Climate Change Indicators in the United States Technical Documentation Arctic Sea Ice

Arctic Sea Ice

Identification

1. Indicator Description

This indicator tracks the extent, age, and melt season length of sea ice on the Arctic Ocean. The extent
of Arctic sea ice is considered a particularly sensitive indicator of global climate because a warmer
climate will reduce the amount of sea ice present. The proportion of sea ice in each age category can
indicate the relative stability of Arctic conditions as well as susceptibility to melting events. The timing of
melt and freeze onset dates and the length of melt season are also important indicators of Arctic sea ice
conditions. An earlier melt onset allows for earlier development of open water areas that in turn
enhance the ice-albedo feedback (Markus et al., 2009). The open water season is important for certain
human activities (e.g., boating, access to natural resources) but restricts other activities (e.g., indigenous
populations' hunting and transportation on ice) and affects wildlife (e.g., polar bear access to food
sources).

Components of this indicator include:

• Changes in the March and September average extent of sea ice in the Arctic Ocean since 1979
(Figure 1).

• Changes in the proportion of Arctic sea ice in various age categories at the September weekly
minimum since 1983 (Figure 2).

• Changes in the start, end, and total length of the Arctic sea ice melt season since 1979 (Figure
3).

2. Revision History

April 2010:
December 2012:
May 2014:

June 2015:

December 2015:
April 2016:
August 2016:

Indicator published.

Updated indicator with data through 2012.

Updated indicator with data through 2013.

Updated indicator on EPA's website with data through 2014. Added annual March sea
ice extent to Figure 1 with data through 2015.

Updated indicator on EPA's website with data through September 2015.

Updated indicator on EPA's website with data through March 2016.

Added Figure 3 to show melt season length.

Data Sources

3. Data Sources

Figure 1 (extent of sea ice) is based on monthly average sea ice extent data provided by the National
Snow and Ice Data Center (NSIDC). NSIDC's data are derived from satellite imagery collected and
processed by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center
(GSFC). NSIDC also provided Figure 2 data (age distribution of sea ice), which are derived from weekly

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NASA satellite imagery and processed by the team of Maslanik and Tschudi at the University of
Colorado, Boulder. Data in Figure 3 come from an analysis conducted by NASA. It is an updated version
of an analysis originally published by Markus et al. (2009).

4. Data Availability

Figure 1. March and September Monthly Average Arctic Sea Ice Extent, 1979-2016

Users can access monthly map images, geographic information system (GlS)-compatible map files, and
gridded daily and monthly satellite data, along with corresponding metadata, at:
http://nsidc.org/data/seaice index/archives.html. From this page, users can also download monthly
extent and area data. From this page, select "FTP Directory" under the "Monthly Extent and
Concentration Images" heading, which will lead to a public FTP site

(ftp://sidads.colorado.edu/DATASETS/NOAA/GQ2135). To obtain the March or September monthly data
that were used in this indicator, select the "Mar" or "Sep" directory, then choose the "...area.txt" file
with the data. To see a different version of the graph in Figure 1 (plotting percent anomalies rather than
square miles), return to the parent directory and open the "...plot.png" image.

NSIDC's Sea Ice Index documentation page (http://nsidc.org/data/docs/noaa/g02135 seaice index)
describes how to download, read, and interpret the data. It also defines database fields and key
terminology. Gridded source data developed by NASA GSFC can be found at:
http://nsidc.org/data/nsidc-0051.html and: http://nsidc.org/data/nsidc-0081.html.

Figure 2. Age of Arctic Sea Ice at Minimum September Week, 1983-2015

NSIDC published a map version of Figure 2 at: http://nsidc.org/arcticseaicenews/2015/10/2Q15-melt-
season-in-review. EPA obtained the data shown in the figure by contacting NSIDC User Services. The
data are processed by Dr. James Maslanik and Dr. Mark Tschudi at the University of Colorado, Boulder,
and provided to NSIDC. Earlier versions of this analysis appeared in Maslanik et al. (2011) and Maslanik
et al. (2007).

