Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.
From a perspective of climate change, it is important to know how fast the total volume of sea ice is changing. In addition to sea ice thickness (Kern et al., 2015), this requires reliable estimates of sea ice concentration (SIC). Consistency in sea ice climate records is crucial for understanding of internal variability and external forcing (e.g. Notz and Marotzke, 2012) in the observed sea ice retreat in the Arctic (Cavalieri and Parkinson, 2012) and expansion in the Antarctic (Parkinson and Cavalieri, 2012).
Accuracy and precision serve as measures of performance of a SIC algorithm.
Accuracy (expressed by bias) is the difference between the mean retrieval and
the true value. Precision (expressed by standard deviation, SD) is the range
within which repeated retrievals of the same quantity scatter around the mean
value (see also Brucker et al., 2014, where precision is addressed in
detail). The average accuracy of commonly known algorithms, such as NASA Team
(Cavalieri et al., 1984) and Bootstrap (Comiso, 1986), is reported to be
within
In this study we focus on the following four error sources, to which the
algorithms have different responses: (1) sensitivity to emissivity and
physical temperature of sea ice, (2) atmospheric effects, (3) melt ponds, and
(4) thin ice. The sensitivity to emissivity and physical temperature of sea
ice depends on the selection of input brightness temperatures (Tbs) available
at electromagnetic frequencies between 6 and near 90
The second error source is represented by atmospheric effects, such as water vapour, cloud liquid water (CLW) and wind roughening of the water surface. It causes the observed Tb to increase and to change as a function of polarisation and frequency, season and location (Andersen et al., 2006). This effect is usually larger during summer and early fall and over open water (also in the marginal ice zone) because of the larger amounts of water vapour and CLW in the atmosphere, and generally more open water areas present.
Algorithms with different sensitivities to surface emissivity and atmospheric effects produce different estimates of trends in sea ice area and extent on seasonal and decadal time scales (Andersen et al., 2007). Effect of diurnal, regional and inter-annual variability of atmospheric forcing on surface microwave emissivity was also reported in a model study of Willmes et al. (2014). This means that not only sea ice area has a climatic trend, but atmospheric and surface parameters affecting the microwave emission may also have a trend. Such parameters can be wind patterns, atmospheric water vapour and CLW (Wentz et al., 2007), snow depth and snow properties, and the fraction of multi-year ice (MYI).
However, some algorithms are less sensitive than others to these effects (Andersen et al., 2006; Oelke, 1997), and it is thus important to select an algorithm with low sensitivity to them. It is particularly important to have low sensitivity to error sources which are currently impossible to correct for, e.g. extinction and emission by CLW or sea ice emissivity variability. We therefore designed a set of experiments to test a number of aspects related to SIC algorithm performance, and ultimately to allow us to select an optimal algorithm for retrieval of a SIC climate data record.
Melt ponds on Arctic summer sea ice represent an additional source of errors
due to their microwave radiometric signatures being similar to open water.
Virtually all SIC algorithms based on the passive microwave channels around
19, 37, and 90
Thin ice is known to be another challenge for the passive microwave
algorithms as they underestimate SIC in such areas (Heygster et al., 2014;
Kwok et al., 2007; Cavalieri, 1994). Recent studies of aerial
(Naoki et al., 2008) and satellite (Heygster et al., 2014) passive microwave
measurements show an increase in Tb with sea ice thickness
(
For the first time this many (30) SIC algorithms are evaluated in a consistent and systematic manner including both hemispheres, and their performance tested with regard to high and low SIC, areas with melt ponds, thin ice, atmospheric influence and tie points; and covering the observing characteristics of the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I) and AMSR-E. The novelty of the presented approach to algorithm inter-comparison is in the implementation of all the algorithms with the same tie points, which helps to avoid subjective tuning, and without applying weather filters, which have their weaknesses (also addressed in this study). When evaluating the algorithms, we have focused in particular on achieving low sensitivity to the error sources over ice and open water, performance in areas covered by melt ponds in summer and thin ice in autumn. We suggest that an optimal algorithm should be adaptable to using: (1) dynamic tie points in order to reduce inter-instrument biases and sensitivity to error sources with potential climatological trends and/or seasonal and inter-annual variations and (2) regional error reduction using meteorological data and forward models.
The algorithms' evaluation of algorithms was carried out in the context of
European Space Agency Climate Change Initiative, Sea Ice (ESA SICCI) and is
described in the following sections. Section 2 describes the algorithms and
the basis for selection of the 13 algorithms to be shown in the
following sections. Section 3 describes the data and methods. Section 4
presents the main results of the work: the inter-comparison and evaluation of the selected algorithms, suggested atmospheric correction and dynamic tie points approach.
All the input data and obtained results are collocated and composed into a
reference data set called round robin data package (RRDP). This is done in
order to achieve equal treatment of all the algorithms during the
inter-comparison and evaluation, as well as to provide an opportunity for
further tests in a consistent manner. This data set is available from the
Integrated Climate Data Center (ICDC,
During the experiment, we implemented 30 SIC algorithms and found that they can be grouped according to the selection of channels and how these are used in each algorithm. We also found that algorithms within each group had very similar sensitivity to atmospheric effects and surface emissivity variations. This is in agreement with sensitivity studies (Tonboe, 2010; Tonboe et al., 2011) using simulated Tbs generated by combining a thermodynamic ice/snow model to the microwave emissivity model for layered snow packs (MEMLS) (Wiesmann and Mätzler, 1999; Tonboe et al., 2006). To avoid redundancy we only include here a selection of 13 sea ice algorithms (Table 1), which were chosen as representatives of the groups.
