TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-2051-2018Thin Arctic sea ice in L-band observations and an ocean reanalysisThin Arctic sea ice in L-band observations and an ocean reanalysisTietscheSteffens.tietsche@ecmwf.inthttps://orcid.org/0000-0002-2961-0289Alonso-BalmasedaMagdalenahttps://orcid.org/0000-0002-9611-8788RosnayPatriciahttps://orcid.org/0000-0002-7374-3820ZuoHaohttps://orcid.org/0000-0003-0860-5832Tian-KunzeXiangshanKaleschkeLarshttps://orcid.org/0000-0001-7086-3299European Centre for Medium-Range Weather Forecasts, Reading, UKInstitute of Oceanography, University of Hamburg, Hamburg, Germanynow at: Max Planck Institute for Meteorology, Hamburg, GermanySteffen Tietsche (s.tietsche@ecmwf.int)14June2018126205120723November20171December201714May201829May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://tc.copernicus.org/articles/12/2051/2018/tc-12-2051-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/2051/2018/tc-12-2051-2018.pdf
L-band radiance measurements of the Earth's surface such as those from the
SMOS satellite can be used to retrieve the thickness of thin sea ice in the
range 0–1 m under cold surface conditions. However, retrieval uncertainties
can be large due to assumptions in the forward model, which converts
brightness temperatures into ice thickness and due to uncertainties in
auxiliary fields which need to be independently modelled or observed. It is
therefore advisable to perform a critical assessment with independent
observational and model data before using sea-ice thickness products from
L-band radiometry for model validation or data assimilation. Here, we discuss
version 3.1 of the University of Hamburg SMOS sea-ice thickness data set
(SMOS-SIT) from autumn 2011 to autumn 2017 and compare it to the global ocean
reanalysis ORAS5, which does not assimilate the SMOS-SIT data. ORAS5
currently provides the ocean and sea-ice initial conditions for all coupled
weather, monthly and seasonal forecasts issued by ECMWF. It is concluded that
SMOS-SIT provides valuable and unique information on thin sea ice during
winter and can under certain conditions be used to expose deficiencies in the
reanalysis. Overall, there is a promising match between sea-ice thicknesses
from ORAS5 and SMOS-SIT early in the freezing season (October–December),
while later in winter, sea ice is consistently modelled thicker than
observed. This is mostly attributable to refrozen polynyas and fracture
zones, which are poorly represented in ORAS5 but easily detected by SMOS-SIT.
However, there are other regions like Baffin Bay, where biases in the
observational data seem to be substantial, as comparisons with independent
observational data suggest. Despite considerable uncertainties and
discrepancies between thin sea ice in SMOS-SIT and ORAS5 on local scales,
interannual variability and trends of its large-scale distribution are in
good agreement. This gives some confidence in our current ability to monitor
climate variability and change in thin sea ice. With further improvements in
retrieval methods, forecast models and data assimilation methods, the huge
potential of L-band radiometry to derive the thickness of thin sea ice in
winter will be realised and will provide an important building block for
improved predictions in polar regions.
Introduction
Sea ice has been regularly observed by satellites since the late 1970s. The
observations most widely used in the context of large-scale weather and
climate models are passive microwave radiances in the range of 6 to 90 GHz. These observations have continuous daily pan-Arctic coverage at a
resolution of 50 km or better. However, because of the very small
penetration depth of microwave radiation into sea ice at these frequencies,
these observations only provide information about the fraction of an area
covered by sea ice, not about its thickness.
Considering the importance of sea-ice thickness for atmosphere–ocean surface
heat fluxes and for predicting the further evolution of the sea-ice cover,
information about it is indispensable. Substantial heat conduction occurs
through thin sea ice in winter, when the temperature contrast is large
between the cold surface atmosphere and the relatively warm ocean water below
the ice. Approximate calculations show that surface heat fluxes resulting
from heat conduction through thin sea ice can easily reach 100 W m-2.
Predicting the evolution of the sea-ice cover days to months ahead also
crucially depends on the sea-ice thickness: thin ice will evolve much more
quickly than thick ice because it is more susceptible to dispersion or
compression by winds and because the larger surface heat fluxes it allows
can change the mass of ice much faster.
The thickness of sea ice is much harder to derive from satellite observations
than its area coverage, and each of the existing methods has its own strong
limitations. Infrared emission measurements of the ice surface temperature
only work for very thin ice without snow
cover and can only be used under cloud-free conditions. Laser and radar
altimetry suffer from high measurement
noise and narrow footprints and are unfeasible for thicknesses below
0.5 m and in the presence of surface waves. Finally, the thickness of thin
sea ice of up to 1 m can be retrieved from L-band microwave radiance
measurements such as those made by the SMOS satellite .
The retrieval of sea-ice thickness from L-band brightness temperatures (TBs)
requires a complex radiative transfer model, and the calculated emissivities
can be very sensitive to the retrieval assumptions and auxiliary fields used.
A reliable retrieval crucially depends on high-quality constraints on the
other parameters which the TBs are sensitive to, most importantly, sea-ice
concentration, and temperature and salinity profiles within the ice
. These external data dependencies introduce
uncertainties that are often difficult to quantify. For instance,
near-surface temperature over Arctic sea ice can vary by several degrees
between atmospheric analyses from different centres .
Moreover, different radiative transfer models exist to calculate the L-band
emissivity of a given sea-ice slab, and the calculated L-band TBs can vary
considerably depending on the model chosen .
For prognostic sea-ice models as included in climate and numerical weather
forecasting models, simulating thin sea ice is challenging as well. Although
climate models have been including prognostic sea ice for many years, two
factors limit their usefulness for investigating thin sea ice. Firstly, sea-ice
thickness is often represented in a mono-category approach similar to that in
, with very simplified treatment of thin sea ice (although
in the latest generation of climate models there is a clear trend towards a
multi-category approach to simulate ice thickness; ). Secondly,
thin-sea-ice features are often short-lived (a few days or less) and local in
scale (smaller than 100 km). These temporal and spatial scales are usually
not well resolved in climate models, the output of which tends to be monthly mean
fields on grids with cell sizes of 100 km or more.
Prognostic sea-ice models as included in numerical weather forecasting models
are usually run at higher spatial resolutions (e.g. around 10–15 km in the
Arctic for the set-up discussed in this study), and usually their output is
analysed based on daily mean or instantaneous values. Thus, they clearly
resolve many of the small-scale, short-lived thin sea-ice features. However,
they often use the same simplified mono-category approach towards simulating
ice thickness and hence suffer from the same structural problems as the
sea-ice component in climate models.
These prognostic models are combined with observations using data
assimilation to arrive at the best estimate of the true state, called an
analysis. If the same system is applied to observations spanning multiple
years, it is usually called a reanalysis, a convention which we will follow
here. State-of-the-art ocean reanalyses employ prognostic sea-ice models at
relatively high spatial resolution, which are suitable for numerical weather
prediction. These ocean reanalyses have many users (see e.g.
) who might not have the resources to carry out an
assessment on how the reanalysis product compares to observations. Overall
assessments of several reanalyses have been carried out in the past
but have not addressed the
specific issue of thin sea ice.
This study aims to provide an overview assessment of agreements and
discrepancies of sea-ice thickness between an observational product from
L-band radiometry on the one hand and a ocean reanalysis that does not
assimilate these observations on the other hand. This assessment is a first
necessary step towards the eventual assimilation of these observational data,
because large systematic errors in either the observations or the forecast
model will make successful data assimilation difficult. Previous studies
report slightly positive results overall when assimilating L-band sea-ice
thickness observations but without doubting the
validity of the observational data. As we will show here, both reanalysis and
observations can contain large and systematic errors. We argue that these
need to be characterised, understood and properly treated in any future data
assimilation system in order to obtain an improved estimate of the true
sea-ice thickness.
Being an overview assessment, this study provides guidance and inspiration
for future research by identifying the characteristic main agreements and
discrepancies between sea-ice thickness from L-band retrievals and an ocean
reanalysis. We offer plausible hypotheses for the identified discrepancies
and are able to verify some of them quantitatively. However, due to the
nature of our methods, there are many discrepancies where we cannot offer
conclusive evidence of their root causes. This would require systematic
numerical experimentation with the retrieval and reanalysis models, a
substantial technical, computational and analytical effort that is beyond the
scope of the diagnostic overview study presented here. First steps in this
direction have already been taken by and ,
who perform sensitivity experiments with the retrieval model, and by
and , who perform sensitivity experiments with
the forecast model and data assimilation methods.
The remainder of the paper is structured as follows: we start with a
description of the methods used to produce the observational sea-ice
thickness product SMOS-SIT in Sect. and the
ocean reanalysis system ORAS5 in Sect. . The pan-Arctic
reanalysis–observation departures are discussed in
Sect. , followed by a more detailed discussion on the
regional differences in Sect. .
Section makes the point that, despite often large
reanalysis–observation departures, climate variability and trend of the thin
sea-ice area are in broad agreement between reanalysis and observations. A
discussion on the results is presented in Sect. , and
Sect. summarises the main results. More detailed
technical information and discussion on the limits of SMOS-SIT can be found
in Appendices –.
