An Arctic and Antarctic sea ice area and extent dataset has been generated by
EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using
the record of microwave radiometer data from NASA's Nimbus 7 Scanning
Multichannel Microwave radiometer (SMMR) and the Defense Meteorological
Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special
Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset
covers the period from October 1978 to April 2015 and updates and further
developments are planned for the next phase of the project. The methodology
for computing the sea ice concentration uses (1) numerical weather prediction
(NWP) data input to a radiative transfer model for reduction of the impact of
weather conditions on the measured brightness temperatures; (2) dynamical
algorithm tie points to mitigate trends in residual atmospheric, sea ice, and
water emission characteristics and inter-sensor differences/biases; and
(3) a hybrid sea ice concentration algorithm using the Bristol algorithm over
ice and the Bootstrap algorithm in frequency mode over open water. A new sea
ice concentration uncertainty algorithm has been developed to estimate the
spatial and temporal variability in sea ice concentration retrieval accuracy.
A comparison to US National Ice Center sea ice charts from the Arctic and the
Antarctic shows that ice concentrations are higher in the ice charts than
estimated from the radiometer data at intermediate sea ice concentrations
between open water and 100 % ice. The sea ice concentration climate data
record is available for download at
The Arctic sea-ice-covered area and extent has decreased since
the 1970s (Cavalieri and Parkinson, 2012). In Antarctica there are large
regional differences in trends but overall the sea ice extent is increasing
because of changing atmospheric circulation patterns and regional cooling
(Comiso et al., 2011; Holland and Kwok, 2012). The climatic trends in sea ice
extent have been documented using models (Zhang and Walsh, 2006; Goosse and
Zunz, 2014), ice charts (Rayner et al., 2003) and in particular the passive
microwave data record from US satellite microwave radiometers (Parkinson and
Cavalieri, 2008; Cavalieri and Parkinson, 2012). Throughout this paper the
sea ice extent is defined as ice-covered waters with ice concentrations
derived from microwave radiometer data greater than 30 % and at a grid
resolution of
The brightness temperatures measured by the satellite radiometers at the atmospheric window channels are dominated by surface emission. However, the measured brightness temperatures are also affected by weather conditions such as wind roughening of the ocean surface, water vapour, and cloud liquid water (Wentz, 1983, 1997; Andersen et al., 2006b). These parameters have trends over the observing period (Wentz et al., 2007). Even though the sensitivity to these parameters is minimized in ice concentration algorithms in general, different algorithms still have different sensitivities (Andersen et al., 2006b). Here we define the noise as the ice concentration fluctuations caused by the instrument electronic components, ice and water surface emissivity variability, and weather conditions, i.e. estimated ice concentration variability not caused by changes in the actual ice concentration.
Because of the algorithms' different sensitivities to the noise, and because the noise has climatic trends, the differences also appear as trends in the sea ice extent trends (Andersen et al., 2007). To minimize these artificial trends caused by noise we must (1) find algorithms with low sensitivities to the atmospheric and surface emissivity variability, (2) correct the brightness temperatures for the properties that we are able to quantify (numerical weather prediction (NWP) data: near-surface wind and air temperature and columnar atmospheric water vapour content), (3) calibrate the algorithms to the actual ice and water signatures using dynamical tie points, and finally (4) quantify the residual uncertainties. The EUMETSAT sea ice concentration climate data record (ESICR) is generated according to these principles, (1)–(4), and is based on the NASA's Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) (1978–1987), the Defense Meteorological Satellite Program's (DMSP) Special Sensor Microwave/Imager (SSM/I) (1987–2009), and the DMSP's Special Sensor Microwave Imager and Sounder (SSMIS) (2003–today) radiometer data. It uses a combination of the Bristol (Smith, 1996) and the Bootstrap (Comiso, 1986) algorithms with dynamical tie points and explicit atmospheric correction using NWP data for error reduction, and it comes with spatially and temporally varying sea ice concentration uncertainty estimates describing the sea ice concentration accuracy.