The sea ice concentration (SIC) algorithms shown in this study.
The first group of algorithms, represented by Bootstrap polarisation mode
(BP, Comiso, 1986), includes polarisation algorithms. These algorithms
primarily use 19 or 37 GHz polarisation difference (difference between Tbs
in vertical and horizontal polarisations of the same frequency) or
polarisation ratio (polarisation difference divided by the sum of the two
Tbs). The next group uses 19V and 37V channels and is represented here by
CalVal (CV, Ramseier, 1991). Commonly known algorithms in this group are
NORSEX (Svendsen et al., 1983), Bootstrap frequency mode (BF, Comiso, 1986)
and UMass-AES (Swift et al., 1985). Bristol (BR, Smith, 1996) represents the
group that uses both polarisation and spectral gradient information from the
channels 19V, 37V and 37H. The NASA Team algorithm (NT, Cavalieri et
al., 1984) uses the polarisation ratio at 19
All the algorithms were evaluated without applying open water/weather filters, since our aim was a comparison of the algorithms themselves. We consider performance of an open water/weather filter separately in Sect. 4.4.
A necessary parameter for practically every algorithm is a set of tie points – typical Tbs of sea ice (100 % SIC) and open water (0 % SIC). Under certain conditions, such as wind-roughened water surface or thin sea ice, it is difficult to define a single tie point to represent the surface. In nature, Tb may have a range of variability for the same ice type or open water due to varying emissivity, atmospheric conditions, and temperature of the emitting layer. Therefore the scatter of retrieved SIC near the tie points, which correspond to 0 and 100 %, may lead to negative or larger than 100 % SICs. Instead of using a set of single tie points to represent the radiometric values (e.g., brightness temperature) for each surface type, the input to the ECICE algorithm is a set of probability distributions of the radiometric observations. Some 1000 sets are randomly and simultaneously selected from the distributions. The optimal solution for SIC is then obtained using each set, and the final solution is found based on a statistical criterion that combines the 1000 possible solutions (see Shokr et al., 2008 for details).
In order to perform a fair comparison of the algorithms, we developed a special set of tie points (Appendix A) based on the RRDP for both hemispheres and for each of the three radiometers: AMSR-E, SSM/I and SMMR. This enabled us to exclude differences between the algorithms caused by different tie points and thus compare the algorithms directly. The set of the RRDP tie points differs from the original tie points provided with the algorithms. This is caused by the fact that we use different versions of the satellite data, which may have different calibrations. Also, the tie points published with the algorithms are typically valid for one instrument and need to be derived for each new sensor. In this study the RRDP tie points were used for all the algorithms except ASI, NASA Team 2 and ECICE, where such traditional tie points were not applicable, and therefore the original implementations of these algorithms were used.
Single swath Tbs were used as input to the algorithms. The SMMR data were
obtained from the US National Snow and Ice Data Centre – NSIDC (25 October
1978 to 20 August 1987, Njoku, 2003), EUMETSAT CM-SAF provided the SSM/I data
(covering 9 July 1987 to 31 December 2008, Fennig et al., 2013), and AMSR-E
data were from NSIDC (from 19 June 2002 to 3 October 2011; Ashcroft and
Wentz, 2003). The footprints of all the channels were matched and projected
onto the following footprints: the 6 GHz footprint of
Coverage graphs for the SSM/I subset of the Northern Hemisphere's round robin data package (RRDP) in winters 2007 and 2008. Both brightness temperature (Tb) and spatial coverage are displayed. Open water (OW) and closed ice (CI) locations are shown by triangle circle symbols respectively. In the Tb diagrams, the OW symbols are coloured according to Tb22V values (left colour scale), while the CI symbols are coloured according to Tb37H values (right colour scale) (also in the embedded map). Solid and dashed lines show ice and OW lines respectively. FYI – first year ice, MYI – multi-year ice.
It is important to note that different Tb data sets may have different calibration (an operation used to convert the radiometer counts into Tbs), and this can even be the case for different versions of the same data set. Therefore the results presented in the following (especially the derived tie points) should be applied to other data sets with caution.
Ideally, every algorithm should be evaluated over open water, at intermediate
concentrations and over 100 % ice cover. In practice, it is difficult to
find high quality reference data at intermediate concentrations, especially
over the entire satellite footprint (e.g.,
For the open water (OW) validation data set (SIC
To create the closed ice (CI) validation data set (SIC
Same as Fig. 1, but in the Southern Hemisphere.