Model and dataSMOS-SIT sea-ice thickness product
Thin sea-ice thickness (nominal cut-off at 1.5 m) has been retrieved at the
University of Hamburg from L-band brightness temperatures (TBs) at 1.4 GHz
measured by the MIRAS radiometer on board SMOS. The retrieval algorithm
consists of a thermodynamic sea-ice model and a single-ice-layer radiative
transfer model . The resulting plane
layer thickness is multiplied by a correction factor assuming a log-normal
thickness distribution . The algorithm has been used
for the production of a SMOS-based sea-ice thickness data set in
polar-stereographic projection in 12.5 km grid resolution from 2010
(http://icdc.cen.uni-hamburg.de/1/daten/cryosphere/l3c-smos-sit.html, last access: 12 June 2018)
. Note that the SMOS-SIT grid is finer than the actual
physical resolution of the MIRAS radiometer for technical and practical
reasons.
In this study, we use the most up-to-date version (v3.1, based on v620 L1C
brightness temperatures), which has been produced operationally since October
2016. The v3.1 data for the previous winter seasons had been reprocessed
using the same algorithm. In the first 2 years of SMOS operation, the
signals were strongly influenced by radio frequency interference (RFI), so we
exclude winter 2010–2011 from our discussion. Previous versions of the
algorithm have been described in and
, who also provide a comparison with EM-bird measurements,
infrared-derived and modelled sea-ice thickness.
Brightness temperature used in the algorithm is the daily mean intensity,
which is the average of horizontal and vertical polarisation. Over sea ice,
the intensity is almost independent of incidence angle. The average over the
incidence angles 0–40∘ is taken in order to reduce the brightness
temperature uncertainty to about 0.5 K. In the algorithms prior to v3.1, RFI-contaminated snapshots have been discarded using a threshold value of 300 K,
applied either to horizontal or vertical polarisation. However, in v3.1 the
new quality flags given in the v620 L1C data have been implemented to
identify the data contaminated not only by RFI but also by the sun or
geometric effects.
The retrieval method needs additional auxiliary data as boundary conditions
for the thermodynamic as well as the radiation model: bulk ice temperature is
estimated from surface air temperature extracted from the JRA-55 atmospheric
reanalysis . Bulk sea-ice salinity is calculated with the
methods described in based on a weekly climatology of
sea surface salinity from a simulation with the MIT general circulation model
covering the years 2002–2009. Brightness temperatures
over sea ice depend on the dielectric properties of the ice layer, which vary
with ice temperature and ice salinity . The temperature profile within the ice is assumed to be
linear, which is a good approximation for thin ice and slow changes in the
meteorological conditions. The retrieval algorithm works only under cold
conditions: the presence of surface melting invalidates the retrieval
assumptions.
Ice thickness retrieval uncertainties are given pixel-wise each day in the
data set. There are several factors that cause uncertainties in the sea-ice
thickness retrieval: the uncertainties in the SMOS TBs, the uncertainties in the
auxiliary data sets, the uncertainties in ice temperature and ice salinity,
and the assumptions made for the radiation and thermodynamic models, for
example 100 % ice coverage.
The uncertainty of daily mean TB is mostly less than 0.5 K, except for the
years 2010 and 2011, when, due to RFI problems, the percentage of RFI-contaminated TB measurements was relatively high near the coasts of Russia
and Greenland. The uncertainties caused by bulk ice temperature and bulk ice
salinity depend on the uncertainties in surface air temperature and sea
surface salinity, which are the boundary conditions in the retrieval. As a
first approximation, a sea-ice surface temperature uncertainty of 1 K has
been assumed. The uncertainty of sea surface salinity is estimated from
the standard deviation of an ocean simulation for the years 2002–2009
.
In addition to the uncertainty factors discussed above, version 3.1 of
SMOS-SIT also considers the uncertainty in the fitted parameter σ of
the assumed log-normal distribution for the subgrid-scale sea-ice thickness
. The fit uncertainty is the standard deviation of the
natural logarithm lnσ, and it is derived from 6 years of NASA OIB
airborne observations of ice thickness . The average ice
thickness uncertainty from this contribution is less than 0.1 m.
The total ice thickness uncertainty provided in SMOS-SIT is the sum of the
above-mentioned uncertainties in TB, ice temperature and salinity, and ice
thickness distribution function. Errors caused by assumptions about heat fluxes
and snow thicknesses have not yet been included. The radiation model used in
the retrieval is a single-layer model. Thus, with this radiative transfer model,
it is not possible to discuss the impact of ice temperature and salinity
profiles on the ice thickness retrieval. Generally, the uncertainty increases
with increasing ice thickness. For thinner ice the relationship between ice
thickness and ice thickness uncertainty is almost linear. A fit function
between ice thickness and ice thickness uncertainty is derived from one
winter period of SMOS data. This function is then implemented in the
retrieval for the calculation of ice thickness uncertainty.
In addition to the retrieval uncertainty, the data set contains the
saturation ratio for each SMOS pixel, which gives a useful estimate of the
sensitivity of the SMOS brightness temperature to ice thickness for the
values of the auxiliary fields valid for the SMOS pixel. The saturation ratio
is defined as the ratio of the retrieved ice thickness to the maximal
retrievable ice thickness, which is reached when SMOS brightness temperature
changes by less than 0.1 K cm-1 due to the change in ice thickness
.
For more detailed technical information and a discussion on the limits of
SMOS-SIT please refer to the Appendices. Appendix shows
that there are some substantial differences in the SMOS-SIT data set between
the current version 3.1 and the previous version 2.3. In Appendix , the fundamental limits of retrieving sea-ice thickness from
SMOS brightness temperatures are touched upon, and evidence for these limits
from the data themselves is presented. Appendix discusses
unrealistic day-to-day fluctuations in retrieved sea-ice thickness, and
Appendix demonstrates that using SMOS-SIT without removing
high-uncertainty data points can lead to the wrong conclusions when studying
year-to-year variability of thin sea ice.
ORAS5 sea-ice–ocean reanalysis
The ECMWF ocean reanalysis system 5 (ORAS5) is a state estimate of the global
ocean and sea ice from 1979 to today and is being used to provide ocean and
sea-ice initial conditions for operational forecasts at ECMWF
.
The NEMO ocean model version 3.4.1 has been used for ORAS5
in a global configuration with a tripolar grid with a resolution of 1/4∘ at the equator. One of the poles of the grid is located on the
Antarctic continent, and the other two are in central Asia and northern Canada.
Horizontal resolution in northern high latitudes ranges from less than 5 km
(Canadian Archipelago south of Victoria Island) to about 17 km (Bering Sea and Sea of Okhotsk). There are 75 vertical levels,
with level spacing increasing from 1 m at the surface to 200 m in the deep
ocean.
ORAS5 contains the dynamic–thermodynamic sea-ice model LIM2
. The sea-ice model is run with a viscous-plastic
rheology. LIM2 has fractional ice cover, a single ice thickness category
and calculates vertical heat flux within the ice
according to the three-layer Semtner scheme . Snow on sea
ice is modelled, but melt ponds are not.
The single-thickness approach of LIM2 necessitates a very simplified
treatment of open-water sea-ice formation: as in , a
critical ice thickness h0 is introduced that distinguishes thin from
thick ice. In ORAS5, h0 is equal to 0.6 m in the Arctic.
The critical ice thickness determines how fast the ice concentration
increases under freezing conditions and is therefore also called the
lead-close parameter (see ). In a model grid cell that was
previously ice-free, new sea ice forms thermodynamically at a constant actual
floe thickness that is equal to h0. This is obviously an overly simplistic
representation of how sea ice really forms from open water: by formation and
solidification of grease ice . This modelling assumption
introduces an artificially increased frequency of the occurrence of equivalent
ice thicknesses around h0, where equivalent ice thickness means ice volume
per grid cell area. New generations of sea-ice models, for instance LIM3
or CICE5 , have a much smaller and
state-dependent h0, which avoids this problem.
Forcing fields for ORAS5 were derived from the atmospheric reanalysis
ERA-Interim until the end of 2014 and from the operational
ECMWF atmospheric analysis from the beginning of 2015. Sea surface
temperature for years from 2008 is constrained to observations from the UK
Met Office Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)
by a strong restoring term. Assimilation of subsurface ocean temperature and
salinity, sea-ice concentration and sea level anomalies is performed using
a 3DVar-FGAT procedure . The length of the data assimilation
window is 5 days.
Sea-ice concentration in ORAS5 is assimilated from the level 4 OSTIA product
. OSTIA sea-ice concentration is created by interpolating
and in-filling the sea-ice concentration product of the EUMETSAT Ocean and
Sea Ice Satellite Application Facility (http://osisaf.met.no/p/ice, last access: 12 June 2018) to
a global regular grid with 1/20∘ resolution and filling in missing
values. The sea-ice concentration assimilation is univariate; i.e. there is
no direct impact on the floe ice thickness. However, sea-ice concentration
assimilation increments directly change the equivalent ice thickness, as
discussed by . Since no observations of sea-ice thickness
are assimilated in ORAS5, it is completely independent of SMOS-SIT.
