TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-2073-2018A new tracking algorithm for sea ice age distribution estimationNew sea ice age algorithmKorosovAnton Andreevichanton.korosov@nersc.nohttps://orcid.org/0000-0002-3601-1161RampalPierrePedersenLeif Toudalhttps://orcid.org/0000-0001-7913-6282SaldoRobertohttps://orcid.org/0000-0003-4716-438XYeYufanghttps://orcid.org/0000-0001-6520-3851HeygsterGeorgLavergneThomashttps://orcid.org/0000-0002-9498-4551AaboeSignehttps://orcid.org/0000-0002-5618-4537Girard-ArdhuinFannyhttps://orcid.org/0000-0001-7819-7665Nansen Environmental and Remote Sensing Center, Thormohlensgate 47,
Bergen, 5009, NorwayInstitute for Space Research and Technology,
Technical University of Denmark, Lyngby, DenmarkDepartment of
Space, Earth and Environment, Microwave and Optical Remote Sensing, Chalmers
University of Technology, Gothenburg, SwedenInstitute of
Environmental Physics, University of Bremen, Bremen, GermanyDepartment of Remote Sensing And Data Management, Norwegian
Meteorological Institute, Oslo, NorwayDepartment of Remote
Sensing And Data Management, Norwegian Meteorological Institute, Tromsø,
NorwayIfremer, Univ. Brest, CNRS, IRD, Laboratoire
d'Oceanographie Physique et Spatiale (LOPS), IUEM, 29280, Brest, FranceAnton Andreevich Korosov (anton.korosov@nersc.no)15June2018126207320854November201715November201722May201825May2018This 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/12/2073/2018/tc-12-2073-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/2073/2018/tc-12-2073-2018.pdf
A new algorithm for estimating sea ice age (SIA) distribution based on the
Eulerian advection scheme is presented. The advection scheme accounts for the
observed divergence or convergence and freezing or melting of sea ice and predicts
consequent generation or loss of new ice. The algorithm uses daily gridded sea
ice drift and sea ice concentration products from the Ocean and Sea Ice
Satellite Application Facility. The major advantage of the new algorithm is
the ability to generate individual ice age fractions in each pixel of the
output product or, in other words, to provide a frequency distribution of the
ice age allowing to apply mean, median, weighted average or other statistical
measures. Comparison with the National Snow and Ice Data Center SIA product
revealed several improvements of the new SIA maps and time series. First, the
application of the Eulerian scheme provides smooth distribution of the ice
age parameters and prevents product undersampling which may occur when a
Lagrangian tracking approach is used. Second, utilization of the new sea ice drift product void of
artifacts from EUMETSAT OSI SAF resulted in more
accurate and reliable spatial distribution of ice age fractions. Third,
constraining SIA computations by the observed sea ice concentration
expectedly led to considerable reduction of multi-year ice (MYI) fractions.
MYI concentration is computed as a sum of all MYI
fractions and compares well to the MYI products based on passive and active
microwave and SAR products.
Introduction
Sea ice age (SIA) is one of the components of the essential climate variable
(ECV) for sea ice as defined by the Global Climate Observing System (GCOS)
. It is an important climate change indicator in polar regions,
which describes the sea ice cover state in addition to its concentration and
thickness. More generally, the changes in SIA can be seen as a proxy
of sea ice decline in Arctic.
Numerous studies have been focused on the estimation and evolution of the
Arctic SIA over the last decades
.
They all report that the amount of old/thick ice in the Arctic Ocean has been
decreasing dramatically over the last decades. For example, the very old ice
(> 4 years old) comprised 20 % of the ice pack in 1985, decreasing to
3 % in 2015, while the 3- to 4-year-old ice decreased by one-third over the
same time period . ,
and reported significant decline in the area covered by
multi-year ice (MYI) in the central Arctic since 1999, to which they
associated a significant decrease in the mean sea ice thickness and volume in
the same region.
Sea ice age is an important parameter of the Arctic ocean system and may be a
good indicator of significant changes in the dynamical and thermodynamical
regimes that have taken place since the beginning of the current century, such
as sea ice thinning and faster ice drift. The younger seasonal ice cover
present in the Arctic nowadays is in general thinner, which makes it more
vulnerable to break up, deformation and drift under the actions of waves,
winds and currents. This has been confirmed by calculating the trends in sea
ice drift and deformation over the last 3 decades, which are significant and
respectively about 13 and 50 % per decade . If more
mobile, sea ice might also be exported more easily and rapidly out of the
Arctic basin, e.g., through Fram Strait, especially the MY sea ice pack
located north of Greenland and Ellesmere Island. A more fragmented sea ice
cover is also more vulnerable to summer melt. For example, during the winter
of 2007–2008 large chunks of MYI resulting from the intense break up of the
thickest part of the Arctic ice cover located in the north of the Canadian
Archipelago were melted away while drifting in the Beaufort Sea within the
few following summer months . A positive feedback on the loss
of MYI due to the more frequent sea ice cover fracturing, increased sea ice
drift and enhanced melting rate is therefore potentially activated,
contributing to the observed negative trend in SIA.