Figure 1 (Northern Hemisphere) and Fig. 2 (Southern Hemisphere) show the coverage of a subset of the RRDP for the SSM/I instrument during winters of 2007 and 2008, which contains about 30 000 data points. The data set also includes the areas where there normally should not be any ice (blue triangles in the left panels of the figures) in order to test the ability of the algorithms to capture these correctly. The coverage of the RRDP is displayed both in terms of Tbs in the six channels of the SSM/I instrument (main panels), and spatial distribution (embedded maps). The other years, mentioned above and not shown in the figures, include approximately 4000 data points per year, except the SMMR period with about 1000 points per year, but the full data set extends from 1978 to 2011. We are confident that these locations represent the full amplitude of weather influence on measured Tbs and hence retrieved SICs. The left panels of Figs. 1 and 2 show the RRDP SSM/I subset in a classic (Tb37V, Tb19V)-space, which is the one sustaining the BF algorithm (or CV). The ice line extends along different ice types. In the Northern Hemisphere, ice types vary from MYI with lower values of Tb37H (colouring) to first-year ice (FYI) with higher values of Tb37H. In the Southern Hemisphere, the ice line extends between ice types A, representing FYI, and B, sea ice with a heavy snow cover (Gloersen et al., 1992). The so-called FYI and MYI tie points would typically lie along this line. The location of these different ice types can be seen on the embedded maps, and matches the expected distribution of older and younger ice in the Northern Hemisphere. In the (Tb37V, Tb19V)-space, the OW symbols are grouped mostly in one point (OW tie point), but also present some spread due to the noise induced by geophysical parameters such as atmospheric water vapour, liquid water- and ice clouds, surface temperature variability and surface roughening by wind (all collectively called geophysical noise). Note that the majority of the symbols is grouped around one point and a lot less are spread along the line; however this is not easy to see from the plots because many points are hidden behind each other. The Tb22V colouring of the OW symbols illustrates how the variability of the OW signature is mostly driven by factors impacting also the 22 GHz channel (atmospheric water vapour content). The length and orientation of the OW spread, and especially the distance from the OW points to the line of ice points, determines the strength of algorithms built on these frequencies (e.g. BF or CV) at low SIC.
The right panels show the same areas but in a (Tb85V, Tb85H)-space. The ice line is very well defined (limited lateral spread), almost with a slope of one. However, it is difficult to define an OW point in this axis, since samples are now spread along a line. This “weather line” even intersects the ice line, illustrating that algorithms based purely in the (Tb85V, Tb85H)-space (like the ASI and N90 algorithms) have difficulties at discriminating open water from sea ice under certain atmospheric conditions (Kern, 2004).
The embedded maps display the winter location of the OW samples (same
location for the whole RRDP, for all instruments). In both hemispheres, these
locations follow sea ice retreat in summer months to always capture
ocean/atmosphere conditions in the vicinity of sea ice (not shown). The
absence of data near the North Pole is due to the ENVISAT ASAR not covering
areas north of
After validation of the algorithms using the obtained data sets at 0 and 100 % we found that some of the algorithms are hard to validate at these values because they are not designed to enable retrievals outside the SIC range of 0–100 % (NASA Team2, ECICE) or are affected by a combination of large bias and nonlinearity at high SIC (ASI). This complicates comparison of these algorithms directly to other algorithms because these effects cut part of the SD of the retrieved SIC, while we aim at evaluating the full variability around these reference values (0 and 100 %). We implemented the algorithms (except these three) without cut-offs, thus allowing SIC values below 0 % and above 100 % as well. In order to be able to include these three algorithms in the inter-comparison, we have produced reference data sets of Tbs in every channel that correspond to values of SIC 15 and 75 % for an additional evaluation. We find that the algorithms' performance at 15 % is representative of that at 0 %, and so is 75 % representative of 100 %. Therefore we show the results of evaluation only at SIC 15 and 75 %. By “representative” here we mean that the algorithms' ranking does not change significantly (more details in Sect. 4.1. and Table 2) even though the absolute values of SD are different.
The SIC 15 % data set was constructed by mixing the average FYI signature
(Tb) with the OW data set, i.e.
The SIC 75 % data set was generated similarly to the SIC 15 % data
set, but with full variability of ice and 25 % of the average OW
signature:
It is noteworthy that we originally had designed a reference data set of SIC 85 %, but the positive biases of the ASI and NASA Team 2 algorithms were larger than 15 % and thus part of the SD was still cut-off at 100 %. Therefore it was necessary to use a SIC 75 % data set instead. The performance of the algorithms was consistent between the SIC 75, 85, and 100 % data sets, and therefore we consider such substitution acceptable. This way of mixing Tbs is not entirely physical since we are mixing Tbs seen through two different atmospheres. However, since the majority of the signal originates from either open water or ice, and we use fixed Tbs for the remaining fraction, we consider the results to be still reasonably representative for algorithm performance evaluation.
Normally, SIC products are truncated at 0 and 100 % to allow only
physically meaningful SIC values, though this does not apply to ECICE because
it employs the inequality constraint of 0 %
A daily gridded SIC and melt pond fraction (MPF) reference data set for the
Arctic (Rösel et al., 2012a) was derived from clear-sky measurements of
reflectances in channels 1, 3 and 4 of the MODerate resolution Imaging
Spectroradiometer (MODIS) in June–August 2009. The MPF is determined from
classification based on a mixed-pixel approach. It is assumed that the
reflectance measured over each MODIS
The MODIS data were corrected for bias (Mäkynen et al., 2014) based on an inter-comparison between ENVISAT ASAR wide swath mode (WSM) imagery, in situ sea ice surface observations, weather station reports and the daily MODIS MPF and SIC data set. It was found that the MODIS SIC was negatively biased by 3 % and MPF was positively biased by 8 %. An investigation of the 8-day composite data set of the MODIS MPF and SIC data set with regard to their seasonal development during late spring/early summer confirmed the existence of such biases.