Satellite data used in historical and ongoing monitoring of sea ice age can be found at the following
websites:

• Defense Meteorological Satellite Program (DMSP) Scanning Multi Channel Microwave
Radiometer (SMMR): http://nsidc.org/data/nsidc-0071.html.

• DMSP Special Sensor Microwave/lmager (SSM/I): http://nsidc.org/data/nsidc-0001.html.

• DMSP Special Sensor Microwave Imager and Sounder (SSMIS): http://nsidc.org/data/nsidc-
0001.html.

• NASA Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E and
AMSR2): http://nsidc.org/data/amsre.

• Advanced Very High Resolution Radiometer (AVHRR): http://nsidc.org/daac/data-sets.html.

Age calculations also depend on wind measurements and on buoy-based measurements and ice motion
vectors. Wind measurements (as surface flux data) are available at:

www.esrl.noaa.gov/psd/data/reanalvsis/reanalvsis.shtml. Data and metadata are available online at:
http://iabp.apl.washington.edu/data.html and: http://nsidc.org/data/nsidc-0116.html.

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Figure 3. Arctic Sea Ice Melt Season, 1979-2015

Figure 3 is based on many of the same satellite datasets as Figures 1 and 2, including the SMMR, SSM/I,
SSMIS, and AMSR. Gridded datasets and summary graphs are publicly available at:
http://neptune.gsfc. nasa.gov/csb/index.php?section=54. The NASA team that conducts the analysis
provided data from the summary graphs to allow the time series to be replicated for EPA's indicator. In
the future, these summary numbers are expected to be posted on NASA's website along with the
gridded data and summary graphs.

Methodology

5. Data Collection

This indicator is based on maps of sea ice extent in the Arctic Ocean and surrounding waters, which
were developed using brightness temperature imagery in the microwave wavelengths collected by
satellites. Data from October 1978 through June 1987 were collected using the Nimbus-7 SMMR
instrument, and data since July 1987 have been collected using a series of successor SSM/I instruments.
In 2008, the SSMIS replaced the SSM/I as the source for sea ice products. These instruments can identify
the presence of sea ice because sea ice and open water have different passive microwave signatures.
They can also identify the surface temperature and detect whether free water is present on the surface
of the ice or within the snowpack on top of the ice—information that helps to identify the start and end
dates of the melt season.

The satellites that supply data for this indicator orbit the Earth continuously, collecting images that can
be used to generate daily maps of sea ice extent. They are able to map the Earth's surface with a
resolution of 25 kilometers. The resultant maps have a nominal pixel area of 625 square kilometers.
Because of the curved map projection, however, actual pixel sizes range from 382 to 664 square
kilometers.

The satellites that collect the data cover most of the Arctic region in their orbital paths, but the sensors
cannot collect data from a circular area immediately surrounding the North Pole due to orbit inclination.
From 1978 through June 1987, when coverage was from the SMMR instrument, this "pole hole"
measured 1.19 million square kilometers. From July 1987 through December 2007, when coverage was
from the SSM/I instrument, it measured 0.31 million square kilometers. Since January 2008, using
SSMIS, it has measured 0.029 million square kilometers. For more information about this spatial gap and
how it is corrected in the final data, see Section 6.

To calculate the age of ice (Figure 2), the SSM/I, SSMIS, and SMMR imagery have been supplemented
with three additional data sets:

• AVHRR satellite data, which come from an optical sensing instrument that can measure sea ice
temperature and heat flux, which in turn can be used to estimate thickness. AVHRR also covers
the "pole hole."

• Maps of wind speed and direction at 10 meters above the Earth's surface, which were compiled
by the National Oceanic and Atmospheric Administration's (NOAA's) National Centers for
Environmental Prediction (NCEP).

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• Motion vectors that trace how parcels of sea ice move, based on data collected by the
International Arctic Buoy Programme (IABP). Since 1979, the IABP has deployed a network of
14 to 30 in situ buoys in the Arctic Ocean that provide information about movement rates at
six-hour intervals.