Thin sea ice for 2 selected days representing typical conditions
in early and late winter: 15 November 2015 (a–c) and 15 April
2016 (d–f). Panels (a) and (d) show the sea-ice
thickness retrieved by SMOS-SIT. The colours saturate at 1 m, because ice
thicknesses beyond that can normally not be retrieved. Panels (b)
and (e) show sea-ice concentration from the OSTIA product. The
difference between sea-ice thickness analysed in ORAS5 and retrieved by
SMOS-SIT is shown in panels (c) and (f). The difference is
only shown for data points for which the retrieved SMOS-SIT ice thickness is
lower than 90 % of the maximal retrievable thickness (see
for details) and for which the SMOS-SIT total retrieval
uncertainty is less than 1m. The yellow circles in the Laptev Sea and
Baffin Bay in panels (d–f) indicate the representative locations
discussed in Sect. .
ORAS5 consists of five ensemble members which are obtained by perturbing the
surface forcing and by assimilating perturbed observations (see
for details).
For a full description of the immediate predecessor of ORAS5, see the
documentation on ORAP5 in and . ORAP5 has been found
to simulate the overall ice thickness in the Arctic well in comparison with
other state-of-the-art ocean reanalyses .
Pan-Arctic reanalysis–observation departures
The SMOS-SIT data provide essential information about sea ice that is
complementary to observations of sea-ice concentration using higher-frequency
passive microwave channels. To illustrate this, Fig. shows
SMOS-SIT sea-ice thickness together with observed sea-ice concentration from
the OSTIA product for a day early in the freezing season and for a day late
in the freezing season. Note that here and elsewhere, unless explicitly
stated otherwise, sea-ice thickness denotes the equivalent
sea-ice thickness, i.e. the sea-ice volume per area (see for
further details).
Early in the freezing season, there are large areas of newly formed sea ice
that is thin. Figure a shows that sea-ice thicknesses of
0.6–0.7 m dominate in the Beaufort and Chukchi seas, as well as in part of
the central Arctic Ocean adjacent to them. In Baffin Bay, sea-ice
thickness from SMOS-SIT is even thinner, at around 0.2–0.3 m. All these
regions exhibit sea-ice concentrations of virtually 100 %
(Fig. b), which demonstrates that sea-ice concentration
observational products like OSTIA cannot be used to distinguish areas with
thin new sea ice from areas of old thick sea ice; sea-ice thickness
observational products like SMOS-SIT are needed to do that.
Sea-ice thickness in ORAS5 in early winter is comparable to that of
SMOS-SIT (Fig. c). However, the model tends to simulate
thicker ice on average. Note that the departures in
Figure c, f are only shown for SMOS-SIT data points with a
saturation ratio of less than 90 % and total retrieval uncertainty of less than
1 m (see Sect. for definitions of these).
Positive departures dominate, especially close to regions of thick ice. There
are a few places in the Beaufort and the Siberian Shelf seas with negative
departures, but in most of the thin-ice areas ORAS5 simulates ice around
0.4 m thicker than that retrieved by SMOS-SIT.
As the freezing season progresses, the ice edge moves further south, out
of the Arctic Basin, and previously formed thin ice in the Arctic Basin
becomes thicker. Polynyas and fracture zones begin to form. These refreeze
very quickly, which is evident from the near-100 % sea-ice concentration but
greatly reduced sea-ice thickness in these features.
Figure d shows extensive refrozen polynyas in the Kara and
Laptev seas, as well as a fracture zone covering the whole of the Beaufort Sea. In
Baffin Bay, sea-ice thickness derived by SMOS-SIT is mostly below 0.3 m.
Again, none of these features within the ice pack are picked up by the
sea-ice OSTIA concentration product, which shows homogeneously high ice
concentration throughout the ice pack (Fig. e).
The departures between ORAS5 and SMOS-SIT in late winter are large and
positive throughout (Fig. f), with values of 1 m or more
dominating. Most of this is due to the failure of the reanalysis to simulate
relevant features like the refrozen coastal polynya in the Laptev Sea or
refrozen fracture zones like the one visible in the SMOS-SIT data for the
Beaufort Sea.
There are multiple plausible reasons for the poor representation of refrozen
polynyas and fracture zones in the reanalysis: various deficiencies in the
ocean and sea-ice models (e.g. too thick ice, inappropriate rheology,
insufficient modelling of open-water ice growth, too strong upper-ocean
stratification), the data assimilation methods (e.g. inappropriate
background error covariance between ice concentration and ice volume) or
deficiencies in the atmospheric forcing (e.g. too weak offshore winds).
Further investigation of this reanalysis deficit is clearly needed, but for
the most part this requires dedicated experimentation and is therefore out of
the scope of this study. However, it can be said that there are conspicuous
features in maps of sea-ice concentration increments (not shown), which
directly affect ice thickness through implied ice volume increments as
discussed by . For the day in question, 15 April 2016, the
sea-ice concentration increments are large and positive in the refrozen
polynyas and fracture zones. This would suggests that the model dynamics tend
to produce the features, but the assimilation increments suppress them in the
reanalysis.
In the Barents Sea there is good agreement between ORAS5 and SMOS-SIT, with a
positive departure of 20 cm or less. Finally, Baffin Bay stands out as
having extensive thin ice cover in SMOS-SIT but thick ice in ORAS5. The
North Water Polynya at the northern end of Baffin Bay is captured both by
SMOS-SIT and ORAS5.
Normalised joint frequency distribution (scatter density) of
co-located pairs of daily observed and analysed thin sea ice:
(a) October to December 2011–2017, (b) February to April
2012–2017. The text insets in the lower-right corner give information on the
pre-filtering of the data before the scatter density is produced: data points
are only considered if the retrieval uncertainty is below 1 m
(unc < 1 m), the sea-ice concentration from OSTIA is above 30 %
(sic > 30 %) and the saturation ratio is below 90 %
(srat < 90 %). The last line of the text inset gives the total number
of data points for which the scatter density was calculated.
The previous example maps show typical conditions in early and late winter,
and typical departures between ORAS5 and SMOS-SIT. For a more quantitative
assessment, we calculate departures for co-located daily sea-ice thickness in
(a) the early-winter period 15 October to 15 December for the years
2011–2017, and (b) the late-winter period 15 February to 15 April for the
years 2012–2017. We exclude data points where the SMOS-SIT retrieval is
known to be unreliable: data points with a retrieval uncertainty of more than
1 m, a saturation ration of above 90 % or a sea-ice concentration below 30 %
are not considered (see Sect. for
explanations of retrieval uncertainty and saturation ratio).
From these co-located pairs of observed and modelled daily sea-ice
thicknesses we calculate the normalised bivariate joint frequency
distribution, which we will call “scatter density” in the following for
the sake of brevity. Scatter density plots give quite a complete picture of
the departure statistics. For a good match, density should be high on the
one-to-one line and low elsewhere. High density above the one-to-one line
indicates a positive bias, high density below the one-to-one line indicates
a negative bias. Conditional departure characteristics, e.g. for a certain
range of observed values, can also easily be derived visually.
As can be seen from the scatter density in Fig. a, in early
winter the agreement between SMOS-SIT and ORAS5 sea-ice thickness is quite
promising as the distribution is close to the one-to-one line. However, the
overestimation of sea-ice thickness by ORAS5 is confirmed but was already visually
apparent from the maps in Fig. . For observed
sea-ice thickness between 0 and 0.3 m, ORAS5 sea-ice thickness is about
0.3 m higher. The agreement becomes better for higher observed sea-ice
thickness in the range 0.5–1 m. Note that the scatter density distribution
has wide tails. For instance, for an ice thickness of 0.4 m in SMOS-SIT,
ORAS5 values of up to 1.5 m exist. This is not so obvious in the scatter
density but is clearly visible in the corresponding scatter plot, which tends
to highlight outlier data points (not shown).
Part of the reason for ORAS5 having higher ice thickness than SMOS-SIT early
in the freezing season is the simplified representation of thin ice in LIM2,
the sea-ice component of ORAS5 (see Sect. ): thermodynamic
formation of new ice in LIM2 happens at a fixed actual (floe) thickness of
0.6 m, a value that has been chosen to approximate growth processes in the
presence of thick sea ice . Quite obviously, this is not a
good representation of how sea ice forms from open water, which is the
dominant regime at the ice edge early in the freezing season. As can be seen
in Fig. c, the simplified LIM2 treatment of thin sea ice
leads to an artificially high frequency in the occurrence of equivalent ice
thicknesses around 0.6 m, because ice growth rates are artificially enhanced
for equivalent ice thicknesses below that value (see ,
and for further discussion on this).