Dynamical tie points are typical signatures of sea ice and water required to compute the sea ice concentration from the measured brightness temperatures. These are derived on a daily basis for each hemisphere and therefore adjust the algorithms to the current signatures of ice and water (see Sect. 2.1).
The sea ice concentration uncertainty estimates are needed when the ice
concentration data are compared to other datasets or when the ice
concentrations are assimilated into numerical models. The mean accuracy of
some of the more common algorithms used to compute ice concentration from
SSM/I data, such as the NASA Team and Bootstrap, is reported to be 1–6 %
in winter (Steffen and Schweiger, 1991; Emery et al., 1994; Belchansky and
Douglas, 2002). The overall accuracy of the SMMR total ice concentrations is
estimated to be
The ESICR data are available at the EUMETSAT's Ocean and Sea Ice Satellite
Application Facility (OSISAF) home page (
The SMMR instrument on board the Nimbus 7 satellite operated from October
1978 to August 1987 (Gloersen et al., 1992). The instrument had 10 channels
at five frequencies (6.6, 10.7, 18.0, 21.0, 37.0
The SSM/I instruments onboard the DMSP satellites are conically scanning
instruments with seven channels at 19.35v, 19.35h, 22.2h, 37.0v, 37.0h,
85.5v, and 85.5h. The spatial resolution on the ground is
The satellite missions carrying the SMMR, SSM/I, and SSMIS instrument and the periods they cover.
The SSMIS is a continuation of the SSM/I series of instruments onboard the
DMSP satellites but with an extension in the number of channels. SSMIS has
24 channels between 19 and 183
The NWP model meteorological data are used for reduction of the brightness temperatures for atmospheric noise with a radiative transfer model. European Centre for Medium-range Weather Forecast (ECMWF) ERA-40 data are used for the period from 1978 to 2002, and ECMWF data from the operational models are used from 2002 onwards. A description of the ERA-40 meteorological data archive and the reanalysis can be found in Kållberg et al. (2004).
The coarse resolution of the passive microwave brightness temperature
measurements gives rise to an additional uncertainty when sea ice
concentration is computed at finer grid spacing. We call this the smearing
uncertainty and it is estimated using a smearing model (see Sect. 2.4.2).
High-resolution ice concentration data are used as input to the smearing
model: cloud-free and non-calibrated MODIS scenes from the NASA image gallery archive
(
The operational sea ice charts from the US National Ice Center (NIC) are used for comparison with the ESICR sea ice concentration. The ice charts, intended for aiding navigation, are produced on a weekly basis covering all seasons and both the Southern and Northern Hemisphere, and the time series cover the entire climate record period except for the period December 1994 to January 2006 in the Southern Hemisphere. The ice charts used for comparison are a combination of three datasets: (1) the NIC ice charts for the Northern Hemisphere in 1972–2007, available at NSIDC in gridded format (Fetterer and Fowler, 2009), (2) the NIC ice charts for the Southern Hemisphere in 1973–1994, available at the NSIDC (Fetterer, 2006), and (3) the NIC ice charts for both hemispheres from 2006 to 2015, available from NIC.
The more recent ice charts are based partly on satellite Synthetic Aperture Radar (SAR) data, e.g. RADARSAT 1 since 1995 and ENVISAT since 2002, and various scatterometers together with visual/infrared line scanners, e.g. Advanced Very High Resolution Radiometer (AVHRR), MODIS, and Operational Linescan System (OLS), whenever possible for daylight and cloud cover conditions. Also, the passive microwave data from SMMR and SSM/I used in this reprocessing of ice concentrations have been extensively used for making the ice charts, in particular before the launch of wide-swath SAR instruments in 1995. In addition to the satellite data, ice charts are based on information from ships and aircraft reconnaissance. For an ice chart different sea ice categories are delineated manually by polygons and assigned a range of sea ice concentrations, thicknesses, type, etc. found within the polygon by an ice analyst. This information is represented on the satellite pixel grid by averaging the range of ice concentrations and other properties given within the polygon (Dedrick et al., 2001).
