Download Aerial observations of sea ice and melt ponds near the North Pole

Acta Oceanol. Sin., 2017, Vol. 36, No. 1, P. 64–72
DOI: 10.1007/s13131-017-0994-2
http://www.hyxb.org.cn
E-mail: [email protected]
Aerial observations of sea ice and melt ponds near the North Pole
during CHINARE2010
LI Lanyu1, KE Changqing1*, XIE Hongjie2, LEI Ruibo3, TAO Anqi1
1 Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing
210023, China
2 Department of Geological Sciences, University of Texas at San Antonio, Texas 78249, USA
3 Polar Research Institute of China, State Oceanic Administration, Shanghai 200136, China
Received 7 November 2015; accepted 28 January 2016
©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2017
Abstract
An aerial photography has been used to provide validation data on sea ice near the North Pole where most
polar orbiting satellites cannot cover. This kind of data can also be used as a supplement for missing data and
for reducing the uncertainty of data interpolation. The aerial photos are analyzed near the North Pole collected
during the Chinese national arctic research expedition in the summer of 2010 (CHINARE2010). The result
shows that the average fraction of open water increases from the ice camp at approximately 87°N to the North
Pole, resulting in the decrease in the sea ice. The average sea ice concentration is only 62.0% for the two flights
(16 and 19 August 2010). The average albedo (0.42) estimated from the area ratios among snow-covered ice,
melt pond and water is slightly lower than the 0.49 of HOTRAX 2005. The data on 19 August 2010 shows that
the albedo decreases from the ice camp at approximately 87°N to the North Pole, primarily due to the decrease
in the fraction of snow-covered ice and the increase in fractions of melt-pond and open-water. The ice
concentration from the aerial photos and AMSR-E (The Advanced Microwave Scanning Radiometer-Earth
Observing System) images at 87.0°–87.5°N exhibits similar spatial patterns, although the AMSR-E
concentration is approximately 18.0% (on average) higher than aerial photos. This can be attributed to the 6.25
km resolution of AMSR-E, which cannot separate melt ponds/submerged ice from ice and cannot detect the
small leads between floes. Thus, the aerial photos would play an important role in providing high-resolution
independent estimates of the ice concentration and the fraction of melt pond cover to validate and/or
supplement space-borne remote sensing products near the North Pole.
Key words: sea ice, melt pond, albedo, concentration, aerial observation, North Pole
Citation: Li Lanyu, Ke Changqing, Xie Hongjie, Lei Ruibo, Tao Anqi. 2017. Aerial observations of sea ice and melt ponds near the North
Pole during CHINARE2010. Acta Oceanologica Sinica, 36(1): 64–72, doi: 10.1007/s13131-017-0994-2
1 Introduction
Arctic sea ice and its variability play a crucial role in the regional and Northern Hemisphere climate system (Liu et al., 2004;
Sedláček et al., 2012; Chen et al., 2013). Sea ice moderates the energy balance by changing the surface albedo of ocean and controls the vertical fluxes of heat, mass and momentum between
the ocean and atmosphere (Grenfell and Perovich, 2004; Screen
et al., 2013; Li and Zhao, 2014). A summer sea ice cover in the
arctic has decreased over recent decades, with record loss in recent years (Cui et al., 2015), and the mean summer sea ice extent
decreased by (51.5±4.1)×103 km2/a (or (4.1±0.3%) per decade)
between 1979 and 2010 (Cavalieri and Parkinson, 2012). The latesummer ice cover, mainly composed of thick multiyear ice, has
been shrinking, thinning (Rothrock et al., 2008; Kwok and Rothrock, 2009). The arctic summer melt season, characterized by a
mixture of ice patches, melt ponds on the ice surface, and open
water,
is a critical component of the annual cycle of a sea ice
growth and decay due to enhanced radiative feedback processes
affecting the decay rate of an ice volume (Hudson, 2011).
