range and their standard deviations are relatively small in
the surface layer with all the individual reanalyses differ-
ing by < 0.2
C from the MMM. The sub-surface layer
(100–300 m), where the MMM has a clear warm bias com-
pared to observations, is characterised by a larger scatter
with differences reaching 0.4
C. Although one ORA is
close to WOA13 (C-GLORS025v5), all others are warmer
than this observational product. GLORYS2v4, GloSea5-
GO5 and UR025.4 are much warmer than observations
and the MMM. Below 300 m, observations and the MMM
agree well due to compensation between the different
reanalyses, with the majority being within 0.2
C of the
MMM. In agreement with Fig.20, GloSea5-GO5 and
ORAP5 display the largest systematic positive anoma-
lies compared to the WOA13 but others also have large
positive or negative differences in many layers, such as
GECCO2, ECDA3 and SODA3.3.1. The largest biases,
reaching nearly 0.4
C are found in GloSea5-GO5 which
has a warm bias over the whole water column.
Fig. 25 Averaged salinity
profiles (a) and their depar-
tures (errors) from the WOA13
climatology (b) in the Antarctic
region shown in Fig.1. In a,
thin dashed blue line shows the
non-depth averaged WOA13
salinity profiles. In b horizontal
bars indicate mean errors and
black, horizontal lines their
standard deviations as error ±
deviation. In cases where the
mean error or its standard devia-
tion is too large to fit inside the
panel, their values are indicated
as numbers
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1641An assessment often ocean reanalyses inthepolar regions
1 3
The vertical salinity profiles for the reanalysis products
are shown in Fig.25. Overall, the MMM salinity is close
to WOA13 and EN4.2.0.g10 in all layers, in particular in
layers deeper than 700 m. WOA13 and EN4.2.0.g10 agree
rather well, although in the 100–300 m layer WOA13 is
slightly fresher with relatively large standard deviation of
basin-wide differences. In the 300–700 m layer, the MMM
salinity is smaller than WOA13 and EN4.2.0.g10 by
The majority of the reanalyses are also close to observa-
tions, except in the 0–100 m layer where mean differences
can reach more than 0.01 and standard deviations are large.
Below 100 m, GECCO2 has a considerable fresh bias which
reaches a maximum in the 300–700 m layer, but remains
large in the deeper layers. This makes GECCO2 an excep-
tion in the OSC patterns of ORAs for the upper 1500 m, as
shown in Fig.21. In the 100–300 m layer, noticeable devia-
tions in salinity from the WOA13 occur also in ECDA3,
GloSea5-GO5, GLORYS2v4 and ORAP5.
The surface layer (0–100 m) is characterized by low tem-
perature and salinity and is composed of the Antarctic Sur-
face Water. Among the reanalyses, GECCO2, SODA3.3.1
and UR025.4 have water mass properties that are closest to
the MMM values, while MOVE-G2i and ORAP5 have the
largest deviations from the MMM values, with their poten-
tial density higher/lower than the MMM by
0.06 and 0.1
kg m
, respectively.
In the subsurface layer (100–300 m), the MMM water
mass properties are much closer to EN4.2.0.g10 than to
WOA13, due to the lower temperature in WOA13 leading to
a higher density. The largest deviations in water mass prop-
erties occur for GloSea5-GO5 and GLORYS2v4, which have
considerably higher temperature than the MMM and obser-
vations, and for GECCO2 which has a much lower potential
density than the MMM, by 0.12 kg m
, associated with the
exceptionally low salinity in this layer.
In the 300–700 m layer, where the presence of the Upper
Circumpolar Deep Water (UCDW) leads to the highest
temperatures and salinities, the agreement is good between
EN4.2.0.g10, WOA13 and the MMM, with a slightly higher
potential density in WOA13. In this layer, GECCO2 is the
only dataset that has considerable potential density devia-
tion from the MMM, due to its low salinity. In the 700–1500
m layer, mainly occupied by the Lower Circumpolar Deep
Water (LCDW), and in the deep layers below 1500 m, the
densities are even closer between EN4.2.g10, WOA13 and
the MMM, as noted for temperature and salinity profiles.
The range among ORAs is also smaller, with GECCO2 still
standing out due to its large potential density deviation from
the MMM.
5 Discussion
After presenting individual diagnostics in the previous
sections, we concentrate here on processes connecting the
results. Table3 shows ORA departures, or errors, from
observed data as their basin-wide means and standard devia-
tions for all diagnostics at a glance. This approach lets read-
ers to understand if the errors are really a problem or within
what is deemed acceptable.
5.1 Sea ice andsnow
The ORAs agree well on the location of the sea-ice edge
in the Arctic and the Southern Ocean. In winter, individual
ORAs show both positive and negative sea-ice concentration
biases in the Labrador Sea, the Greenland Sea, and the sea
of Okhotsk. On average, the sea-ice area and concentration
tend to be overestimated both in the Arctic Ocean and the
Southern Ocean (Table3). The Antarctic sea ice is a mix of
ice with medium and high sea-ice concentration, at a more
balanced ratio than in the Arctic. Thus, the general posi-
tive sea-ice area bias appears more clearly in the Antarctic
with only a narrow MIZ present near the ice edge. The sea-
ice edge itself is well constrained, even in summer at the
Antarctic sea-ice minimum, when it is controlled by small-
scale coastal processes and not well represented in rather
coarse resolution free running ocean-ice models without
data assimilation (Downes etal. 2015).