MODIS SIC was only used for the summer period to evaluate the algorithms' performance over melt ponds, but not for the SIC validation. This is due to the lack of a sufficiently quality-controlled MODIS SIC product with potential of a validation data set. The cloud filters developed for lower latitudes are not reliable enough in the polar latitudes. Moreover, identification of ice/water in the images depends on thresholds, which will bring the problem of tie points. The validation of the MPF data set by Rösel et al. (2012a) revealed accuracy of 5–10 %. Because of the methodology used, the MPF is tied to the other two surface types: open water in leads and openings between the ice floes and sea ice/snow. Therefore it can be assumed that the accuracy of the fraction of these two other surface types is of the same magnitude as that of the MPF: 5–10 %, which can be considered as insufficient for quantitative SIC evaluation.
Sensitivity of the algorithms to thickness of thin (
SIC retrievals can be contaminated due to wind roughening of the ocean
surface, atmospheric water vapour and CLW, as well as precipitation.
Traditionally, the atmospheric effects on the SIC retrievals are removed by
applying an open water/weather filter based on gradient ratios of Tbs for
SMMR (Gloersen and Cavalieri, 1986) and SSM/I (Cavalieri et al., 1995):
Therefore we chose not to use the open water/weather filters, but implement
an alternative solution, following Andersen et al. (2006) and Kern (2004).
The suggested method consists of applying a more direct atmospheric
correction methodology, where the input SSM/I Tbs in all the channels used by
the algorithms are corrected with regard to atmospheric and surface effects
using a radiative transfer model (RTM):
In order to evaluate the effect of suggested atmospheric correction for SSM/I we selected six test cites in the Arctic, which are subject to different weather types: for some it is more common to have storms and strong winds, and some are typically quieter. The total amount of points sampled at these locations is 2320 and covers the entire year 2008. The results obtained were similar for AMSR-E (not shown here).
Tbs from the three microwave radiometer instruments (AMSR-E, SSM/I and SMMR, Sect. 3.1) were extracted and collocated with the reference data sets introduced above for open water, closed ice, melt ponds, and thin ice in the RRDP. These Tb data were then used as input to the SIC algorithms.
The criteria for the validation and evaluation procedure were aimed at
minimising the sensitivity to the atmospheric effects and surface emissivity
variations as described in the Introduction. In addition, we considered the
following aspects: (1) data record length: algorithms using near 90 GHz
channels cannot be used before 1991 when the first functional SSM/I 85 GHz
radiometer started to provide consistent data, (2) spatial resolution: ranges
from over 100
To evaluate performance of the algorithms, SD (Table 2) and bias relative to
the validation data sets (Sect. 3.2) were calculated for summer and winter
separately. The algorithms in Table 2 are sorted by the average SD of all the
cases, starting with the smallest one. These values are averages weighted by
the number of years when data were available for each instrument, thus giving
more weight to SSM/I as the one providing the longest data set. SSM/I data
cover 21 years (1988–2008) for low-frequency algorithms, i.e. the algorithms
using frequencies up to 37
The high-frequency algorithms ASI and N90 have a clear difference in SDs at
low and high SIC. This is also true for the CV
We chose not to show the bias in detail here because it was found to be
sensitive to the choice of tie points. Since we thus were able to eliminate
the bias for those algorithms which allowed implementation of the same set of
tie points, we put more weight on SD in the algorithm evaluation. In the
Northern Hemisphere, stronger negative biases were dominated by the high SIC
cases (with the exception of the N90, CV
At SIC 15 % the CV (BF) algorithm had the second lowest SD (3.8 % in the Northern Hemisphere and 3.5 % in the Southern Hemisphere) after the 6H algorithm. Even though the 6H showed such a low SD, we did not consider it as a suitable algorithm for a climate data set because this algorithm could not be applied to SSM/I data, which shortens the time series significantly. At SIC 75 % the BR algorithm had the lowest SD of 3.1 % in the Northern Hemisphere and 2.9 % in the Southern Hemisphere.
The difference in SD between summer and winter (only SIC 15 %) was lowest
for the algorithms NT, NT
The SIC and MPF from MODIS were collocated with daily SIC retrieved by the
algorithms in the Arctic Ocean for June–August 2009 to investigate the
sensitivity of the algorithms to melt ponds. Due to the low penetration
depth, we expect that passive microwave SIC algorithms interpret melt ponds
as open water and hence in summer they provide the net ice surface fraction
(
There is a pronounced overestimation of the net ice surface fraction by the
CV and BR algorithms that compose the OSISAF combination (however only BR is
used for high SIC). For example, at
Sea ice concentration (SIC) in % (
Sea ice concentration (SIC) calculated by the SIC algorithms (shown
in colours) as a function of SMOS sea ice thickness (SIT) in areas of the
Arctic Ocean, which are known to be
The sensitivity of selected SIC algorithms (CV, BR, OSISAF, N90, NT and 6H) to thin sea ice thickness was investigated. Figure 4 shows SIC obtained by these algorithms as a function of sea ice thickness from SMOS (Sect. 3.4). The data are shown as averages for each sea ice thickness bin of 5 cm width with the number of measurements in each bin shown on the figure (total number of measurements is 991). The grey shading shows SD, which is calculated from all the SIC retrievals in the given bin. These SDs are calculated for each algorithm individually, but overlap each other on the figure. Since in the OSISAF combination the BR algorithm has weight of 1 for high SIC, these algorithms show identical results; therefore BR is not visible.