Satellite remote sensing has provided a reliable tool for continuously monitoring changes in the arctic sea ice cover since
1972 (Cavalieri et al., 2003). However, the usefulness of satellite
instruments is limited by certain characteristics of remote
sensors. Clouds and polar nights severely constrain the utility of
an optical imagery, while passive microwave sensors are limited
by their coarse spatial resolution from distinguishing open water
with small size from sea ice (Inoue et al., 2008).
The AMSR-E sensor measures a microwave radiation (brightness temperatures) from the earth at six different frequencies
from 6.9 GHz to 89.0 GHz at both horizontal and vertical polarizations . Sea ice concentration products, derived by an arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) algorithm (Spreen et al., 2008), use vertically and horizontally polarized data at 89 GHz and offer a spatial resolution of 6.25 km,
Foundation item: The National Natural Science Foundation of China under contract No. 41371391; the Program for Foreign Cooperation of
Chinese Arctic and Antarctic Administration, State Oceanic Administration of China under contract No. IC201301; the National Key
Research and Development Program of China under contract No. 2016YFA0600102.
*Corresponding author, E-mail: [email protected]
LI Lanyu et al. Acta Oceanol. Sin., 2017, Vol. 36, No. 1, P. 64–72
which is the highest among sea ice concentration algorithms applied to AMSR-E data and provides an accurate distribution of
sea ice. However, the use of AMSR-E at the 89 GHz channels is
limited by considerably higher atmospheric influence from water vapor and clouds than lower-frequency channels (Spreen et
al., 2008). Furthermore, most polar orbiting satellites cannot cover the North Pole. For example, the AVHRR and the AMSR-E,
providing widely used sea ice products, cover latitudes only up to
87.5°N, and the SSM/I only covers up to 87.2°N. A data interpolation for the missing North Pole is often needed for climate modeling and could produce large uncertainty (Comiso, 2003). The
data interpolation issue near the North Pole is more critical during summer than winter due to the more variable sea ice concentration during summer.
Ship-based observations and aerial photography of arctic sea
ice near the North Pole during summer are important to validate
the climatic interpolation results and numerical models (Connor
et al., 2009; Perovich et al., 2009), especially in the region surrounding the North Pole, where the satellite data are unavailable.
Aerial photography surveys have long been an effective method
in understanding the spatial variability in sea ice with higher resolution (Markus and Cavalieri, 2000; Perovich et al., 2009; Haas et
al., 2010). Moreover, the aerial photography can serve as validation data in some tough or dangerous areas where validation
data cannot obtain by ship expedition.
Some studies provided snapshots of sea ice properties by the
aerial photography for particular regions and time in the Arctic
Ocean (Hall et al., 2008; Connor et al., 2009). For example,
Derksen et al. (1997) used a camera mounted on a tethered balloon to monitor changes in the pond fraction of first-year ice during the onset of melt. A series of images were created from a
downward-looking video camera mounted on the underside of a
research aircraft as it flew in the vicinity of the surface heat
budget of the Arctic Ocean (SHEBA) camp, which drifted with the
pack ice in the Beaufort Sea and eastern Chukchi Sea (Tschudi et
al., 2001). Those data were processed to determine the distribution of melt ponds and open water in addition to the albedo variability (Perovich et al., 2002). Eight helicopter flights were conducted in the Pacific arctic sector (PAS) during the
CHINARE2008, and more than 9 000 aerial images were obtained (Lu et al., 2010). The results show a significantly higher
amount of melt ponds and a significantly smaller areally-averaged albedo than those reported in earlier studies in the same region in the PAS, because aerial observations allow to see more
details in the ice cover.
In this study, we evaluate the aerial photographs obtained
during the ice camp period (August 7–19) of the CHINARE2010
expedition. The spatial variability of open water, melt pond and
snow-covered ice near the North Pole is determined. In addition,
we determine the areally-averaged albedo on the basis of the aerial photos and compare them conducted with measurements in
the PAS in the previous years. Finally, we determine ice concentration based on the aerial photos and compare it with that from
the AMSR-E using the 89 GHz ASI algorithm.