While direct estimates of sea ice thickness can be obtained from airborne and satellite systems using
laser and radar altimeters, as well as from submarines using sonar, these data sources cannot provide
sufficiently long and consistent time series to be used as an indicator.

For documentation of passive microwave satellite data collection methods, see the summary and
citations at: http://nsidc.org/data/docs/noaa/g02135 seaice index. For further information on AVHRR
imagery, see: http://noaasis.noaa.gov/NOAASIS/ml/avhrr.html. For motion tracking methods, see
Maslanik et al. (2011), Fowler et al. (2004), and: http://nsidc.org/data/nsidc-0116.html.

6. Indicator Derivation

Figure 1. March and September Monthly Average Arctic Sea Ice Extent, 1979-2016

Satellite data are used to develop daily ice extent and concentration maps using an algorithm developed
by NASA. Data are evaluated within grid cells on the map. Image processing includes quality control
features such as two weather filters based on brightness temperature ratios to screen out false positives
over open water, an ocean mask to eliminate any remaining sea ice in regions where sea ice is not
expected, and a coastal filter to eliminate most false positives associated with mixed land/ocean grid
cells.

From each daily map, analysts calculate the total "extent" and "area" covered by ice. These terms are
defined differently as a result of how they address those portions of the ocean that are partially but not
completely frozen:

• Extent is the total area covered by all pixels on the map that have at least 15-percent ice
concentration, which means at least 15 percent of the ocean surface within that pixel is frozen
over. The 15-percent concentration cutoff for extent is based on validation studies that showed
that a 15-percent threshold provided the best approximation of the "true" ice edge and the
lowest bias. In practice, most of the area covered by sea ice in the Arctic far exceeds the 15-
percent threshold, so using a higher cutoff (e.g., 20 or 30 percent) would yield different totals
but similar overall trends (for example, see Parkinson et al., 1999).

• Area represents the actual surface area covered by ice. If a pixel's area were 600 square
kilometers and its ice concentration were 75 percent, then the ice area for that pixel would be
450 square kilometers. At any point in time, total ice area will always be less than total ice
extent.

EPA's indicator addresses extent (the area within the 15-percent concentration contour) rather than
area (the area-integrated concentration). Both of these measurements are valid ways to look at trends
in sea ice, but in this case, EPA chose to look at the time series for extent because it is more complete
than the time series for area. In addition, the available area data set does not include the "pole hole"
(the area directly above the North Pole that the satellites cannot cover), and the size of this unmapped
region changed as a result of the instrumentation changes in 1987 and 2008, creating a discontinuity in
the area data. In contrast, the extent time series assumes that the entire "pole hole" area is covered

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with at least 15 percent ice, which is a reasonable assumption based on other observations of this area.
See https://nsidc.org/data/clocs/noaa/g02135 seaice index/#pole hole for more information about the
"pole hole" and how NASA's data address it.

NASA's processing algorithm includes steps to deal with occasional days with data gaps due to satellite
or sensor outages. These days were removed from the time series and replaced with interpolated values
based on the total extent of ice on the surrounding days.

From daily maps and extent totals, NSIDC calculated monthly average extent in square kilometers. EPA
converted these values to square miles to make the results accessible to a wider audience. By relying on
monthly averages, this indicator smooths out some of the variability inherent in daily measurements.

Figure 1 shows trends in March and September average sea ice extent. September is when Arctic sea ice
typically reaches its annual minimum, after melting during the summer months. By looking at the month
with the smallest extent of sea ice, this indicator focuses attention on the time of year when limiting
conditions would most affect wildlife and human societies in the Arctic region. Six months later, March
is when Arctic sea ice typically reaches its annual maximum, after cold winter months freeze new ice.
Presenting the month with the greatest extent of sea ice highlights the extent to which the Arctic region
recovers melted sea ice.

