TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-10-2003-2016Near-real-time Arctic sea ice thickness and volume from CryoSat-2TillingRachel L.rachel.tilling.12@ucl.ac.ukhttps://orcid.org/0000-0002-3433-7651RidoutAndyShepherdAndrewCentre for Polar Observation and Modelling, Department of Earth
Sciences, University College London, London, WC1E6BT, UKCentre for Polar Observation and Modelling, School of Earth and
Environment, University of Leeds, Leeds, LS29JT, UKRachel L. Tilling (rachel.tilling.12@ucl.ac.uk)7September20161052003201225January20162February201625July20162August2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/10/2003/2016/tc-10-2003-2016.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/10/2003/2016/tc-10-2003-2016.pdf
Timely observations of sea ice thickness help us to
understand the Arctic climate, and have the potential to support seasonal
forecasts and operational activities in the polar regions. Although it is
possible to calculate Arctic sea ice thickness using measurements acquired by
CryoSat-2, the latency of the final release data set is typically 1 month due
to the time required to determine precise satellite orbits. We use a new
fast-delivery CryoSat-2 data set based on preliminary orbits to compute
Arctic sea ice thickness in near real time (NRT), and analyse this data for
one sea ice growth season from October 2014 to April 2015. We show that this
NRT sea-ice-thickness product is of comparable accuracy to that produced
using the final release CryoSat-2 data, with a mean thickness difference of
0.9 cm, demonstrating that the satellite orbit is not a critical
factor in determining sea ice freeboard. In addition, the CryoSat-2
fast-delivery product also provides measurements of Arctic sea ice thickness
within 3 days of acquisition by the satellite, and a measurement is
delivered, on average, within 14, 7 and 6 km of each location in the
Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT
sea-ice-thickness data set provides an additional constraint for short-term
and seasonal predictions of changes in the Arctic ice cover and could support
industries such as tourism and transport through assimilation in operational
models.
Introduction
Near-real-time (NRT) measurements of sea ice thickness allow
timely assessments of Arctic environmental change and have the potential to
improve the skill of short-term forecasts that are, in turn, a resource for
operational activities. The US Navy's Arctic Cap Nowcast/Forecast System
(ACNFS) (Posey et al., 2015; Hebert et al., 2015), for example, provides
short-term (1-to-7-day) forecasts of conditions such as the location of the
sea ice edge, which can improve the safety and efficiency of their
operational missions (Posey et al., 2015; U.S. Navy, 2014). Although the
ACNFS currently assimilates NRT sea ice concentration data, it has been
suggested that forecast model skill could be further improved by also
assimilating NRT measurements of sea ice thickness (Day et al., 2014). On
slightly longer (seasonal) timescales, forecast models are currently able to
predict the area of September sea ice with good confidence if the
distribution of sea ice thickness is known in late spring (Sigmond et
al., 2013). To initialize such models with known thickness distributions
(Chevallier and Salas-Melia, 2012) and analyse their output, rapid and
reliable satellite observations are required. Despite these potential
benefits, it is nevertheless recognized that the value of NRT
sea-ice-thickness observations derived from repeat satellite altimetry does
have limits. For example, some model systems show higher forecast skill when
initialized with thickness distribution (and for some months volume anomaly)
estimates from early summer (Chevallier and Salas-Melia, 2012). Summer is a
period when sea-ice-thickness measurements are traditionally unavailable in
the Arctic due to the presence of melt ponds (e.g. Tilling et al., 2015).
Similarly, although forecasts could benefit the planning of Arctic operations
(Meier et al., 2014; Stewart et al., 2007), day-to-day activities require
measurements with far greater spatial and temporal sampling than can be
achieved using a single satellite altimeter.
A range of Arctic sea-ice-thickness measurements are currently available,
with varying spatial and temporal sampling. The Beaufort Gyre Exploration
Project (BGEP) has measured year-round sea ice draught using three upward
looking sonar buoys moored in the Beaufort Sea since 2003
(http://www.whoi.edu/beaufortgyre). On a larger scale, NASA's Operation
IceBridge utilizes a suite of research aircraft each spring (March and April)
to produce tracks of sea-ice-thickness estimates (Kurtz et al., 2013)
concentrated around northern Greenland, the ocean region north of the
Canadian Archipelago and the Beaufort Sea. Currently the final and “quick
look” IceBridge data are available for spring 2009–2012 and spring
2013–2015 respectively. The quick look product is experimental and is
designed only to be applicable for time-sensitive projects such as sea ice
forecasting. On a larger spatial scale there are currently three publicly
available data sets that provide sea-ice-thickness estimates across the whole
Arctic Ocean. These are produced by NASA (Kurtz et al., 2014), Germany's
Alfred Wegener Institute (AWI) (Ricker et al., 2014) and the UK's Centre for
Polar Observation and Modelling (CPOM) (Tilling et al., 2015) using final
release data from the European Space Agency's (ESA) CryoSat-2 satellite
(Wingham et al., 2006). NASA provide experimental monthly averaged
sea-ice-thickness data for March 2014 and March 2015 within a 7.2×106km2 area of the central Arctic known as the ICESat domain
(Kwok et al., 2009). AWI provide monthly averaged thickness data starting
from January 2011 with a current lag of about 6 months, and these data again
cover a central area of the Arctic Ocean. CPOM provide sea-ice-thickness
estimates for spring (March/April average) and autumn (October/November
average) at all latitudes above and including 40∘ N beginning in
autumn 2010, also with a lag of about 6 months, depending on the availability
of sea ice concentration data (Cavalieri et al., 1996). Here we use
fast-delivery CryoSat-2 data to produce NRT measurements of Arctic sea ice
thickness and volume and evaluate the product.
