In the last few decades, the sea ice extent (defined as the total area with at least 15 % of ice cover) and area of multiyear ice (MYI) has been declining at even faster rate than that of the total perennial ice (Comiso, 2012). This tendency of the sea ice towards thinner and younger ice has a strong impact on the energy balance of the system atmosphere–sea ice–ocean (Perovich et al., 2011; Wang et al., 2014), where 96 % of the total annual solar heat input through sea ice occurs during the melt season from May to August (Arndt and Nicolaus, 2014). The above-mentioned effects are of importance within the context of a changing Arctic due to the sensitivity of the regional climate balance and its importance for the global climate (Shindell and Faluvegi, 2009).

The extent of the Arctic sea ice cover has a pronounced seasonal cycle with the maximum typically in March (in February in 2015) and the minimum in September. The main feature of the sea ice in summer is the presence of melt ponds. Knowing their spatial and temporal dynamics during the melt season and over several years can help improve understanding of the current state and trends of the energy balance in the region. The sea ice extent has been showing a general declining trend ever since the beginning of continuous satellite observations in the late 1970s. This decline has been stronger for the September minimum than for the March maximum (Cavalieri and Parkinson, 2012; Stroeve et al., 2011). The decline of the minimum sea ice extent has become very significant since 2007: in September 2007, the minimum sea ice extent of 4.15 million km2 was about 39 % below the average minimum of 1981–2000, and in 2012 the minimum (3.41 million km2) was 49 % below the 1981–2000 average (Perovich et al., 2012). While the minima of Arctic sea ice extent were less dramatic in the years following 2007, it has to be noted that all minima after 2007 were below all yearly minima observed before 2007 (since beginning of satellite observations). What is the role of melt ponds in the above-mentioned yearly dynamics of the sea ice cover? The clear connection between the area of melt ponds in spring and sea ice extent during the sea ice minimum in autumn is highlighted in Schröder et al. (2014).

The temporal dynamics of melt can be subdivided into four stages (Eicken et al., 2002). Stage 1 occurs after the onset of positive air temperatures and is characterized by widespread ponding and lateral melt water flow. During stage 2 both the surface albedo and melt pond fraction (MPF) decrease due to removal of snow cover and due to pond drainage. During stage 3, as the meltwater penetrates deeper into the ice, the pond coverage continues to evolve and MPF to grow. Stage 4 corresponds to freeze-up. This publication is dedicated to the application of the algorithm to retrieve MPF and sea ice albedo described and validated in the companion publications (Istomina et al., 2015; Zege et al., 2015). As the Melt Pond Detection (MPD) retrieval does not see separate ponds, but detects the spectral signature of melt ponds in the measured top-of-atmosphere signal, the retrieved MPF dynamics will somewhat deviate from above-mentioned stages of melt observed in the field. The reason for this deviation is the ambiguity of the top-of-atmosphere signal, where e.g. the spectrum of melting sea ice (before stage 1) can be represented as a linear mixture of pond spectrum and non-melting ice spectrum, as can be seen from field spectra in e.g. Istomina et al. (2013). The problem is addressed in MPD by setting boundary conditions on optical parameters given in Eq. (18) in Zege et al. (2015). However, as the MPD uses only a limited amount of spectral bands in the visible (VIS) and near infrared (NIR) surfaces which are similar in these channels may all be retrieved as MPF, e.g. submerged ice, blue ice and water-saturated ice without yet actual meltwater on it. This may introduce a bias in the MPF product (not sea ice albedo); however, it does not imply limitations on the usage of the MPD product for the studies of radiative balance and global circulation models. Another source of bias of the MPD product are the unscreened thin clouds.

All available Medium Resolution Imaging Spectrometer (MERIS) reduced resolution data for the Arctic summers from 2002 to 2011 are processed and compiled in lower resolution in daily and weekly averages to increase the coverage and quality of the data. As the temporal evolution of the MPF and the surface albedo is naturally correlated with the evolution in air temperature at the surface, a comparison to National Centers for Environmental Prediction (NCEP) reanalysis temperature data for various ice types was performed to check the performance of the algorithm. This has been done for extended periods of time (over the whole summer); also, in the context of the above-mentioned connection of the MPF and sea ice extent, the MPF dynamics have been studied for the 2 years 2007 and 2011 and also compared to the data by Rösel and Kaleschke (2012) for selected cases. The performed comparisons showed clear agreement both to reanalysis data and to the MPF by Rösel and Kaleschke (2012).