This indicator does not attempt to estimate values from before the onset of regular satellite mapping in
October 1978 (which makes 1979 the first year with March and September data for this indicator). It
also does not attempt to project data into the future.

For documentation of the NASA Team algorithm used to process the data, see Cavalieri et al. (1984) and:
http://nsidc.org/data/nsidc-0051.html. For more details about NSIDC methods, see the Sea Ice Index
documentation and related citations at: http://nsidc.org/data/docs/noaa/g02135 seaice index.

Other months of the year were considered for this indicator, but EPA chose to focus on March and
September, which represent the annual maximum and minimum extent of sea ice. September extent is
often used as an indicator. One reason is because as temperatures start to get colder, there may be less
meltwater on the surface than during the previous summer months, thus leading to more reliable
remote sensing of ice extent, as the passive microwave signal is influenced by surface meltwater.
Increased melting during summer months leads to changes in the overall character of the ice (i.e., age
and thickness) and these changes have implications throughout the year. Thus, September conditions
are particularly important for assessing the overall health of Arctic sea ice. Conversely, March is the
month when sea ice experiences its peak extent for the year.

Evidence shows that the extent of Arctic sea ice has declined in all months of the year. Comiso (2012)
examined the seasonal pattern in Arctic sea ice extent for three decadal periods plus the years 2007,
2009, and 2010 and found declines throughout the year. Figure TD-1 shows monthly means based on an
analysis from NSIDC—the source of data for this indicator. It reveals that Arctic sea ice extent has
generally declined over time in all months, with the most pronounced decline in the summer and fall.

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Figure TD-1. Arctic Sea Ice Extent for Each Month, 1978/1979-2015/2016

March
^—February

April
^—January

May
¦^—December
June

November
July
^—October
^—August
September

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

Data source: NSIDC: http://nsidc.org/data/seaice index/archives.html. Accessed April 2016.

Figure 2. Age of Arctic Sea Ice at Minimum September Week, 1983-2015

A research team at the University of Colorado at Boulder processes daily sequential SSM/I, SMMR,
AMSR-E, and AVHRR satellite data from NASA, then produces maps using a grid with 12 kilometer (km)-
by-12 km cells. The AVHRR data help to fill the "pole hole" and provide information about the
temperature and thickness of the ice. Like Figure 1, this method classifies a pixel as "ice" if at least 15
percent of the ocean surface within the area is frozen over. Using buoy data from the IABP, motion
vectors for the entire region are blended via optimal interpolation and mapped on the gridded field.
NCEP wind data are also incorporated at this stage, with lower weighting during winter and higher
weighting during summer, when surface melt limits the performance of the passive microwave data.
Daily ice extent and motion vectors are averaged on a weekly basis. Once sea ice reaches its annual
minimum extent (typically in early September), the ice is documented as having aged by one year. For
further information on data processing methods, see Maslanik et al. (2011), Maslanik et al. (2007), and
Fowler et al. (2004). Although the most recently published representative study does not utilize AMSR-E
brightness data or NCEP wind data for the calculation of ice motion, the results presented in Figure 2
and the NSIDC website incorporate these additional sources.

Figure 2 shows the extent of ice that falls into several age categories. Whereas Figure 1 extends back to
1979, Figure 2 can show trends only back to 1983 because it is not possible to know how much ice is five
or more years old (the oldest age class shown) until parcels of ice have been tracked for at least five
years. Regular satellite data collection did not begin until October 1978, which makes 1983 the first year
in which September minimum ice can be assigned to the full set of age classes shown in Figure 2.

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Like Figure 1, Figure 2 is based on the most recent data available. The December 2015 data update
involved a slight adjustment that NSIDC applied to previous data points to better account for the "pole
hole."