2 Aerial photography
Two helicopter flights (i.e., F1 to F2) with photographic surveys (Fig. 1, Table 1) were conducted in the central Arctic Ocean
to extend the surface-based measurements to a larger scale during the ice camp period of the CHINARE2010. Figure 2 shows
some typical surface features of the ice camp, which was established on an ice floe over 100 km2 in size and approximately 1.8
65
Fig. 1. The two helicopter flight trajectories (August 16 or F1 and
August 19 or F2) in the central Arctic Ocean, with green arrows
indicating the flight direction, during the 12 d ice camp period
(7–19 August 2010).
Table 1. Survey flight information
Flight No. Time (UTC) Distance/km
Number of
images
F1
August 16
160
861
F2
August 19
441
1 400
Area range
87.0°–87.5°N,
168.0°–180.0°W
87.0°–89.2°N,
171.0°–180.0°W
m in mean thickness at 86.92°N, 178.88°W on 7 August 2010. Subsequently, the ice camp drifted a total of 175.7 km and stopped at
87.2°N, 172.3°W on August 19 (Xie et al., 2013; Lei et al., 2015).
A rigid plastic box was mounted outside the helicopter in a
downward vertical orientation. The box contained a Canon G9
camera (with a resolution of 3 264×2 248 pixels) with a portable
GPS connected to the camera to record the location of each image and a pressure differential altimeter to record the height of
the helicopter. A data cable was used to connect the camera to a
laptop inside the helicopter. The computer controlled the camera for image collection at a rate of one image per 6 seconds and
also acted as a data logger for image collection.
The flight distance was 160 km and 441 km on August 16 (F1)
and on August 19 (F2), respectively. The surveyed area of the two
flights mainly covered 87.0°–89.2°N, 168.0°–180.0°W, located near
the North Pole (Fig. 1). Flights were typically made at relatively
low altitudes of 150–500 m because of the presence of low clouds
during the campaigns. Therefore, each snapshot covered an area
between 147 m×100 m and 490 m×335 m, or 5–25 cm in pixel size.
The images were spaced without overlapping, which can assure
the statistical independence of samples. More than 2 261 photos
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LI Lanyu et al. Acta Oceanol. Sin., 2017, Vol. 36, No. 1, P. 64–72
Fig. 2. Photos of typical snow, sea ice and melt ponds taken on the 12 d ice camp from the CHINARE2010 cruise. a. R/V Xuelong on an
ice floe over 100 km2 in size and approximately 1.8 m in mean thickness; b. photographic surveys of summer sea ice surface features
over the ice camp with the helicopter; c. sea ice covered by decomposed ice and fresh snow on top; d. sea ice melting and refreezing,
and surrounded by fresh snow; e. a large melt pond covered by refreezing ice, and the green apple-shaped room for protecting
scientists from polar bear attacks; and f. thin fresh snow covering refrozen ice of melt pond surface.
in total were taken, and only approximately 3% of them cannot be
processed due to poor contrast under lower cloud or fog conditions.
3 Method
3.1 Aerial photos processing
Sea ice, including first-year ice and multiyear ice, normally
has very high reflectance and is easy to identify and classify.
Ponds are discriminated from the surrounding pack ice because
their albedos are substantially lower than snow or ice. In addition, the pond reflectance is greater in the blue portion of the
spectrum than that in the red (Grenfell and Maykut, 1977). Using
these criteria, pond pixels are classified (Tschudi et al., 2001).
The open water with low reflectance is easy to separate from
snow-covered ice or bare ice. Its spectral signature is also distinguished from the ponds and can be easily classified.
Actually, all of the aerial images have been screened at first.
