The presence of melt ponds on the Arctic sea ice strongly affects the energy
balance of the Arctic Ocean in summer. It affects albedo as well as
transmittance through the sea ice, which has consequences for the heat
balance and mass balance of sea ice. An algorithm to retrieve melt pond
fraction and sea ice albedo from Medium Resolution Imaging Spectrometer
(MERIS) data is validated against aerial, shipborne and in situ campaign
data. The results show the best correlation for landfast and multiyear ice
of high ice concentrations. For broadband albedo,
Melt ponds on the Arctic sea ice affect the albedo, mass balance and heat balance of the ice (e.g. Perovich et al., 2009) by translating the increase of air temperature into drastic and rapid surface type changes. They introduce a positive feedback within the sea ice albedo feedback loop (Curry et al., 1995), thus facilitating further ice melt. In the context of changing Arctic climate (Shindell and Faluvegi, 2009), knowledge of melt pond fraction (MPF), its spatial distribution and the length of the melt season is required to reflect and predict the role of the sea ice cover in the radiative balance of the region. Schröder et al. (2014) show the potential of predicting the minimum sea ice extent in autumn by the spring MPF. In addition to applications in climate studies, e.g. global circulation modelling, knowledge of the MPF can be helpful for navigation purposes. Findings from numerous in situ campaigns (Barber and Yackel, 1999; Hanesiak et al., 2001; Yackel et al., 2000) provide data of excellent quality and detail, but unfortunately lack in coverage. To fill in this gap, a remote sensing approach needs to be employed.
The present work is dedicated to validation of a MPF and sea ice albedo retrieval algorithm, the Melt Pond Detector (MPD), described by Zege et al. (2015). The algorithm differs from existing satellite remote sensing algorithms, e.g. Rösel et al. (2012) or Tschudi et al. (2008), by (1) utilizing a physical model of sea ice and melt ponds with no a priori assumptions on the surface spectral reflectances, and (2) providing daily averaged MPF instead of weekly averaged MPF, which is beneficial in case of rapid melt evolution. Field observations (Fig. 1) show faster melt evolution on first-year ice (FYI) as compared to multiyear ice (MYI). Due to the fact that MPF depends not only on air temperature and available melt water volume but also on the ice topography (Eicken et al., 2004; Polashenski et al., 2012), the melt evolution is different for FYI and MYI. Melt onset proceeds rapidly to the MPF maximum on FYI with rapid pond drainage and moderate MPFs afterwards. On multiyear ice, the evolution of melt up to the melt maximum takes longer. The peak MPF value is lower and the MPF decrease is slower than that on FYI (Fig. 1). A detailed description of melt stages and melt water distribution mechanisms can be found in Polashenski et al. (2012). These details of melt evolution are responsible for the spatial variability of MPF and sea ice albedo. The temporal variability of MPF is driven by air mass transport and changing air temperature. This introduces complications in the MPF modelling and creates the need for an MPF and sea ice albedo data set of possibly high temporal and spatial resolution, which can be retrieved from satellite data.
The manuscript is structured as follows: in Sect. 2 the MPD algorithm, its input and output data are described. Section 3 is dedicated to validation of the cloud screening (Sect. 3.1), albedo (Sect. 3.2) and MPF (Sect. 3.3) products. The additional cloud screening developed for the purpose of quality validation is presented in Sect. 3.3.2. The conclusions are given in Sect. 4.
The data used for the present study are the pond fraction and broadband sea ice albedo swath data products retrieved from MERIS (Medium Resolution Imaging Spectrometer) swath Level 1b data over the ice-covered Arctic Ocean using the MPD retrieval. The present chapter presents a short summary of the MPD retrieval. The full description of the algorithm can be found in Zege et al. (2015).
The MPD is an algorithm for retrieving characteristics (albedo and melt pond fraction) of summer melting ice in the Arctic from data of satellite spectral instruments. In contrast to previously developed algorithms (Rösel et al., 2012; Tschudi et al., 2008), MPD does not use a priori values of the spectral albedo of constituents of the melting ice (melt ponds, drained surface, etc.).
Pond coverage taken from various field campaigns (see legend) vs. days from onset of ponding on first-year ice (filled dots) and multiyear ice (empty dots). Melt onset proceeds rapidly to the MPF maximum on FYI with following pond drainage and moderate MPFs afterwards; on multiyear ice, the evolution of melt up to the melt maximum takes longer, the peak MPF value is lower and the MPF decrease is slower than that on FYI. Figure courtesy C. Polashenski.