Figure 3. Arctic Sea Ice Melt Season, 1979-2015

Figure 3 depends on a consistent definition of "start date" and "end date" for the annual Arctic sea ice
melt season. Start date refers to the date on which free water begins to be consistently present within
the snowpack on the surface of the ice (i.e., wet snow). Consistency is important to distinguish this melt
onset date from earlier melt events that might not have persisted. End date refers to the date on which
the surface temperature begins to stay consistently at the freezing point of water, and ice begins to
form in the open ocean. Consistency is important to distinguish the freeze date from early freeze events.
Markus et al. (2009) provides more detail about these definitions and how they relate to definitions
used in other analyses.

NASA conducted the analysis by dividing the Arctic Ocean into a grid and determining each year's melt-
season start and end dates for each individual grid cell. NASA averaged the dates across all grid cells to
derive an average start date and an average end date for the entire Arctic region. Figure 3 shows the
aggregated regionwide averages. For additional detail about these methods, see Markus et al. (2009).

NASA reports start and end dates in terms of Julian days (i.e., the number of days since January 1).

Figure 3 is based on Julian days, but the corresponding non-leap year calendar dates have been added to
the y-axis to provide a more familiar frame of reference. This means that a melt date of May 31 in a leap
year will actually be plotted at the same level as June 1 from a non-leap year, for example, and it will
appear to be plotted at June 1 with respect to the y-axis. Plotting the data this way facilitates consistent
year-to-year comparison.

7. Quality Assurance and Quality Control

Image processing includes a variety of quality assurance and quality control (QA/QC) procedures,
including steps to screen out false positives (i.e., ice is detected where it is not actually present). These
procedures are described in NSIDC's online documentation at:

http://nsidc.org/data/docs/noaa/g02135 seaice index as well as in some of the references cited
therein.

NSIDC Arctic sea ice data have three levels of processing for QC. NSIDC's most recent data come from
the Near Real-Time SSM/I Polar Gridded Sea Ice Concentrations (NRTSI) data set. NRTSI data go through
a first level of calibration and quality control to produce a preliminary data product. The final data are
processed by NASA's GSFC, which uses a similar process but applies a higher level of QC. Switching from
NRTSI to GSFC data can result in slight changes in the total extent values—on the order of 50,000 square
kilometers or less for total sea ice extent.

GSFC processing requires several months of lag time. At the time EPA published this report, the GSFC
data for 2015 had not yet been finalized.

Melt-season data for Figure 3 undergo a set of key QA/QC procedures as described in Markus et al.
(2009). For example, to eliminate spurious data points, the results from each individual grid cell are
compared with results from neighboring cells. Wide discrepancies between neighbors lead to potentially

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erroneous data points being excluded from subsequent analysis. NASA also checks results through
comparisons with other similar datasets.

Analysis

8. Comparability Over Time and Space

All three figures for this indicator are based on data collection methods and processing algorithms that
have been applied consistently over time and space. NASA's satellites cover the entire area of interest
with the exception of the "pole hole" for Figure 1. Even though the size of this hole has changed over
time, EPA's indicator uses a data set that corrects for this discontinuity.

The total extent shown in Figure 2 (the sum of all the stacked areas) differs from the total extent shown
for September in Figure 1 because Figure 2 shows conditions during the specific week in September
when minimum extent is reached, while the series in Figure 1 shows average conditions over the entire
month of September. It would not make sense to convert Figure 2 to a monthly average for September
because all ice is "aged" one year as soon as the minimum has been achieved, which creates a
discontinuity after the minimum week.

9. Data Limitations

Factors that may impact the confidence, application, or conclusions drawn from this indicator are as
follows:

1. Variations in sea ice are not entirely due to changes in atmospheric or ocean temperature.

Other conditions, such as fluctuations in oceanic and atmospheric circulation and natural annual
and decadal variability, can also affect the extent of sea ice, and by extension the sea ice age
indicator.

2. Changes in the age and thickness of sea ice—for example, a trend toward younger or thinner
ice—might increase the rate at which ice melts in the summer, making year-to-year
comparisons more complex.