Although a CORE-II comparison is meaningful for the
ocean, it is less so for sea ice, as the ORA sea-ice concen-
tration is rather strongly constrained by data assimilation.
Therefore, we do not compare the ORA sea ice with CORE-
II in the following discussion.
Chevallier etal. (2017) noted that the sea-ice concentra-
tion is one of the most consistent features of the sea-ice
cover amongst the reanalyses due to constraints imposed by
direct assimilation of ocean and sea-ice concentration obser-
vations, and to the strong restoring towards near-surface air
temperatures through the atmospheric reanalyses. All rea-
nalyses in the present study, except the coupled ECDA3, are
driven by a prescribed atmosphere through bulk formulae. In
addition to missing sea-ice data assimilation, the vanishing
Antarctic summer sea ice in ECDA3 is possibly related to
the stronger surface shortwave radiation compared to pre-
scribed atmospheric forcing. Other ORAs also have more
correct marginal versus pack-ice area/extent ratios.
The fact that ORAs realistically reproduce the seasonal
cycle of total sea-ice area (Figs.3, 16) and agree in the
location of the sea-ice edge (Figs.2, 15), is owing to the
compensation of errors in their simulation of two sea-ice
regimes, too small MIZ area and respectively too large pack-
ice area. This is consistent with the results of Chevallier
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1642 P.Uotila et al.
1 3
Table 3 A summary of diagnostic performance of ten ORAs and their mean (MMM) in the Arctic and Antarctic
For each diagnostics mean errors and their standard deviations, calculated from differences between the ORAs and observed reference data, are presented, where applicable. The observed refer-
ence data are described in text. Abbreviations of diagnostics: sea-ice area in February (SIA-F, 10
), sea-ice area in March (SIA-M, 10
), sea-ice area in September (SIA-S, 10
snow depth (SND, cm), sea-ice thickness (SIT, m), mixed-layer depth in winter (MLD-W, m) and in summer (MLD-S, m), liquid ocean heat transport (OHT, TW), liquid ocean volume transport
(OVT, Sv), sea temperature (ST,
C), sea salinity (SS), ocean heat content (OHC,
C m), ocean salt content (OSC, psu m), and the temperature of the warm Atlantic Water (AW at 300–700 m
C) in the Eurasian basin
C-GLORS025v5 − 0.24 ± 0.06 − 0.60 ± 0.09 − 0.15 ± 0.17 − 0.16 ± 0.67 − 8.9 ± 5.6 39.0 ± 150.5 − 34 ± 5 − 0.17 ± 0.70 − 0.09 ± 0.86 − 133 ± 242 − 9.3 ± 75.9 − 0.40 ± 0.38
ECDA3 0.20 ± 0.14 − 0.01 ± 0.34 − 0.09 ± 0.13 − 0.79 ± 1.01 − 8.3 ± 5.1 66.0 ± 133.6 − 51 ± 5 − 0.35 ± 0.77 − 0.36 ± 5.57 − 331 ± 289 − 125.9 ± 198.6 − 0.35 ± 0.43
GECCO2 − 0.02 ± 0.16 − 0.45 ± 0.58 − 0.37 ± 0.71 − 8.0 ± 5.1 10.9 ± 65.8 − 7 ± 5 − 0.08 ± 0.79 − 0.36 ± 0.76 250 ± 198 − 72.6 ± 71.6 − 0.16 ± 0.39
GLORYS2v4 0.13 ± 0.06 0.24 ± 0.07 − 0.13 ± 0.16 0.70 ± 1.08 − 9.3 ± 5.1 55.3 ± 178.8 − 48 ± 5 − 0.10 ± 0.75 0.11 ± 1.15 − 112 ± 218 − 12.7 ± 153.7 − 0.60 ± 0.43