The retrieval algorithm is based on the observations of optical properties of
constituents of sea ice (Perovich, 1996). A sea ice pixel is considered to
consist of two components: white ice and melt ponds. The reflection
properties of surface are described by the spectral bi-directional
reflectance distribution function (BRDF)
The white ice is considered as an optically thick weakly absorbing layer.
The BRDF of this sub-pixel
The BRDF of a melt pond
It is supposed that the pixel surface consists of white ice (highly
reflective) and melt ponds with area fraction The input to the algorithm is the MERIS level 1B data, including the radiance
coefficients The data is sent to the three independent blocks.
The atmospheric correction preprocessing block – The atmosphere reflectance
Separation of the sea ice pixels – In this procedure the ice pixels are
separated from the cloud, land and open water pixels, using a brightness
criterion on the channels Setting the bounds for ice and pond parameters – These border values serve to
stabilize the algorithm and are set to correspond to values observed in
nature (obtained by analyzing the field data from the Polarstern cruise,
Istomina et al., 2013; and from the CRREL field observations, Polashenski et
al., 2012). The main part of the algorithm is an iterative procedure to retrieve ice and
pond parameters and the pond fraction The resulting characteristics and the value of Output is the melt pond area fraction, the spectral albedo, and the
estimation of the retrieval error in the pixel. The spectral albedo is
retrieved at six wavelengths specified by the user. For the validation
studies presented in this paper, the broadband sea ice albedo has been
calculated as an average of the six spectral albedo values at 400–900 nm in
steps of 100 nm.
A satellite scene is processed pixel by pixel, producing an hdf5-formatted
map of output values.
The MPD algorithm has been preliminarily verified numerically, using a synthetic data set of top of atmosphere radiances from melting Arctic ice as the input of a satellite spectral instrument. This data set was computed with software developed based on the radiative transfer code RAY (Tynes et al., 2001; Kokhanovsky et al., 2010) for calculating signals reflected by the melting sea ice–atmosphere system. Thus the radiances in the MERIS spectral channels were simulated for a set of ice pixels for a few typical situations, including “standard” white ice, bright ice (snow-covered), as well as dark- and light-blue melt ponds. The numerical experiment showed that the melt pond fraction can be retrieved with high accuracy (error less than 1%) for the most common case of “standard” white ice and light blue (young) melt pond. The retrieval error increases with deviation from the “standard” case, e.g. the retrieved pond fraction can be underestimated more than twice for the case of bright (snow-covered) ice and dark (mature) melt ponds. However, this situation is rare, because in the case of an open (exposed) mature pond, snowfall only affects the surrounding ice surface for a short time due to melt temperature. The case of lid-covered melt ponds is a separate topic, which is discussed in detail in Sect. 3.3.3. Submerged sea ice or water-saturated ice surfaces are optically identical to melt ponds and are retrieved as such. At the same time the MPD algorithm provides accurate retrievals of the spectral albedo in all considered cases, even in the situations when the error of the pond fraction retrieval is high. The spectral albedo is retrieved much better with the MPD algorithm than with the conventional algorithms using the Lambert approximation for surface reflection, which underestimates the albedo at about 0.05 all over the spectral range, whereas the error of the MPD retrieval in the worst case (“bright ice – dark pond”) is 0.01 and lower in all other considered cases.
The data sets used for the validation of the MPD algorithm are shown in Table 1.
Data sets used for validation of the MPD algorithm.
These validation data sets contain a wide range of pond fractions and were obtained over landfast ice, FYI and MYI of various ice concentrations. Therefore the performance of the satellite retrieval can be thoroughly tested for a variety of conditions and conclusions on the more or less suitable conditions for the application of the MPD retrieval can be drawn. Such conclusions are especially important, as the MPD retrieval was initially designed for a limited set of ice and pond parameters, namely for the conditions of the melt evolution with open melt ponds surrounded by dry white ice within the pack ice. A sensitivity study based on modelled input data shows the algorithm's better performance for bright melt ponds as opposed to dark melt ponds (Zege et al., 2015). Therefore, it is expected that the MPD algorithm shows the best performance over MYI of high ice concentrations. The performance over lower ice concentrations, in case of subpixel ice floes, saturated wet dark ice or thin ponded ice is compromised due to the limitations of the retrieval (Zege et al., 2015). We, however, perform the comparison to the in situ data for all available conditions anyway in order to evaluate the performance of the algorithm at the global scale.