The SIC is known to be
Out of the five algorithms shown, N90 levels off, that is the SIC value
varies by less than 5 % between the neighbouring bins of SIT, at the
lowest thicknesses (20–25
Demonstration of the open water/weather filter performance: gradient ratio (GR) 19/22 is plotted as a function of GR19/37 for SSM/I data in 2008 (entire year) for the Northern Hemisphere for sea ice concentration (SIC) of 0, 15, 20, and 30 %. The red square shows the value range outside which the open water/weather filter sets SIC values to 0 % (open water).
First we implemented traditional open water/weather filters (Eqs. 3 and 4), which work as ice-water classifiers. These filters set pixels to SIC 0 % when they are classified as ones subjected to a high atmospheric influence over open water. This efficiently removes noise due to the weather influence in open water regions.
However, we found, as did also Andersen et al. (2006), that open
water/weather filters also eliminate low concentration ice (up to 30 %).
This is illustrated in Fig. 5, where intermediate concentration data sets
were generated using equations similar to Eq. (1) from the same Tbs as used
for the algorithms' inter-comparison (Sect. 4.1). The filter correctly identifies the pixels, which do not contain any ice (SIC
In order to avoid this truncation of real SIC by the open water/weather filter, we investigated an alternative approach where we applied atmospheric correction to the Tbs, as described in Sect. 3.5, before using them as input to the algorithms. The correction reduced the Tb variance by 22–35 % (19 and 37 GHz channels) and up to 40 % (near 90 GHz channels) when water vapour, wind speed and 2 m temperature were used in the correction scheme. Adding CLW as the fourth parameter worsened the results (19 and 37 GHz channels). CLW has high spatial and temporal variability and the current ERA Interim resolution and performance for CLW is not suitable for this correction. In the following the satellite data are therefore not corrected for the influence of CLW.
Histograms for SSM/I sea ice concentration (SIC) obtained by the
OSISAF algorithm over open water (SIC
To illustrate the effect of the correction, we compared the SD of SIC computed from Tbs with and without correction for water vapour, wind speed and 2 m temperature (Fig. 6). The top plots show histograms of the SIC over open water for the OSISAF algorithm before the correction (left) and after (right). The distribution becomes clearly less noisy and tends to be more Gaussian-shaped. To show the effect of the correction on performance of all the algorithms (Table 1, except NT2 and ECICE), the SD of SIC is shown in the bottom plot. The SD has decreased by 48–65 % (of the original value) after the atmospheric correction for all the shown algorithms. The improvement due to the RTM correction shown in Fig. 6 is an average measure for all the 2320 samples. It should be noted that the tie points need to be adjusted to the atmospherically corrected data. The tie points given in Appendix A are for uncorrected data.
As mentioned in the Introduction, not only sea ice area/extent is characterised by seasonal variability and has a trend, but so do also atmospheric and surface effects influencing the measured microwave emission. In order to compensate for these effects, we suggest that in an optimal approach tie points should be derived dynamically.
In order to generate dynamically adjusted daily tie points we first define
the sampling areas for consolidated ice and open water at a distance of
100
Then 5000 Tb measurements (every day) in these areas were randomly selected
among the total of 15 000 data points and averaged using a 15-day running
window (
An example of an ice tie point is shown in Fig. 7 by Tb19V and Tb37V (top and
middle panels) and slope of the ice line according to the Bootstrap scheme
(bottom panels). We chose to not show the tie points of the Bristol algorithm
because the polarisation and frequency information from 19V, 37V and 37H
channels is transformed into a 2-D plane defined by
Figure 7 demonstrates that the tie points are not constant values as it is
assumed traditionally (static tie points from the RRDP, also averaged FYI and
MYI values, are shown by horizontal lines), but rather geophysical parameters
showing seasonal and inter-annual variations. This applies particularly to
the melt season, which is highlighted by the grey vertical bars for three
selected years in Fig. 7, bottom plots. Therefore the dynamic approach is
more suitable for the SIC algorithms. The ice tie point may vary by about
30
A detailed description of the procedure to obtain dynamic tie points is given in the Appendix B. The tie points will vary with calibration of the input data/version number and source, so the tie points obtained here should not be used with other versions of the input data with potentially different calibration. The procedure on the other hand can be applied to all versions/calibrations of the input data.
Examples of tie points time series for the Bootstrap F algorithm in the Northern Hemisphere (left panels) and in the Southern Hemisphere (right panels) (marked nh and sh respectively). Upper and middle panels show ice tie points Tb19V and Tb37V (brightness temperatures in 19V and 37V channels) respectively, and bottom panels show slopes. The vertical bars in light grey to dark grey colours denote the progressing melt season from May to September in the Northern and from November to March in the Southern Hemisphere.
Based on validation data sets of SIC 15 and 75 % we used variability (SD)
in the SIC produced by the different algorithms as a measure of the
sensitivity to geophysical error sources and instrumental noise. The errors
from geophysical sources over open water are generated by wind induced
surface roughness, surface and atmospheric temperature variability and
atmospheric water vapour and CLW. Over ice, the errors are dominated by snow
and ice emissivity and temperature variability, where parameters such as snow
depth, and to some extent variability in snow density and ice emissivity are
important (Tonboe and Andersen, 2004). The atmosphere plays only a minor role
over ice except at near 90
The algorithms 6H, CV, BR, OSISAF, NT and NT
The differences in SDs between summer and winter are reflecting the
sensitivity of different algorithms to wind, atmospheric humidity and other
seasonally changing quantities. In addition, some of these quantities may
have climatological trends. Therefore, small difference between the summer and
winter SDs is an asset for an algorithm. The algorithms NT, NT
Note that the two modes of the Bootstrap algorithm in this study were tested
separately. The frequency mode (BF) of the original algorithm is applied only
when Tb19V is below the ice line minus 5
Evaluation of typical processing chain components, such as climatological masks, land contamination correction and gridding from swath to daily maps, is not covered by this study. This work is devoted to a systematic evaluation of algorithms using a limited but very accurate reference data set (the RRDP). For the consistent evaluation exercise completed here, areas in the vicinity of land were excluded.