Tie points are typical signatures of ice and open water which are used in the ice concentration algorithms as a reference. The tie points are derived by selecting brightness temperatures from regions of known open water and ice.
During winter, in the consolidated pack ice well away from the ice edge, the
ice concentration is very near 100 %. This has been established using
high-resolution SAR data, ship observations, and by comparing the estimates
from different ice concentration algorithms (Andersen et al., 2007). The
apparent fluctuations in the derived ice concentration in the near-100 %
ice regime are primarily attributed to variations in snow/ice surface
emissivity and temperature around the tie-point signature and only
secondarily to actual ice concentration fluctuations. In the marginal ice
zone at intermediate ice concentrations and over open water the atmospheric
emission and wind-induced water surface roughness and smearing dominates as
error sources. The ice concentration algorithm sensitivity to atmospheric and
surface emission is systematic, meaning that different algorithms with
different sensitivity to atmospheric and surface emission can provide very
different trends in sea ice extent on seasonal and decadal timescales
(Andersen et al., 2007). This means not only that does the estimated sea ice
extent has a climatic trend but also that the atmospheric and surface constituents
affecting the microwave emission are changing. In an attempt to compensate
for the influence of these artificial trends, the tie points are derived
dynamically using a window of width
There is no attempt to compensate explicitly for sensor drift or inter-sensor calibration differences (even though the SSM/I data have been intercalibrated by RSS) or possible biases in the NWP fields used for atmospheric noise reduction of the brightness temperatures. The dynamical tie-point method is in principle compensating for these problems in a consistent manner.
Using an emission model, the brightness temperatures are corrected for the
influence of water vapour in the atmosphere and open water surface roughness
caused by wind. The emission model used for atmospheric noise reduction of
the SMMR brightness temperatures,
The representation of atmospheric liquid water column in the NWP data is not
suitable to use for brightness temperature correction because of the spatial
and temporal variability of clouds, which is higher than the model grid cell
size and model time step size. The brightness temperatures are therefore not
corrected for the influence of atmospheric liquid water. Assuming a neutral
atmospheric temperature profile, the wind speed at 10
The analysis of atmospheric sensitivity in Andersen et al. (2006b) showed that the Comiso Bootstrap frequency-mode (CF) algorithm (Comiso, 1986; Comiso et al., 1997) had the lowest sensitivity to atmospheric noise at low ice concentrations. Furthermore, the comparison to high-resolution SAR imagery in Andersen et al. (2007) indicated that among the algorithms using 19 and 37 GHz channels available on both SMMR and SSM/I–SSMIS, the Bristol algorithm (Smith, 1996) had the lowest sensitivity to ice surface emissivity variability. In addition the Bristol algorithm had low sensitivity to atmospheric emission in particular at high ice concentrations.
Consequently, we use a combination of the Bristol algorithm and the CF algorithm – a so-called hybrid algorithm.
The CF algorithm uses
The Bristol algorithm (Smith, 1996) is conceptually similar to the Bootstrap
algorithm. In a three-dimensional scatter plot spanned by
The Bootstrap algorithm is used over open water and the Bristol algorithm is
used over ice. At intermediate concentrations up to 40 % (from the
Bootstrap ice concentration estimate) the ice concentration is an average
weighted linearly between the two algorithms, i.e.
The uncertainties described in the following sections are generally
independent and the squared sum of the two estimated components of
uncertainty is assumed to represent the total uncertainty squared. Each of
the components is quantified as the standard deviation (SD) of sea ice
concentration. The tie-point uncertainty
Both the water surface and ice surface emissivity variability and emission and scattering in the atmosphere affect the brightness temperatures and the computed ice concentrations. To reduce the uncertainties due to atmospheric noise, the brightness temperatures are corrected using NWP data for atmospheric water vapour, near-surface air temperature, and open water roughness caused by wind. The remaining tie-point uncertainties are given as the tie-point ice concentration standard deviation in regions with open water or 100 % ice.