3. Many factors can diminish the accuracy of satellite mapping of sea ice. Although satellite
instruments and processing algorithms have improved somewhat over time, applying these new
methods to established data sets can lead to trade-offs in terms of reprocessing needs and
compatibility of older data. Hence, this indicator does not use the highest-resolution imagery or
the newest algorithms. Trends are still accurate, but should be taken as a general representation
of trends in sea ice extent, not an exact accounting.

4. As described in Section 6, the threshold used to determine extent—15-percent ice cover within
a given pixel—represents an arbitrary cutoff without a particular scientific significance.
Nonetheless, studies have found that choosing a different threshold would result in similar
overall trends. Thus, the most important part of Figure 1 is not the absolute extent reported for
any given year, but the size and shape of the trend over time.

5. Using ice surface data and motion vectors allows only the determination of a maximum sea ice
age. Thus, as presented, the Figure 2 indicator indicates the age distribution of sea ice only on

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the surface and is not necessarily representative of the age distribution of the total sea ice
volume.

10. Sources of Uncertainty

NSIDC has calculated standard deviations along with each monthly ice concentration average. NSIDC's
Sea Ice Index documentation (http://nsidc.org/data/docs/noaa/g02135 seaice index) describes several
analyses that have examined the accuracy and uncertainty of passive microwave imagery and the NASA
Team algorithm used to create this indicator. For example, a 1991 analysis estimated that ice
concentrations measured by passive microwave imagery are accurate to within 5 to 9 percent,
depending on the ice being imaged. Another study suggested that the NASA Team algorithm
underestimates ice extent by 4 percent in the winter and more in summer months. A third study that
compared the NASA Team algorithm with new higher-resolution data found that the NASA Team
algorithm underestimates ice extent by an average of 10 percent. For more details and study citations,
see: http://nsidc.org/data/docs/noaa/g02135 seaice index. Certain types of ice conditions can lead to
larger errors, particularly thin or melting ice. For example, a melt pond on an ice floe might be mapped
as open water. The instruments also can have difficulty distinguishing the interface between ice and
snow or a diffuse boundary between ice and open water. Using the September minimum minimizes
many of these effects because melt ponds and the ice surface become largely frozen by then. These
errors do not affect trends and relative changes from year to year.

NSIDC has considered using a newer algorithm that would process the data with greater certainty, but
doing so would require extensive research and reprocessing, and data from the original instrument (pre-
1987) might not be compatible with some of the newer algorithms that have been proposed. Thus, for
the time being, this indicator uses the best available science to provide a multi-decadal representation
of trends in Arctic sea ice extent. The overall trends shown in this indicator have been corroborated by
numerous other sources, and readers should feel confident that the indicator provides an accurate
overall depiction of trends in Arctic sea ice over time.

Accuracy of ice motion vectors depends on the error in buoy measurements, wind fields, and satellite
images. Given that buoy locational readings are taken every six hours, satellite images are 24-hour
averages, and a "centimeters per second (cm/sec)" value is interpolated based on these readings,
accuracy depends on the error of the initial position and subsequent readings. NSIDC proposes that "the
error would be less than 1 cm/sec for the average velocity over 24 hours"
(http://nsidc.org/data/docs/daac/nsidc0116 icemotion/buov.html).

Uncertainty has not yet been quantified for the detection and analysis of ice melt. While absolute error
may be fairly high in a given location for a given year, when considering the trends and variability for the
entire basin over a long time series, the results provide a fairly accurate indicator of change.

11. Sources of Variability

Many factors contribute to variability in this indicator. In constructing the indicator, several choices have
been made to minimize the extent to which this variability affects the results. The apparent extent of
sea ice can vary widely from day to day, both due to real variability in ice extent (growth, melting, and
movement of ice at the edge of the ice pack) and due to ephemeral effects such as weather, clouds and
water vapor, melt on the ice surface, and changes in the character of the snow and ice surface. The
intensity of Northern Annular Mode (NAM) conditions and changes to the Arctic Oscillation (specific

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patterns of variability in atmospheric circulation) also have a strong year-to-year impact on ice
movement. Under certain conditions, older ice might move to warmer areas and be subject to increased
melting. Weather patterns can also affect the sweeping of sea ice out of the Arctic entirely. For a more
complete description of major thermodynamic processes that impact ice longevity, see Maslanik et al.
(2007) and Rigor and Wallace (2004).