During the algorithm evaluation and inter-comparison exercise the SICCI
algorithm was introduced. It is a slightly modified version of the OSISAF
algorithm in order to achieve better performance over areas with thin ice.
Similar to the OSISAF algorithm, it is constructed as a weighted combination
of CV and BR algorithms. In order to take more advantage of the better
performance of CV for thin ice, the weights are defined as follows. For SIC
below 70 %, as obtained by CV, the weight of this algorithm is
Figure 3 illustrates that the four algorithms shown (but this is also valid
for all other algorithms) are sensitive to the MPF, which may mean that melt
ponds are interpreted as open water by the algorithms. This is because
microwave penetration into water is very small. Rösel et al. (2012b)
showed that in areas with melt ponds SIC algorithms (ASI, NT2 and Bootstrap)
underestimate SIC by up to 40 % (corresponding to a MPF close to
40 %). One may still argue that melt ponds should have different
signature from that of open water due to the difference in their salinity.
However, for frequencies as high as those used in the algorithms
(19
For some applications it is important to interpret ponded ice as ice and not as open water. However, we believe that satellite microwave radiometry is incapable of estimating SIC correctly if a certain fraction of the sea ice is submerged under water. Therefore, we suggest accepting what microwave sensors actually can do: estimate the net ice surface fraction. The latter is similar to the well known SIC during most of the year until melt ponds have formed on top of the ice in the melting season. Additional data sources (for example MODIS) could be used to supplement summer retrievals of SIC. Unlike with microwave radiometry, open water in leads and openings between the ice floes can be discriminated from open water in melt ponds on ice floes by means of their different optical spectral properties.
The algorithms shown in Fig. 3 overestimate SIC, which can be caused by
higher Tbs in the areas between melt ponds. During summer these areas
comprise wet snow and/or bare ice with a different physical structure than
during winter. Therefore these areas have radiometric properties potentially
different from those of winter, when the RRDP ice tie points were developed.
This is demonstrated by Fig. 7 where the grey bars highlight that seasonal
changes in the dynamic tie points to be used in the SICCI algorithm vary
particularly during the summer months. The comparison of passive microwave
algorithms and MODIS SIC in Rösel et al. (2012b) showed that in the areas
without melt ponds the passive microwave SIC was larger than that of MODIS.
Note also, however, that the tie points used here differ from those in
Rösel et al. (2012b). This complicates a quantitative comparison of their
results with ours and, in turn, calls for such kind of systematic, consistent
evaluation and inter-comparison as shown in the present paper. Using the
dynamic tie points approach (Sect. 4.5) decreases this effect: the OSISAF
algorithm on average overestimated SIC by 24 % when fixed RRDP tie points
were used (same as in the Fig. 3) and by 17 % with dynamical tie points
(this example is not shown in the figure). However, even with dynamic tie
points, it is likely that the areas selected to derive the 100 % ice tie
point during summer contain melt ponds. If this would be the case and if the
selected area would have an average melt pond fraction of 10 %, then the
100 % ice tie point would not represent 100 % ice but a net ice
surface fraction of only 90 %. When estimating dynamic tie points, an
initial SIC estimate is needed. In our case this was done using pixels with
NT SIC
Another relevant aspect is effect of refrozen melt ponds on passive microwave signatures, which was not addressed in this study. It has not yet been covered thoroughly in the literature (except Comiso and Kwok, 1996) and thus represents an interesting topic for future studies. Per definition, refrozen melt ponds occur on the MYI and they are formed of fresh water, which means these two surfaces have different density and structure with presumably much less air bubbles in the refrozen melt pond than in MYI. This may partially explain the large variability in MYI signatures.
All the algorithms shown for the thin ice test (Fig. 4) underestimate the SIC for ice thicknesses up to 35 cm, which confirms findings by others (see Introduction). The 6H algorithm showed the highest sensitivity to the sea ice thickness, which is in agreement with Scott et al. (2014) showing that Tbs at 6 GHz can be used to estimate thin ice thickness. The least sensitivity to thickness of thin ice was observed for the N90 algorithm; the SIC obtained by this algorithm was independent of SIT values already at thicknesses of 20–25 cm. This is caused most likely by a smaller penetration depth in the near-90 GHz channels (shorter wave length) (see also Grenfell et al., 1998). OSISAF and CV had the second least sensitivity (levelled off at 25–30 cm), which adds more weight to the choice of an OSISAF-like combination as an optimal algorithm. We suggest that, when areas of thin ice are interpreted as reduced concentration, this should be clearly stated along with an eventual SIC product. This issue is similar to melt ponds in a way that there is no simple solution, and one should be aware of the limitation, which we demonstrate by the Fig. 4. In this study we manage to quantify the effect and thus allow modellers to assimilate SIC data in a more proper way. Implementation of an algorithm that accounts for thin ice (Röhrs and Kaleschke, 2012; Röhrs et al., 2012; Naoki et al., 2008; Grenfell et al., 1992) as an additional module to this optimal algorithm could be a potential improvement. For shorter data sets, a thin ice detection technique developed for AMSR-E and SSMIS (Mäkynen and Similä, 2015) can be incorporated in order to provide a thin-ice flag.