Data and methods
We use fast-delivery radar altimeter measurements from the ESA CryoSat-2
satellite synthetic aperture radar (SAR) and SAR interferometric (SARIn)
altimeter modes (Wingham et al., 2006) to produce NRT estimates of Northern
Hemisphere (latitudes above 40∘ N) sea ice thickness and volume. The
data are Level 1b and consist of an echo for each point along the ground
track of the satellite. Prior to the release of Level 1b data, ESA perform
some on-ground processing of the raw satellite data. Before 26 March 2015,
ESA applied a processing chain known as “Baseline-B” to the raw
fast-delivery data and an updated processor, “Baseline-C”, has been applied
since. The number of range bins for each waveform depends on the satellite
operating mode and the baseline of the data – Baseline-B SAR mode has
128 bins, Baseline-C SAR mode has 256 bins, Baseline-B SARIn mode has
512 bins and Baseline-C SARIn mode has 1024 bins. The larger number of bins
in SARIn mode is due to an increase in the range window for capturing the
slope variation in ice sheet margins. To allow for identical processing of
both SAR and SARIn mode data acquired over Arctic sea ice, we crop all
waveforms to 128 bins, ensuring that the waveforms are positioned at
approximately the same location within the 128 bins.
In the fast-delivery data the wet-tropospheric, dry-tropospheric and
inverse-barometer corrections are missing in 94 % of cases for Baseline-B
data, but in less than 1 % of cases for Baseline-C data. In these
instances, all three of the corrections are missing. The fast-delivery
CryoSat-2 data are available from ESA on average 36 h after
acquisition by the satellite, although we run our sea ice processor with a
latency of 3 days to ensure sufficient data are available. The main
difference between the fast-delivery and final release CryoSat-2 data is the
orbits applied. For both data sets, an accurate determination of the
satellite orbit is required to determine surface elevations above a reference
ellipsoid. For the final release data product, ESA perform a ground-based
precise orbit determination (POD), which requires modelling of the forces
acting on the satellite as well as a dense set of measurements regarding its
position and velocity (Wingham et al., 2006). The primary means of making
these measurements is with the on-board Doppler Orbit and Radio positioning
Integration by Satellite (DORIS) receiver, which makes measurements of the
relative velocity of the satellite using an extensive network of
ground beacons. The messages uplinked from the beacons include time signals
that allow the DORIS receiver time to be accurately determined. The DORIS
receiver also includes software for the real-time, on-board computation of
the orbit, known as the DORIS Navigator orbit. The DORIS Navigator orbit is
estimated to be accurate to 30 cm in the radial direction and is
included in the fast-delivery CryoSat-2 data to provide good-quality orbit
estimates before the POD can be produced. However, the fast-delivery data are
more susceptible to orbit dropout, meaning that certain orbits, for which the
orientation of the satellite could not be sufficiently determined, are not
included in the data set. There is also a difference in the time frame of
on-ground processing of the raw fast-delivery and final release data by ESA.
Before 22 February 2015, ESA applied the Baseline-B processing chain to the
raw final release data, and an updated processor, Baseline-C, has been
applied since 1 April 2015. Between these dates, a hybrid processor known as
“Baseline-BC” was applied. On average, it takes us 6 h to process 1
day of data.
Near-real-time (NRT) Arctic sea-ice-thickness estimates from
CryoSat-2. (a–c) Thickness estimates for the final 2, 14 and
28 days of October 2014 respectively. (d–f) Thickness estimates for
the final 2, 14 and 28 days of March 2015 respectively. NRT sea-ice-thickness
data are output Arctic-wide on a 5 km square grid. All thickness
measurements within a 25 km radius of the centre of the grid are averaged,
with all points receiving equal weight. The sea ice extent mask is shaded in
light grey and highlights unmapped areas of the sea ice.
The processing steps for fast-delivery CryoSat-2 data are identical to those
used for the final delivery data and are described in Tilling et al. (2015).