It is the purpose of the present paper to describe a method and a derived
dataset that allow us to shed more light on the development of the age
distribution of the Arctic sea ice. For this purpose, we have taken advantage
of some new datasets on sea ice drift and concentration developed and
distributed by the EUMETSAT Ocean and Sea Ice Satellite Application Facility
(OSI SAF). In addition, we have developed a new Eulerian scheme of advection
supported by the Sea Ice Climate Change Initiative (SICCI) project of the
European Space Agency (ESA). These improvements have allowed us to avoid the
problem of the tracers dispersion and to
produce a new SIA dataset which in each grid box contains not only
the age of the oldest ice, but the actual age distribution provided as
fractions of ice of different age categories (hereafter referred to as
“sea ice age fractions”). The dataset will be presented and compared
with earlier attempts to map Arctic SIA as well as with the standard
products for sea ice type classification from scatterometer and microwave
radiometer observations.
DataSea ice drift
Information on sea ice motion was acquired from two sources. First, the
National Snow and Ice Data Center (NSIDC) sea ice drift (SID) product v.0116
was downloaded from the NSIDC portal .
Weekly SID fields from NSIDC were accessed on an Equal Area Scalable Earth
(EASE) grid with 25 km spacing for the period from
October 1978 to December 2015. As pointed out by this ice
drift product contains artifacts due to the composition of the ice drift
derived from satellite data, observed by in situ buoys and predicted using a
simple free drift model. On short timescales, these artifacts result in
large openings in the predicted distribution of MYI which
are filled with the first-year ice (FYI). On longer timescales the openings
get bigger and the shape of the predicted ice age distribution may be
significantly distorted.
Sea ice age fractions for 1 January 1985 in the Greenland and
Lincoln seas. It is clearly seen that in many areas the fraction of 8YI does
not exceed 20 %, but the corresponding SIA map shows that the entire
pixel is assigned to contain 8YI.
The second SID product was produced by the Ocean and Sea Ice Satellite
Application Facility (OSI SAF) High Latitude Processing Center. The
low-resolution sea ice drift product from the EUMETSAT OSI SAF is operational
since 2009. It implements the continuous maximum cross-correlation algorithm
of for retrieving ice drift vectors from daily composited
maps of various medium resolution passive and active microwave (PAMW)
satellite sensors such as the SSMIS, AMSR2 and ASCAT. A multi-sensor
analysis is also provided . The retrieval of SIA
requires sea ice drift information during summer. This was recently achieved
by the OSI SAF product using the 18.7 GHz channels of
the GCOM-W1 AMSR2 instrument . For this study, a short
re-processed record of OSI SAF ice drift product (October 2012–May 2017) was
accessed, complemented by the operational product available from
http://osisaf.met.no (May 2017 onwards). The SID product from OSI SAF
was filtered component-wise with 3 × 3 pixels median filter and then
upscaled using linear interpolation onto a grid with 10 km spatial
resolution. Gaps in the product were filled with nearest neighbor values.
Sea ice concentration
Sea ice concentration (SIC) product v.1.6 was produced by
the OSI SAF portal at 1-day temporal resolution and 10 km spatial resolution
for the period September 2012–September 2017 covering the Northern
Hemisphere. Validation of the product indicates that SIC can be retrieved
with 10 % accuracy with slight seasonal variations .
Sea ice type (MYI concentration)
Sea ice types can be discriminated with PAMW satellite observations since the
physical signatures of sea ice change significantly after the influence of
summer melt and brine rejection. Therefore sea ice that has survived at least
one summer melt is referred to as multi-year ice, and seasonal ice is
referred to as first-year ice.
The algorithm of Environment Canada Ice Concentration Extractor (ECICE)
can combine several different observation inputs, e.g.,
passive and active microwave satellite data. Combined PAMW data can help to
identify MYI; however, the retrieval shows flaws under specific weather
conditions. The improved MYI concentration retrievals developed at University
of Bremen is based on the ECICE algorithm using brightness temperatures from
AMSR2 and radar backscatter from C-band scatterometer ASCAT. Two correction
schemes were applied to the initial MYI concentration: one using temperature
records from atmospheric reanalysis to identify MYI anomalies caused by warm
spells and replace with interpolated MYI concentrations and
the other utilizing mainly ice drift records to constrain the MYI changes
within a plausible contour .