A second reason for higher ice thickness in ORAS5 than SMOS-SIT early in the
freezing season is that SMOS-SIT retrieves sea-ice thickness under the
assumption of 100 % sea-ice concentration. If the area captured by a SMOS
pixel has only partial ice cover, the SMOS-SIT ice thickness retrieval is
biased thin . As can be seen from
Fig. b, there is an almost perfect linear relationship
between SMOS TBs and sea-ice concentration for intermediate sea-ice
concentration values, which clearly indicates that geometrical averaging of
open-water and sea-ice emissivity within a SMOS pixel plays a role. When
excluding data points from Fig. a, where sea-ice concentration
is below 95 % (not shown), the scatter density conditional on SMOS-SIT
thickness being below 0.2 m is almost zero, which is a good indication that
all thickness retrievals at least up to this thickness are likely to have a low bias due to neglecting the open-water contribution to L-band
emissivity.
Normalised joint frequency distribution (scatter density) of
observed and analysed thin sea ice in late winter from February to April
2012–2017: (a) Barents and Kara seas (15–90∘ E,
70–85∘ N), (b) Laptev Sea (90–150∘ E,
70–85∘ N), and (c) Baffin Bay (75–53∘ W,
65–80∘ N). For an explanation of the text insets, see caption of
Fig. .
In late winter, ORAS5 has much higher sea-ice thickness than SMOS-SIT
(Fig. b). Departures between 0.5 m and 1m are common
throughout the SMOS-SIT thickness range of 0–1 m. There is a more linear
shape in the scatter density distribution – this is promising in principle,
but could result from errors compensating for each other in different
regions, which would make the relationship less relevant. The scatter
distribution is also much wider than for early winter, indicating larger and
more uncertain reanalysis–observation differences. The larger discrepancy in
late winter has several causes. Figure d–f illustrate the
most obvious one: the ocean reanalysis does not simulate polynyas and
fracture zones well. However, there are other causes, some of which are
related to the properties of SMOS-SIT data. In the following section, we
analyse the late-winter departures in more detail.
Regional contrasts
There is considerable regional dependence of the departures in late winter
(February to April). Figure shows the SMOS-SIT/ORAS5
scatter density as in Fig. b but for three key regions
separately: the Barents and Kara seas, the Laptev Sea and Baffin Bay.
For the Barents and Kara seas (Fig. a), the departure
statistics are almost as good as for the pan-Arctic in early winter
(Fig. a). We can conclude that this region has relatively
good agreement between ORAS5 and SMOS-SIT sea-ice thickness throughout the
winter. In the Laptev Sea (Fig. b), ORAS5 has no ice
thickness below 1 m, whereas SMOS-SIT detects a lot of ice thinner than 1 m.
There is a very low correlation between ORAS5 and SMOS-SIT ice thickness.
This behaviour is consistent with our earlier assessment that refrozen
polynyas occur frequently in the Laptev Sea in late winter and that they
are detected by SMOS-SIT but are not well represented in ORAS5.
Finally, Fig. c shows the late-winter scatter density
for Baffin Bay, which again has characteristics that are very different
from the other two regions. In general, ORAS5 simulates much thicker ice than
that retrieved by SMOS-SIT, but in contrast to the Laptev Sea case, there is a
quite high rank correlation between SMOS-SIT and ORAS5; i.e. higher SMOS-SIT
values are often associated with higher ORAS5 values but the correspondence
is not necessary linear. This suggests systematic rather than random sources
for the departures.
An interpretation of the results in Fig. needs to
start from the appreciation that the regions shown have quite different
physical characteristics: in the Barents and Kara seas, sea ice is strongly
affected by warm Atlantic water being advected towards and under the ice,
which means the ice cover is constrained by SST. At the same time, prevailing
winds modulate the location of the ice edge by transporting the ice. Both
processes are expected to be reasonably well simulated by ORAS5, because
winds are prescribed as forcing, and the SST are ingested from an
observational product. For the observations, most of the calibration
and validation campaigns for SMOS-SIT have been carried out in this area
. Thus, the Barents and Kara seas can be expected to be
where the reanalysis–observation agreement is best.
In the Laptev Sea, sea ice is still relatively well observed when it comes to
SMOS-SIT validation, but it is more difficult to simulate in ORAS5. Because
there is no ice edge in the Laptev Sea, SST information cannot be used to
constrain the ice cover. Furthermore, as clearly visible in
Fig. , extensive polynyas form there in February to April,
mainly when offshore winds push back the ice from land or land-fast sea ice.
These processes are not well simulated by the reanalysis, which tends to keep
a compact thick sea-ice cover even in the presence of offshore winds. As a
result, major departures can be expected.
Time series of daily sea-ice thickness during the 2011–2012 winter
at (a) a representative location in the Laptev Sea at
74.5∘ N, 127∘ E and (b) a representative location
in Baffin Bay at 72∘ N, 62∘ W. Blue represents SMOS-SIT (full
line) with added and subtracted uncertainty standard deviation (dotted
lines). Red shows the five realisations of ORAS5. Black horizontal lines are
the CryoSat2 average thickness for March–April provided by CPOM; black star
is an EM-bird overfly for the Laptev Sea on 20 April 2012. The corresponding
time series of sea-ice concentration are shown in Fig. b.
In Baffin Bay, the occurrence of thinner ice of varying thickness is
modelled and observed, but the modelled ice is roughly twice as thick. There
is independent information that suggests that SMOS ice thickness has a low bias
there (see ). CryoSat2 estimates
(http://www.cpom.ucl.ac.uk/csopr/seaice.html, last access: 12 June 2018) indicate that between
February and April, the ice in this region is typically 1.5 m thick. This is
confirmed by independent expert judgement by ice analysts, who estimate that
ice in this region and this season would typically be at least 1 m thick (Nick Hughes, personal communication, 2016).
To further illustrate and consolidate the findings from
Fig. , we plot time series for two representative
locations in the Laptev Sea and Baffin Bay in Fig. .
Both show the typical behaviour of reanalysis–observation departures:
SMOS-SIT and ORAS5 match well early in winter, but later on ORAS5 ice keeps
thickening while SMOS-SIT thickness saturates, albeit with some strong
fluctuations. We choose to present a full freezing season in winter
2011–2012, because this allows co-location with independent data in both
locations. For the Laptev Sea (Fig. a), there was a
campaign in April that measured the ice thickness using an EM-bird.
This measurement method has demonstrated uncertainties of less than 0.1 m
; hence we can use it as the ground truth to benchmark
remote sensing observations and reanalysis results. The EM-bird measurement
confirms that the ice was indeed only about 0.5 m thick, which indicates the
presence of new thin ice in the Laptev Sea polynya .
The reanalysis is not able to simulate this. The CryoSat2 estimate for this
location is around 1 m averaged over March and April, halfway between ORAS5
and SMOS-SIT.
For a representative location in Baffin Bay (Fig. b),
there is reasonable match between reanalysis and observations until January.
After that, the sea ice in ORAS5 keeps growing to reach thicknesses of 1.5–2 m in mid-April, whereas SMOS-SIT observations level off between 0.5 and 1 m
until mid-April. The CryoSat2 estimate for this location, averaged over
March–April 2012, is 1.8 m. This behaviour is generic: it occurs in all years and also when considering an area average over the west of Baffin Bay as
defined by
Time series for relevant SMOS-SIT and ORAS5 parameters for the Baffin Bay location (72∘ N, 62∘ W) for the full freezing
season 2011–2012. Blue curves are SMOS-SIT parameters (except in
panel b, where blue is observed ice concentration from OSTIA), and red
curves are model parameters.
When judging the compatibility of observational and model-based estimates of sea-ice thickness, their uncertainties should be taken into account. The
available uncertainty estimates are indicated in Fig. in
the form of five perturbed ensemble members of the ORAS5 reanalysis and in
the form of lower and upper bounds of the SMOS-SIT uncertainty estimate
provided with the data set. The estimated ORAS5 uncertainty is very small –
well below 0.3 m most of the time. It is almost certainly too small, as it
does not account for structural uncertainty in the model and data
assimilation methods. By contrast, the SMOS-SIT uncertainty range (see
Sect. ) is very variable and often very
large. Sometimes it covers the whole range of fathomable values and sometimes it
is small, but independent evidence suggests that the truth lies far outside
the uncertainty range provided. An example of the former case is the SMOS-SIT
ice thickness in the Laptev Sea (Fig. a) in February: the
retrieved value is 1.2 m, but the uncertainty ranges from 0 m to more
than 2 m. An example for the latter case is the SMOS-SIT ice thickness in Baffin Bay in April 2012 (Fig. b): the retrieved value is
0.5 m with an uncertainty estimate of only 0.1 m. As argued before, the
true sea-ice thickness was very likely much higher than that.
Given that ORAS5, CryoSat2 and expert judgement agree that sea ice in Baffin Bay at this time of year should be considerably thicker than
SMOS-derived thicknesses, we tentatively suggest that there is a problem with
the retrieval assumption of SMOS-SIT in this region. From
Fig. a, e it can be seen that the slight decrease in
SMOS TBs from February onwards is interpreted as a strong decrease in
retrieved sea-ice thickness in SMOS-SIT, in disagreement with the ORAS5
reanalysis and independent observations.
A sea-ice concentration slightly below 100 % (Fig. b) might
also play a role: for the high SMOS TBs typical of late-winter conditions in
Baffin Bay, even a few percent of open water within the SMOS footprint
will lower TBs significantly by geometrical averaging (note that differences
of open-water fraction of a few percent are difficult if not impossible to
observe reliably; ). The assumption of 100 % sea-ice cover
made by SMOS-SIT will then lead to a thickness retrieval that has a low bias.