The first step is the computation of sea ice freeboard, which is the
difference in elevation between the snow–ice interface and that of the
surrounding ocean. We do this by using the return echo shape to discriminate
between measurements of the ocean surface and the ice surface (Peacock and
Laxon, 2004). We define sea ice regions as those with a NRT sea ice
concentration (Maslanik and Stroeve, 1999) greater than 75 %. NRT ice
concentration data are taken from the National Snow and Ice Data Center
(NSIDC) and are available to us by 01:00 UTC, 2 days after measurement. A
correction is applied to each freeboard measurement to account for the
reduced speed of the radar pulse as it passes through any snow cover on sea
ice. The next step is to convert sea ice freeboard to sea ice thickness. We
assume that the ice floes are in hydrostatic equilibrium, under which
circumstances sea ice thickness can be calculated using the following:
Ti=fcρw+hsρsρw-ρi,
where Ti is the sea ice thickness, fc is the
corrected sea ice freeboard, hs is snow depth, ρw
is seawater density, ρs is snow density and ρi
is sea ice density. We use a fixed estimate of first-year ice (FYI) density
of 916.7 kgm-3 (Alexandrov et al., 2010), multi-year ice (MYI)
density of 882 kgm-3 (Alexandrov et al., 2010) and a fixed
seawater density of 1023.9 kgm-3 (Wadhams et al., 1992). To
obtain snow depth and density we average the values from a climatology
(Warren et al., 1999) that fall within the ICESat domain, where the
climatology is constrained by in situ measurements. Snow depth is halved over
FYI to account for reduced snow accumulation (Kurtz and Farrell, 2011;
Webster et al., 2014). NRT ice-type data from the Norwegian Meteorological
Service Ocean and Sea Ice Satellite Application Facility
(http://osisaf.met.no/p/ice/#type) are used to classify FYI and MYI
for each individual freeboard measurement, and this data set becomes
available to us by 01:00 UTC the day after measurement. During the sea ice
melt season it becomes difficult to discriminate between measurements of the
ocean and the ice due to melt ponds that form on the sea ice surface, and
because of this we do not currently produce measurements of sea ice thickness
between May and September. We compute NRT estimates of sea ice on a 5 km
square grid encompassing the entire Arctic region (Fig. 1). To obtain grid
values, we average all thickness measurements within a 25 km radius of the
centre of each grid cell, with all points receiving equal weighting. Although
this resolution is coarser than the maximum afforded by the CryoSat-2
altimeter and the satellite orbit (Wingham et al., 2006), it allows the NRT
sea-ice-thickness product to be compared with estimates computed from the
entire archive of CryoSat-2 data which, because it extends over a greater
time period, has been evaluated with respect to in situ observations (Tilling
et al., 2015).
We then compute sea ice volume Arctic-wide and within fixed oceanographic
basins (Nurser and Bacon, 2014; Tilling et al., 2015) by averaging individual
thickness and concentration values during each calendar month on a
0.1∘×0.5∘ grid and defining the sea ice margin by
applying a 15 % sea ice concentration mask using data from the 15th day
of each month. Empty thickness grid cells within the sea ice extent mask,
including those north of 88∘ N, are filled by nearest-neighbour
interpolation with a maximum search radius of 300 km. Monthly
estimates of sea ice volume are then calculated by summing the product of the
ice thickness, the ice concentration and the ice area within the sea ice
extent mask.
We estimate monthly errors in sea ice volume by considering the contributions
due to uncertainties in sea ice freeboard (∼ 9 cm), snow depth
(4.0–6.2 cm in Warren et al., 1999), snow density
(60.0–81.6 kgm-3 in Warren et al., 1999), sea ice density
(7.6 kgm-3 from data in Romanov (2004) and calculated in Tilling
et al., 2015), sea ice concentration (5 % according to the NSIDC at
http://nsidc.org/data/docs/daac/nsidc0051_gsfc_seaice.gd.html) and sea
ice extent (20 000–30 000 km2 according to the NSIDC at
http://nsidc.org/arcticseaicenews/faq/#error_bars). Uncertainties in
seawater density are neglected because they have a negligible impact (Kurtz
et al., 2013; Ricker et al., 2014).
Errors in our freeboard estimates arise through speckle in the radar echoes,
which averages 8 cm across the Arctic but decorrelates from one
measurement to the next, and from uncertainties in sea surface height, which
may be correlated in space due to our interpolation scheme based on a linear
regression of measurements along 200 km sections of the ground track. We
examined the variability of sea surface heights over this scale, and their
standard deviation at orbit crossing points is 4 cm. As a
conservative estimate, we assume that this variability remains correlated
within the 200 km window of our freeboard calculation and include it as an
additional source of uncertainty in our gridded product. The freeboard error
is then a combination of that due to spatially uncorrelated speckle on floe
heights and that due to spatially correlated errors in the interpolation of
sea surface heights. This results in a 2 cm freeboard uncertainty, which
scales to ∼ 20 cm thickness, or 11 % of a typical growth season
thickness of 1.8 m (Tilling et al., 2015) for our gridded 28-day
product.