The manuscript is structured as follows: Sect. 2 is dedicated to the comparison of the MPF and albedo data to the reanalysis air temperature for various locations. Global applications of the MPD algorithms over the whole MERIS data set (2002–2011) are given in Sect. 3. There the weekly averages for 2007 and 2011 are analyzed (Sect. 3.1); for these cases a comparison to another MPF retrieval is performed (Sect. 3.2) and MPF (Sect. 3.3) and sea ice albedo trends (Sect. 3.4) are presented. The conclusions are given in Sect. 4.

The processed swath MERIS Level 1b data obtained with the MPD algorithm as described in the companion publications (Istomina et al., 2015; Zege et al., 2015) have been gridded daily into the 12.5 km polar stereographic grid (the so-called National Snow and Ice Data Center (NSIDC) grid) with the criterion of more than 50 % valid pixels (both spatially and temporally) within a grid cell to produce a valid grid cell. The standard deviation of such a mixed spatial and temporal average is also provided. Thus, the resulting NetCDF file contains four data sets: MPF, broadband albedo and their standard deviations (STDs). On average, there were around 13 overflights per day, with the density of overlapping swaths highest at about 80∘ N latitude. Even from a single overflight, there are at least 100 up to about 1000 data points for averaging into a single grid cell. Assuming a stable retrieval and low variations within a single day, the STD gives information about the spatial variation of MPF and broadband albedo within the grid cell. This kind of averaging does not provide a guarantee of a valid data point for every single day and grid point. The gridding method with a threshold on the amount of cloud-free pixels allowed to form a valid grid cell assists with cloud screening because it does not allow partly screened out clouds or potentially unscreened cloud edges to appear in the end product. However, since the Arctic is one of the most cloud-covered regions with up to 80 % cloud cover throughout the year (Serreze and Barry, 2005), some of unscreened clouds will still affect the gridded product. The amplitude of this effect tends to be the greatest (up to 15 % MPF bias) before melt onset as it is the most challenging to separate clouds and bright snow/sea ice. Later in the season the surface becomes darker due to melt, which aids the cloud screening, and the bias in MPF and albedo introduced by unscreened clouds decreases.

The weekly resolution has been obtained by averaging the gridded daily product. As in the case of daily resolution, a weekly averaged grid cell is obtained from no less than 50 % of valid (cloud-free) pixels. Should a given grid cell contain more than 50 % of invalid pixels, it is assigned not a number value. No weight or threshold on STDs is applied. The resulting STD is then written into the resulting NetCDF file together with the averaged value for the broadband albedo and MPF.

These weekly averages have a much higher data density since there is a higher probability for the satellite to observe cloud-free areas within 1 week than it is the case for 1 day.

For this study, the daily averaged product was taken in the area 75∘ N, 155∘ W (Beaufort Sea) and 84.5∘ N, 35∘ W (North Greenland) and it was compared to the time sequence of daily averaged air temperature at the surface (0.995 sigma level) from NCEP reanalysis data (Kalnay et al., 1996). The difference between melt evolution in the selected location is mainly that melt onset happens about a month earlier in lower latitudes: beginning of June on FYI as opposed to beginning of July for MYI. Then, due to FYI roughness being much less than that of MYI, the maximum MPF on FYI can be about 4 times higher than that on MYI (maximum melt 0.2 on MYI as opposed to up to 0.8 on FYI, Fig. 1 in Istomina et al., 2015). While the melt onset occurs rapidly on both ice types, the later stage of melt – drainage of melt ponds – happens much sooner on FYI than on MYI. On MYI, this stage is generally substituted with MPF decrease due to freezing and snowfall events. One more difference between the two chosen locations is the sea ice concentration: for the MYI, the ice concentration stays very high throughout the whole summer, whereas for the FYI region, the effect of ice concentration and also ice drift (in the swath data for consecutive days separate floes and their drift is clearly visible) can affect the time sequence analysis, affecting the noisiness of the retrieved values.