Unfortunately, MERIS only features VIS (visible) and NIR (near infrared) channels, whereas for effective cloud screening over snow, IR (infrared) and TIR (thermal infrared) channels would be more suitable. Therefore MERIS is not the best instrument for cloud screening over snow and ice, and there remains a risk of cloud contamination in the swath data and final gridded product. To avoid this, an additional cloud screening (Sect. 3.3.2) was implemented which proved to give a much better result on swath data. For the gridded product, a restriction on the amount of valid data pixels to form one grid cell was applied to screen out cloud edges. These issues will be addressed below.
Schematic representation of the spatial distribution of the validation data. Red dots show the location of in situ field measurements; tracks – ship cruises, rectangles – approximate area of airborne measurements. The data includes FYI and MYI.
The summary of data set locations is shown in Fig. 2. Among the above-mentioned data sets, the airborne measurements and transect estimates are more accurate than visual estimations; in case of ship cruise bridge observations or visual estimations of melt pond fraction in the field, the measurement accuracy is hard to evaluate.
In order to test the performance of the cloud screening presented in Zege et
al. (2015), we have employed data from the AATSR (Advanced Along-Track Scanning Radiometer) sensor aboard the same
satellite platform. The advantage of this sensor is that it has suitable IR
channels for cloud screening over snow and ready procedures to perform this
task. For this study, a cloud screening method for AATSR developed by
Istomina et al. (2010) is used. For that, the swath data of both MERIS and
AATSR was collocated and cut down to only AATSR swath. Then, the two cloud
masks (the reference mask by AATSR and test mask by MERIS) have been
compared as follows: for each swath, an average pond fraction in cloud-free
areas as seen by AATSR (Fig. 3, blue curve) and
by MERIS (Fig. 3, red curve) has been derived.
This has been done for the period from 1 May 2009 to 30 September 2009.
The resulting Fig. 3 shows the effect of clouds
on the MERIS MPD swath data: before the melt season, clouds have lower
albedo than the bright surface and may be seen as melt ponds by the MPD
retrieval. In the case of developed melt, the situation is the opposite: the
melting surface is darker than clouds, and unscreened clouds are taken as
lower pond fraction by the retrieval. Overall, the unscreened clouds in the
MPD product result in smoothing out of the pond fraction toward the mean
value of about 0.15. However, the temporal dynamics is preserved even in
swath data. Partly the problem of unscreened clouds can be solved at the
stage of gridding swath data into daily or weekly averages, by constraining
the amount of valid pixels that form a valid grid cell so that cloudy areas
which are only partly unscreened in the swath data are still not included in
the gridded data (see Sect. 2 in the companion paper Istomina et al., 2015).
It is important to note the positive MPF bias even in the data cloud
screened with the reference AATSR cloud mask (blue curve in Fig. 3) both
in May and in September 2009 where no melt ponds should be present. One of
the reasons for the bias in September might be the specifics of the MPD
retrieval which detects also frozen ponds as MPF (see Sect. 3.3.3 for
details). Another reason might be the actual accuracy issues of the MPD
retrieval for dark ponds (see Zege et al. (2015) for details). Given the
geographical coverage of the study region (Arctic Ocean to the north of
65
Validation of the sea ice albedo satellite retrieval is a non-trivial task
due to high spatial variability. In summer this variability is even more
pronounced as each given duration and intensity of melt or refreeze creates
an optically unique surface type (various grain sizes of sea ice and snow,
drained, forming, over-frozen melt ponds, deep or shallow ponds on MYI or
FYI, intermediate slushy areas, etc). For a satellite pixel size of
1.2 km
Swathwise comparison of the MERIS cloud mask used in the MPD retrieval to the
AATSR cloud mask presented in Istomina et al. (2010). The region covered is the Arctic
Ocean to the north of 65
Transect measurements of surface type fractions in the Canadian Arctic, POL-ICE 2006,
where the relative surface type fractions are as follows:
Integrated (320–950 nm) albedo for various surface types and total obtained from
transect radiance measurements in Canadian Arctic, POL-ICE 2006, vs. corresponding
retrieved broadband (400–900 nm) albedo averaged within 5 km around the location.