To calculate uncertainties in sea ice volume, we compute the monthly rate of
change of volume with respect to each parameter that has an associated error.
We do this by individually adjusting the value for each parameter 6 times at
even increments and recomputing the volume each time. The computed rates of
change are then multiplied by the error in each parameter in question to
estimate their partial contributions to the total volume error. Finally, we
combine the monthly contribution to the volume error for all significant
error sources in a root-sum-square manner to arrive at an estimate of the
total monthly sea ice volume error:
σV=∂V∂hs⋅σhs2+∂V∂ρs⋅σρs2+∂V∂ρi⋅σρi2+∂V∂ei⋅σei2+σVc2,
where σV is the uncertainty in sea ice volume in a given
month, V is sea ice volume, hs is Arctic-wide snow depth,
σhs is the uncertainty in snow depth, ρs
is Arctic-wide snow density, σρs is the uncertainty in
snow density, ρi is Arctic-wide ice density,
σρi is the uncertainty in sea ice density,
ei is sea ice extent, σei is the uncertainty in sea
ice extent, and σVc is the uncertainty in sea
ice volume due to uncertainty in sea ice concentration. We estimate that
year-to-year uncertainties in Arctic-wide sea ice volume are typically about
13.5 %, with small variations from month to month (Tilling et al., 2015).
Estimating local errors in sea ice thickness is complicated due to a lack of
knowledge of the distances over which the contributing factors decorrelate.
The main factors for which this information is important and lacking are snow
depth, snow density and sea ice density. In our sea ice volume error budget,
we estimate their uncertainty over large scales as the standard deviation of
monthly averaged sparse field observations collected across the
9 millionkm2 central Arctic region. However, these factors and
their variability are influenced by synoptic-scale meteorology, and we
suppose that the length scale over which they are correlated is comparable to
that of a typical polar vortex – around 2000 km in diameter
(http://www.cpc.ncep.noaa.gov/products/stratosphere/polar/polar.shtml).
Taking snow depth as an example, over areas that are large in comparison to
this correlation scale, the variability of spatially averaged snowfall
fluctuations will diminish in the ratio 1n, where n
is the effective number of independent values of accumulation sampled. We
take n∼Aπ20002, where A is the area
in square kilometres. If n< 1, we set it equal to 1. For the
9 millionkm2 central Arctic region, over which the large-scale sea
ice volume and thickness uncertainty is estimated to be 13.5 %,
n∼ 3, leading to an uncertainty of 23 %. Using this approach and
accounting additionally for short-scale correlated errors in freeboard
associated with interpolating sea surface heights, we estimate the
uncertainty in sea ice thickness increases to 25 % at the 5 km scale of
our 28-day NRT grid.
We acknowledge that this is only a first attempt to characterize local
uncertainty in sea ice thickness, and that more detailed observations of snow
depth, snow density and sea ice density are required to establish the extent
to which their variability impacts on the retrieval accuracy. However, a
25 % local error in our gridded 28-day estimates of Arctic sea ice
thickness derived from CryoSat-2 observations corresponds to an uncertainty
of 45 cm for a typical thickness of 1.8 m. This uncertainty
is consistent with the spread of differences relative to independent
estimates acquired from airborne and ocean-based platforms (34–66 cm
in Tilling et al., 2015). However, grid cell thickness uncertainty will
increase with fewer days of data coverage. For example, for 2 days of data
the averaged freeboard measurements often come from just one satellite pass.
Therefore the full 4 cm uncertainty in sea surface height contributes to the
freeboard error, which scales to ∼ 40 cm for thickness, or
22 % of a typical thickness of 1.8 m. Combined with the error of
23 % from other sources this brings the total error on the 2-day 5 km
grid sea-ice-thickness data to 32 %.
Comparison of near-real-time (NRT) and archive estimates of Arctic
sea ice freeboard, thickness and volume, from CryoSat-2.
(a) Cross-plot of point-by-point sea ice freeboard for an Arctic
pass in April 2015. Also shown is the difference (archive minus NRT) in sea
ice freeboard between the data sets. (b) Normalized distribution of
NRT and archive thickness estimates over the period October 2014-April 2015
for all grid cells where measurements are available for both data sets.
(c) Cross-plot of sea ice volume for October 2014-April 2015. Also
shown is the difference (archive minus NRT) in sea ice volume between the
data sets.