GloSea5− GO5 0.14 ± 0.07 0.18 ± 0.05 − 0.14 ± 0.17 − 0.38 ± 0.84 − 9.1 ± 5.1 25.4 ± 86.5 − 7 ± 5 − 0.10 ± 0.54 0.02 ± 1.27 − 8 ± 171 14.8 ± 73.2 − 0.20 ± 0.23
MOVE− G2i 0.08 ± 0.10 − 0.09 ± 0.12 − 0.13 ± 0.16 − 0.52 ± 0.80 − 5.6 ± 5.2 29.0 ± 112.8 − 83 ± 5 0.03 ± 0.72 0.01 ± 0.75 110 ± 208 45.0 ± 41.1 − 0.01 ± 0.27
ORAP5 0.25 ± 0.08 − 0.02 ± 0.05 − 0.08 ± 0.12 0.07 ± 0.66 − 8.6 ± 5.7 57.2 ± 228.0 − 16 ± 5 − 0.20 ± 0.61 − 0.02 ± 0.52 − 112 ± 129 41.8 ± 40.3 − 0.35 ± 0.22
SODA3.3.1 0.97 ± 0.37 1.60 ± 0.72 0.05 ± 0.13 − 0.34 ± 0.69 − 10.6 ± 5.1 8.0 ± 60.0 − 31 ± 5 0.04 ± 0.71 − 0.03 ± 1.15 97 ± 162 − 71.7 ± 120.2 − 0.20 ± 0.37
TOPAZ4 − 0.19 ± 0.11 0.13 ± 0.09 0.01 ± 0.12 − 0.43 ± 0.69 − 5.5 ± 5.2 49.4 ± 125.8 − 0.14 ± 0.99 − 0.03 ± 0.75 5 ± 232 − 53.5 ± 85.2 − 0.52 ± 0.49
UR025.4 0.04 ± 0.09 − 0.07 ± 0.05 − 0.20 ± 0.24 − 0.85 ± 1.06 − 6.5 ± 5.0 56.4 ± 127.7 − 23 ± 5 − 0.14 ± 0.49 0.06 ± 0.92 − 112 ± 129 − 4.5 ± 42.4 − 0.28 ± 0.30
MMM 0.11 ± 0.08 0.09 ± 0.10 − 0.10 ± 0.16 − 0.31 ± 0.84 − 7.8 ± 5.0 34.0 ± 108.3 − 38 ± 5 − 0.11 ± 0.64 − 0.06 ± 0.88 − 25 ± 124 − 27.8 ± 65.4 − 0.30 ± 0.31
WOA13 − 0.07 ± 0.59 0.04 ± 0.59 34 ± 113 16.3 ± 35.0 − 0.14 ± 0.27
EN4.2.g10 − 0.12 ± 0.68 0.05 ± 0.83 − 118 ± 160 9.6 ± 127.0 − 0.30 ± 0.33
C− GLORS025v5 − 0.90 ± 0.16 − 1.08 ± 0.09 0.03 ± 0.24 − 7.9 ± 9.4 52.3 ± 73.1 − 28.9 ± 10.9 − 0.06 ± 0.62 − 0.01 ± 0.15 − 131 ± 235 7.1 ± 24.7
ECDA3 0.05 ± 0.58 − 2.28 ± 0.33 − 0.03 ± 0.31 − 18.5 ± 9.1 5.4 ± 53.4 3.5 ± 10.7 − 0.05 ± 0.65 0.01 ± 0.18 − 277 ± 317 − 0.7 ± 30.7
GECCO2 1.97 ± 0.39 0.22 ± 0.39 0.09 ± 0.31 − 14.3 ± 7.4 − 9.2 ± 64.0 5.1 ± 11.0 0.07 ± 0.69 − 0.10 ± 0.17 − 190 ± 503 − 53.5 ± 53.8
GLORYS2v4 0.86 ± 0.29 − 0.35 ± 0.08 0.05 ± 0.23 − 10.0 ± 7.1 3.1 ± 54.1 − 13.9 ± 11.0 0.10 ± 0.62 0.02 ± 0.16 11 ± 220 − 9.6 ± 73.5
GloSea5-GO5 0.14 ± 0.22 − 0.08 ± 0.09 0.06 ± 0.29 − 4.6 ± 10.0 24.0 ± 67.4 − 55.7 ± 13.5 0.19 ± 0.66 − 0.00 ± 1.78 34 ± 291 − 7.8 ± 38.2
MOVE-G2i 1.01 ± 0.76 − 0.46 ± 0.19 − 0.05 ± 0.23 7.7 ± 12.0 49.4 ± 91.5 − 3.3 ± 11.1 0.17 ± 0.65 0.02 ± 0.12 39 ± 261 7.4 ± 21.4
ORAP5 0.31 ± 0.37 − 0.40 ± 0.11 0.03 ± 0.25 2.6 ± 12.4 41.1 ± 67.9 − 48.6 ± 11.3 0.15 ± 1.00 − 0.04 ± 0.22 138 ± 388 1.8 ± 39.5
SODA3.3.1 5.43 ± 0.70 5.64 ± 1.17 0.08 ± 0.27 − 19.8 ± 9.0 − 30.0 ± 52.2 − 25.5 ± 14.0 0.04 ± 0.63 0.00 ± 0.15 − 63 ± 221 − 5.8 ± 30.4
UR025.4 0.13 ± 0.30 − 0.47 ± 0.08 − 0.02 ± 0.21 − 1.0 ± 8.3 74.2 ± 67.6 − 22.3 ± 11.2 0.04 ± 0.64 0.01 ± 0.16 − 57 ± 255 − 7.6 ± 28.4
MMM − 0.61 ± 0.19 − 0.17 ± 0.15 0.03 ± 0.19 − 7.4 ± 6.7 23.8 ± 52.2 − 21.1 ± 11.0 0.08 ± 0.65 − 0.01 ± 0.40 − 55 ± 208 − 7.7 ± 21.8
EN4.2.g10 − 0.09 ± 0.56 0.00 ± 0.10 − 96 ± 179 4.9 ± 20.7
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1643An assessment often ocean reanalyses inthepolar regions
1 3
etal. (2017), and shows that in spite of recent physics and
data assimilation improvements, ORAs still tend to simulate
too high pack-ice concentration in winter.