According to NSIDC's documentation at: http://nsidc.org/data/docs/noaa/g02135 seaice index, extent
is a more reliable variable than ice concentration or area. The weather and surface effects described
above can substantially impact estimates of ice concentration, particularly near the edge of the ice pack.
Extent is a more stable variable because it simply registers the presence of at least a certain percentage
of sea ice in a grid cell (15 percent). For example, if a particular pixel has an ice concentration of 50
percent, outside factors could cause the satellite to measure the concentration very differently, but as
long as the result is still greater than the percent threshold, this pixel will be correctly accounted for in
the total "extent." Monthly averages also help to reduce some of the day-to-day "noise" inherent in sea
ice measurements.

12. Statistical/Trend Analysis

The key points associated with Figure 3 report total change in melt dates, freeze dates, and melt season
duration. These changes are all based on ordinary least-squares linear regression slopes for the full
period of record (1979-2015). All three trends are significant to at least a 95-percent confidence level (p
< 0.05). Specific trends are as follows:

• Start date: -0.295 days/year (p < 0.001)

• End date: +0.730 days/year (p < 0.001)

• Duration: +1.025 days/year (p < 0.001)

This indicator does not report on the slope of the apparent trends in sea ice extent and age distribution,
nor does it calculate the statistical significance of these trends. However, several other peer-reviewed
publications (e.g., Cavalieri and Parkinson, 2012; Parkinson, 2014), have performed linear regressions on
these data and reported statistically significant decreases in both monthly sea ice extent and the extent
of multi-year sea ice. NSIDC's website provides the standard deviation for each trend shown in the
monthly sea ice extent anomaly graphs at: http://nsidc.org/data/seaice index.

References

Cavalieri, D.J., P. Gloersen, and W.J. Campbell. 1984. Determination of sea ice parameters with the
NIMBUS-7 SMMR. J. Geophys. Res. 89(D4):5355-5369.

Cavalieri, D.J., and C.L. Parkinson. 2012. Arctic sea ice variability and trends, 1979-2010. The Cryosphere
6:881-889.

Comiso, J. 2012. Large decadal decline of the Arctic multiyear ice cover. J. Climate 25(4):1176-1193.

Fowler, C., W.J. Emery, and J. Maslanik. 2004. Satellite-derived evolution of Arctic sea ice age: October
1978 to March 2003. IEEE Geosci. Remote S. 1(2):71—74.

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Markus, T., J.C. Stroeve, and J. Miller. 2009. Recent changes in Arctic sea ice melt onset, freezeup, and
melt season length. J. Geophys. Res. 114:C12024.

Maslanik, J.A., C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery. 2007. A younger, thinner
Arctic ice cover: Increased potential for rapid, extensive sea-ice loss. Geophys. Res. Lett. 34:L24501.

Maslanik, J., J. Stroeve, C. Fowler, and W. Emery. 2011. Distribution and trends in Arctic sea ice age
through spring 2011. Geophys. Res. Lett. 38:L13502.

Parkinson, C.L. 2014. Global sea ice coverage from satellite data: Annual cycle and 35-year trends. J.
Climate 27:9377-9382.

Parkinson, C.L., D.J. Cavalieri, P. Gloersen, H.J. Zwally, and J.C. Comiso. 1999. Arctic sea ice extents,
areas, and trends, 1978-1996. J. Geophys. Res. 104(C9):20837-20856.

Rigor, I.G., and J.M. Wallace. 2004. Variations in the age of Arctic sea-ice and summer sea-ice extent.
Geophys. Res. Lett. 31:L09401. http://iabp.apl.washington.edu/research seaiceageextent.html.

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