Random instrument noise also results in ice concentration uncertainties. The SSM/I instrument noise results in an ice concentration uncertainty of 1.4 % for the Bristol algorithm and 1.7 % for the Bootstrap algorithm in frequency mode (Andersen et al., 2006a). Systematic sensor drift is critical issue for ice concentration algorithms using static tie points. Here we use inter-sensor calibration and dynamical tie points intended for alleviating problems with sensor drift.
Footprint sizes for the channels used for ice concentration mapping are
uneven and range from about 50–70
The smearing simulation model uses high-resolution brightness temperature input to compute the brightness temperatures as would be measured by the coarse-resolution radiometers on board the satellite. The high-resolution input is compared to the coarse-resolution output and realizations of ice concentrations in the hybrid sea ice concentration algorithm.
Reference SIC is derived from the brightness of cloud-free MODIS scenes
resampled to
The SD of the difference between the simulated SSM/I–SSMIS satellite ice concentration and the reference ice concentration resampled to different grid resolutions in percent.
The MODIS image used for estimating the smearing uncertainty is shown in Fig. 1. The image has regions of open water, intermediate concentrations, and 100 % ice cover. The simulated SSM/I sea ice concentration using Fig. 1 as input to the hybrid OSISAF algorithm is shown in Fig. 2.
The representativeness uncertainty is computed as a function of ice
concentration using a model. The other error sources are computed using the
hemispheric standard deviation of the ice concentration estimates over open
water and over near-100 % ice respectively. The ice concentration
algorithm provides ice concentrations which are greater than 100 % and
less than 0 % because of the natural variability of the measured
brightness temperatures around the ice and open water tie points. These
unphysical concentrations are truncated in the processing. ic is the ice
concentration calculated by the algorithm and
The 1 km cloud-free MODIS image
The simulated ice concentrations using the SSM/I sensor specifications and the OSISAF hybrid ice concentration algorithm and the data in Fig. 1 as input. Ice concentrations between 0 % (black) and 100 % (white).
Using Eq. (2) and assuming the uncertainty for the ice and water part is
independent leads to a total tie-point uncertainty of
The SD of the ice concentrations where NASA Team ice concentrations are
greater than 95 % is
The ice concentration uncertainty is a function of sea ice concentration
(Fig. 3) where the total uncertainty squared is the sum of the two
uncertainty components squared (see Eq. 5). The smearing uncertainty is zero
for open water and for 100 % ice and at these two points on the curve the
total uncertainty equals the tie-point uncertainty (including sensor and
residual atmospheric noise) for open water and ice respectively (see Eqs. 6
and 7). The smearing uncertainty reaches a maximum at intermediate
concentrations between (0 %
The transition from level 2 swath projection data to the final level 4 daily predefined EASE grid includes the gridding of the swath data, the filtering of coast line grid cells, the maximum ice extent masking and spatial and temporal interpolation of data gaps. Whenever a pixel is altered by any of these processing steps it is indicated with a flag value in the product file.
The time window of 24
The total uncertainty in blue and its two components: the smearing uncertainty in red and the tie-point uncertainty in green as a function of ice concentration.
Due to the coarse spatial resolution of the radiometers the data may be
influenced by land up to 70
Occasionally spurious sea ice is detected in open water regions far from the
ice edge due to atmospheric noise affecting the ice concentration estimate.
These spurious sea ice detections are masked out using the monthly maximum
extent climatology by the NSIDC
(
Grid cells with missing data are filled with interpolated values in the level 4 processing and the affected pixels are flagged. Daily data coverage is never complete due to the observation gap near the North Pole (see Sects. 1.1 and 1.2) and occasionally there are missing scan lines and missing orbits. Spatial interpolation can fill small gaps, e.g. one or two missing scan lines but it is deceiving when large areas are missing and filled with interpolated values. To overcome this issue, and also implement a general approach for all cases, both temporal and spatial interpolation is used.