Snow volume differences among ORAs grow during
autumn and winter. They are linked to precipitation biases,
and deviations in ice formation, the timing of freeze-up,
melt, flooding and sublimation. Lindsay etal. (2014) showed
that the rate of precipitation in the polar regions is highly
uncertain between reanalyses. Most of the ORAs analyzed
here are using the ERA-Interim reanalysis with the excep-
tions of ECDA3 (which has a coupled ocean-atmosphere,
with the atmosphere relaxed towards the NCEP-NCAR
reanalysis), MOVE-G2i (forced by the JRA-55 reanalysis)
and SODA3.3.1 (forced by the NASA MERRA2 reanaly-
sis), see Table1. Differences in atmospheric forcing result
in discrepancies in the air–ice surface energy balance, ice
growth and melt, and upper ocean characteristics. In addi-
tion, sea-ice dynamics impacts where the ice and snow drifts
to, and therefore their spatial distributions. For example,
sea-ice dynamics includes the process where ORAs with
larger open water fraction have lower snow volume because
in these products more snow melts in the open water. To
some extent, the ice-snow relationships are affected by the
sea-ice concentration and sea surface temperatures, both
controlled by data assimilation. For instance, many ORAs
have higher than observed sea-ice concentration in the pack-
ice region. Accordingly, in these ORAs data assimilation
tends to reduce the sea-ice area and correspondingly increase
snow and ice melt. Due to these reasons varying physical
parametrisations and data assimilation schemes affect the
evolution of snow volume.
Despite the differences in atmospheric forcing, all ORAs
have a dipole bias of the Arctic sea-ice thickness, with too
thick ice in the Beaufort Gyre and too thin in the Eurasian
basin north of the Fram Strait. A consequence of the dipole
bias is that their total Arctic sea-ice volumes agree rather
well with the observed estimates. Similarly, when compared
to Arctic ITRP observations, the ORA sea-ice volumes tend
to have cancelling positive and negative biases and, as a
result, the MMM sea-ice volume is surprisingly close to the
ITRP sea-ice volume (Table3).
Perhaps analogously, the ORA ensemble sea-ice thick-
ness cannot be deemed inconsistent with the ASPeCt data
in the Antarctic, while most individual ORAs are them-
selves inconsistent with this observational data set. This
points towards the important role of model error in the
misrepresentation of sea-ice thickness. In other words, the
spread of the ORAs appears large enough to reflect what
is uncertain in the experimental setup and in the models
used, but this data set is not sufficient to provide trustful
reconstructions and estimations of past Antarctic sea-ice
thickness. We argue that the global ensemble diagnostics
provides realistic insights to the ocean state, especially
when compared to global-individual and regional-ensemble
diagnostics results. These insights remain limited due to the
large ORA spread.
The MMM also has a rather realistic looking snow and
ice volume. This is because most individual ORAs have
snow/ice volumes and ice areas close to observations, indi-
cating that their ice and snow related processes are linked
rather similarly by physical mechanisms. An exception is
SODA3.3.1 which has a very large ice area and thick snow,
although its Arctic sea-ice thickness looks reasonable.
SODA3.3.1 ice and snow biases are related to delayed ice
melt due to the thicker snow. We checked that for SODA3.3,
the NASA MERRA2 forcing produces too much ice, ERA-
Interim clearly less and JRA-55 produces ice volumes
between those two in the Southern Hemisphere. These find-
ings are consistent with Chevallier etal. (2017). The very
thin snow cover in UR025.4 may also explain its thin ice in
the Arctic and Antarctic.
5.2 Mixed layer
In agreement with our findings in Table3, an overestima-
tion of average winter Arctic and Antarctic MLDs was
also found from the forced ocean simulations conducted
in the CORE-II framework (Downes etal. 2015; Ilicak
etal. 2016). The sea-ice biases noted above may be linked
to this winter MLD bias. The MMM sea-ice concentra-
tion has a positive bias in winter, but is close to observed
in summer which implies that the simulated sea ice must
have a larger than observed seasonal amplitude and indi-
cates increased ice formation and melt. Hence, during the
freezing period more salt is rejected to the upper ocean
destabilising it more in ORAs than in observations. In
winter, the CORE-II MMM has a too extensive deep mixed
layer in the Weddell Sea and a very limited region of deep
convection in the Ross Sea (Downes etal. 2015). These
spatial patterns of deep convection are less realistic that
in the ORA MMM. Accordingly, it seems that in winter in
the Antarctic the representation of mixed layers in ORAs
are improved.
On average, the ORA MMM tends to have a close to the
observed amount of ice in the Arctic and too little ice in the
Antarctic in summer. The ORAs also uniformly have too
shallow summer mixed layers (Table3). This is the only
diagnostics where the ORAs systematically show similar
biases with basin-wide means larger than their standard
deviations. As explained in the previous paragraph, the
amplified seasonal sea-ice cycle may cause large surface
freshwater fluxes, reducing the summer MLD. Given the
vertical model resolution, the summer MLD may even be
at the lowest possible model level for a couple of ORAs,
for example at 10 m, although most ORAs have upper layer
thicknesses in the order of 1 m. The fact that the largest
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1644 P.Uotila et al.
1 3
MLD discrepancies between ORAs and observations are
found in ice-covered regions suggests that the MLD biases
could also arise from issues in the ice-ocean coupling or in
the vertical mixing of melt water by high-frequency wind
events. The shallow MLD is therefore possibly caused by
missing or poor representation of some mixing processes
such as surface waves, Langmuir circulation and sub-mes-
oscale eddies. Shallow summer MLD biases are a common
issue of current coupled and forced models (Huang etal.
2014; Barthélemy etal. 2015). For example, the shallow
summer bias has been found in CMIP5 models (Sallée
etal. 2013).
It is worth mentioning that the quality of observations
may be poor in the Arctic Ocean and, for example, under
thick ice MLD is not even observed. Importantly, the MLD
differences between ORAs and observations appear large
and robust, and it is likely that the results are not quali-
tatively affected by the methodological issues related to
the different calculation procedures of ORA MLDs com-
pared to observation based MLDs (Toyoda etal. 2017a).