Then, we delete some unavailable photos, such as confused or
single-targeted, etc. We select some typical photos for each flight
and corresponding training sets for snow-covered ice or bare ice,
melt ponds and open water (training sets are manually defined in
the photos). Maximum likelihood method (Conese and Maselli,
1992) is performed on all photos according to the extracted statistical characteristics of those training sets for the three surface
classes: snow-covered ice or bare ice (I), melt ponds (P) and
open water (W). Finally, the results are optimized by a visual interpretation. After classification, the areal fractions of I, P, and W,
as well as ice concentration (I plus P) (0%–100%) are calculated.
If image continuity is interrupted because of poor quality or other reasons, we interpolate the areal fractions and the ice concentration according to their neighborhood values. Certainly, other
processing method of the aerial photograph can be used to obtain sea ice surface features (Miao et al., 2015).
3.2 Areally averaged albedo calculation
We use the area fractions derived from the aerial photos to
compute the really averaged albedo (®
¹ ) using Eq. (1), which was
used during the HOTRAX2005 by Perovich et al. (2009):
®=®IA I+®P A P + ®W A W ;
(1)
where A is the area fraction; α is the albedo; and the subscripts I,
P and W refer to the ice, melt ponds and open water, respectively.
During late summer, the sea ice albedo (α I ) changes with the
snow cover conditions. There were several snowfall events before August 17 when a rainfall event occurred. Therefore, photographed sea ice on August 16 (F1) is mostly snow-covered sea ice
with a mean albedo value of 0.75 (Xie et al., 2013; Lu et al., 2010),
which is used for sea ice albedo . After the rainfall event of August 17, the snow cover was melted. An albedo value of 0.65 (Perovich et al., 2009), corresponding to the typical value for bare or
snow melted ice, is used for sea ice albedo under the flight on August 19 (F2). Determining a melt pond albedo (αP) is more difficult because it is associated with the pond developments (Tucker III et al., 1999). On the basis of visual analysis of the aerial photographs from the CHINARE2010 and previous observations conducted in the transpolar melting zones, a value of 0.25 is selected
in this study (Perovich et al., 2009). The open water albedo (αW) is
set to 0.07 (Tschudi et al., 2001).
3.3 Sea ice concentration estimation based on AMSR-E
Sea ice concentration data from the AMSR-E sensor, downloaded from the University of Bremen, Germany (http://www.
iup.uni-bremen.de: 8084/amsredata/), are derived with the ASI
algorithm, which is developed as a way to obtain more detail
from the high spatial resolution of the 89 GHz channels and
shows a finer performance than other sea-ice algorithms (Spreen
et al., 2008). As a detection of high efficiency, the value of the polarization difference P of the brightness temperatures Tb should
be calculated at first:
P = Tb; v ¡ Tb; h;
(2)
where v refers to vertical and h refers to horizontal polarization.
LI Lanyu et al. Acta Oceanol. Sin., 2017, Vol. 36, No. 1, P. 64–72
Then, a cubic polynomial function of P is used to fit the sea ice
concentration c from 0% to 100%:
c = 1:640 £ 10¡5 P 3 ¡ 0:001 618P 2 + 0:019 16P + 0:971 0: (3)
On the basis of AMSR-E ice concentration grid, all aerial
photo-derived concentrations within one AMSR-E cell (6.25 km)
from the same day are averaged for comparison with the corresponding AMSR-E concentration. The average value of the ice
concentration over the observation area of each flight during the
CHINARE2010 is also compared with the AMSR-E data (north
latitude limitation is 87.5°N).