The weighting parameters are computed as follows:
The interpolation on a given date,
The interpolated value at grid cell (i,j) for day
For the SMMR, which was operated every second day, the temporal interpolation
is
We compared the ESICR to sea ice charts for reference during the period from October 1978 to April 2015 in both hemispheres. There is a gap in the comparison in the Southern Hemisphere between 1994 and 2006 (see Sect. 1.5). The overlap period during July and August 1987 between the SMMR and the SSM/I instruments is analysed in more detail in Sect. 3.2.
The ice charts are produced to support ship and offshore operations and not to monitor sea ice as a climate parameter. However, they do well in identifying areas of open water and ice and the comparison does in fact reveal trends in the ESICR noise levels.
The NIC ice charts and the ESICR are gridded onto the 12.5 km EASE grid and compared pixel by pixel. The total concentration in the ice chart is given as the average of the range of sea ice concentrations, e.g. 10–30 %, describing the variability within each ice chart polygon. The bias and SD between ice chart and the ice concentration are computed for ice (ice chart concentration greater than 0 %) and for open water (ice chart concentration equal to zero).
The ESICR ice concentration is higher than the ice chart over open water by
5–15 % in the Northern Hemisphere (Fig. 4). This is due to the fact
that the radiometer ice concentration is affected by atmospheric noise and
smearing near the ice edge, which increases the ESICR ice concentration above
zero, while the ice charts have a nominal value of zero over open water.
Actually, the mean open water ESICR ice concentration is zero at swath level
(level 2). However, all negative ice concentration estimates are truncated to
zero, which leaves the small positive bias in the final product (level 4). The
uncorrected noise from, in particular, cloud liquid water, but also
atmospheric water vapour and wind over open water, gives a positive bias in the
ESICR ice concentrations. The SMMR to SSM/I transition in 1987 is hardly seen
even though the SSM/I 19.35
The Arctic ESICR–NIC ice chart mean difference (bias) for areas of ice in red and for areas of open water in black.
The Arctic ESICR–NIC ice chart standard deviation of the difference for areas of ice in red and for areas of open water in black.
The Antarctic ESICR-NIC ice chart mean difference (bias) for areas of ice in red and for areas of open water in black. No ice charts were available to us from 1994 to 2006.
Both the standard deviation of open water and ice has a clear seasonal cycle with higher standard deviations during summer than during winter (Fig. 5) and the standard deviation of open water has a decreasing trend during the latter part of the record. This could be a result of higher-quality wind and water vapour data in the recent part of the ERA-40 reanalysis and in the operational ECMWF model used since 2002.
There is also a small positive bias over open water in the Southern
Hemisphere due to the truncation of spurious sub-zero ice concentrations in
the ESICR (Fig. 6). Over ice, the ESICR and NIC ice chart difference is
negative around
The standard deviation of the difference between the ESICR and the NIC ice charts (Fig. 7) is higher and has more interannual variability in Antarctica than in the Arctic, except for the comparison over open water where the difference is between 0 and 5 % from 2006 onwards.
The ESICR and NIC ice chart standard deviation of the difference around Antarctica. The red curve is for ice and the black curve is for water. No ice charts were available to us from 1994 to 2006.
The overlapping SMMR–SSM/I difference in the Arctic during summer melt. The red curve shows the ice bias.
The overlapping SMMR–SSM/I difference around Antarctica during austral winter. The red curve shows the ice bias.
The overlap period between SMMR and SSM/I during July and August 1987 is short because 15 days prior and after the actual date are needed in order to establish the tie points properly. Subtracting 15 days in each end of the overlap period leaves only a few days where the tie points are fully established. For the periods where the tie points are not fully developed the tie points for SMMR and for SSM/I cover different time periods and they are therefore expected to differ. In the Northern Hemisphere (Fig. 8) the bias is below 4 % and this may be due to melt ponds with diurnal variability in their signatures and the two instruments' different orbits and data coverage.