Using the RTM of Wentz (1997), we concluded that over open water, most of the algorithms were sensitive to CLW although the sensitivities of CV and 6H were small (not shown). However, we found that CLW and precipitation are less reliable in ERA Interim data and therefore represent error sources, which we cannot correct for using the suggested method. This is also confirmed in literature (Andersen et al., 2006). Therefore, it is important to select a less sensitive algorithm (e.g., CV). The algorithms BP, ASI and N90 were very sensitive to this component (not shown). Most of the algorithms were sensitive to water vapour over open water, especially BP, ASI and N90. Some of the algorithms show some sensitivity to wind (ocean surface roughness), e.g. NT and BR. But we corrected for the water vapour and wind roughening by applying the RTM correction (see Fig. 6).
It was found that atmospheric correction of Tbs for wind speed, water vapour and temperature reduces the SD in retrieved SIC for all tested algorithms at low SIC. In addition, the shape of SIC distribution got closer to Gaussian after the correction (Fig. 6). The OSISAF combination (19V/37V) improved significantly after correction over open water. Over ice the atmospheric influence is small, as was shown by the ERA Interim data we used – total water vapour and CLW content over ice were much smaller than over ocean. The atmosphere over ice is generally much colder than over ocean, and cold air can contain much less moisture (including clouds) than warmer air. In addition, when the emissivity is much larger over sea ice (e.g. FYI) than open water, a change in the atmospheric water vapour imposes a smaller change in the Tb measured over sea ice compared to the one measured over open water (Oelke, 1997). Correction for the effect of surface temperature variations at SIC 100 %, where 2 m temperature was used as a proxy, was not effective. This can be explained by the fact that different wavelengths penetrate to different depth in the ice and thus should retrieve different temperatures.
The limitation of the applied correction is that, even though it reduces the atmospheric noise considerably, it does not remove it completely. There will therefore be some residual atmospheric noise over the ocean. We argue that this noise is more acceptable in a SIC algorithm than the removal of ice, but admit that this is debatable and for some applications the removal of ice may be preferable.
The advantages of the suggested dynamical approach to retrieve tie points can be listed as follows. Firstly, it ensures long-term stability in sea ice climate record and decreases sensitivity to noise parameters with climatic trends. This is of importance because both sea ice area/extent and the geophysical noise parameters (sea ice emissivity, atmospheric parameters) have climatic trends. Also, as model study by Willmes et al. (2014) showed, emissivity of FYI covered by snow is characterised by seasonal and regional variations caused by atmospherically driven snow metamorphism. Secondly, the dynamical tie points are needed when accurately quantifying the SIC uncertainties. Thirdly, the dynamic tie point method in principle compensates for inter-sensor differences in a consistent manner, so no additional attempt was considered necessary to compensate explicitly for sensor drift or inter-sensor calibration differences (the SSM/I data have been inter-calibrated but not with the SMMR data set).
The seasonal cycle in the tie points can be tracked across platforms
(Fig. 7). Thus, the tie points are naturally changing geophysical parameters
(or quantities obtained from such parameters), and should be dynamic as
opposed to the traditional static approach. The variation amounts to
approximately 20–30
The dynamic tie points approach is only applied in time, not in space. The aim of this study is to identify an optimal SIC algorithm for a climate data set, which requires transparent description of techniques and uncertainties. It would be difficult to come up with a proper uncertainty estimate in case we divide our region of interest – more or less arbitrarily – into sub-regions.
One might argue that different tie points for MYI and FYI can still be used. However, computation of the uncertainty at the boundary of both regions will become problematic. How shall one treat mixed pixels? And – most importantly – one would need a validated quality-controlled ice type data set spanning the entire period. Therefore, we would recommend that regional (dynamic) tie points would be an ideal tool for regional applications and for near-real time SIC retrieval of spatially limited areas, but not for a climate data set.
A sea ice concentration (SIC) algorithm for climate time series
should have low sensitivity to error sources, especially those that we cannot
correct for (cloud liquid water (CLW) and precipitation, see Sect. 5.5) and
those, which may have climatic trends. When correcting for errors it is
important to adjust the tie points in order to avoid introducing artificial
trends from the auxiliary data sources (e.g., numerical weather prediction,
NWP, data). Therefore the preferred algorithm should allow the tie points to
be adjusted dynamically. The latter is necessary to compensate for climatic
changes in the radiometric signature of ice and water, as well as eventual
instrumental drift and inter-instrument bias. In addition, this algorithm
should be accurate over the whole range of SIC from 0 to 100 %. Along the
ice edge spatial resolution and sensitivity to new ice and atmospheric
effects is of particular concern. In order to produce a long climate data
record, it is also important that the algorithm is based on a selection of
channels for which the processing of long time-series is possible, which are
currently 19 and 37 The CalVal algorithm is among the best (low standard deviation (SD),
Table 2a) of the simple algorithms at low SIC and over open water. The Bristol algorithm is the best (lowest SD, Table 2b) for high SIC. OSISAF-like combination of CalVal and Bristol is a good choice for an
overall algorithm, using CalVal at low SIC and Bristol at high SIC. Melt ponds are interpreted as open water by all algorithms. Thin ice is seen as reduced SIC by all algorithms. After atmospheric correction of Tbs, low SIC became less uncertain (less
noisy) than high SIC. Near 90 GHz algorithms are very sensitive to atmospheric effects at low
SIC. All 10 algorithms shown in the Fig. 6 improve substantially when
brightness temperatures (Tbs) are corrected for atmospheric effects using
radiative transfer model (RTM) with NWP data. The additional 3 algorithms by
nature could not be corrected/tested for this. The dynamic tie points approach can reduce systematic biases in SIC and
alleviate the seasonal variability in SIC accuracy.