4 Results
4.1 Accuracy assessment
We randomly select 154 (7% of totals) photos from the two
flights to show the accuracy assessment of aerial photo classification. Visual interpretation results of the aerial photos are used as
the “true values” to assess the classification accuracy (Table 2). A
higher value of each accuracy parameter means more obvious
differentiation among sea ice surface features, and the physical
significance of each parameter is listed in the notes under Tables
2 and 3 . The accuracy for open water and sea ice is very high,
with a user accuracy (UA) of 99%. The UA for melt pond is 83%,
because the melt pond could be misclassified as water when it is
too deep (albedo closer to 0.1–0.2) or as sea ice when it is refrozen with either thin ice or snow on top (albedo closer to
0.4–0.5) . The producer accuracy (PA) for sea ice and melt pond is
also very high, 97% and 95%, respectively. Figure 3 shows examples of the classification results. Table 3 shows the classification accuracy of 154 photos randomly selected from the two
flights, with 87%–91% for the overall accuracy and 0.80–0.86 for
the kappa coefficients (KC). All aforementioned error metrics in-
67
dicate that the classification accuracies are high and that the
classified results are reliable (Congalton, 1991).
Table 2. Classification accuracy of the aerial photos of the two
flights
Class
Open water Sea ice Melt ponds Total UA/%
Open water
342 147
993
4 150
347 290 99
Sea ice
1 376
653 357
12 881 667 614 98
Melt ponds
54 212
8 921
307 813 370 946 83
Total
397 735
663 271
324 844 1 385 850
PA/%
86
97
95
Note: PA (producer accuracy) is the ratio of the number of pixels
correctly classified as a given class to the total number that actually
belong to that class and UA (user accuracy) is the ratio of number of
pixels correctly classified as a given class to total number of pixels
classified as that class.
Table 3. Overall accuracy and kappa coefficients of the aerial
photos classification of the two flights
Flight No. Time (UTC)
Photos
OA/%
KC
F1
August 16
58
87
0.80
F2
August 19
96
91
0.86
Note: OA (overall accuracy) is the ratio of number of correct
pixels to total number of pixels and KC (kappa coefficient) is a coefficient for evaluating agreement between the classified and reference
images.
4.2 Area fraction
The results from individual images along the flight lines are
plotted in Fig. 4 and summarized in Tables 4 and 5 . The sea ice
(AI), melt ponds (AP) and open water (AW) fractions along each
flight track are shown. There was a considerable spatial variability along the trajectory of each flight and between the two flights.
For the flight F1, there were two segments (in the first 14 km and
Fig. 3. The aerial photographs selected (a and c) and classified results (b and d).
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Fig. 4. Area fractions of ice or snow, melt ponds and open water along the aerial survey flight paths on August 16 (160 km) (a), August
19, 2010 (205 km) (b) and August 19, 2010 (226 km) (c).
Table 4. The average values of area fraction of sea ice (I) (including snow-covered and bare ice), melt ponds (P) and open water (W),
a really averaged albedo and ice concentration from the aerial survey (A) and AMSR-E (S) on August 16 (F1) and August 19, 2010 (F2)
Area fraction
Ice concentration /%
Albedo (0–1)
I
P
W
A
S
A
S
F1
87.0°–87.5°N
0.50
0.08
0.42
58
83
0.42
0.63
F2
87.0°–89.2°N
0.55
0.11
0.34
66
/
0.42
/
87.0°–87.5°N
0.63
0.14
0.23
77
88
0.46
0.58
F21)
Average
0.61
0.52 (0.562))
0. 10 (0.112)) 0.38 (0.332))
62 (682))
862)
0.42 (0.442))
Note: F21) denotes a part of F2 flight, with latitude ranging 87.0°–87.5°N, and 2) denotes the average value of F21) in order to compare with
the AMSR-E in a similar latitude range.
Flight No.
Latitude
Table 5. The average values of areal fractions, ice concentration
and areal average albedo in different latitude ranges along the
flight on August 19, 2010
North latitude/(°)
<87.5
87.5–88.0
88.0–88.5
88.5–89.0
>89.0
Average
Ice or
snow
0.63
0.42
0.63
0.60
0.46
0.55
Melt
ponds
0.14
0.08
0.10
0.12
0.12
0.11
Open
water
0.23
0.50
0.27
0.28
0.42
0.34
Ice concentration/%
77
50
73
72
58
66
Albedo
(0–1)
0.46
0.33
0.45
0.44
0.36
0.42
closer to the end (128–139 km)), the water fraction was 0, with
100% ice concentration. But most of the area, there was substantial open water, even up to 100%, with mean fraction of 0.42 for
the entire flight path. The fraction of open water under Fa2 varied considerably, with the maximum 0.50 in the area
87.5°–88.0°N, followed by 0.42 in the North Pole region (north of
89.0°N) (Table 5). The mean fraction of open water was 0.34, less
than 0.42 of F1. The fraction of melt ponds fluctuated around 0.11
(Table 5).