During POL-ICE 2006 the spatiotemporal evolution of surface features and their spectral reflectance properties were monitored by collecting a series of transect measurements on landfast FYI (FI) also in the vicinity of Resolute Bay, Nunavut between 26 June 2006 and 11 July 2006. For each transect, a 200 m transect line was established perpendicular to the predominant major-axis pond direction to maximize the frequency of changes between ponds and snow/bare ice patches. For the relatively uniformly distributed network of ponds and snow/bare ice patches characteristics of smooth FYI, this orientation yields a representative areal fraction of cover types (Grenfell and Perovich, 2004). A total of 12 transects were collected with surface cover types classified as melt pond, snow/bare ice, or mixed at 0.5 m intervals. The mixed-cover type was introduced to classify the slushy mixture of water-saturated ice that could be neither classed as discrete pond or snow/bare ice. The data is shown in Table 2.
For 8 of POL-ICE 2006 transects when lighting conditions were suitable, cosine-corrected downwelling and upwelling radiance (0.35 m height) measurements were made at 2m intervals using a TriOS RAMSES spectrometer (320–950 nm). Spectral data were processed using the calibration files and software bundled with the RAMSES spectrometer, with radiation measurements integrated across the bandwidth of the instrument to create integrated albedo measurements from each sample. Each albedo measurement was matched to a surface class, and average broadband albedo statistics by class and for each transect were derived. For these locations, the MPD retrieval has been performed and the broadband albedo average within 5km around the location has been produced. Satellite overflights closest in time to the field measurements were taken. The result is shown in Table 3, the comparison itself in the last column “Results”. The not available (NA) values in the retrieved data are gaps due to cloud cover. Only four cases were cloud free. Overall, slight overestimation of the satellite albedo is visible. The discrepancies between the field and satellite albedo can be explained by difference in the spatial resolution of the two data sets and varying melt pond distribution within the studied area.
The validation has been performed for selected cloud-free satellite swaths
at the reduced resolution of the retrieval (MERIS data, reduced resolution,
1.2 km
UTC time of aerial measurements (mpf and alb) and satellite overflights (sat) for each day of available aerial measurements of MELTEX 2008 and NOGRAM 2011. Cases with large time difference (greater than 1.5 h) between satellite and field measurements are shown in red.
The aircraft campaign MELTEX (“Impact of melt ponds on energy and momentum fluxes between atmosphere and sea ice”) was conducted by the Alfred Wegener Institute for Polar and Marine Research (AWI) in May and June 2008 over the southern Beaufort Sea (Birnbaum et al., 2009).
The campaign aimed at improving the quantitative understanding of the impact of melt ponds on radiation, heat, and momentum fluxes over Arctic sea ice. For determining broadband surface albedo, the BASLER BT-67 type aircraft POLAR 5 was equipped with two Eppley pyranometers of type PSP (precision spectral pyranometer) measuring the broadband hemispheric down- and upwelling shortwave radiation. The radiation sensors were mounted on the aircraft in a fixed position. For clear-sky conditions, data of the upward facing pyranometer, which receives direct solar radiation, were corrected for the misalignment of the instrument (based on a method described by Bannehr and Schwiesow, 1993) and the roll and pitch angles of the aircraft to derive downwelling hemispheric radiation flux densities for horizontal exposition of the sensor (see Lampert et al., 2012).
Weather conditions in May 2008 were characterized by warming events interrupted by cold-air advection from the inner parts of the Arctic towards the coast of the southern Beaufort Sea. A warming event on 23 and 24 May 2008, caused the onset of melt pond formation on ice in a large band along the coast from the Amundsen Gulf to Alaska. On 26 May 2008, numerous melt ponds in a very early stage of development were overflown. However, from 27 May to 1 June 2008, a new period with prevailing cold-air flow caused a refreezing of most melt ponds, which were still very shallow at that time. During the last week of the measurements, a tongue of very warm air was shifted from Alaska to the Beaufort Sea. It reached its largest extension over the ocean on 4 and 5 June 2008, which again strongly forced the development of melt ponds.
The available validation data consist of five flight tracks for 5 days on 26 May and 3, 4, 6 and 7 June 2008. Only the cloud-free data are selected. The measurements were performed at different altitudes, as low as 50 m and reaching 400 m, with correspondingly different numbers of measurement points for each satellite pixel. The collocation of such an uneven data set with the satellite data has been performed by calculating an orthodromic distance of every pixel within a satellite swath to a given aerial measurement point, and collecting those aerial points lying at the minimum distance to the centre of a given satellite pixel. This ensures that aerial measurements performed at any height are collocated to the corresponding satellite pixel correctly. The number of data points per flight is in the order of tens to hundreds of thousands with up to 500 points per satellite pixel.