To assess the reliability of the NRT sea ice data set we compared it to values
derived from the final CryoSat-2 data release (the archive product), which
have shown excellent agreement with an extensive set of independent
observations (Tilling et al., 2015). It is currently not possible to evaluate
the NRT product directly against in situ measurements, as the overlap between
coverage periods is too short. During archive processing we use final sea ice
concentration from NSIDC (Cavalieri et al., 1996), rather than the NRT
concentration data used in NRT sea ice calculations. Aside from this, the
CryoSat-2 SAR and SARIn mode data are processed identically to the NRT case.
First, we assessed our processing at orbit scale by calculating
point-by-point differences of NRT and archive sea ice freeboards using a
single track of CryoSat-2 data from April 2015 for which all geophysical
corrections were present in both data sets. The track consisted of 3968 lead
and 5246 freeboard measurements for the NRT data compared with 3970 lead and
5242 freeboard measurements for the archive data. Along this track, NRT and
archive freeboards showed excellent agreement, with a mean difference of
0.02 cm (Fig. 2a). We then compared sea ice thickness and volume
based on the NRT and archive products, using 7 months of data acquired
between October 2014 and April 2015, which corresponds to a season of ice
growth. The thickness comparison was done over the 5 km square grid on which
NRT data are output. In general, our NRT and archive estimates of sea ice
thickness are in excellent agreement, with a mean difference of
0.9 cm (Fig. 2b). NRT and archive estimates of sea ice volume are
also in excellent agreement, with an average difference of 175 km3
(Fig. 2c) across the entire Arctic region. The negative freeboard and
thickness values apparent in Fig. 2a and b respectively are a consequence of
negative freeboard measurements that occur due to random noise in radar
echoes from thin ice floes, caused by radar speckle. These freeboards are
included in our processing to ensure that the average freeboard, and
therefore thickness, is not biased high. Overall, differences between NRT and
archive estimates of sea ice thickness and volume fall well within the
corresponding estimates of their uncertainties (Tilling et al., 2015).
Our archive estimates of sea ice volume are larger than NRT estimates in part
as they are computed using the final sea ice concentration data set, which
contains higher values than its NRT counterpart. For example, we recalculated
sea ice volume using the NRT sea ice thickness and final sea ice
concentration data sets, and the departure from the archive estimate reduced
to 100 km3. A contribution to the remaining difference is likely the
combined absence of the wet-tropospheric, dry-tropospheric and
inverse-barometer corrections in 93.8 % of the Baseline-B fast-delivery
CryoSat-2 data. This is reduced to 0.3 % for Baseline-C data. The mean
sea ice thickness for both the NRT and archive data sets is
∼ 1.8 m, and there is no bias between them, with or without
geophysical corrections applied. When the corrections are missing, the NRT
and archive thickness values at any given location differ, on average, by
just 1.1 cm with a standard deviation of 23.0 cm (Fig. 3a).
This is reduced to 0.1 cm with a standard deviation of 7.4 cm
when the corrections are present (Fig. 3b). There is no spatial pattern to
these differences. Despite the improvement in performance of Baseline-C NRT
data compared with Baseline-B we conclude that the satellite orbits and
on-ground processing applied to fast-delivery CryoSat-2 data are sufficient
to determine accurate measurements of Arctic sea ice thickness and volume for
both baselines. The thickness differences between the archive and NRT data
products are not significant for either baseline given the estimated
uncertainty on thickness and the typical thickness of sea ice floes.
The impact of geophysical corrections on near-real-time (NRT) Arctic
sea-ice-thickness estimates from CryoSat-2. (a) Percentage change in
archive minus NRT thickness estimates for the final 28 days of March 2015. In
March 2015 the wet-tropospheric, dry-tropospheric and inverse-barometer
corrections were missing in 80 % of cases. (b) Percentage change
in archive minus NRT thickness estimates for the final 28 days of April 2015.
In April 2015 the wet-tropospheric, dry-tropospheric and inverse-barometer
corrections were missing in 0 % of cases.
Results
The spatial distribution of the NRT sea-ice-thickness data (Fig. 1) for any
given time period depends on the nature of the CryoSat-2 orbit over that
period. CryoSat-2 has an orbit repeat period of 369 days, which is built up
by successive shifts of a 30-day repeat subcycle, meaning that uniform
coverage of the Arctic Ocean is achieved every 30 days (Wingham et
al., 2006). The density of orbit crossovers increases with latitude up to the
CryoSat-2 limit of 88∘ N and with the number of days of coverage.
CryoSat-2 orbit patterns are visible in maps of thickness for 2-day
(Fig. 1a, d) and 14-day (Fig. 1b, e) coverage. The orbits are clearer at
lower latitudes, below about 80∘ N. Over 28 days (Fig. 1c, f),
almost complete coverage across the sea ice pack is achieved. However, there
are still small areas of unmapped sea ice, and these typically occur at the
ice edge (see Fig. 1). In these unmapped areas the sea ice concentration is
above 15 %, which we use as the sea ice margin threshold, but below
75 %, which is the concentration required for a region to be classed as
containing sea ice (see Sect. 2).