A large open water area was present during most of the aerial
observations. The open water accounted for an average of 38% of
the total area covered by the two flights from 87.0°N to 89.2°N
(Table 4). In addition, the maximum pond fraction during the
flights was approximately 0.14. The results from this cruise are
similar to the situation previously encountered by the HOTRAX
2005 cruise in the summer of 2005 (Darby et al., 2005; Perovich et
al., 2009). They found that there was a major exception from
88.4°N to 89.5°N (from 8 to 11 September), where there was a
large area of open water, nilas and thin young ice for over 100 km
of the cruise track (Darby et al., 2005; Perovich et al., 2009).
4.3 Albedo
The resulting albedos according to the fractional area-averaged calculation [Eq. (1)] for the two flights are shown in Fig. 5a,
with the average albedo for each flight summarized in Tables 4
and 5 . Along each flight track, the albedo for individual scenes
ranged from 0.07 to 0.75. The smallest albedo occurred in the
photos with the largest area fraction (1.00) of open water, and the
largest albedo was in the photos with the largest area fraction
(0.98) of snow-covered ice. For example, the albedo was low
overall along the F1, except for the first 10 km where the albedo
was greater than 0.60. The albedo along the F2 (August 19) varied greatly with an overall decreasing trend northward except for
the latitude range of 87.5°–88.0°N where the average albedo 0.33
was the lowest (Table 5). This exception was due to a much higher fraction of open water in this portion (50%). The average albedo over the two flights was approximately 0.42, which was
slightly lower than the value of 0.49 on August 22, 2005 (HOTRAX 2005) (Perovich et al., 2009) in the similar latitudinal area.
These results can be explained by the appearance of large open
water areas in 2010 (Table 4).
Using the same Eq. (1) and parameter values, we calculate the
albedo based on the sea ice concentration of the AMSR-E. The
results indicate that the albedos are higher than those estimated
with the aerial photographs (Table 4).
4.4 Concentration
According to Eq. (1), the sea ice concentration refers to the
coverage of snow-covered ice and melt ponds, and is shown in
both Fig. 5b and Table 4. The mean ice concentration of 58% (F1)
is less than the 87% from the CHINARE2008 cruise for the similar
latitude region between 86.8°N and 86.9°N (Lu et al., 2010). The
ice concentration in the higher latitude areas (>85°N) in 2005
(HOTRAX) was over 90% (Darby et al., 2005; Perovich et al.,
2009), which is much higher than the 62% in 2010 (F2) in this
study.
Comparisons of the ice concentrations derived from both the
aerial photos and the AMSR-E are shown in Fig. 6. The r2 (coefficient of determination) value (0.545) on August 16 (Fig. 6a) is
close to that of Xie et al. (2013) where they compared the shipbased observation of the ice concentration and the AMSR-E concentration for the same cruise but mostly at the lower latitudes.
LI Lanyu et al. Acta Oceanol. Sin., 2017, Vol. 36, No. 1, P. 64–72
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Fig. 5. Areally averaged albedo (a) and ice concentration (b) along the aerial survey flight paths on August 16 (F1) and August 19,
2010 (F2).
Fig. 6. Scatter plots of sea ice concentrations derived from both the aerial observations and the AMSR-E on August 16 (a) and August
19, 2010 (b).