The validation effort has been done on swath satellite data. The quality of retrieval conditions for the MPD algorithm differs for each overflight depending on weather conditions, ice concentration and ice type. In addition, time difference between the satellite overflight and aerial measurements affect the comparison (Table 4) due to ice drift.
Examples of ice conditions present during MELTEX 2008 flights over landfast ice on 6 June 2008 (top panel) and over separate ice floes of various sizes on 4 June 2008 (bottom panel). The black tracks depict the flight tracks with albedo measurements. The colour code illustrates the satellite retrieved broadband albedo. The background consists of the coral filled land mask and grey filled data gaps due to cloud contamination or surface type other than sea ice.
Altitude of the airborne broadband albedo measurements on 6 June 2008,
MELTEX campaign (left panel). Correlation between retrieved broadband albedo from satellite data
and measured broadband albedo over landfast ice (no drift) (flight track shown on the top
panel of Fig. 3). STD is calculated from all collocated aerial measurements for a given
satellite pixel. Only pixels with STD smaller than the mean STD are used.
Correlation between broadband albedo retrieved from airborne measurements and
from a satellite overflight, respectively, for the 4 June 2008, MELTEX campaign (bottom
panel of Fig. 3) with respect to time difference.
Correlation between broadband albedo retrieved from airborne measurements
(MELTEX campaign) and from a satellite overflight, respectively, for the 26 May 2008 (left
panel),
Correlation between broadband albedo retrieved from airborne measurements
(MELTEX campaign) and from a satellite overflight, respectively, for the 7 June 2008, with
respect to the time difference.
Example of aerial photo from MELTEX campaign in 2008, flight over landfast ice on 4 June 2008. The image width is approximately 400 m. Only quality assessed images were taken (see text for details).
An example of such different conditions is shown in Fig. 4, where the flight tracks over FI and over separate ice floes are shown.
The time difference between the aerial measurement and satellite overflight
varies for the presented cases, which adds to the validation data uncertainty
for cases with lower ice concentrations due to drifting separate floes. Where
possible in the case of drift, the time difference was limited to 1.5 h around
the satellite overflight. Two exceptions with time difference of 2–3 h are
marked in Table 4. Figure 5 shows the altitude and the correlation of the
measured and retrieved broadband albedo for the only flight over FI on
6 June 2008. The rest of the flights were flown over separate floes. As no
screening of albedo data was possible, it was decided to limit the time
difference to 1.5 h around the satellite overflight for the asymmetrically
distributed flights. Some points of low measured albedo but high retrieved
albedo feature time difference up to 2 h and are most probably connected to
the drift of separate ice floes. These are flights on 4 June 2008,
26 May 2008, 3 and 7 June 2008. They are shown in Figs. 6–8. Due to ice
drift, the aerial measurements are displaced relative to the satellite
snapshot which causes different areas to be compared to each other. The
resolution differences of the two sensors may increase this difference even
more. Therefore, slight over or underestimation due to the ice concentration
difference of aerial and satellite measurements is visible. As the numerical
experiment shows that accuracy of the albedo retrieval in all cases is high
(Zege et al., 2015), and the case of no drift shows high correlation of
retrieved and measured albedo (fast ice (FI) case shown in Fig. 5), we
conclude that the discrepancy is due to the specifics of data used for
validation and not a weak point of the MPD retrieval. To conclude, the best
correlation for albedo retrieval is observed for the landfast ice, which are
the conditions of the best algorithm performance with
For the validation of the melt pond product, the aerial photos from the same airborne campaign MELTEX 2008 have been used. Although the flight tracks are the same, the criteria for data selection are different for albedo and melt pond measurements. This is why the validation data for melt pond and albedo data do not overlap entirely for the same flight. The number of points per flight is in the order of hundreds with about 5 images per satellite pixel (example photograph is shown in Fig. 9). Additionally, one more flight over MYI near the coast of North Greenland during the aerial campaign NOGRAM-2 2011 has been used.
For the evaluation of the aerial photographs a supervised classification
method (maximum likelihood) was applied. For every pixel
More than 10 000 aerial photographs were recorded during the MELTEX campaign
during the different flight tracks. As the quality of the data was not
uniform, only images which meet the following requirements were chosen:
images taken during horizontal flight tracks (to minimize the geometric
distortions) and clear sky flight tracks (to prevent a wrong classification
because of fog, clouds and shadows of the clouds). The camera was operated
with a non-constant exposure, so that the sea ice in images with a large
fraction of open water was overexposed and useless for further evaluation.