The improved MYI concentrations were provided as gridded products on polar
stereographic grid with 12.5 km spatial resolution for the winter months
(November–April) of 2013–2017. Compared to the MYI dataset, for which the
corrections were developed, the retrievals from AMSR2 and ASCAT are much
coarser (12.5 km vs. 4.45 km). The coarser resolution requires adjustment
of the thresholds in the drift correction, which results in suboptimal
performance of the drift correction and therefore could lead to unexpected
problems in the final dataset.
The OSI SAF sea ice type is another algorithm that combines both passive and
active microwave data in a Bayesian approach that computes
the probability of occurrence of the most likely ice type – first-year ice
or multi-year ice. The OSI SAF type product is a near-real-time product that
has been operational since 2005 with data available from the OSI SAF High
Latitude processing center (http://osisaf.met.no). Since the start of
operational production the algorithm has been upgraded several times by
including new sensors and improving the methods. For the period of interest
in this study, 2013–2017 summer, the retrieval is based on scatterometer
data from the ASCAT instrument and atmospherically corrected brightness
temperatures from SSMIS. From October 2015 an algorithm upgrade resulted in
the implementation of dynamical training data updated on a daily basis in
order to replace the previous fixed training data. The ice type product is
provided for the winter period of October until mid-May on polar stereographic
projection with 10 km grid resolution.
Sea ice age
Information on SIA independent of the method introduced here
was acquired from the NSIDC portal . Weekly SIA fields were
provided at EASE grid with 12.5 km spatial resolution
for the period from October 1978 to December 2015. The gridded SIA product is
generated from positions of the virtual Lagrangian ice parcels (initially
released on a regular grid) when the grid cell is assigned
the age of the oldest parcel. This does not take into account fractions of
younger ice present in the same cell. It may happen that most of the drifters
within a cell are very young and only one drifter is old but nonetheless the
entire cell is still assigned to be the oldest ice. Such a case is shown in Fig. for 1 January 1985. For generating this figure we
have implemented the NSIDC ice age algorithm and applied it to the NSIDC ice
drift product starting from 1978. Concentration of each ice age category is
calculated as a relative number of ice parcels with respective age.
Methodology
The SIA computation is implemented in two stages as explained in
detail below. First, SIA fractions are independently advected using
the satellite-derived sea ice drift and concentration observations and the
Eulerian advection scheme. Second, the SIA is computed, accounting for
concentration of each ice age fraction.
Eulerian advection scheme
At each time step the observations of sea ice drift velocity components (U
and V) and sea ice concentration (COBS) are provided as gridded
products on the polar stereographic grid with the same spatial resolution
with pixel size in x and y directions equal to Rx and Ry,
correspondingly. Uncertainties in U, V and COBS products impact
the accuracy of the end product but are not considered in the present study.
We assume that a sample ice parcel in a pixel has shape of one grid cell and
an initial ice concentration C and corresponding velocity components U
and V. An example is presented in Fig. , where the ice
parcel originating from the pixel 31 is shown as a blue square. The parcel
drifts from point A over time Δt and the coordinates of the
destination point B can be computed as follows:
XB=XA+UΔtYB=YA+VΔt.
Scheme of Eulerian advection of sea ice. Points A and B denote start
and end of drift of an ice parcel shown by the blue square.
The areal fluxes of sea ice out of the donor pixel 31 into recipient pixels
12, 13, 22 and 23 are calculated as follows:
F31▸12=C31ad,F31▸13=C31bd,F31▸22=C31ac,F31▸23=C31bc,
where a, b, c and d are sides of the rectangles formed by
intersection of the displaced donor pixel and recipient pixels
(Fig. ). The recipient pixels are selected based on
position of point B – the four closest pixels become recipients.
Concentration of sea ice at the next step in a recipient pixel (e.g., pixel 13
in Fig. ) (C13*) is calculated as
C13*=F21▸13+F22▸13+F31▸13+F32▸13
or in generalized form:
CR*=∑4donorsCD1-|XR-XD-UΔt|Rx1-|YR-YD-VΔt|Ry,
where CD is the concentration of a donor pixel, XD and
YD are the coordinates of a donor pixel, XR and YR are the
coordinates of the recipient pixels and Rx and Ry are pixel size.