The scatter density of OSTIA sea-ice concentration versus SMOS-SIT sea-ice
thickness for the west of Baffin Bay in late winter (not shown) shows
moderate correlation between the two; i.e. there was open water present and
it was usually associated with lower ice thicknesses in the SMOS-SIT
retrievals. This is an indication – but not proof – that SMOS-SIT might
systematically underestimate ice thickness in Baffin Bay because of
non-negligible amounts of open water.
Monthly November means of the pan-Arctic area covered by ice
thicker than given
thresholds in SMOS-SIT (a) and ORAS5 (b).
Sea-ice surface temperature (Fig. d) is almost always
colder in ORAS5 than in SMOS-SIT. This is consistent with the ice being
thicker in ORAS5 than in SMOS-SIT: the thicker the ice, the smaller the
surface heating by conductive heat flux from the relatively warm ocean water
below the ice to the relatively cold surface of the ice. However, different
near-surface temperatures in the two reanalyses (JRA-55 and
ERA-Interim,) might also play a role (see ), because they
will have a direct impact on the implied sea-ice bulk temperature. Note that
there is an apparent artefact in the ice surface temperature in the SMOS-SIT
product: it has a constant value of around -4 ∘C for extended
periods in November and December. Differences in snow thicknesses
(Fig. c) mirror differences in the ice thickness, because
SMOS-SIT assumes an empirical piecewise linear relationship between the two
. Furthermore, sensitivity studies by
suggest that the decrease in TB could be the result of the sea ice becoming
fresher at a different rate than assumed by the empirical rate assumed by
SMOS-SIT. Testing all these hypothesis in detail is beyond the scope of this
paper, because neither does SMOS-SIT deliver the assumed sea-ice salinity as
part of the data product, nor does the ORAS5 sea-ice model have a good
treatment of ice salinity. Further investigation should be undertaken, and we
suggest that the assumed sea-ice salinity be made part of the SMOS-SIT data
product.
Interannual variability
Despite the uncertainties on a local scale discussed in the previous
sections, there is good agreement in the large-scale distribution of thin
(< 1 m) sea ice and its interannual variability. Figure shows time series of the area covered by sea ice with
thickness above various thresholds in November from 2011 to 2017. The
uppermost curve is the area of sea ice with at least 0.1 m thickness. The
0.1 m curve corresponds quite well to the NSIDC sea-ice extent if the
observational gap around the North Pole is taken into account. The lowermost
curve is the area of sea ice with at least 0.9 m thickness.
The overall magnitude, variability and trend of the area for the various ice
thickness thresholds generally agree quite well between ORAS5 and SMOS-SIT.
The extreme summer minimum in 2012 is visible as the sea-ice area reduces in
November for all thickness classes. In 2013, there was a marked recovery.
Since then, there has been a downward trend in all classes, with a small
uptick in November 2017. Importantly, this indicates that the
well-established summer sea-ice decline in recent years has started to affect
the wintertime state. These signals of interannual variability are in good
agreement with ice volume estimates derived from CryoSat2 radar altimetry
.
It is important to recall that, in the thickness range 0.9 m and above,
SMOS-SIT relies heavily on auxiliary fields to retrieve the sea-ice thickness
from SMOS brightness temperature. To produce Fig. it
was necessary to consider all SMOS-SIT data points, even those with high
uncertainty and/or saturation ratio close to 100 %. As shown in Appendix , the resulting maps and scatter densities are not
realistic, and one should be cautious when interpreting the lowermost curve
in Fig. a. Nevertheless, it is encouraging to see that
overall the same interannual variability and trends of thin sea-ice area are
derived from ORAS5 and SMOS-SIT.
Interannual variability and trends in sea ice in the Arctic do not occur in
synchrony in different regions. Figure shows November
conditions, when sea ice is present not only in the central Arctic Ocean, but
also in the adjacent seas, in the Canadian Archipelago, Baffin Bay,
Labrador Sea and Hudson Bay. All these regions are exposed to regional
climate variability and change that is not necessarily aligned: the Barents,
Kara and Laptev seas are heavily influenced by the North Atlantic inflow. In
the East Siberian, Chukchi and Beaufort seas the role of the North Atlantic
diminishes, and other processes related to the Siberian High and Pacific
climate are more important.
In the East Siberian, Chukchi and Beaufort seas
(Fig. a, b), interannual variability of area
cover is higher for thicker ice than it is for thinner ice. This feature is
detected by both SMOS-SIT and ORAS5; it is more pronounced in ORAS5, where
the area covered by ice thicker than 0.7 m more than doubled between 2012 and
2013 and then decreased in each subsequent year to reach the same level as 2012
in 2016.
Monthly November means of the regional area covered by ice thicker than given
thresholds in SMOS-SIT (left) and ORAS5 (right). The boundaries of the
longitude–latitude boxes are 0–150∘ E, 70–90∘ N for
panels (a) and (b); 150∘ E–120∘ W,
70–90∘ N for panels (c) and (d); and
120–70∘ W, 55–83∘ N for panels (e) and
(f).
The Barents, Kara and Laptev seas (Fig. c, d)
also exhibit a strongly reduced area coverage in 2012 for all thickness
categories. However, ice cover continued to increase until 2014, by which
time the area covered was almost twice as high as 2012 in some categories.
The unusually high area cover in 2014 might at least in parts be due to an
unusual circulation in autumn 2014: anomalously high pressure over
Scandinavia combined with low pressure over Siberia in September–November led
to anomalous high northerly components in the winds in these seas, which
would have both encouraged thermodynamic ice growth and spreading of the ice
by advection.
Another interesting feature in the Barents, Kara and Laptev seas is the
increasing area of ice thicker than 0.9 m simulated by ORAS5. The
year-to-year changes in thicker ice area as seen by SMOS-SIT are very
different, but we would advise caution when interpreting the SMOS-SIT time
series for these thicker ice categories for the reasons detailed in
Appendix .
Finally, in Canadian waters, Baffin Bay and the Labrador Sea
(Fig. e, f), no decrease in ice area for any
category is detected, neither by SMOS-SIT nor by ORAS5. Relative year-to-year
variations in ice area also tend to be much smaller than in the other two
areas.
The consistency in the time series presented in this section demonstrates
that large-scale variability and trend of thin sea ice early in the freezing
season can be monitored by both SMOS-SIT and ORAS5 with relative confidence.
Both products indicate that year-to-year variability in the pan-Arctic area
of thin sea ice is currently strong enough to mask any expected negative
trend, and that different regions show distinct – even opposed – variability
and trends. These can be related to specific regional anomalies in
atmospheric circulation and surface conditions for any given year.
Discussion
In light of the previously discussed shortcomings and uncertainties both in
the current version of the SMOS-SIT data and the current version of the ocean
reanalysis, we suggest proceeding with caution. It is clear that there is a
generic trend for analysed sea ice to be thicker than what is retrieved from
SMOS. Indications are that both problems in the model and in the observations
contribute to this.
For the model, an overly simplistic treatment of open-water sea-ice
growth (see Sects. and and
) leads to overestimation of ice thicknesses during
the freeze-up season (October–December). Later in winter, the reanalysis is
mostly incapable of simulating the polynyas and fracture zones present in the
interior of the ice pack.
For the observations, low sensitivity of the SMOS brightness
temperatures for ice thicknesses larger than 0.5 m is compensated for in the
SMOS-SIT retrieval algorithm by heavily relying on auxiliary fields from
external sources, such as 2 m temperature and winds, sea-ice salinity and
snow thickness on sea ice. These have considerable and poorly quantified
uncertainties associated with them (e.g. ), which leads to
the uncertainty in the retrieved ice thickness. For ice thicknesses below
0.5 m, the assumption of 100 % sea-ice concentration becomes questionable.
The previous example illustrates that reanalysis–observation departures have
several distinct root causes, and future data assimilation studies using SMOS
should treat each of the following scenarios differently:
The model over- or underestimates large-scale ice thickness in the areas of first-year ice.
Overestimation is typical in October–December in the Arctic Shelf seas. Sea-ice thickness as derived
by SMOS is within the range of the unconstrained sea-ice model, so that data assimilation will unequivocally
provide a better estimate of the truth than the model or observations alone.
SMOS-SIT systematically underestimates ice thickness. We argue that this typically occurs in Baffin Bay and Labrador Sea during late winter. Assimilating SMOS-SIT data here would deteriorate the
simulated state. We would argue that the quality of the observational product in this region needs to
be improved before it is used for data assimilation.
SMOS-SIT detects the presence of thin ice in fracture zones and polynyas, but there are fundamental
structural deficits in the reanalysis (see discussion in Sect. ) that prevent it from
simulating them. Here, SMOS-SIT can contribute to model validation and improvement. Assimilating SMOS-SIT
data would lead to a better state estimate but would force the model outside the range of states it would
normally occupy. Assimilation is probably beneficial for arriving at better state estimates and initial
conditions, but an investigation is needed to ensure no undesired unphysical side effects are triggered during the assimilation.