To simplify the automated classification, images of each day were separated
into different flight tracks with similar exposure, ice conditions and same
flight level. Nevertheless almost 3000 images were classified and evaluated
for the MELTEX campaign. Two suitable flight tracks of the NOGRAM-2 campaign
that contain about 1000 images were chosen to complement the quantification
of the melt stages. Depending on the flight level, each image covered an area
between 0.2 and 3 km
Overall the validation data used features four types of sea ice: thin and thick FYI as well as FI for the MELTEX images, and MYI for NOGRAM-2. Most of the investigation area of the MELTEX campaign was covered by thin FYI or FI. Only on 7 June 2008, the most northerly part of the flight track contained a notable amount of thick FYI. This part showed a different behaviour during the melting process and contained different surface classes than the thin FYI or FI.
Most flight tracks of the campaign were subdivided in several sub-flight tracks. For every sub-flight track, a representative image was chosen, which contained all classes. In cases where there were no representative images with all classes for a given sub-flight track, two or more images were merged for the determination of the training data. The threshold for the maximum likelihood method was set to 0.95. This means that the probability of belonging to a defined class must be 0.95 or higher. Otherwise the pixels were not classified. Within the presented study, the amount of unclassified pixels per image is uniformly about 1–2 %.
The sea ice conditions varied greatly for each of the studied flights, with the cases ranging from land fast ice of 100 % ice concentration, separate drifting ice floes to brash ice with subpixel ice floes (example in Fig. 10). The cases with no separate ice floes and no ice drift are shown in Fig. 11 (FI) and Fig. 12 (left panel, MYI) with quite good correspondence of the retrieved and measured pond fractions. Right panel in Fig. 12, on the other hand, shows higher retrieved MPF than measured from the aircraft. The reason for this discrepancy is 2-fold: relatively large time difference and the challenging surface conditions. The surface state at the time was as follows: the reported cold air intrusion in the area on 1 June 2008 prevented the forming melt ponds from evolving further (an overview on surface conditions in the area can be found in Scharien et al., 2012), and the large floes were covered with frozen ponds at the beginning of their evolution. Frozen shallow ponds at the beginning of their evolution were classified as sea ice from the aerial images, but retrieved as melt ponds from the satellite. For the applications connected to the radiation budget studies (e.g. GCM), a generalization where darker types of sea ice and melt ponds are put into one class is appropriate due to similar radiative characteristics of the two.
Examples of ice conditions present during MELTEX 2008 flights over landfast ice on 6 June 2008 (top panel) and over separate ice floes of various sizes on 4 June 2008 (bottom panel). Black dots: the flight track. The coloured filled background: the satellite retrieved melt pond fraction. The background is the coral filled land mask and grey filled data gaps due to cloud contamination or surface type other than sea ice.
Altitude of the airborne melt pond measurements on 6 June 2008 (left panel).
Correlation between retrieved melt pond fractions from satellite and airborne classified MPF
over landfast ice with no drift (right panel), 6 June 2008 during MELTEX campaign. The flight
track shown on the top panel Fig. 9.
Correlation between retrieved melt pond fractions from satellite- and airborne-classified MPs (melt ponds) over MYI (no drift, ice pack), 21 July 2011, NOGRAM-2, 2011, campaign
north of Greenland (left panel).
Correlation between retrieved melt pond fractions from satellite- and airborne-classified MPs over FYI, possible drift, 7 June 2008, MELTEX2008, Beaufort Sea. This case
features larger ice floes than flights on 4 June or 26 May 2008.
Retrieved melt pond fractions from satellite- vs. airborne-classified MPs over
FYI, possible drift, 26 May 2008 (left panel),
Example of a spatial dynamic cloud filtering for MERIS swath data: original swath subset with the cloud filters from (Zege et al., 2015) applied (top panel), same swath subset after applying the dynamic spatial filter (see text).