In addition we conclude that
It is clear from these conclusions that there is no one single algorithm that is superior in all criteria, and it seems that a combination of algorithms (e.g., OSISAF or SICCI) is a good choice. An additional advantage of using a set of 19 and 37 GHz algorithms is that the data set extends from fall 1978 until today and into the foreseeable future.
Over ice the Bristol algorithm, chosen for the high SIC retrievals, is sensitive to the snow and ice temperature profile as well as to ice emissivity variations. Surface temperature is quantified in most NWP models, which means that there is a potential for correction. The Bristol algorithm performance over melting ice is good because the SIC as a function of net ice surface fraction has a slope close to one. The Bristol algorithm as other algorithms has a clear seasonal cycle in the apparent ice concentration at 100 % SIC when using static tie points. This means that dynamic tie points are an advantage when using Bristol (as with most of the other algorithms).
Over open water the CalVal algorithm, chosen for the low SIC retrievals, is among the algorithms with the lowest overall sensitivity to error sources including surface temperature, wind, and atmospheric water vapour. Importantly, the CalVal is relatively insensitive to CLW, which is a parameter we cannot correct for due to the uncertainty of this parameter in the NWP data at high latitudes. The response of CalVal to atmospheric correction gives a substantial reduction in the noise level. The response of CalVal to thin ice is better than that of the other 19 and 37 GHz algorithms and comparable to near 90 GHz algorithms.
Therefore we suggest that an OSISAF or SICCI type of algorithm with dynamic tie points and atmospheric correction could be a good choice for SIC climate data set retrievals. The selection of tie points should be done with careful attention to the melt pond issues in order to avoid melt pond contamination of the tie points in summer. Correction for wind speed, water vapour and surface temperature provides a clear noise reduction, but we found no improvement from correcting for NWP CLW.
In spite of their high resolution and good skill over ice, the near-90 GHz algorithms have some limitations for a SIC climate data set because the near-90 GHz data were not available before 1991, and they are very sensitive to the atmospheric error sources over open water and near ice edge such as CLW. Finer spatial resolution achieved by the high-frequency channels does not reduce the weather-induced SIC errors over open water and near ice edge. Model data used in the RTM to correct for the influence of surface wind speed, water vapour and air temperature have a coarser spatial resolution, and hence will cause artifacts in the RTM-based correction. The remaining weather effects we cannot correct for (CLW and precipitation) will become even worse and more difficult to correct for because the model is even less capable of providing the information for this parameters at the same spatial scale as would be required. Their skill over ice is approximately the same as the one of the selected Bristol algorithm.
In the presented work we suggested a number of parameters, which could be used in order to select an optimal approach to retrieval of SIC climate data set. We also suggested an approach that satisfies these requirements. However, we do not claim the suggested approach to be the best one, taking into account that there is still a lot of potential for improvement in passive microwave methods.
The RRDP tie points: brightness temperatures in K.
OW: open water, FYI: first year ice, MYI: multi-year ice.
Computing of the dynamic tie points involves two steps. First, a large number of characteristic Tb samples are selected for each day. Then, these data samples are aggregated over a temporal sliding window.
The open water data samples are selected geographically within the limits of
two 200 km wide belts, one in each hemisphere. Each belt follows the mask of
a maximum sea ice extent climatology, which was first extended 150
The daily open water tie point is computed as the average Tb of all selected
open water data samples in a centred temporal sliding window (
The sea ice data samples are selected geographically within a maximum sea ice extent climatology for each hemisphere. The ice tie point data must in addition correspond to a SIC greater than 95 %, as retrieved by the NASA Team algorithm using the tie points from the Appendix A. Additional masks ensure that samples are taken away from the coastal regions. A maximum of 5000 sea ice data samples are kept per day.
The daily sea ice tie point is computed over the same temporal sliding window as the open water tie point, and is computed separately for each hemisphere. The slope and offset of the ice line are computed using principal component analysis. The ice line is the line in Tb space that goes through the FYI and MYI points (type-A and type-B ice in the Southern Hemisphere, see Figs. 1 and 2). Since the total SIC is our target (and not the partial concentrations of ice types), alternative versions of the CV and Bristol algorithms that rely on the slope and offset of the ice line were implemented. Additional criteria would be needed for further splitting the sea ice data samples into tie points based on ice types; this is not considered here.
A similar approach to deriving dynamic tie points is implemented for the sea ice concentration reprocessed data set, and operational products of the EUMETSAT OSISAF.
This work was completed in the context of ESA Climate Change Initiative, Sea
Ice project (SICCI) and was funded by ESA. The work of S. Kern was supported
by the Center of Excellence for Climate System Analysis and Prediction
(CliSAP). Support from the International Space Science Institute (ISSI),
Bern, Switzerland, under project No. 245: Perta Heil and Stefan Kern, “Towards
an integrated retrieval of Antarctic sea ice volume” is acknowledged. The
EUMETSAT OSISAF (