The SMMR and SSM/I overlap period coincides with the ice maximum in the Southern Hemisphere, which is ideal for comparison (Fig. 9), and the bias is even smaller than in the Northern Hemisphere (less than 2 %). Inspecting the differences geographically (not shown) indicates that when environmental conditions have not changed significantly during SMMR and SSM/I passes the SSM/I is slightly higher over open water while over ice the two estimates are close to each other.
The uncertainties in the NIC sea ice charts are described in Dedrick et
al. (2001). Another study of the differences between ice charts from
Greenland and Norwegian ice centres covering the same region shows relatively
large (up to 30 %) discrepancies in ice concentration SD of the
difference especially at intermediate concentrations (Breivik et al., 2015).
Compared to microwave radiometer ice concentrations (the OSISAF operational
algorithm in Andersen et al., 2006b) the ice concentration in Greenland ice
charts is systematically about 30 % higher at intermediate
concentrations. Trials with the ice concentration model described in
Sect. 2.5.3 show that the estimates from most sea ice concentration
algorithms including the Bootstrap and the Bristol agree very well with the
actual ice concentration and that there are very small differences between
the overall response of different algorithms (ice concentration differences
The bias between ice charts and radiometer ice concentrations at intermediate concentrations, i.e. near the ice edge and in the marginal ice zone, can be caused by two effects: (1) the estimated radiometer ice concentrations are lower than real ice concentration for new ice and lower when the surface is melting or refrozen after melting. Both new ice and melting/refreezing is abundant in regions with intermediate ice concentrations and this will thus lead to the radiometer underestimating the real ice concentration. A hybrid algorithm such as OSISAF mitigates biases due to melting/refreezing to some extent but usage of hemispheric tie points cannot account for existing regional differences in melt progress. (2) The ice charts' ice concentration is a subjective estimate which is made for the safety of navigation and an overestimation of the ice concentration in the ice chart, particularly near the ice edge and in the marginal ice zone, might stem from “better-safe-than-sorry” practices within the ice charting community.
The differences between sea ice climate data records from the same set of
satellite microwave radiometer data (SMMR, SSM/I and SSMIS) are primarily due
to different spatial resolution, land masks and land spill over correction
methodologies, and different ice concentration thresholds for delineating the
sea ice extent. The choice of sea ice concentration algorithms and
atmospheric correction methods also influences the sea ice extent
estimate (Kern et al., 2014). The NSIDC sea ice extent uses the NASA Team
sea ice concentration algorithm and a 15 % threshold for delineating the
sea ice extent. The land masks are similar to the ones used in the ESICR. The
mean monthly sea ice extent from the NSIDC is shown together with the ESICR
in Table 3a and b for comparison. In the Arctic (Table 3a) the differences
between the NSIDC and the ESICR data records are small (less than
0.4
In the following we give examples of the ESICR dataset for estimating sea ice climate statistics and trends. The applied climate period here is the full length of the ESICR from October 1978 to the end of 2014. We give examples for both hemispheres.
In this context, the sea ice extent is defined as the area covered by sea ice within the ice edge. The ice edge is defined as the 30 % contour. Ice concentrations greater than 30 % are considered as ice covered while concentrations less than 30 % are considered open water. This threshold is higher than e.g. the 15 % threshold used in Parkinson and Cavalieri (2008). The higher threshold is needed here because we are not using weather filters in the processing and therefore there may be more noise over open water resulting in an unwanted overestimation of the ice extent. The noise level over open water depends on the success of the Tb correction, i.e. partly on the quality of the NWP data, and the levels of cloud liquid water, which we cannot yet correct for.