To determine the utility of the 5 km grid measurements of NRT sea ice
thickness, we performed a detailed assessment of the spatial and temporal
distribution of the data and compared these to the equivalent for archive
data. Over the 2-, 14- and 28-day time periods for which NRT data are
available, we calculated the percentage of sea ice covered by NRT and archive
data in 1∘ latitude bands from 60 to 90∘ N, for the final 2,
14 and 28 days of each month. This was done for data from October 2014 to
April 2015, and data were averaged over all months
(Fig. 4a). We produced the equivalent plot for the mean data separation in
each latitude band, where separation is simply the square root of the number
of measurements in each band divided by the sea-ice-covered area (Fig. 4b).
For 28-day data coverage, sea ice at latitudes between 85 and 88∘ N
is mapped in its entirety by the NRT and archive products and the data
separation drops to 5.0 km in each 1∘ latitude band, which is
simply the grid separation. For 14-day coverage the CryoSat-2 orbit pattern
achieves its maximum coverage for NRT data of 98 %, between 86 and
87∘ N, but achieves 100 % coverage for archive data between 86
and 88∘ N. These correspond to mean data separations of 5.1 and
5.0 km (the grid separation). The maximum NRT coverage over 2 days is
91 %, between 87 and 88∘ N, where the mean data separation is
5.2 km. This increases to 99 %, between 87 and 88∘ N for
archive data, with a mean data separation of 5.1 km. For both NRT and
archive data the percentage of ice mapped decreases with decreasing
latitudes, and the separation between data points increases, although there
is some fluctuation in these trends that is likely due to the shift in the
CryoSat-2 orbit pattern producing less favourable coverage for a given month.
CryoSat-2 does not observe sea ice north of 88∘ N, so the percentage
of ice mapped drops to 0 % for 2-, 14- and 28-day coverage in the region
88–90∘ N for both data sets. On average, the NRT sea-ice-thickness
data maps 20, 51 and 66 % of the Arctic sea ice north of 60∘ N
every 2, 14 and 28 days respectively. This corresponds to a measurement
within 14, 7 and 6 km of each location in the Arctic every 2, 14 and
28 days. For archive data the coverage increases to 23, 57 and 69 % every
2, 14 and 28 days respectively, which corresponds to a measurement within 13,
7 and 6 km of each location in the Arctic.
Spatial and temporal sampling of the Centre for Polar Observation
and Modelling (CPOM) near-real-time (NRT) and archive Arctic
sea-ice-thickness products, north of 60∘ N. (a) Percentage
of sea ice cover mapped in 1∘ latitude bands, averaged over each
month from October 2014 to April 2015. Data are plotted for the final 28, 14
and 2 days of all months. Solid lines show NRT data, dashed lines show
archive data. (b) Mean separation between measurement points in
1∘ latitude bands, averaged over each month from October 2014–April
2015. Data are plotted for the final 28, 14 and 2 days of all months. Solid
lines show NRT data, dashed lines show archive data.
Regional and temporal sampling of the Centre for Polar Observation
and Modelling (CPOM) near-real-time (NRT) and archive Arctic sea-ice-thickness products. (a) Arctic Ocean regions. The regions are the
Amerasian Basin (1), Eurasian Basin (2), Canadian Archipelago and Northwest
Passage (3), Hudson Bay and Foxe Basin (4), Baffin Bay (5), Greenland Sea (6),
Iceland Sea (7), Barents Sea (8), Kara Sea (9), Siberian shelf seas (10),
Bering Sea (11), Sea of Okhotsk (12), White Sea (13), Baltic Sea and
surrounding gulfs (14), Labrador Sea (15), the Gulf of St Lawrence and Nova
Scotia Peninsula (16) and the Beaufort Sea (17). Regions 1–10 encompass all
autumn sea ice, and regions 1–16 encompass all spring sea ice. Region 17 is
a subregion of regions 1 and 3. (b) Plot showing the percentage of
sea ice cover mapped by the NRT product in each month for six key
oceanographic basins. (c) Plot showing the difference (archive –
NRT) in percentage ice cover mapped.