With further progress in the retrieval algorithms and the modelling for thin
sea ice, the distinction between the above three departure scenarios might
become obsolete, and unqualified use of the data for model validation and
data assimilation will become possible without the need for manual
intervention and interpretation. Until then, we suggest using SMOS-SIT data
as a means of detecting the presence of thin sea ice and designing data
assimilation studies with the above three departure scenarios in mind.
Summary and conclusions
In this study, we carry out an overall assessment of agreement and
discrepancies between sea-ice thickness in the range 0–1 m between SMOS-SIT,
an observational product derived from L-band radiances, and ORAS5, an ocean
reanalysis that does not assimilate the SMOS-SIT data. We start from the
premise that neither the observational product nor the reanalysis can be
unequivocally trusted to be closer to the truth, because both of them contain
systematic errors that are dependent on the region and feature under
consideration. Thus, a careful overall assessment of agreements and
discrepancies is advisable before using the observational data routinely for
model validation, data assimilation and forecast verification.
We find that SMOS-SIT and ORAS5 are broadly consistent in distinguishing
between areas of newly formed thin sea ice and areas of old thick sea-ice
early in the freezing season. This is true regarding the spatial
distribution but also regarding regional and pan-Arctic interannual
variability and trends. However, in terms of reanalysis–observation
departures, it is evident that ORAS5 almost always simulates sea-ice thicker
than observed in SMOS-SIT. This happens to a greater or lesser degree and
with various unrelated root causes, depending on the region and feature under
consideration.
Early in the freezing season (October–December), there is reasonable
correspondence between sea-ice thickness from SMOS-SIT and ORAS5, but sea ice
is thicker in ORAS5 than in SMOS-SIT. We suggest that this discrepancy is
explained in roughly equal parts by known systematic deficiencies in both
products: SMOS-SIT underestimates the true ice thickness because it ignores
the open-water contribution to L-band emissivity, and ORAS5 overestimates the
true sea-ice thickness because of exaggerated ice growth rates due to
limitations inherent to the mono-category approach to modelling the sea-ice
thickness distribution.
As the freezing season progresses, ice thicknesses continuously grow
almost everywhere in ORAS5 but are stagnating and often even decreasing in
SMOS-SIT. This stagnation and saturation of sea-ice growth in SMOS-SIT occurs
even when only considering data that are deemed to be reliable according to
the diagnostic uncertainty parameters provided with the product. The result
of this is large discrepancies between SMOS-SIT and ORAS5 sea-ice thickness
late in the freezing season (February–April) for all regions except the
central Arctic and the Barents and Kara seas. In the central Arctic Ocean
(excluding the surrounding marginal seas), both SMOS-SIT and ORAS5 agree that
there is no thin sea ice late in the freezing season. In the Barents and
Kara seas, the departures are moderate throughout the freezing season.
The large positive reanalysis–observation departures late in the freezing
season fall into two distinct categories. The first category is prevalent in
the Laptev, East Siberian, Chukchi and Beaufort seas, where extensive
refrozen polynyas and fracture zones exist, as evidenced by independent
observations from campaigns and visual imagery. These are easily detected by
SMOS-SIT, but ORAS5 is mostly unable to simulate them. In this case, the
discrepancy can be attributed to errors in the model and data assimilation
methods. The second category of large positive departures is most apparent in
Baffin Bay: here, SMOS-SIT ice thickness saturates at values around
0.7 m, whereas simple energy budget considerations and ORAS5 as well as
independent observations from radar altimetry suggest values closer to 1.5 m.
Hence, it seems that SMOS-SIT has a systematic low bias in this case. We
suggest several plausible hypothesis for the bias, the most appealing being
that SMOS-SIT misinterprets the contribution of appreciable area fractions of
open water to L-band emissivity.
The discrepancies described above illustrate that a robust and reliable
quantification of the thickness of thin sea ice is from L-band observations
and ocean reanalysis is an open challenge. Meeting it will require
improvements in the observational methods but also in the forecast model and
data assimilation methods. It should be kept in mind that our capacity to
observe and model the thickness of thin sea ice on a pan-Arctic scale is less
than a decade old, and many improvements are already imminent. In this light,
the consistencies that do already exist are encouraging. We hope that the
discrepancies described here will provide inspiration and guidance in future
in-depth studies addressing current deficiencies of observational, modelling and data assimilation methods, so that subsequent improvements can unlock the
full potential of L-band radiometry for measuring the thickness of thin sea
ice and contributing to an improved characterisation and prediction of polar
regions.
The SMOS-SIT data are publicly available from
https://icdc.cen.uni-hamburg.de/1/daten/cryosphere/l3c-smos-sit.html. The ORAS5 data will be publicly available from the
Copernicus Marine Environment Monitoring Service under
http://marine.copernicus.eu/ (last access: 12 June 2018).
Changes from the previous SMOS-SIT version
In the previous SMOS-SIT version 2.1, look-up tables were used in the
retrieval algorithm to speed up processing. The resulting discretisation
leads to substantial retrieval artefacts. As Fig.
demonstrates, the frequency distribution of retrieved sea-ice thickness (SIT)
has an unphysical multi-mode structure, with local minima at around 15, 25,
45 and 80 cm. These modes are very strong; for instance SMOS-SIT has four
times more sea ice at 30 cm than at 25 cm. This artefact could potentially
cause major problems in a correct geophysical interpretation of the data and
could cause spurious results when using SMOS-SIT for data assimilation. In
the current version 3.1 of the data, the problem has been addressed by
introducing more entries in the look-up table with finer spacing.
Furthermore, in the process of converting plane-layer ice thickness into
heterogeneous mean ice thickness instead of using a look-up table, a
parameterised conversion function is applied, which avoids the abrupt
transition caused by dividing the ice thickness into discrete entries.
SMOS-SIT thickness frequency distribution for winter 2015–2016
for (a) SMOS-SIT version 2.1, (b) SMOS-SIT version 3.1 and
(c) the ORAS5 ocean/sea ice reanalysis.
Ambiguities when retrieving sea-ice thickness from SMOS TBs
Sea-ice thickness (SIT) retrieved from L-band microwave radiance is limited
by penetration depth of the radiation in sea ice. The maximum retrievable ice
thickness is reached when the L-band brightness temperature has no more useful
sensitivity to SIT or when it is dominated by uncertainty in the
ice bulk salinity and temperature .
Figure shows that for SMOS-SIT, throughout the data set,
there is a strong functional relationship between retrieved SIT and
brightness temperature (TB). TB is very sensitive to SIT of up to 50 cm or so,
but beyond that the slope TB/SIT of the relationship is small, meaning that
SIT is only poorly constrained by TB, and auxiliary data become more
important for determining the retrieved SIT.
Scatter density of (a) SMOS TB and SMOS-SIT-derived sea-ice
thickness, (b) SMOS TB and sea-ice concentration,
(c) sea-ice concentration and SMOS-SIT sea-ice thickness. The
scatter density is calculated from all SMOS-SIT data points over the period
15 October 2015 to 15 April 2016; no filtering has been applied.
Unfortunately, for footprints which are partially open water, SMOS-SIT does
not take into account the emission of the open water. As shown in
Fig. (middle and right), in the range up to 0.5 m, there
is typically a sizeable open-water fraction, and there is a linear
relationship between the ice concentration and SMOS TBs. This suggests that
SMOS-SIT erroneously ascribes lower TBs to thinner ice instead of open water,
and hence below 50 cm we must expect SMOS to have a low bias (see also
). However, this might be compensated for by the fact
that retrievals for sea-ice concentration often also has a low bias for areas
of thin sea ice . For
retrieved ice thicknesses above 0.5 m, the open-water fraction is usually
low so does not contribute much to the TB; however, in this range the
retrieved thickness is dominated by potentially uncertain assumptions
about snow, ice temperature and ice
salinity.
Day-to-day variability
Sea-ice thickness at a particular location retrieved from SMOS-SIT varies
much more from one day to the next than that analysed by ORAS5.
Figure shows that the distribution of daily
SIT changes is much broader for SMOS-SIT than for ORAS5. Extreme daily
thickness changes of more than 0.2 m occur around 6 % of the time in
SMOS-SIT but less than 1 % of the time in ORAS5. These changes can have
either thermodynamic causes (ice mass changes) or advective causes (ice is
moved in/out of grid cell). A SMOS-SIT grid cell has a width of 12.5 km. For
reference, an advective change of 0.2 m would require a nearby step change
of 0.2 m in the ice thickness, combined with strong winds or ocean currents
that are able to move the ice by 12.5 km in a day. Alternatively, if the
change was thermodynamic, a surface heat flux of 700 W m-2 over that
day for the whole of the 12.5 km grid cell would be required. These extreme
conditions should only be expected to occur near the ice edge, and in
polynyas and fracture zones, and therefore daily changes of 0.2 m or more
should be rare.