For the Arctic there is a negative trend in the monthly mean extent for all
months of the year (Table 3a). The negative slope is largest in September
(
The mean sea ice extent for the Arctic for years 1979 through 2014 is shown in Fig. 10 together with the September 2012 sea ice extent. The lower two panels display the seasonal variability of the sea ice extent and the long-term mean monthly sea ice extent in March and in September, the months with maximum and minimum extent respectively. In this panel we have included the extent for the most recent 11 years of ESICR (2004–2014) for comparison. September 2012 was the lowest sea ice extent on record in the Arctic since beginning of the satellite era. Over the 35 years of ESICR there is a negative trend in sea ice extent for all months of the year with the largest negative trend during the summer and the beginning of autumn (July–October).
The upper panel: the September 2012 sea ice extent in the Arctic compared to the mean extent shown with the red line. The blue lines on either side of the mean extent line (red) are the 5 and 95 percentiles of ice extent. The lower two panels show the annual cycle of sea ice extent. The shaded areas are the 5 and 95 % percentiles of the interannual and daily variability respectively. The lower panel show the long-term (1978–2014) Arctic sea ice extent near its maximum in March and near its minimum in September.
The upper panel: the September 2012 sea ice extent in the Antarctic compared to the mean extent shown with the red line. The blue lines on either side of the mean extent line are the 5 and 95 percentiles of ice extent. The lower two panels show the annual cycle of sea ice extent. The shaded areas are the 5 and 95 % percentiles of the interannual and daily variability respectively. The lower panel show the long-term (1978–2014) Antarctic sea ice extent near its maximum in March and near its minimum in September.
The linear trend in open water days in the Arctic (1978–2014).
The probability that the trend in Fig. 12 is not significant (test
of the null hypothesis). A low value (
The linear trend in open water days in the Antarctic (1978–2014).
The probability that the trend in Fig. 14 is not significant (test
of the null hypothesis). A low value (
The mean sea ice extent for the Antarctic for years 1979 through 2014 is shown together with the September 2012 sea ice extent in Fig. 11. The lower two panels show the seasonal variability of the sea ice extent and the long-term mean monthly sea ice extent in March and in September. The sea ice extent has experienced an overall positive trend around Antarctica, especially along the ice edge in the Weddell and Ross Seas downstream of the northward branches of the cyclonic atmospheric circulation.
In order to assess the length of the ice season for a given pixel, the annual spatial distribution of dates of freeze-up and break-up were calculated using a simple methodology, the results of which are comparable to Parkinson (2014). The freeze-up date for a given point is defined as the date when the sea ice concentration exceeds 30 % and remains so for at least 5 days. Similarly, the break-up date for a given point is defined as the date when the sea ice concentration falls from above to below 30 % and remains so for at least 5 days.
Since the sea ice does not retreat and expand completely every year, not all areas experience the same number of freeze-ups and break-ups over an equal period of years. Therefore, some regions may experience relatively few freeze-ups and break-ups, thus reducing the confidence in the trend of the region. As a consequence, only areas having experienced more than six freeze-ups/break-ups are considered.
The open water days are calculated as the difference in days between freeze-up and break-up and the decadal trends in the open water days are shown in Fig. 12 for the Arctic and in Fig. 14 for the Antarctic.
In the Arctic, over the record of 35 years the number of open water days has been increasing by at least 60 days in the Davis Strait and in large parts of the Barents Sea. The ice season (the opposite of open water days) has been shortening consistently all over the Arctic except in the Bering Strait region and the Greenland Sea (Fig. 12). The negative trend in the Greenland Sea is not significant and based on an insufficient number of data points. In fact, the large areas with new ice formation which used to characterize the ice cover in Greenland Sea have appeared rarely since 2000 (Tonboe and Toudal, 2005; Rogers and Hung, 2008). The shortening of the ice season in the Arctic in general is due both to a delay of the freeze-up and earlier break-up in combination (not shown). This is consistent with e.g. Close et al. (2015).