In short, the mean mixed layer biases in the ORAs, includ-
ing the MMM, are similar in both hemispheres: they have
a shallow mean MLD bias in summer, which is larger
than the standard deviation of the bias (see MLD-S and
MLD-W in Table3. In winter the ORAs, generally a deep
bias in winter, but the standard deviation of the winter bias
is comparably large.
5.3 Ocean transports andhydrography
Observational products Sumata, WOA13 and EN4.2.g10
show somewhat different hydrographies. Sumata is from
1980 to 2015, but with few observations early on, WOA13
from 1995 to 2015 and EN4.2.g10 from 1993 to 2010. To
some extent, deviations between these three observational
data sets are probably due to oceanic decadal variability. The
2010–2015 period missing from EN4.2.g10 may be the main
cause for its differences from WOA13. Because of the most
recent observations, the Arctic Ocean in Sumata appears
warmer than in the other two observational products. In the
Southern Ocean, where Sumata is not available, EN4.2.g10
is somewhat colder than WOA13, but it is hard to say which
one is more realistic. The MMM is closer to EN4.2.g10
probably because many ORAs are adjusted towards it, or its
earlier versions, instead of WOA13 (Table1).
The MMM reveals salient biases among ORAs. Its MLD
is too deep in the northern North Atlantic where its warm
AW cools and consequently its heat flux to the Arctic Ocean
is reduced. This low heat transport, mainly through the Fram
Strait, impacts the hydrography in the Arctic Ocean and
results in a colder than observed Atlantic layer. Moreover,
in the Barents Sea, the winter heat loss is high and many
ORAs unrealistically convect to the bottom. Despite this,
most ORAs have a realistic location of the sea-ice edge in
the Barents–Kara Sea being apparently unaffected by the
heat transport anomalies. A similar relationship is seen on
the Pacific side of the Arctic, where the ORA heat transport
through the Bering Strait is low but the sea-ice edge location
is realistic, on average. The prescribed atmospheric forc-
ing, and assimilated sea-ice concentration and sea surface
temperature data constrain the location of the sea-ice edge,
obscuring the link between it and the oceanic heat trans-
ports. However, in many ORAs in the Greenland Sea the
sea-ice edge is more to the east than observed (Fig.2) and
the maximum MLD is shifted to the east as a result (Fig.7).
This could, partially at least, block the AW transport through
In CORE-II, the total Atlantic ocean heat transport to
the Arctic varies with some models having positive and
some negative biases through the Fram Strait. These biases
co-vary with the amount of cold water entering the Arctic
through the St. Anna Trough (Wang etal. 2016b). In CORE-
II models with a cold Arctic, the warm AW layer is eroded
due to excessive cold water transport to the Arctic in the St
Anna Trough. The excess of cold water is a result of nega-
tive sea-ice biases near shelf regions exposing the ocean to
the cold atmosphere.
These relationships are not apparent in the ORAs—prod-
ucts with a low Fram Strait transport also have high BSO
transports, but these products do not necessarily have a cold
Arctic Ocean. The MMM heat transport through the Fram
Strait is lower than observed, its heat transport through the
BSO is close to the observed and its AW layer is too cold.
This indicates that in the ORAs, probably due to their bet-
ter resolution compared to CORE-II, and to data assimila-
tion, the transport over the St. Anna Trough is more realistic
while, on average, their Fram Strait heat fluxes remain too
cold resulting in a cold Arctic Ocean.
Of individual products, GECCO2 and MOVE-G2i show
excessive transports through the BSO but very low in the
Fram Strait (Fig.8). However, their Arctic Oceans are much
warmer than observed. For example, the MOVE-G2i Eura-
sian basin mean temperature is the highest among the ORAs
in the top 700 m (Fig.13). In GECCO2 the warm Arctic can
be explained by its extremely warm Nordic Seas and high
BSO heat transport. MOVE-G2i, on the other hand, has a
low average heat transport in the Fram Strait and rather cold
Nordic Seas indicating that its warm Arctic Ocean must be
of different origin than the one of GECCO2. MOVE-G2i
heat content in the top 100 m is very high in the Barents Sea,
compared to other ORAs and observational data, pointing
to a major heat pathway to the Arctic in MOVE-G2i (Figure
S9). It is probable that this exceptional heat transport pattern
results in a particular heat and temperature distribution in the
Eurasian basin. However, a realistic looking heat transport
alone does not guarantee the correct Arctic hydrography. For
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1645An assessment often ocean reanalyses inthepolar regions
1 3
example, GloSea5-GO5 and ORAP5 capture the transports
from the Atlantic well, but are still too cold in the Arctic.
There is a possible link between the ORA MLD and the
cold biases in the top 100 m layer. As shown, the MMM
MLD is too shallow in summer and too deep in winter in
association with cool biases in the Arctic Ocean and the
coastal Antarctic waters (Table3). These biases could be
related in the following way: in summer a too shallow mixed
layer (
100 m) reduces the flux of the atmospheric heat to
the ocean and the top 100 m layer stays cooler, and possibly
fresher due to more winter ice melt in ORAs, as noted ear-
lier. The shallow mixed layer loses heat faster and becomes
colder, allowing for an earlier freeze-up in autumn, and the
increased salt flux from ice contributes to the deepening of
mixed layer in ORAs. In winter, there is always at least nar-
row open water areas even in compact ice fields due to ice
dynamics so that a deeper mixed layer loses more heat to
the cold atmosphere, which in non-coupled ORAs acts as
an infinite heat sink, generating a cooler and denser upper
ocean in the Arctic Ocean and the coastal Antarctic waters.