The r 2 value on August 19 (Fig. 6b) is even greater (0.66). As
shown in Table 4, the mean ice concentration from the AMSR-E
was larger than the corresponding mean ice concentration of the
aerial photos of F1 and F2 and was approximately 18% higher for
the entire area relative to the aerial photos. This situation was
also observed during the CHINARE2008 (14% higher) (Lu et al.,
2010).
The higher sea ice concentration and albedo based on an
AMSR-E estimation as compared with those from the aerial photos could be due to three reasons. The first one could be the
coarse resolution of the AMSR-E. Some small leads or cracks
among the floes cannot be recognized by the AMSR-E sensor,
whereas those small water bodies are clearly classified in the aerial photos in this study. Therefore, although an increase of the
water fraction from the aerial photos was observed, the AMSR-E
did not see this increase, resulting in a higher estimation of the
sea ice concentration relative to that from the aerial photos. The
second possible reason could be the fixed tie points currently
used for operational calculations (P0 = 47 K for open water, and
P1 = 11.7 K for closed ice cover). Owing to the enhanced atmospheric and weather influence on the brightness temperature
during the summer months and the change in radiometric properties of the ice caused by melting and refreezing, several studies
have found seasonal, monthly or even daily adapted tie-points to
be useful (Spreen et al., 2008). Comparisons with shipborne observations also argued for such a difference for the ASI algorithm
during summer (Heygster et al., 2009), since the ship route might
be biased toward lower ice concentrations compared with the
daily AMSR-E SIC product, which is a daily average over several
overlapping swaths. Finally, the third reason could be a temporal
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sampling difference. The AMSR-E has a wide swath and instantly
acquires an image of a large area at 01:30 pm local time, while the
helicopter takes the aerial photos one-by-one along the flight.
Therefore, the acquisition time for those images isnot identical.
These conditions result in differences in th e sea ice concentration estimation between the aerial observations and satellite
methods.
5 Discussion
The two aerial flights examined in this study were conducted
in the later summer of 2010 (August 16 and 19) when significant
surface melting and bottom melting have already occurred. Melt
ponds and pond networks were extensively developed and observed all the way to the North Pole throughout the flight path
(F2). Only a few very large ice floe (mostly multiyear ice) existed
where the pond fraction was less than 20% and pond network
was not well developed, such as the one (over 100 km2) with the
12 d drift ice station/camp located at (Xie et al., 2013). Two types
of melt ponds were generally observed: blue melt ponds, occurring predominantly over multiyear ice, while black or dark melt
ponds, occurring over first-year ice (or melting through the sea
ice). Most of the well-developed pond networks had dark-colored
ponds. Ponds that melted through could further speed the sea ice
melt.
In the high latitude area between 84.0°–87.5°N and 170°–
180°W, the monthly mean ice concentrations in August, derived
from the AMSR-E, are basically higher than 90% from 2002 to
2011 (Fig. 7), including the HOTRAX 2005 and the CHINARE2008
(Fig. 8), except 2007 and 2010. The sea ice extent of the Arctic
Ocean in 2007 has been the first low ice extent in satellite era
since the 1979. The sea ice concentration was exceptionally low
in this area in August 2010 (Fig. 8c), and it was even lower on August 19 (Fig. 8e).
The arctic sea ice extent in the summer of 2010 was the fifth
lowest in past years, with the summer of 2012 as the new lowest
(3.41×106 km2) in the satellite record (http://nsidc.org/arcticseaicenews/2012/09/arctic-sea-ice-extent-settles-at-record-seasonal-minimum/). A continuous zone of the low ice concentration was detected remotely across the central Arctic Ocean Basin
(Cavalieri and Parkinson, 2012). Because all of these phenomena
occurred within the transpolar or circumpolar regions in the center of arctic ice pack, named as transpolar melting zones (Rabenstein et al., 2010; Stanton et al., 2012; Tucker III et al., 1999),
which were caused either by the heavy sea ice melt and/or sea ice
drift. In the past decade, the low ice concentration strips started
to appear in the central arctic sea ice pack near the North Pole
(Perovich et al., 2009). These transpolar melting zones were separately dotted into the multiyear pack sea ice zones, usually in
August. However, in the summer of 2010, these zones covered a
much larger area and almost completely traversed the multiyear
transpolar melting zones in the central Arctic Ocean Basin (Cavalieri and Parkinson, 2012).