The significance of the trends in the number of open water days is shown in Figs. 13 and 15 for the Arctic and Antarctic, respectively, as a test of the null hypothesis, i.e. testing the probability of no trend. This means that a low probability indicates that the trend is in fact significant. It is noted that the trend is significant in most Arctic regions (Fig. 13). There is a negative decadal trend in the number of open water days around Antarctica in regions with a seasonal sea ice cover (Fig. 14), except in the Bellingshausen Sea/Amundsen Sea and the Indian Ocean. The trend is significant in large regions in the Weddell Sea and in the Ross Sea (Fig. 15). The negative trend in the number of open water days in the Ross and in the Weddell Seas indicates that the ice is staying longer in these areas now than before.
A sea ice climate record covering the period from autumn 1978 to
the end of 2014 has been produced based on past satellite microwave
radiometer data from SMMR, SSM/I, and SSMIS. The climate record has been
produced according to four principles to ensure consistency and to minimize
the sensitivity to noise sources:
Finding algorithms with low sensitivities to geophysical noise. Two
algorithms have been selected in combination based on the evaluation in
Andersen et al. (2007), the Bristol over ice, and the Bootstrap in frequency
mode over open water. An independent evaluation of algorithms in Ivanova et
al. (2015) pointed at the same two algorithms. Regional error reduction correcting the brightness temperatures for
water vapour in the atmosphere and wind over open water. The scheme described
in Andersen et al. (2006b) is used to reduce the noise over both ice and
water. Calibrate the algorithms to the actual ice and water signatures and
sensor drift using dynamical tie points. The result of using dynamical
tie points has been demonstrated here at the transition from SMMR to SSM/I
with satisfactory results. In addition, we do not see any jumps at sensor
transitions in the comparison to the independent ice chart dataset. Quantify the residual uncertainties. A forward model for the residual
uncertainties has been developed and applied. The total uncertainty as a
combination of the tie-point variability and the representativeness
uncertainty is a function of the ice concentration and it is applied on each
individual measurement.
It is clear that the sea ice covers in both hemispheres have undergone large
changes over the 35-year period. In the Arctic the linear trend at sea ice
minimum month in September is
Around Antarctica there has been an increase of the total sea ice extent during all months especially downstream of the Weddell Sea and in the Ross Seas. However, there are regional differences and the ice extent has decreased along the Antarctic Peninsula in the Bellinghausen and the Amundsen Seas.
The sea ice climate record will be updated at irregular intervals. The next update is planned for autumn 2016. It will include development from the ESA sea ice climate change initiative project working towards improved sea ice climate record methodologies (Ivanova et al., 2015).
In addition, the daily near-real-time OSISAF sea ice concentration product and the ESICR are using the same algorithms and similar methodologies. One of the differences is related to the tie-point selection period, which is either the last 30 days (near-real-time) or 15 days before and after (ESICR).
In order to extend the sea ice climate record with past data the possibility of to retrieving the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR) 19 GHz swath data from 1972 to 1977 is being investigated. These single channel data are significantly different from SMMR and SSM/I–SSMIS data and a new sea ice algorithm would have to be used.
The sea ice concentration climate data record is available for download from
Tonboe et al. (2015a,
We would like to thank Irene Rubinstein, Walter Meier, and Georg Heygster for their constructive and helpful comments on the manuscript. The work was completed with support from EUMETSAT's Ocean and Sea Ice Satellite Application Facility. Stefan Kern acknowledges support given by the Center of Excellence for Climate System Analysis and Prediction (CliSAP). The SMMR data were provided by the NSIDC, the SSM/I data by Remote Sensing Systems, the numerical weather prediction model data by the ECMWF, and the SSMIS data were processed at NOAA. The ice chart data are from the US National Ice Center and NSIDC and the sea ice extent data used for comparison were provided by the NSIDC. Edited by: C. Haas Reviewed by: G. Heygster, W. Meier, and I. Rubinstein