Example of advection of an ice fraction from 1 October 2012 to
1 October 2013 shown for every third month.
When the observed sea ice drift field diverges then a gap appears between the
ice parcels (e.g., in pixel 23). If the sum of fluxes
(Eq. ) into a recipient pixel falls behind the actually
observed concentration then this is interpreted as opening of leads, freezing
of water in the leads and generation of new ice. Then, ice is presented by
two fractions: the partial concentration of the older ice COI is
computed as the sum of incoming fluxes (Eq. ) and the
partial concentration of the young ice CYI is computed as a
remainder:
CYI=COBS-COI.
When the observed sea ice drift field converges then the advected ice parcels
overlap (e.g., in the pixel 12) and the sum of fluxes into a recipient pixel
may exceed the concentration observed by satellites. This is interpreted as
generation of pressure ridges and increase in thickness of the older fraction
of sea ice. In that case the total ice concentration at the next step is
assigned to be the observed ice concentration.
During the freeze-up period the observed concentration increases and becomes
higher than the sum of fluxes into a recipient pixel. This is also
interpreted as generation of young ice (YI) only and Eq. () is used for
computing YI concentration. During melting the observed concentration
decreases and it may become less than the total predicted concentration (sum
of OI and YI fractions). In that case the YI concentration is decreased
first, and if YI is absent then the OI concentration is decreased.
Advection of sea ice age fractions
Advection of a SIA fraction is initiated on 10 September, the
approximate date when ice extent reaches minimum in the Arctic, area of
FYI is zero and all observed sea ice is MYI
by definition. Initialization before this date has little impact, but
initialization after this date increases risk of considering the observed FYI
as MYI. The age of each ice fraction is increased by 1 year on 10 September
of each consecutive year of advection.
In our study we initiated SIA calculation on 1 October 2012 when continuous
high-quality observations of ice drift started to be available from AMSR2
(Fig. 3). For this date the total observed concentration was computed as the
minimum concentration during the period 1 September–1 October 2002.
Propagation of ice age fractions from later years started from 10 September
or respective years.
We did not know the spatial distribution of ice of different age within the
pack at the first moment of time (1 October 2012), but we can postulate that
all observed ice is at least in the second-year ice (2YI) category. We have
to make a simplification: the concentration of 2YI on 1 October 2012
is assigned to be equal to the total observed concentration: C2YI=CTOT (Fig. a). Understandingly, this
simplification allows us to initialize the SIA algorithm but does not
allow to provide full SIA frequency distribution before a
sufficiently long spin-up period (e.g., 5 years). Such a spin-up period is
also needed for the NSIDC product. Then the developed advection scheme is
applied on a daily basis utilizing the available daily ice drift and
concentration products (Fig. b–e). From
1 October 2012 to 10 September 2013 (Fig. a–d) the
advected fraction contains 2YI but on 10 September 2013 the
age of this fraction is increased by 1 year and later it becomes the fraction
of the third-year ice (3YI) (Fig. e).
On 10 September 2013 the total ice concentration is the sum of all MYI fractions and therefore the initial concentration of the 2YI
is calculated as
C2YI=CTOT-C3YI,
where CTOT is the ice concentration observed by satellites and
C3YI is the ice fraction advected from 1 October 2012. Both 2YI and
3YI fractions are advected further using the developed scheme and after N
years of advection on each of 10 September the concentration of 2YI is
calculated as
C2YI=CTOT-∑iCi,
where Ci is the concentration of the ice fraction advected from
previous years.
Computation of sea ice age
At any moment of time, sea ice in each cell is characterized by a vector of
concentrations of ice fractions of various age with the sum equal to the
total sea ice concentration (Fig. ). In other words the
new algorithm provides ice age probability distribution. A single number that
characterizes a probability density function can be computed in several
standard ways: minimum, maximum, mean, median, mode, etc. We propose that SIA can also be computed from the concentration of ice age fractions
using several approaches depending on the preferred definition of the ice age
(Fig. ):
Maximum age is the age of the oldest ice fraction that exceeds a given
threshold (e.g., 5 % concentration).
Average age is the mean of age of the ice fraction that exceeds a given threshold.
Modal age is the age of the ice fraction that has the highest concentration.
Median age is the age of the ice fraction that splits the age distribution
into two equal parts. Linear interpolation between the ice age fractions or approximation
of ice distribution by a predefined function may be needed to compute median age correctly.
More generally, median (the value of the 50th percentile) can be replaced by any percentile.