Inspection of maps of daily changes reveals that large sea-ice thickness
(SIT) changes in SMOS-SIT are not restricted to the ice edge, polynyas and
fracture zones but occur over extended large-scale areas that correspond to
changing synoptic weather patterns. An example is given in
Fig. . On 16 November 2015, ice surface temperatures
derived by SMOS-SIT were around -15 ∘C in the Laptev Sea and
SMOS-derived ice thicknesses ranged between 0.5 and 1 m. The next day,
SMOS-derived ice surface temperatures in this region increased by 5 K in a
very coherent and homogeneous structure, while brightness temperatures
decreased only slightly and with less spatial coherence. The SMOS-derived SIT
over the Laptev Sea thinned coherently by more than
0.2 m in some areas. Given that it is impossible
for the ice to change that way in reality, taking into account both
thermodynamic and advective forcing, it must be concluded that this
widespread ice thinning by 0.2 m from one day to the next is an error in
the retrieval algorithm: strong changes in the ice surface temperature, in
reality caused by synoptic changes, together with unremarkable change in
brightness temperatures, are erroneously interpreted as a strong thinning of
the ice.
The unrealistically strong day-to-day fluctuations in the SMOS-SIT data are
likely due to either errors in the auxiliary fields or due to the assumption
of a linear temperature profile within the ice. If there are relevant errors
in the auxiliary fields, a quick change in the field will lead to a quick
change in the retrieved ice thickness that is not realistic. The limits to
the validity of the assumption of a linear temperature profile have been
investigated in detail by . They found that, after abrupt
changes in the meteorological conditions, the temperature profile within the
ice can take several days to adjust. Based on these results, we tentatively
suggest that the assumption of the linear temperature profile within the ice
is responsible for the unrealistic day-to-day changes in the SMOS-SIT data.
However, this question can only be answered satisfyingly by further research
which has full control both over the SMOS-SIT retrieval model and the
auxiliary meteorological and oceanographic fields. Most of these auxiliary
fields are the output of complex data assimilation systems, and therefore
advanced and well-studied uncertainty estimates are available. It would be a
valuable first step towards the assimilation of SMOS brightness temperatures
for SIT if the SMOS-SIT retrieval model could be installed at one of the
centres that produces the auxiliary fields, and in order to test the sensitivity of the retrieved SIT to their known
uncertainties.
Frequency distribution of daily sea-ice thickness changes from
(a) SMOS-SIT and (b) ORAS5 in the period 15 October 2015 to
15 April 2016. To produce these histograms, only those differences between
consecutive days at the same location have been taken into account where the
uncertainty diagnostics provided with SMOS-SIT for both days indicate a
reliable retrieval (saturation ration < 100 %, uncertainty < 1 m,
sea-ice concentration > 50 %). Day-to-day thickness changes are
outside ±0.4 m in less than 1 % of the cases.
SMOS-derived information on 15 November 2015 (a–c) and
daily difference between 16 and 15 November 2015 (d–f) for SMOS
TBs (a, d), SMOS-SIT ice thickness (b, e) and SMOS-SIT ice
surface temperature (c, f). Correspondence between unrealistic
SMOS-derived changes in ice thickness (e) and changes in ice surface
temperatures (f) is evident.
Representation of thicker ice
When interpreting sea-ice thicknesses of 0.5 m or higher from SMOS-SIT, it
is essential to inspect the provided uncertainties. Neglecting to do so
easily results in the wrong conclusions. As an example, Fig.
shows sea-ice thickness on a single day (15 November 2012) as seen by SMOS-SIT and
ORAS5. When considering all data from SMOS-SIT (Fig. a),
a false impression of almost uniform 1 m thick sea ice throughout the
Arctic Ocean is given, which is unrealistic given the well-known fact that
the multi-year ice north of Greenland and the Canadian Archipelago is several
metres thick, whereas the newly formed first-year ice in the marginal seas of
the Arctic Ocean is probably thinner than 1 m. Sea-ice thickness in ORAS5
(Fig. b) clearly shows the expected structure, in good
agreement with other observations and modelling results .
Representation of thicker ice in SMOS-SIT and ORAS5.
Panels (a) and (b) show sea-ice thickness on 15 November
2012 in the range 0–2 m derived from (a) SMOS-SIT and
(b) ORAS5. Panels (c) shows the scatter density of ice
thickness from SMOS-SIT and ORAS5 for all observation points without any
filtering from 15 October to 15 December 2012.
Figure c shows the corresponding scatter density between
SMOS-SIT and ORAS5 sea-ice thickness for the freeze-up season
15 October–15 December
2012. It is evident that SMOS-SIT, without any filtering, has many ice
thickness values in the 1–1.5 m range, which do not correlate at all with the ORAS5
ice thickness.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was partly supported by ESA under the contract
4000101703/10/NL/FF/fk. We thank Nina Maaß, Matthias Drusch, Leif T. Pederson, and Nick Hughes for helpful discussions.
Edited by: Julienne Stroeve
Reviewed by: two anonymous referees
ReferencesBalmaseda, M., Hernandez, F., Storto, A., Palmer, M., Alves, O., Shi, L.,
Smith, G., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D., Boyer, T.,
Chang, Y.-S., Chepurin, G., Ferry, N., Forget, G., Fujii, Y., Good, S.,
Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A., Lee, T.,
Martin, M., Masina, S., Masuda, S., Meyssignac, B., Mogensen, K., Parent, L.,
Peterson, K., Tang, Y., Yin, Y., Vernieres, G., Wang, X., Waters, J., Wedd,
R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J.-F., Dupont, F., Kuragano,
T., Kamachi, M., Awaji, T., Caltabiano, A., Wilmer-Becker, K., and Gaillard,
F.: The Ocean Reanalyses Intercomparison Project (ORA-IP), J. Oper. Oceanogr., 8, s80–s97, 10.1080/1755876X.2015.1022329,
2015.Bauer, P., Magnusson, L., Thépaut, J.-N., and Hamill, T. M.: Aspects of
ECMWF model performance in polar areas, Q. J. Roy.
Meteor. Soc., 142, 583–596, 10.1002/qj.2449, 2016.Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii,
Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T.,
Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S., Ferry,
N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T., Masina,
S., Tang, Y., Tsujino, H., and Wang, X.: Intercomparison of the Arctic sea
ice cover in global ocean–sea ice reanalyses from the ORA-IP project,
Clim. Dynam., 10.1007/s00382-016-2985-y, 2016.
Daget, N., Weaver, A. T., and Balmaseda, M. A.: An ensemble three-dimensional
variational data assimilation system for the global ocean: Sensitivity to the
observation and background-error variance formulation, ECMWF Technical
Memorandum, 562, ECMWF, Reading, UK, 38 pp., 2008.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N.,
and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, 10.1002/qj.828, 2011.Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
10.1016/j.rse.2010.10.017, 2012.Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K., Harada,
Y., Yasui, S., Miyaoka, K., Takahashi, K., Kamahori, H., Kobayashi, C., Endo,
H., Soma, M., Oikawa, Y., and Ishimizu, T.: The Japanese 55-year Reanalysis
“JRA-55”: An Interim Report, SOLA, 7, 149–152,
10.2151/sola.2011-038, 2011.Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics, J. Geophys.
Res., 102, 12609–12646, 10.1029/97JC00480, 1997.Haas, C., Lobach, J., Hendricks, S., Rabenstein, L., and Pfaffling, A.:
Helicopter-borne measurements of sea ice thickness, using a small and
lightweight, digital EM system, J. Appl. Geophys., 67, 234–241,
10.1016/j.jappgeo.2008.05.005, 2009.
Hibler III, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys.
Oceanogr., 9, 815–846, 1979.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.:
CICE : the Los Alamos Sea Ice Model Documentation and Software User's
Manual Version 5.1, Tech. rep., Los Alamos National Laboratory, Los Alamos
NM, USA, 2015.Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G.,
Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and
Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards
further identification of challenges and optimal approach using passive
microwave observations, The Cryosphere, 9, 1797–1817,
10.5194/tc-9-1797-2015, 2015.Kaleschke, L., Maaß, N., Haas, C., Hendricks, S., Heygster, G., and
Tonboe, R. T.: A sea-ice thickness retrieval model for 1.4 GHz radiometry
and application to airborne measurements over low salinity sea-ice, The
Cryosphere, 4, 583–592, 10.5194/tc-4-583-2010, 2010.Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch, M.:
Sea ice thickness retrieval from SMOS brightness temperatures during the
Arctic freeze-up period, Geophys. Res. Lett., 39, L05501,
10.1029/2012GL050916, 2012.Kaleschke, L., Tian-Kunze, X., Maaß, N., Beitsch, A., Wernecke, A.,
Miernecki, M., Müller, G., Fock, B. H., Gierisch, A. M.,
Schlünzen, K. H., Pohlmann, T., Dobrynin, M., Hendricks, S., Asseng,
J., Gerdes, R., Jochmann, P., Reimer, N., Holfort, J., Melsheimer, C.,
Heygster, G., Spreen, G., Gerland, S., King, J., Skou, N., Søbjærg,
S. S., Haas, C., Richter, F., and Casal, T.: SMOS sea ice product:
Operational application and validation in the Barents Sea marginal ice zone,
Remote Sens. Environ., 180, 264–273,
10.1016/j.rse.2016.03.009, 2016.Kaleschke, L., Tian-Kunze, X., Heygster, G., Patilea, C., Hendricks, S.,
Ricker, R., Tonboe, R., Makinen, M., Bertino, L., and Xie, J.: ESA Support
To Science Element (STSE) SMOS+Sea Ice Final Report, Tech. rep., University
of Hamburg, Hamburg, available at:
http://icdc.cen.uni-hamburg.de/fileadmin/user_upload/icdc_Dokumente/SMOS_SIT/SMOSICE2_FinalReport_Aug28_2017.pdf (last access: 12 June 2018), 2017.Kurtz, N. T., Farrell, S. L., Studinger, M., Galin, N., Harbeck, J. P.,
Lindsay, R., Onana, V. D., Panzer, B., and Sonntag, J. G.: Sea ice thickness,
freeboard, and snow depth products from Operation IceBridge airborne data,
The Cryosphere, 7, 1035–1056, 10.5194/tc-7-1035-2013, 2013.Kwok, R. and Cunningham, G. F.: ICESat over Arctic sea ice: Estimation of snow
depth and ice thickness, J. Geophys. Res., 113, C08010,
10.1029/2008JC004753, 2008.Kwok, R., Comiso, J. C., Martin, S., and Drucker, R.: Ross Sea polynyas:
Response of ice concentration retrievals to large areas of thin ice, J.