Denser upper ocean waters sink and further deepen the win-
ter mixed layer.
In the Southern Ocean, the ORAs are generally too
warm in the upper 300 m which, combined with too fresh
or close to observed salinity in the upper 100 m (Figs.24,
25). This results in a stratification that is more stable than
observed and is associated with low MLDs in summer. At
deeper levels the ORA hydrography is close to WOA13 and
EN4.2.g10i with the exception of GECCO2 due to its low
salinity. Compared to the CORE-II models, which tend to
have too cold Southern Ocean south of 50
S (Downes etal.
2015), the ORA MMM shows a warm bias in the upper
700 m. As in CORE-II, the ORA hydrographic biases are
likely associated with differences in oceanic transports, for
instance in the ACC. GloSea5-GO5 and ORAP5 have the
lowest volume transports in the ACC linked to significant
cold biases in the Drake Passage (Table3). In contrast, the
ORAs with a higher than MMM ACC (ECDA3, GECCO2,
GLORYS2v4, and MOVE-G2i) have positive heat biases in
the Drake Passage. The model resolution seems to matter as
the low resolution ORAs (ECDA3, GECCO2 and MOVE-
G2i) have high ocean transports in the ACC, while the high
resolution, eddy permitting ORAs have volume transports
matching rather well with observational estimates. This is
consistent with Farneti etal. (2015) who found that the bet-
ter representation of ocean eddies among the CORE-II mod-
els resulted in a more realistic ACC. One might expect that
the realistic ACC due to higher resolution would also repro-
duce realistic temperatures, but this is not apparent from our
diagnostics results (Table3).
5.4 Synthesis ofdiagnostics
As has been seen, basin-wide mean errors are sensitive to
opposing local biases and may give small values when, for
example, large negative and positive biases cancel out. The
basin-wide standard deviation of errors, on the other hand,
is not similarly affected by spatial errors and obtains a large
value in the aforementioned case. Therefore the products are
ranked based on their standard deviations of errors in such
way that for each diagnostics the product with the small-
est standard deviation obtains a score of one, the product
with the next smallest standard deviation a score of two and
so forth. Hence, the products with relatively small stand-
ard deviations get a sum of scores smaller than those with
larger standard deviations and can be assumed to perform
better. However, our ranking approach is somewhat sensitive
to the selection of the observational reference data and to
which diagnostics enter the ranking. Hence, relatively small
differences between ranking scores do not necessarily indi-
cate significant differences in performance. However, even
though a single score might be questioned we think that the
general picture emerging from the sum of rank scores and
linkages between diagnostics are not. Rank scores summed
Fig. 26 Sums of rank scores of various diagnostic variables for indi-
vidual ORAs and their mean product (MMM). The ranking is based
on standard deviations of differences between the ORAs and observa-
tional data in the Arctic (blue bars), Antarctic (orange bars) and both
added together (green bars) shown in Table3. A smaller sum indi-
cates a closer agreement with the observational data and a better per-
formance. The Arctic snow depth and net liquid ocean heat tranport
diagnostic rank scores were excluded from the sums as GECCO2 did
not provide snow depth and the ocean transport diagnostic provided
the mean values of net transports only
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1646 P.Uotila et al.
1 3
across individual diagnostics in the Arctic and Antarctic are
illustrated in Fig.26 for each ORA and the MMM.
In the Arctic, only ORAP5 and GloSea5-GO5 have lower
sums of rank scores than MMM, while UR025.4 is close
to the MMM (Fig.26). The remaining ORA rank score
sums are clearly higher than the MMM one. In the Antarc-
tic, the good performance of the MMM is even clearer as
it obtains the lowest sum of rank scores. Closest to it are
now GLORYS2v4 and C-GLORS025v5, then UR025.4 and
SODA3.3.1, followed by the other ORAs. ORAP5, which is
the best performer in the Arctic has the largest sum of rank
scores in the Antarctic. ECDA3, the only coupled reanalysis,
stands out as having the highest global sum of rank scores,
followed by GECCO2 and SODA3.3.1. Globally, the MMM
has the lowest sum of rank scores, followed by UR025.4
and C-GLORS025v5. In addition to the ranking based on
standard deviation of errors, another ranking was carried
out using absolute values of basin-wide mean errors (not
shown). This ranking produced overall similar results to the
ones based on standard deviations—the sum of MMM rank
scores was smaller than ones for individual ORAs, globally.
Results from the rankings support the good performance
of the MMM and its usefulness in describing polar ocean
6 Conclusion
We have analysed several aspects of ten ocean reanalysis
products in the Arctic and Antarctic. In this paper we con-
centrate on comparing the mean states of the ORAs. This
is the first step towards more comprehensive analyses of
interannual variability and co-variability between differ-
ent fields as proposed for instance in the ongoing European
H2020 APPLICATE project. The biases identified, and their
potential linkages, will assist the developers to improve their
products and inform the users of product quality. We empha-
sise that this paper is a snapshot of a moving target, because
these products are constantly evolving and being updated
regularly, quite often in response to ORA intercomparison
efforts. Nonetheless, the performance may remain represent-
ative for a while, as was found when comparing the Arctic
sea-ice diagnostics results in our study with Chevallier etal.