Weighted average age is the average of ages of individual ice fractions
weighted by their concentrations:A=∑fAfCf∑fCf,where Af is age of a sea ice fraction and Cf is
concentration of a sea ice fraction.
Maps of sea ice age fractions on 1 March 2015. (a) Observed
total sea ice concentration; (b–d) fractions of advected multi-year
ice; (e) FYI fraction. Color bar represents the sea ice age fraction
concentration.
Maps of sea ice age on 1 March 2015 computed with different methods.
(a) Maximum ice age; (b) average ice age;
(c) modal ice age; (d) weighted average ice age;
(e) median (percentile 50) ice age; (f) NSIDC SIA. Color bar
represents the sea ice age in years.
Results
The developed advection scheme (see Sect. ) was
applied to the sea ice drift and concentration products available at OSI SAF
from 1 October 2012 to 1 March 2017. We compared our products to the already
available sea ice products including SIA maps from NSIDC: time series and
maps of MYI from NSIDC, Bremen University, OSI SAF and Denmark Technological
University (DTU). The results of comparison presented below can be used for
indirect assessment of our SIA algorithm.
Comparison with sea ice age product from NSIDC
In order to provide stepwise illustration of the SIA product improvements we
have used four different combinations of forcings and algorithms to produce a
SIA map for the 1 January 2016. First, we have implemented the NSIDC
advection scheme (Lagrangian) and the SIA algorithm (age of the oldest
parcel) and applied it to the ice drift product from NSIDC. Second, we have
applied the NSIDC algorithms to the ice drift from OSI SAF. Third, we have
applied our Eulerian advection scheme and SIA algorithm to the ice drift from
OSI SAF but without accurately accounting for SIC. In this experiment all
pixels with SIC below 15 % were assigned to be open water and other
pixels contain 100 % ice. Finally, we have used the new advection scheme,
the OSI SAF ice drift, and fully accounted for SIC.
Comparison of the generated ice age products is presented in Fig. . Panels a and b contain maps of maximum age
computed with the NSIDC algorithm and panels c and d contain ice age
estimated with the weighted average. The comparison indicates that spatial
distributions of SIA are similar across four combinations of forcings and
algorithms only in general. The change of the forcing significantly affects
distribution of SIA (Fig. a and b) as was also illustrated
in . The belt of very old sea ice in the Lincoln Sea almost
disappears, large areas with old ice in the central Arctic become more
pronounced, long sleeves of 2YI with interspersing of older ice stretch
across the central Arctic towards the Laptev Sea and along the Beaufort Sea coast
into the Chukchi Sea, and vast areas of 2YI in the European Arctic become more
homogeneous.
Comparison of SIA for the 1 January 2016 calculated with the
following combinations of forcings and algorithms: (a) NSIDC drift
and
NSIDC algorithm; (b) OSI SAF drift and NSIDC algorithm;
(c) OSI SAF drift and SICCI algorithm without SIC; (d) OSI SAF
drift and SICCI algorithm with SIC.
Comparison of MYI concentration estimated from the NSIDC SIA
product (a), from the SICCI SIA product (b), downloaded
from OSI SAF (c) and from University of Bremen (d). Daily
maps are generated for 31 December of the years from 2012 to 2016 and for
29 March 2017 from available data. Red contours denote extent of MYI in the
SICCI product at 5 % concentration.
Seasonal dynamics of area of sea ice age fractions (filled plots)
and multi-year ice area (dots) derived from the NSIDC product (a),
from the SICCI product (b), from the UB product (black dots) and
from the OSI SAF (yellow dots).
The change of the advection scheme does not affect the overall spatial
distribution of MYI significantly (Fig. b and c) but
generates a much smoother picture without discontinuities. Using the weighted
average for SIA computation dramatically decreases the observed age
(Fig. c). Few small spots where SIA reaches 4.5–5 years
are observed only near the Canadian Archipelago and in the central Arctic. A
stripe of high SIA appears along sea ice edge in the Fram Strait. This ice
was advected from the central Arctic but fractions of younger ice have melted
and only fractions of older ice remain, making the weighted average value high.
When the new algorithm takes SIC into account then SIA is decreasing even
more and the ice older than 4 years is observed only near the Canadian
Archipelago coast (Fig. d). Moreover, extent of MYI is also
decreased and the sleeves of 2YI towards the Laptev and Chukchi seas almost
disappear.
Intercomparison of multi-year ice concentration products
MYI concentration is an important indicator of abundance of
older, thicker and rougher sea ice and is readily available from several
resources including OSI SAF and University of Bremen
. MYI can also be estimated from the NSIDC SIA product if we
assume that bins with ice older than 1 year contain 100 % MYI and other
bins contain 0 %. Total concentration of MYI from the SICCI product (with
due consideration of SIC) was estimated as a sum of all multi-year fractions.