Anomalous sea-ice reduction in the Eurasian Basin of the
Arctic Ocean during the summer of 2010 was associated with a
wind-driven circulation (low pressure), and it appears that the
wind-forced sea ice divergence led to enhanced absorption of incident solar energy in the expanded areas of open water and thus
to increased sea ice melt (Timmermans et al., 2011). At the same
Fig. 7. The variation of the monthly (August) mean sea ice concentration derived from the AMSR-E between 84.0°–87.5°N and
170°–180°W from 2002 to 2011, with the two daily ice concentrations on August 16 and August 19, 2010.
Fig. 8. The sea ice concentration between 84.0°–87.5°N and 170°–180°W, with monthly average in August 2005 (a), August 2008 (b),
August 2010 (c), and daily concentration on August 16, 2010 (d) and August 19, 2010 (e).
LI Lanyu et al. Acta Oceanol. Sin., 2017, Vol. 36, No. 1, P. 64–72
time, the sea ice concentration was reduced by 30%–40% while
the low pressure persisted (Kawaguchi et al., 2012).
6 Conclusions
The arctic sea ice cover undergoes a dramatic change in surface conditions in response to summer melt. The surface changes
from a homogeneous and highly reflective surface to a heterogeneous mixture of bare or snow-covered ice, melt ponds and
open water. Changes in the surface conditions near the North
Pole are documented by analyzing the aerial photographs from
helicopter survey flights flown on August 16 and 19, 2010 during
the CHINARE2010 cruise. The aerial surveys covered north parts
of the ice camp, with one long track to the North Pole. The average fraction of open water increased from the ice camp at 87.0°N
towards the North Pole, while an opposite trend occurred for
snow-covered ice or bare ice. The melt pond and open water
fractions obtained in this study could assist modelers in evaluating parameterizations of the evolution of the ice pack during the
summer melt season.
The albedo variations are consistent with those of the areal
fraction along the two flight tracks derived from the aerial photography. The comparisons indicate that the average albedo of this
study is slightly lower than that of the HOTRAX 2005 (Perovich et
al., 2009). The albedo decreased from the ice camp to the North
Pole. The key factors contributing to this reduction were the decrease in sea ice, the increase in melt pond and the open water
fractions. The calculated sea ice concentration according to the
classified areal fractions of the aerial photos for each flight reveals that it varies a lot along the flight track; the average value is
62% for the two flights. The sea ice concentrations from the sea
ice observations and AMSR-E data exhibit similar spatial patterns, but the AMSR-E concentrations were approximately 18%
higher than those from the aerial photos.
In fact, many good quality satellite products in the polar region are not continuous, especially in areas close to the poles. As
important remote sensing data, the aerial photos in the polar regain exhibit unique advantages. On the one hand, the aerial images are necessary supplement for null value zone of remote
sensing images. On the other hand, the accuracy of satellite
products can be verified by the photos, which is an excellent
method for reducing errors resulting from rough resolution of
satellite sensors. In addition, “true value” of the aerial images is
an intuitive reflection of surface features. Training samples can
be easily selected for further supervised classification.
The aerial observations in this study provide useful information regarding the surface features of arctic sea ice, particularly in
the satellite pole hole. A mean albedo values for pond, open water and ice pack are obtained by constructing the observed albedo from the areal fractions of each surface type. The a rctic sea
ice is retreating rapidly, and an ice-free arctic in the summer may
occur sometime within the foreseeable future (Wang and Overland, 2009). The aerial observations may play a more important
role in providing high-resolution independent estimates to validate numerical models and remote sensing products. More analyses about variations in melt pond and surface albedo under the
rapid decay of arctic sea ice are needed in the future.
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