Geophys. Res., 112, C12012, 10.1029/2006JC003967, 2007.Landy, J. C., Ehn, J. K., Babb, D. G., Theriault, N., and Barber, D. G.: Sea
ice thickness in the Eastern Canadian Arctic: Hudson Bay Complex & Baffin
Bay, Remote Sens. Environ., 200, 281–294,
10.1016/J.RSE.2017.08.019, 2017.Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen,
R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield,
R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic
sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737,
10.1002/grl.50193, 2013.Le Traon, P. Y., Ali, A., Alvarez Fanjul, E., et al.: The Copernicus
Marine Environmental Monitoring Service: Main Scientific Achievements and
Future Prospects, Mercator Ocean Journal Special Issue, 56, available at:
https://www.mercator-ocean.fr/en/science-publications/mercator-ocean-journal/mercator-ocean-journal-56-special-issue-cmems/
(last access: 12 June 2018), 2017.
Maaß, N.: Remote sensing of sea ice thickness using SMOS data, PhD
thesis, University of Hamburg, Hamburg, Germany, 2013.Madec, G.: NEMO ocean engine, Tech. rep., Institut Pierre-Simon Laplace
(IPSL), available at:
http://www.nemo-ocean.eu/About-NEMO/Reference-manuals (last access:
12 June 2018), 2008.Mäkynen, M., Cheng, B., and Similä, M.: On the accuracy of
thin-ice thickness retrieval using MODIS thermal imagery over Arctic
first-year ice, Ann. Glaciol., 54, 87–96,
10.3189/2013AoG62A166, 2013.Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the ocean on
parallel computers, J. Geophys. Res.-Oceans, 102,
5753–5766, 10.1029/96JC02775, 1997.Mecklenburg, S., Drusch, M., Kaleschke, L., Rodriguez-Fernandez, N., Reul, N.,
Kerr, Y., Font, J., Martin-Neira, M., Oliva, R., Daganzo-Eusebio, E., Grant,
J., Sabia, R., Macelloni, G., Rautiainen, K., Fauste, J., de Rosnay, P.,
Munoz-Sabater, J., Verhoest, N., Lievens, H., Delwart, S., Crapolicchio, R.,
de la Fuente, A., and Kornberg, M.: ESA's Soil Moisture and Ocean Salinity
mission: From science to operational applications, Remote Sens.
Environ., 180, 3–18, 10.1016/j.rse.2015.12.025, 2016.Mellor, G. L. and Kantha, L.: An ice–ocean coupled model, J. Geophys. Res.,
94, 10937–10954, 10.1029/JC094iC08p10937, 1989.Menashi, J. D., St. Germain, K. M., Swift, C. T., Comiso, J. C., and
Lohanick, A. W.: Low-frequency passive-microwave observations of sea ice in
the Weddell Sea, J. Geophys. Res., 98, 22569,
10.1029/93JC02058, 1993.Notz, D., Jahn, A., Holland, M., Hunke, E., Massonnet, F., Stroeve, J.,
Tremblay, B., and Vancoppenolle, M.: The CMIP6 Sea-Ice Model Intercomparison
Project (SIMIP): understanding sea ice through climate-model simulations,
Geosci. Model Dev., 9, 3427–3446, 10.5194/gmd-9-3427-2016,
2016.Richter, F., Drusch, M., Kaleschke, L., Maaß, N., Tian-Kunze, X., and
Mecklenburg, S.: Arctic sea ice signatures: L-band brightness temperature
sensitivity comparison using two radiation transfer models, The Cryosphere,
12, 921–933, 10.5194/tc-12-921-2018, 2018.Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.:
Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on
radar-waveform interpretation, The Cryosphere, 8, 1607–1622,
10.5194/tc-8-1607-2014, 2014.Schweiger, A., Lindsay, R., Zhang, J. L., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116,
C00D06, 10.1029/2011JC007084, 2011.
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical
Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, 1976.Shi, X. and Lohmann, G.: Sensitivity of open-water ice growth and ice
concentration evolution in a coupled atmosphere-ocean-sea ice model,
Dynam. Atmos. Oceans, 79, 10–30,
10.1016/J.DYNATMOCE.2017.05.003, 2017.Smedsrud, L. H. and Martin, T.: Grease ice in basin-scale sea-ice ocean
models, Ann. Glaciol., 56, 295–306, 10.3189/2015AoG69A765, 2015.Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N.,
Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm
baseline, product specifications and initial verification, The Cryosphere, 8,
997–1018, 10.5194/tc-8-997-2014, 2014.Tian-Kunze, X., Kaleschke, L., and Maass, N.: SMOS Daily sea ice thickness
version 3, updated current year, 15 October 2011 to 30 November 2017. ICDC,
icdc.cen.uni-hamburg.de, University of Hamburg, Germany, Digital media,
available at:
https://icdc.cen.uni-hamburg.de/1/daten/cryosphere/l3c-smos-sit.html
(last access: 12 June 2018), 2016.Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Assimilation of
sea-ice concentration in a global climate model – physical and statistical
aspects, Ocean Sci., 9, 19–36, 10.5194/os-9-19-2013, 2013.Tietsche, S., Balmaseda, M. A., Zuo, H., and Mogensen, K.: Arctic sea ice in
the global eddy-permitting ocean reanalysis ORAP5, Clim. Dynam., 49,
775–789, 10.1007/s00382-015-2673-3, 2017.Tilling, R. L., Ridout, A., Shepherd, A., and Wingham, D. J.: Increased
Arctic sea ice volume after anomalously low melting in 2013, Nat. Geosci.,
8, 643–646, 10.1038/ngeo2489, 2015.Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthelemy, A., Bricaud,
C., Carton, J., Fuckar, N., Garric, G., Iovino, D., Kauker, F., Korhonen, M.,
Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson, K. A.,
Sadikni, R., Shi, L., Tietsche, S., Toyoda, T., Xie, J., and Zhang, Z.: An
assessment of ten ocean reanalyses in the polar regions, Clim. Dynam.,
10.1007/s00382-018-4242-z, online first, 2018.Vancoppenolle, M., Fichefet, T., Goosse, H., Bouillon, S., Madec, G., and
Maqueda, M. A. M.: Simulating the mass balance and salinity of Arctic and
Antarctic sea ice. 1. Model description and validation, Ocean Modelling, 27,
33–53, 10.1016/j.ocemod.2008.10.005,
2009.Wang, X., Key, J. R., and Liu, Y.: A thermodynamic model for estimating sea
and lake ice thickness with optical satellite data, J. Geophys.
Res., 115, C12035, 10.1029/2009JC005857, 2010.Xie, J., Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L.:
Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ
system, The Cryosphere, 10, 2745–2761,
10.5194/tc-10-2745-2016, 2016.Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, L., Liu, J.,
Kaleschke, L., and Zhang, Z.: Assimilating SMOS sea ice thickness into a
coupled ice-ocean model using a local SEIK filter, J.Geophys.
Res.-Oceans, 119, 6680–6692, 10.1002/2014JC009963, 2014.
Yu, Y. and Rothrock, D. A.: Thin ice thickness from satellite thermal
imagery, J. Geophys. Res.-Oceans, 101, 25753–25766,
10.1029/96JC02242, 1996.Zuo, H., Balmaseda, M. A., and Mogensen, K.: The new eddy-permitting ORAP5
ocean reanalysis: description, evaluation and uncertainties in climate
signals, Clim. Dynam., 49, 791–811, 10.1007/s00382-015-2675-1, 2015.
Zuo, H., Balmaseda, M. A., Boisseson, E., and Hirahara, S.: A new ensemble
generation scheme for ocean reanalysis, ECMWF Technical Memorandum, 795, ECMWF, Reading, UK, 44 pp.,
2017.