An example image made from the bridge of RV
Figure 13 shows the flight on 7 June 2008, which features larger ice floes than the flights
shown in Fig. 14. The MPF output of the MPD algorithm is not affected by the subpixel
fraction of open water because the almost constant spectrum of open water only affects the
amplitude and not the spectral shape of the mixture of surfaces (sea ice, ponds and open
water) within the pixel; however, the spectral signature of melt ponds is harder to resolve in
case of lower ice concentrations. Subpixel ice floes, brash ice, and blue ice are not appropriate
conditions for the MPD algorithm application, hence the overestimated pond fraction for both
flights in Fig. 14. Overall, the best correlation can be seen for the cases of landfast and
multiyear ice of high ice concentrations
As the aerial validation has been performed on cloud free data, the problem of cloud clearing did not arise. For in situ and ship cruise data, cloud contamination may increase the uncertainty of the satellite-retrieved values, and in these cases this problem has to be addressed additionally. With the gridded product, the unscreened cloud edges and partly screened-out clouds are cut out with the criterion for minimum valid data pixels allowed within one grid cell. For the swath data, such criterion is not applied and the existing cloud filtering proved to be not sufficient for a quality validation. Therefore, an additional spatial dynamic filter was introduced for ship cruise and in situ data. An example is shown in Fig. 15.
The dynamic spatial filter consists of dividing the swath into boxes of
10
This method proved to be successful for the case studies on single swaths which do not undergo gridding with a threshold on the minimum allowed amount of cloud-free pixels, which helps to screen out cloud edges or partly screened clouds. For our MERIS gridded products, the gridding procedure tends to introduce a similar cloud screening effect as the above-mentioned filter. High, thin clouds, however, may still be present within both swath data and gridded products. The consequences are discussed in the Sect. 3.1.
The visual estimations of various sea-ice parameters, including MPF during
the ship cruises differ in accuracy from aerial measurements, transect
measurements, or visual estimations during in situ campaigns which are
dedicated to such measurements. As opposed to the in situ campaign, hourly
bridge observations are performed by many observers with different estimation
experience and skill, which introduces additional noise to the observed
value. The two studied cruises – The Healy–Oden Transarctic Expedition
(HOTRAX), 19 August–27 September 2005 (Perovich et al., 2009), and
RV
Within this work, we apply the MPD algorithm without limitations other than cloud screening (original as described by Zege et al. (2015), and dynamic spatial filter described in Sect. 3.3.2) to illustrate the effect of the above-mentioned underestimation. In cases not dedicated to the study of the algorithm accuracy, it is recommended to use the MPD MPF product in combination with the reanalysis air surface temperature to apply the algorithm only when the melt ponds are not frozen over. Otherwise the (supposedly low) MPF value is ambiguous and could indicate both low MPF of open ponds or high MPF of frozen ponds.
Retrieved MPF vs. observed MPF from the hourly bridge observations during
TransArc 2011, 4 August 2011–6 October 2011. Swath data, no temporal averaging, 15 km
satellite average around the in situ point. All but one point is FYI. Corrected for ice
concentration. Underestimation may be connected to undocumented presence of melted
through or over-frozen ponds at the end of the melt season (see Fig. 16).
Retrieved MPF vs. observed MPF from the hourly bridge observations during
HOTRAX 2005, 19 August–27 September 2005. Swath data, no temporal averaging, 15 km
satellite average around the in situ point. No information on ice type. Corrected for ice
concentration. Underestimation may be connected to undocumented presence of melted
through or frozen over ponds at the end of the melt season.
Both cruises TransArc2011 (Fig. 17) and HOTRAX 2005 (Fig. 18) had only
several days of cloud free collocations. The available swath data and the
hourly ship observations have been compared point by point without temporal
averaging. The only averaging was the 15 km spatially of the satellite data
around the ship location. For both cruises, information on ice concentration
was available from bridge observations, and the ship MP values have been
corrected for ice concentration to give the pond fraction relative to the
visible area and not to the area of sea ice. For the TransArc2011 cruise,
information on MYI and FYI ice concentration was available with corresponding
MPFs. The total MPF was calculated using the linear mix of these values.
However, the resulting cloud-free collocations feature mostly FYI cases. For
the HOTRAX 2005, such information was not available and only total ice
concentrations were used. The correlation between the satellite value and
observed value: mean
The in situ validation has been performed on the swath data using the three
available data sets: transect measurements on the FI just north of Barrow,
AK, approximately 1 km offshore from Niksiuraq in the Chukchi sea, near
71
During C-ICE 2002 visual estimates of MPF fraction were made on a homogeneous and relatively smooth zone of FI in the Canadian Arctic Archipelago approximately 80 km northwest of Resolute Bay, Nunavut between 18 June 2002 and 8 July 2002 (Scharien and Yackel, 2005). Visual estimates were supported by occasional 100 m transect measurements taken at 0.5 m intervals to characterize surface feature types (melt pond or ice) and pond depths, as well as time-lapse photos taken from a tower-based camera mounted at 6 m height. From these data, a nominal 0.1 MPF estimation error was ascribed to the visual estimates. For days where transect measurements were available, the daily average of W–E and N–S transects was used instead of visual estimates.