The distribution of NRT sea-ice-thickness measurements also varies with
region and month, and the nature of the monthly variation depends on the
region being observed. This is an important consideration for those wishing
to use the data in a specific region of interest or over the entirety of the
sea ice growth season. We calculated the percentage of ice cover mapped by
the NRT product for six key oceanographic regions (Fig. 5a) for the final
28 days of each month of the 2014–2015 sea ice growth season (Fig. 5b), then
compared this to the percentage of ice cover mapped by our archive data in
the same regions (Fig. 5c). The percentage of the ice cover mapped in the
Amerasian and Eurasian basins is high (≥ 76 % for NRT data and
≥ 83 % for archive data), with just a small increase over the growth
season. Both regions are almost entirely covered in sea ice year-round, which
means that the areal fraction of unmapped sea ice at the ice edge is fairly
consistent throughout the year. However, this is not the case for regions
with more seasonal ice cover, such as the Canadian Archipelago and Northwest
Passage, Hudson Bay and the Beaufort Sea, where NRT and archive coverage
improves throughout the growth season and peaks in February or March. In
these regions, as the extent of the sea ice cover increases through winter,
the unmapped area at the sea ice edge becomes a decreasing fraction of the
ice-covered area, and a greater percentage of the ice cover is mapped. In
addition, as the sea ice concentration increases through winter, echoes from
sea ice floes becomes less noisy and are more likely to be included in our
processing. Coverage in the Greenland Sea generally improves throughout the
growth season, although there is some variation in this pattern due to
fluctuations in the width of the unmapped area at the sea ice edge, which
could be a consequence of the rapid sea ice transport in this sector.
Overall, coverage is lowest for the Greenland Sea, Canadian Archipelago and
Northwest Passage and Hudson Bay. Due to the location of the Greenland Sea,
there is also a persistent presence of unmapped sea ice along its eastern
edge. The Canadian Archipelago and Northwest Passage and Hudson Bay are in
close proximity to substantial coastal areas, where it is difficult to
construct sea surface height due to the absence of leads in the sea ice pack.
Although there is spatial variation in the coverage of the NRT sea-ice-thickness data, both with latitude (Fig. 4) and oceanographic basin
(Fig. 5b), there is no significant spatial variability in the difference
between the NRT and archive data coverage (Figs. 4, 5c).
Variations in the sampling of CryoSat-2 near-real-time (NRT)
sea-ice-thickness products in 17 Arctic Ocean basins. Regions 1–10 encompass
all October sea ice, and regions 1–16 encompass all March sea ice. Region 17
is a subregion of region 1 (Fig. 5a).
Data coverage (% of ice cover mapped) 2 days 14 days 28 days Oct 2014Mar 2015Oct 2014Mar 2015Oct 2014Mar 2015Amerasian Basin (1)333878829298Eurasian Basin (2)244458737688Canadian Archipelago and Northwest Passage (3)9731373953Hudson Bay (4)06048071Baffin Bay (5)015056081Greenland Sea (6)81331504963Iceland Sea (7)016044057Barents Sea (8)0917321847Kara Sea (9)21715461658Siberian shelf seas (10)112038604985Bering Sea (11)n/a3n/a35n/a40Sea of Okhotsk (12)n/a0n/a21n/a33White Sea (13)n/a0n/a6n/a6Baltic Sea and surrounding gulfs (14)n/a0n/a0n/a0Labrador Sea (15)n/a1n/a13n/a19Gulf of St Laurence and Nova Scotia Peninsula (16)n/an/an/an/an/an/aBeaufort Sea (17)172059836995
We extended our analysis of NRT data sampling by calculating the percentage
of sea ice mapped in all Arctic Ocean basins at the beginning and end of the
sea ice growth season (Table 1) for the final 2, 14 and 28 days of each
month. In each month the coverage improves with the number of days sampling,
in every basin. The coverage also improves from October to March, for each
time period and for all but one basin; the Canadian Archipelago/Northwest
Passage experiences a drop in coverage over the growth season for the 2-day
observation period. However, this change is very small, and over short
observation periods we would expect some variability in the proportion of ice
cover mapped as a consequence of the CryoSat-2 orbital repeat pattern. This
becomes more important in regions such as the Canadian Archipelago, where
there is a high fraction of land interspersed with ocean. The Bering Sea, the
Sea of Okhotsk, the White Sea, the Baltic Sea and surrounding Gulfs and the
Labrador Sea have the smallest proportional ice cover mapped in March 2015.
These are regions of highly seasonal sea ice cover, and by the end of the
growth season the unmapped area at the ice edge still constitutes a sizable
fraction of the ice-covered area. In addition, they are all southerly basins
(below 70∘ N), which are sampled with reduced spatial density by
CryoSat-2. The most extensively sampled areas are in the central Arctic –
the Amerasian and Eurasian basins – which experience substantial year-round
sea ice cover and are at high latitudes. We conclude that the location,
seasonality and dynamic nature of any sea ice region are important
considerations when assessing the reliability of the NRT Arctic sea-ice-thickness product.