Intercomparison of MYI maps from four sources indicated that the general
spatial distribution and temporal evolution is rather similar
(Fig. ): most of MYI is observed in the Canadian sector;
a thin filament is exported through the Fram Strait; in some years the
Beaufort Gyre advects MYI along the Beaufort Sea coast; and sleeves of enhanced
MYI concentration are elongated towards the Laptev Sea.
Distinct features of MYI from NSIDC (Fig. a) include the
following: the map is binary (ice/no ice); numerous discontinuities due to
artifacts in the sea ice drift field are present; rather large lacunas filled
mostly with FYI appear in the ice pack (e.g., 31 December 2015, Lincoln Sea);
the MYI sleeves that extend towards the Laptev Sea are rather wide; and a wide
gap between ice pack and the coastline is observed. The SICCI product
(Fig. b) is continuous and smooth; expectedly it shows
higher MYI concentrations in the central Arctic and near the Canadian coast.
The OSI SAF MYI product (Fig. c) is also binary but the
map is not discontinuous; generally it shows lower extent of MYI than other
products, but in some periods the extent is much higher (e.g., on 29 March 2017
in the Beaufort Sea). The UB product is also continuous and apparently can
resolve smaller spatial details but the atmospheric influence seems to reduce
MYI concentration in the middle of the ice pack (e.g., along the 150∘
meridian in 2013 or above the Yermak Plateau in 2015).
The time series of MYI areas are compared with regard to both seasonal
dynamics (Fig. ) and interannual variations
(Fig. ). Areas of ice age fractions of each age
in the Arctic ocean are estimated individually from the NSIDC and SICCI
products and plotted against time. For the NSIDC product the area of an ice
age fraction is calculated as a number of all pixels corresponding to this
ice age fraction multiplied by the area of the pixel. Similarly the MYI area
from the OSI SAF product is estimated from the number of MYI pixels. For the
SICCI product the area is estimated as the integral of the corresponding ice
age fraction over all pixels. Similarly the MYI area from the UB product is
estimated as the integral of MYI concentration over all pixels.
The seasonal variations of ice age fraction areas follow similar pattern for
the NSIDC and SICCI products (Fig. ): the sea ice area
minimum (4 ×106 km2) is observed in mid-September, and it is
followed by a rapid growth of the FYI (blue in Fig. ) until the beginning of the next year. A stable
winter, when total area remain practically unchanged at level of
7–8 ×106 km2, is over in May and is followed by a rapid
decrease of FYI. Unlike FYI, the area of MYI fractions cannot increase – it
only gradually decreases due to ice convergence and melting. Maximum area of
the 2YI is also observed in mid-September – at this
point all FYI from the previous year is considered as the ice which has
survived summer melting and it becomes 2YI. Other fractions of older ice
increase their age by 1 year in the same fashion.
Interannual dynamics of area of sea ice age fractions (bars) and
multi-year ice area (dots) derived from NSIDC product (left bars), from NERSC
product (right bars), from UB product (black dots) and from OSI SAF (black
stars).
The MYI area is shown in Figs.
and as a demarcation of the blue and orange
areas and as black and yellow dots. A comparison of MYI areas from the four
products reveals several differences. The NSIDC MYI area decreases in two
regimes: a relatively slow decrease during winter solely due to convergence
and a faster decrease at the end of spring when melting reaches MYI. The
SICCI MYI area decrease also accelerates towards spring but more gently –
melting affects MYI already starting from the beginning of spring. The MYI
areas from UB and OSI SAF correspond well to the SICCI algorithm but exhibit
much more sporadic variability, including short periods of an inexplicable
increase in MYI area. Although the MYI extent in the OSI SAF product is
somewhat lower than of the other products (see maps in Fig. ) the OSI SAF MYI area corresponds well to other
estimates (Figs. and )
because concentration of MYI within the ice extent is assumed to be
100 %.
By the beginning of 2013 the SICCI product has only one fraction of MYI (2YI)
because calculations were initiated in 2012 and all MYI was assigned to be
2YI. By the fourth annual cycle (in 2016) the SICCI product has five ice age
fractions and the distribution can be better compared with the NSIDC product.
Clearly, the fractions of the older ice in the SICCI product have lower area
than in the NSIDC product.