Overall, the comparison of the retrieved MPFs and albedos to the surface air temperature (Fig. 2) shows a clear connection during the melt onset: as soon as air temperature assumes constantly positive values, sea ice albedo drops down and MPF increases abruptly. For both FYI and MYI, the maximum MPF is around 0.35, with melt onset happening in the beginning of June for FYI and beginning of July for MYI. This corresponds to the knowledge about melt onset and dynamics from field measurements (Fig. 1 in Istomina et al., 2015). The evolution of melt on MYI follows the air temperature dynamics and goes on till first snowfalls and freezing temperatures around mid August. The FYI region, however, is closer to the ice edge and therefore features a greater range of ice concentrations within the study area. As a result, the corresponding curve appears noisier and interrupts with the area becoming ice free starting 1 August. For periods before melt onset, the retrieved MPFs range from 0 to 10–15 % with relatively high albedos; both might be the effect of unscreened clouds which tend to increase retrieved pond fraction in case of small true pond fraction and decrease it in case of high true pond fractions. The difference of the sea ice type is also visible in the albedo values before the melt onset (Fig. 2): higher albedo of 90 % for MYI region and lower albedo of about 80 %) for the coastal region of Beaufort Sea. For MPF before melt onset, the effect of subpixel ice floes and greater open water fraction as compared to the MYI region may have caused the difference in MPF offset with respect to expected value near zero before melt (Fig. 2).

This chapter presents the main highlights of the processed MERIS data for 2002–2011. Weekly averages have been used for this study due to better data coverage; therefore the shown trends are produced with the weekly resolution.

The most striking and characteristic stage of the melt season is the melt onset and the first stage of melt evolution. Such dynamics are ice type specific, e.g. on FYI this is the rapid melt pond formation followed by a rapid drainage, during which the MPF changes drastically up and down within a scale of days to weeks. MYI features later (starts in July) and slower melt onset, less extreme pond fractions with the absence of rapid melt evolution stages. The pond fractions in the melt maximum on MYI are close to pond fractions of the melt evolution (after melt onset and drainage) on FYI. Thus, the difference of MPF on FYI and MYI is most prominent in June. Therefore in this work we study the temporal variation of MPF and albedo for June of each year of the available MERIS data set.

This publication presents a detailed analysis of the MPD product (Istomina et al., 2015; Zege et al., 2015) consisting of a comparison to reanalysis air surface temperatures, detailed analysis of weekly averages for 2007 and 2011 (which both showed different dynamics of MPF, but resulted in similar minimum sea ice extent), a comparison to the data by Rösel et al. (2012), and an analysis of albedo and MPF trends. The gridded products compare well to independent reanalysis temperature data and show melt onset when the temperature gets above zero degrees Celsius (Fig. 2); however MPD results show an offset at low MPF of about 10 % most probably due to unscreened high clouds. This makes the application of the MPD algorithm to a sensor with a more precise cloud mask desirable (Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi NPP or Ocean Land Colour Instrument (OLCI) onboard Sentinel3). Though absolute daily values of MPF and albedo may be affected by unscreened clouds, relative MPF and albedo differences through the temporal axis are significant and the temporal MPF dynamics correspond to that observed in the field for FYI and MYI (Fig. 2). This is also applicable to weekly averages based on analysis of MPF behaviour in 2007 and 2011 (Figs. 3 and 4) and on the comparison of the MPD product to data by Rösel et al. (2012) (Fig. 6). Thus, the MPD products are suitable for analyzing temporal and spatial dynamics of MPF and sea ice albedo.

Weekly averaged trends show pronounced dynamics of both MPF and albedo: a negative MPF trend in the East Siberian Sea connected to a change of absolute MPF value in its peak but no temporal shift, a positive MPF trend around the Queen Elizabeth Islands connected to the earlier melt onset but with peak MPF values staying the same (Figs. 7, 9 and 10). The MPF dynamics in the East Siberian Sea could indicate a change of ice type prevailing in the region, as opposed to the Queen Elizabeth Island, where the MPF dynamics react to earlier onset of melt. This will be analyzed further in a follow-up publication.