Discussion and conclusions
We have shown that NRT estimates of
sea ice thickness determined from fast-delivery CryoSat-2 data can be
computed within a few days of the raw data acquisition and with a certainty
that is comparable to that of the standard archive product which is typically
available 6 months later. This allows for timely and reliable assessments
of local and regional sea ice conditions, which should benefit activities
that depend on such data. A good example is seasonal forecasts of Arctic sea
ice properties, which have previously utilized sparse airborne measurements
to adjust model-based initial ice-thickness distributions (Lindsay et
al., 2012). Although of coarser spatial resolution, our NRT thickness
estimates complement the airborne data because of their wider spatial and
temporal extent (Posey et al., 2015; Chevallier and Salas-Melia, 2012), and
even though the data do not extend into the summer season, their use should
nevertheless lead to improved model skill (Day et al., 2014; Sigmond et
al., 2013). A previous study (Rinne and Similä, 2016) has highlighted the
potential value of fast-delivery CryoSat-2 data for the classification of sea
ice into discrete stages of its development – thin (< 70 cm) and
thick (> 70 cm) FYI and MYI – in the Kara Sea. We have extended
this initial analysis of the mission potential to provide continuous
measurements of sea ice thickness across the entire Northern Hemisphere.
Together with records of NRT sea ice concentration (Cavalieri et al., 1996;
Maslanik and Stroeve, 1999), which are also available in NRT, NRT estimates
of sea ice thickness determined from CryoSat-2 will allow routine assessments
of Arctic environmental conditions (Stroeve et al., 2005) to report
additional changes in sea ice thickness and volume.
In addition to the CryoSat-2 measurements, our NRT sea-icethickness
estimates depend also on timely availability of sea ice concentration
estimates (Maslanik and Stroeve, 1999) and of classification of sea ice type
(http://osisaf.met.no/p/ice/#type). The sea ice concentration and sea-ice-type data sets are currently available to us 2 days and 1 day after
their measurements respectively. Because the fast-delivery CryoSat-2 data are
typically available 1–3 days after acquisition, the latency of the NRT sea-ice-thickness product is in practice limited by the altimeter data. A more
rapidly delivered product, to support by day-to-day activities in the Arctic,
would first require improvements in the latency of the CryoSat-2 data,
followed by either improvements in the latency of sea ice concentration data
or the use of older sea ice concentration measurements as an approximation.
The NRT estimates are of comparable accuracy to those produced using the
final release CryoSat-2 data, with a mean difference of 0.9 cm
between NRT and archive estimates of sea ice thickness. The NRT and archive
thickness differences, although small, vary temporally. The differences are
reduced when all geophysical corrections are present in the fast-delivery
CryoSat-2 data, which is the case in 99.7 % of the data since 26 March
2015, when the ESA on-ground processing chain switched from Baseline-B to
Baseline-C. There is no spatial variability in the differences between our
NRT and archive data products. For the period from October 2014 to April
2015, the NRT data set covered an average of 20, 51 and 66 % of the Arctic
sea ice north of 60∘ N every 2, 14 and 28 days respectively. This is
equivalent to a measurement within 14, 7 and 6 km of each location in
the Arctic every 2, 14 and 28 days. However, there are temporal and spatial
variations in the data coverage. The time of year, location and dynamic
nature of any region of interest must be considered when assessing the
reliability of the data. The next major step in the advancement of the data
is to develop improved estimates of snow loading on Arctic sea ice. We also
intend to investigate the impact of different gridding methods, including the
application of a distance weighting to our gridded NRT sea-ice-thickness
product. Our sea ice thickness and volume error budget could be further
constrained with improved knowledge on uncertainties in snow loading and sea
ice density, and also by accounting for uncertainties in the propagation
speed of the radar signals through the snow pack.
Data availability
Our NRT sea ice thickness and
volume data are publicly available on the CPOM UCL data portal at
http://www.cpom.ucl.ac.uk/csopr/seaice.html.
The fast-delivery CryoSat-2 Level 1b radar altimeter data used for this work
are available on request via ftp at ftp://science-pds.cryosat.esa.int.
The NRT DMSP SSMIS daily polar gridded sea ice concentration data required for
this work are available from NSIDC via ftp at
ftp://sidads.colorado.edu/pub/DATASETS/nsidc0081_nrt_nasateam_seaice/, and
the NRT sea ice type maps are available from OSI SAF at
http://osisaf.met.no/p/ice/#type.
Rachel L. Tilling and Andy Ridout developed and analysed
the satellite observations. Andrew Shepherd supervised the work. Rachel L.
Tilling, Andy Ridout and Andrew Shepherd wrote the paper. All authors
commented on the text.
Acknowledgements
We wish to thank those who
provided the timely ancillary data that we required to deliver a NRT product:
ESA, for the fast-delivery CryoSat-2 Level 1b data; OSI SAF, for their sea
ice type data; and NSIDC, for hosting the NRT sea ice concentration data.
This work was funded by the UK Natural Environment Research Council, with
support from the UK National Centre for Earth Observation.
Edited by: C. Haas
Reviewed by: two anonymous referees
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