(2017). Additionally, most of our polar diagnostics, with the
exception of some related to Arctic sea ice, were carried out
for the first time for a such an extensive set of ORAs.
In addition to interannual variability, future studies could
focus on assessing the sea-ice dynamics in these reanalyses.
A thorough evaluation of sea-ice drift in both polar oceans,
similar to that performed in Chevallier etal. (2017) for the
Arctic sea-ice cover, could be carried out, as sea-ice advec-
tion is one of the main mechanisms linking the polar regions
with lower latitudes. An important outcome from such a
study would be a more comprehensive understanding of how
the atmosphere–ocean energy transfer is represented in the
current ocean reanalyses, and the role of sea ice in control-
ling this. As climate models presently fail to realistically
replicate the global sea-ice trends, such an understanding
is needed to enhance climate prediction skill (Turner and
Comiso 2017; Rosenblum and Eisenman 2017). However,
such a study would require more detailed diagnostics than
currently available in the ORA-IP database, otherwise the
results would not significantly differ from those of Cheval-
lier etal. (2017).
Ideally, we would like to separate the impact of data
assimilation from, for example, model physics, which would
require the use of analysis increments as a measure. Carry-
ing out such an approach can be considered as an in-depth
assessement of a few ORAs, but is clearly beyond the scope
of our study. In general, the earlier ORA-IP studies, except
one, did not investigate the impact of analysis increments
presumably for similar reasons. Based on six ORAs, where
data were available for calculations, Valdivieso etal. (2014)
found that the analysis increments were compensating for the
inadequacies of the atmospheric reanalysis used to force the
ORA, but the ORA ensemble still gave the best estimates of
the oceanic and sea-ice quantities. Their results support our
approach to pay attention to the performance of the MMM.
For the ORA ensemble mean state, we found that devia-
tions from observational estimates were typically smaller
than individual ORA anomalies, a well known character-
istic of many climate model ensembles, often attributed to
offsetting biases of individual ORAs. While this interpreta-
tion may be challenged (Rougier 2016), the ORA ensem-
ble appears to be a useful product and, while knowing its
anomalies and recognising its restrictions, it can be used
to gain useful information on the physical state of the polar
marine environment.
Acknowledgements We acknowledge Dr. Benjamin Rabe and the two
anonymous reviewers for their comments that significantly improved
the manuscript. EU-COST EOS-1402 Ocean Synthesis action is
acknowledged for their support, in particular to assist the organisation
of the Polar ORA-IP meetings, both physical and virtual, which were
crucial for the study. Work of Uotila was supported by the Finnish
Academy (Grants 264358 and 283034) and by the EU MCSA grant
707262-LAWINE. Chevallier, Fučkar, Haines and Massonnet have
received funding from the European Union’s Horizon 2020 Research
and Innovation programme through Grant agreement No. 727862
APPLICATE. Fučkar was a Juan de la Cierva-incorporacion fellow
supported by the Spanish government. Goosse is a research director and
Massonnet a post-doctoral researcher with the FRS/FNRS, Belgium.
The ORA and MMM data used in this study are provided by Ham-
burg University on the ORA-IP web-site at https ://icdc.cen.uni-hambu /reana lysis -ocean /oraip .html.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1647An assessment often ocean reanalyses inthepolar regions
1 3
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... In Figure 8, we see that the correlation between the MAE and MIZ relative areas almost equals 1. The high correlation means that even not rheology-dependent ML models face the same problem as numerical models [61]. All the results above are given for the general models. ...
... One should also consider exploring more advanced approaches to measure the quality of the forecasts in terms of their value for marine operations. Ref. [61] showed that the ten most modern ocean reanalyses systematically underestimate the area of MIZ during spring and autumn even with data assimilation. Finally, one can try to fuse these approaches and train statistical models to compensate for errors of the numerical models or use them in any other more intricate way. ...
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Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions.
... In figure 10, we see that the correlation between MAE and MIZ relative area almost equals 1. The high correlation means that even not rheology-dependent ML models face the same problem as numerical models [53]. ...
... Another important extension is to compare the performance of numerical ocean-ice models, such as GLORYS12-V1, TOPAZ4, or SODA3.3.1, with the developed data-driven approach. [53] showed that the ten most modern ocean reanalyses systematically underestimate the area of MIZ during spring and autumn even with data assimilation. Finally, one can try to fuse these approaches and train statistical models to compensate for errors of the numerical models or use them in any other more intricate way. ...
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient in this task. Many works have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data they need for operation. In this work, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin and improve its quality by using additional weather data and training on multiple regions, ensuring its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions.
... Instead, only those focusing on the salinity profiles simulated by several individual reanalysis systems have been widely conducted. For example, the author of [21] assessed the quality of upper salinity content above 1500 m reproduced by ten reanalysis systems. The author of [10] indicates that data assimilation has effectively improves the fit of ORAS5 to the in situ salinity measurements. ...