The interannual variability can be seen on the plot of areas of the ice age
fractions for 1 January of 2013, 2014, 2015 and 2016 in Fig. . The total ice area (entire height of the
columns) is lower for the SICCI product because it is calculated as an
integral ice concentration over all pixels whereas for the NSIDC it is
calculated as sum of pixels within ice extent. The total MYI area is rather
similar across the four products, but the area of older sea ice (≥2 years) fractions is lower by almost 20 % in the SICCI product.
Comparison of MYI with SAR
An independent validation of the SICCI MYI product was performed using a
mosaic of synthetic aperture radar (SAR) images in HH polarization from
Sentinel-1 A and B satellites acquired around 1 January 2016. MYI appears as
brighter and more homogeneous texture on SAR images which allows one to draw
an outline and compare it with outlines of MYI from the SICCI product as
shown in Fig. . The SICCI MYI product corresponds very well to
the SAR-derived MYI extent although it has much smoother boundaries due to
low resolution of the input sea ice drift product (65 km). The low
resolution is also the reason why SICCI MYI does not reach the coast, leaving
a 50 km gap along the shoreline. The most considerable difference is
observed in the Beaufort Sea where the SAR-based MYI is seen as a stretched
“archipelago” of MYI “islands”. In this area the SICCI product exhibits
erroneously smooth decline in MYI concentration and fills the gap between the
MYI “archipelago” and the central Beaufort Sea.
Comparison of MYI extent derived by SAR visual interpretation (light
blue) and from SICCI SIA product (red) on top of mosaic of Sentinel-1 SAR
images for 1 January 2016. The thick red line shows 15 % threshold and
thin red line shows 50 % threshold.
Comparison of sea ice age for 1 January 1985 initiated from ice
parcels with various density (1 parcel per 12 × 12 km,
6 × 6 km, 3 × 3 km).
Discussion
The major advantage of the new algorithm is the ability to generate
individual ice age fractions or, in other words, to provide a frequency
distribution of the ice age in each pixel. This allows derivation of a single
number characterizing SIA using one of the statistical methods presented
above. Selection of the method depends on the preferred definition of the SIA
and on the eventual application. For example, in our opinion, weighted
average is the optimal method for graphical representation of SIA maps as it
depicts the smooth nature of SIA distribution and accounts not only for the
oldest ice fractions but also for the younger ones. However, for other
applications, e.g., assimilation into models, a different method may be
preferred. The SIA distribution can also be used to calculate MYI
concentration for various applications,
including calibration/validation of the PAMW-based MYI algorithms,
improvement of the freeboard to sea ice thickness conversion for altimeter
data and estimation of sea ice roughness for assimilation into models.
The motivation for implementing the Eulerian advection scheme in the new
algorithm was to produce continuous and smooth spatial distributions of SIA fractions and also to prevent undersampling of the results. In the
experiments with the NSIDC algorithm it was discovered that the density of
starting points of the sea ice drift vectors indirectly influences the area
of MYI. Too low density (e.g., 1 drifter in each 12 × 12 km box as
designed at NSIDC) leads to undersampling of the ice age observations in the
zone of strong divergence very early – only after a few years of
propagation. With a two or four times higher number of initial points,
undersampling is still experienced after 5 years of Lagrangian propagation.
Observations that the Lagrangian approach introduces problems due to the
dispersion of tracers were also previously reported in
. The NSIDC method was run with three different
initial densities of ice parcels: one drifter per 12 × 12 km or
6 × 6 km or 3 × 3 km box. The results show that lower
initial densities result in a map of SIA with sparse presence of old ice
fractions (Fig. a) and increasing the initial density of
drift vectors leads to increase of concentration of the older ice
(Fig. b and c). Consequently the area of older ice
fractions ≥ 6 years) increases and the area of younger ice fractions
(≤ 5 years) decreases when the initial density is increased. Impact of
the increased initial density on increased MYI area is independent of the
forcing and was demonstrated also for the OSI SAF ice drift field (not shown
here).
The new sea ice drift product derived from AMSR2 also has a significant
impact on the accuracy and reliability of the results. It accurately
reproduces the sea ice circulation in the Arctic, is void of artifacts and
produces homogeneous distribution of SIA fractions. It was discovered
that the initial SID product is contaminated with high-frequency noise.
Cleaning of SID with a median filter reduced small-scale deformations and
prevented appearance of FYI discontinuities in artificial divergence zones.
At the same time the low spatial resolution of SID (65 km) limits the
algorithm from reproduction of fine-scale features in MYI distribution and
may lead to over-smoothing in areas with high ice drift speed gradients
(e.g.,
western Beaufort Sea).