For the remaining two data sets, the transect measurements of MPFs were used as provided.
The data sets feature uniform FI and at times of extremely high pond fractions
and the following drainage events. As the campaigns were performed on the FI,
no correction for the ice concentration was needed. As in the case of ship
cruises, the average MPF 15km around each in situ point was taken. The same
cloud filtering has been applied (original as described by Zege et
al. (2015), and dynamic spatial filter described in Sect. 3.3.2). The total
amount of cloud-free collocated points is
Melt ponds on sea ice affect the radiative properties of the ice cover and its heat and mass balance. In order to assess the change of the energy budget in the region (e.g. with GCMs (general circulation models), among other sea ice and melt pond properties, the sea ice reflective properties and the amount of melt ponds on sea ice have to be known. This work has validated a retrieval of MPF and broadband sea ice albedo from MERIS data (Zege et al., 2015) against aerial, in situ and ship-based observations.
Three in situ campaigns on landfast ice: Scharien 2002 (red dots), Scharien 2006
(blue dots) and Polashenski 2009 (green dots). Total point number
The cloud screening presented in Zege et al. (2015) has been compared to the AATSR cloud screening presented in Istomina et al. (2010) for swath data of both sensors collocated to AATSR swath, for the whole summer 2009. The comparison (Fig. 3) shows that unscreened clouds affect the MPD retrieval in two ways and result in (1) overestimated MPF before melt onset and (2) underestimated MPF during the melt season; the effect of unscreened clouds is not constant and depends on the true surface pond fraction. Unscreened clouds tend to smooth out the melt pond fraction values towards a mean value of about 0.15. As can be seen from the figure, this smoothing effect is most prominent in the beginning of the season and during the melt maximum, and is the smallest in June.
The albedo data from spaceborne and airborne observations have been compared and showed high correlation when there is no ice drift (Figs. 5 and 7). Same comparison for MPF highly depends on the ice conditions and melt stage: for FI and MYI in the beginning of melt the correlation is high (Figs. 11, 12 and 19), for separate FYI floes the correlation is worse maybe due to ice drift (Figs. 13 and 14). The comparison of ship cruise data to satellite retrieved MPF for FYI and MYI at the end of the melt season shows strong underestimation of satellite retrieval. This might be connected to frozen over ponds undocumented in the ASPeCt observations (Figs. 17 and 18). At the same time, comparison to ship observations show that the MPD retrieval shows ambiguity of the retrieved MPF: low retrieved MPF could indicate low MPF of open ponds or high MPF of frozen ponds. It is planned to resolve this ambiguity in the future versions of the algorithm by introducing a decision tree based on the air temperature as a measure of surface energy balance to determine whether ponds are frozen over or not.
The presented melt pond fraction and sea ice albedo retrieval can be applied to other radiometers with sufficient amount of channels in the VIS and NIR regions of spectrum, e.g. VIIRS onboard Suomi NPP and OLCI onboard the Sentinel-3 ESA mission (planned launch late 2015). Thus the continuity of the MPF and sea ice albedo data set can be achieved; this is important for the data set use as input to GCM and for studies of MPF and albedo dynamics in the context of global change and Arctic amplification.
The case studies, time sequence analysis and trends of MPF and sea ice albedo are presented in the companion paper (Istomina et al., 2015).
The authors express gratitude to Stefan Hendricks for providing photos of the hourly bridge observations of the TransArc 2011 cruise, to Daniel Steinhage for providing photos taken with a downward-looking camera during the aircraft campaign NOGRAM-2 2011, to the C-ICE 2002 participants, J. Yackel and the Cryosphere Climate Research Group, Department of Geography, University of Calgary. The Centre for Earth Observation Science at the University of Manitoba and the Polar Continental Shelf Project are gratefully recognized for their logistic and financial support.
The authors are grateful to the two anonymous reviewers and the editor H. Eicken for their effort and valuable comments on the manuscript.
This work has been funded as a part of EU project SIDARUS. The article processing charges for this open-access publication were covered by the University of Bremen. Edited by: H. Eicken