Full-text available
Sea surface salinity (SSS) is one of the Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS). Acquiring high-quality SSS datasets with high spatial-temporal resolution is crucial for research on the hydrological cycle and the earth climate. This study assessed the quality of SSS data provided by five high-resolution ocean reanalysis products, including the Hybrid Coordinate Ocean Model (HYCOM) 1/12° global reanalysis, the Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis, the Simple Ocean Data Assimilation (SODA) reanalysis, the ECMWF Oceanic Reanalysis System 5 (ORAS5) product and the Estimating the Circulation and Climate of the Ocean Phase II (ECCO2) reanalysis. Regional comparison in the Mediterranean Sea shows that reanalysis largely depicts the accurate spatial SSS structure away from river mouths and coastal areas but slightly underestimates the mean SSS values. Better SSS reanalysis performance is found in the Levantine Sea while larger SSS uncertainties are found in the Adriatic Sea and the Aegean Sea. The global comparison with CMEMS level-4 (L4) SSS shows generally consistent large-scale structures. The mean ΔSSS between monthly gridded reanalysis data and in situ analyzed data is −0.1 PSU in the open seas between 40° S and 40° N with the mean Root Mean Square Deviation (RMSD) generally smaller than 0.3 PSU and the majority of correlation coefficients higher than 0.5. A comparison with collocated buoy salinity shows that reanalysis products well capture the SSS variations at the locations of tropical moored buoy arrays at weekly scale. Among all of the five products, the data quality of HYCOM reanalysis SSS is highest in marginal sea, GLORYS12 has the best performance in the global ocean especially in tropical regions. Comparatively, ECCO2 has the overall worst performance to reproduce SSS states and variations by showing the largest discrepancies with CMEMS L4 SSS.
... Overall, the Sea Surface Height (SSH) field is consistent with that observed by satellite (see Wang et al. 2014). The SOSE estimate adequately represents the ACC transport (163 ± 6 Sv) which stands in the upper range of previous estimates: 134 ± 11 Sv estimated from observations achieved in the framework of the International Southern Ocean Studies program in Cunningham et al. (2003); 141 ± 3 Sv and 173 ± 11 Sv based on mooring measurements in Koenig et al. (2014) and Donohue et al. (2016) and an updated estimate of 157± 5 Sv, which combines these observations and a high-resolution model Xu et al. (2020); 152 ± 19.2 Sv estimated from an ensemble of ocean reanalyses in Uotila et al. (2019). SOSE also provides a qualitatively good picture of the two major jets within the ACC, the Polar Front (PF) and the Subantarctic Front (SAF) (see Firing et al. 2011, for a detailed comparison with current observations). ...
The origins of the upper limb of the Atlantic Meridional Overturning Circulation and the partition among different routes has been quantified with models at eddy-permitting and one eddy-resolving model or with low-resolution models assimilating observations. Here, a step towards bridging this gap is taken by using the Southern Ocean State Estimate (SOSE) at the eddy-permitting 1/6° horizontal resolution to compute Lagrangian diagnostics from virtual particle trajectories advected between 6.7°S and two meridional sections: one at Drake Passage (cold route) and the other from South Africa to Antarctica (warm route). Our results agree with the prevailing concept attributing the largest transport contribution to the warm route with 12.3 Sv (88%) compared with 1.7 Sv (12%) for the cold route. These results are compared with a similar Lagrangian experiment performed with the lower resolution state estimate from Estimating the Circulation and Climate of the Ocean. Eulerian and Lagrangian means highlight an overall increase in the transport of the major South Atlantic currents with finer resolution, resulting in a relatively larger contribution from the cold route. In particular, the MC/ACC (Malvinas Current to Antarctic Circumpolar Current) ratio plays a more important role on the routes partition than the increased Agulhas Leakage. The relative influence of the mean flow versus the eddy flow on the routes partition is investigated by computing the mean and eddy kinetic energies and the Lagrangian-based eddy diffusivity. Lagrangian diffusivity estimates are largest in the Agulhas and Malvinas regions but advection by the mean flow dominates everywhere.
... Sea ice plays a key role in regulating the climate system. The impact of the steady decline of Arctic sea ice and the irregular, extreme patterns exhibited by Antarctic sea ice are not fully understood, and sea ice models, in a fully coupled configuration, are not able to reproduce some of these patterns [1]. Despite much effort put into the development of sea ice models in recent years, the full picture of the processes governing its dynamics is not clear, in particular how to interconnect the different scales involved in sea ice dynamics, from the microstructure of sea ice to the seasonal growth and decay on a synoptic scale. ...
Sea ice is not horizontally homogeneous on large scales. Its morphology is inherently discrete and made of individual floes. In recent years, sea ice models have incorporated this horizontal heterogeneity. The modelling framework considers an evolution equation for the probability density function of the floe size distribution (FSD) with forcing terms that represent the effects of several physical processes. Despite the modelling effort, a key question remains: What is the FSD emerging from the collection of all forcing processes? Field observations have long suggested that the FSD follows a power law, but this result has not been reproduced by models or laboratory experiments. The theoretical framework for FSD dynamics in response to physical forcings is presented. Wave-induced breakup is further examined with an emphasis on how it affects the FSD. Recent modelling results suggesting the consistent emergence of a log-normal distribution as a result of that process are further discussed. Log-normality is also found in a dataset of floe sizes, which was originally analysed under the power law hypothesis. A simple stochastic process of FSD dynamics, based on random fragmentation theory, is further shown to predict log-normality. We therefore conjecture that, in some situations, the emergent FSD follows a log-normal distribution. This article is part of the theme issue ‘Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks’.
... The ORAS5 is an assimilated ocean reanalysis product developed using quality-controlled observations of SST, sea level anomaly, sea ice concentration as well as subsurface temperature and salinity profiles. It has been shown to provide realistic variability in ocean heat storage and oceanic transports in the Arctic (75), especially in the BKS (76 ...