The observed differences in MYI spatial distribution and total area are only
marginal for the NSIDC and the SICCI products. This happens due to a short
integration time: MYI spatial distribution is forced by observations on 15
September and then it is modified by an advection scheme (either Lagrangian
or Eulerian) and melting (in case of SICCI product) during only 1 year. In contrast, the integration over longer times and accounting for the sea
ice concentration have significant effect on the area of the older ice.
Analysis of inter-annual dynamics (Fig. ) shows
that after 4 years of integration FYI and MYI areas in the NSIDC and SICCI
products are almost the same (7.1 × 106 and
6.9 × 106 km2) but the area of the third-year and older ice in
the SICCI product is almost 30 % smaller than in the NSIDC product. On
average, the speed of annual multi-year ice decline (calculated as difference
between MYI area in the beginning and in the end of the year) is 22 %
higher for the SICCI product, which may also indicate that the actual
residence time of sea ice is also shorter by one-fifth. It is challenging to
separate the impact of the new sea ice drift product, scheme of advection and
constraining by the SIC but more accurate estimates of long-term changes in
the residence time will be possible when longer time series of the new
SIA product become available.
The sea ice type algorithms based on PAMW satellite observations have higher
spatial resolution than the ice-drift-based algorithms but inevitably suffer
from the unaccounted atmospheric impact or melt ponds and have to be
corrected using information on air temperature or ice motion
. The method in our paper has high potential for
complementing the PAMW algorithms and production of high-resolution and
consistent MYI estimations. Such combined procedure can be realized, for
example, as a machine learning algorithm which is trained to output SICCI MYI
using PAMW brightness temperatures (TB) as input. The machine
learning block can be realized as a simple polynomial regression or as a
more complex neural network . The training can be performed
either on a per image basis, when only one mosaic of brightness temperatures
is used as input and only one snapshot of MYI is used as output, or on a
seasonal (or even satellite mission) basis, when information from many images
is used for training. Alternatively, both SICCI MYI estimations and
brightness temperatures can serve as input to classification algorithms, such
as Bayesian classifier or support vector machines
.
Conclusions
We have developed a new algorithm for estimating sea ice age distribution
using sea ice drift and concentration products. The algorithm is based on the
Eulerian advection scheme which provides smooth distribution of the ice age
parameters and prevents the undersampling problem that may occur when a
Lagrangian tracking approach is used. Another advantage of the selected
scheme is the ability to generate not just a single age characteristic but a
distribution of sea ice age fractions. First, this allows for flexibility in
choosing the ice age definition and application of a statistical measure to
compute SIA and, second, this provides individual spatial distributions of
ice age fractions that can be assimilated into models or used for ice type
delineation. For example, concentration of multi-year ice can be computed as
a sum of multi-year ice fractions and used for defining ice density and snow
thickness for the ice thickness algorithms, ice roughness for the ice
circulation models and so on.
The new algorithm is driven by the new sea ice drift products from OSI SAF,
which is void of potential artifacts due to inclusion of autonomous ice
drifter buoys. This leads to a more homogeneous distribution of ice age
fractions over the Arctic Ocean. The algorithm is also constrained by the
observed sea ice concentration from OSI SAF, which reduces fractions of old
ice and, consequently, ice age by 20–30 %. It was applied to generate
time series of daily sea ice age fraction product from October 2012 to
October 2017. Comparisons with the NSIDC SIA time series indicate that the
fractions of MYI in the new product melt faster during the year and after a
spin-up
time of 3 years the area of older ice in the SICCI product is almost 20 %
lower than in the NSIDC product.
The data generated with the algorithm are openly available
at FTP (for bulk download;
ftp://ftp.nersc.no/ArcticData/esa_cci_sea_ice_age/) and at THREDDS
(subsetting and online visualization;
http://thredds.nersc.no/thredds/arcticData/esa-cci-sea-ice-age.html) in
netCDF format following CF conventions containing values of sea ice age
fractions concentrations, MYI concentration, and sea ice age computed using
weighted average . Detailed information on datasets used
in this paper can be found in Sect. 2.
AK developed and applied the presented algorithm,
with contributions from PR. TL provided the new sea ice drift product. SA
provided the OSI SAF ice type data. YY and GH provided the new sea ice type
product. LTP and RS provided the mosaic of SAR images and multi-year ice
outline. All co-authors participated in fruitful discussions and writing the
manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work has been supported by the Sea Ice Age option of the Sea Ice Climate
Change Initiative project funded by the European Space Agency, contract
number 4000112229/15/I-NB. Edited by: Jennifer
Hutchings Reviewed by: three anonymous referees
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