Towards a Satellite
Based Sea Ice Climate Data Record
Walter N. Meier
, Florence Fetterer
, Julienne Stroeve
, Donald J. Cavalieri
, Claire L. Parkinson
, Josefino C. Comiso
, and Ronald Weaver
National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309
NASA Goddard Space Flight Center, Greenbelt, MD 20771
Sea ice plays an important role in climate by reflecting incoming solar radiation,
modifying the salinity of the upper ocean during ice growth and melt, and insulating
the ocean from the atmosphere. Thus a reliable, complete, and consistent climate
data record of sea ice extent and area is important for climate studies.
Good quality satellite records of sea ice from passive microwave sensors dating
back to 1978 already exist. These records have been used to track interannual
variability in both the Arctic and the Antarctic [1, 2]. In the Arctic, a significant
downward trend in summer ice cover has been detected .
These records are among the longest and most consistent from satellites. However,
there are estimates from several algorithms and no single algorithm has proven to
be superior under all ice conditions. Thus, there is still potential to create higher
quality estimates to obtain a unified sea ice climate data record.
Physical Basis for Retrieval:
The character of naturally emitted microwave
radiation is particularly sensitive to the phase of water. In particular, the solid phase
of sea ice is generally distinct from the liquid ocean waters allowing sea ice to be
distinguished from the open ocean using satellite passive microwave data. There
are several complicating factors such as emission from melting snow or meltwater
on the ice surface, wind
roughened ocean, and water in the atmosphere. These
factors lead to uncertainties in retrievals of sea ice cover.
Passive Microwave Sensors:
The first satellite
borne passive microwave
sensor was the Electrically Scanning Microwave Radiometer (ESMR), launched in
1972. However, this was a single
channel radiometer and had several major data
gaps, limiting the usefulness of its sea ice products for climate studies. The launch
of the Scanning Multichannel Microwave Radiometer (SMMR) in 1978 marked the
beginning of near
continuous coverage of sea ice that continued through the launch
of a series of Special Sensor Microwave/Imagers (SSM/I) beginning in 1987 through
to the present. Since 2002, the NASA EOS Advanced Microwave Scanning
E) sensor, with more channels, increased spatial resolution, and
enhanced algorithms, has yielded improved sea ice estimates.
Sea Ice Algorithms:
Several algorithms have been developed to obtain
estimates of sea ice from the observed brightness temperatures. Two of the most
commonly used algorithms, the NASA Team  and Bootstrap , are presented
here. Both algorithms were developed at the NASA Goddard Space Flight Center
and the timeseries of sea ice conditions from the algorithms are archived at the
National Snow and Ice Data Center (NSIDC).
Sea Ice Products:
Time series of sea ice extent (total ocean area containing at
least 15% ice), sea ice area (total area covered by ice), and sea ice concentration
(percentage of area covered by ice) have been produced for both algorithms that
span the entire multichannel radiometer period, November 1978
products are already quite mature and contain numerous quality control
enhancements, including inter
sensor calibration to assure a consistent timeseries.
The products also filter contamination from mixed land
ocean pixels and weather
effects (from atmospheric emission and/or wind
roughening of the ocean). Missing
data are filled in through spatial and temporal interpolation to provide a complete
timeseries. The resulting daily and monthly estimates of sea ice cover, as well as
climatologies, browse imagery, and other ancillary data are available from NSIDC at:
. Browse images, animations, and monthly mean data
for the NASA Team algorithm are also available from NSIDC’s Sea Ice Index pages,
. Other sea ice data products include operational
ice charts from the U.S. National Ice Center  and other national ice services,
based fields such as the Hadley Center climatology , and fused fields
such as a combined ESMR
NIC timeseries ; these use a variety of
sources to provide complete fields for their given purposes.
Sea Ice Products
Timeseries of Arctic (left) and Antarctic (right) monthly sea ice extent from the Bootstrap
and NASA Team (Nov. 1978
Sep. 2005) algorithms; Jan. 2004
Sep. 2005 NASA Team values
from the Sea Ice Index, all other values from the NASA Goddard SMMR
SSM/I Timeseries. Tick
marks represent January of each year.
The NASA Team (NT) and Bootstrap (BT) algorithms
provide complete information on the ice cover
throughout the year (Figure 1). While care is taken to
insure each algorithm is internally consistent via inter
sensor calibration and quality control techniques, the
two algorithms yield different results, as illustrated in
Figure 2. The NT algorithm typically shows less ice
than the BT, particularly during summer. Each
algorithm uses a different approach to estimate sea ice
cover, including different passive microwave channels
and different reference brightness temperatures (T
For example, the NT algorithm uses brightness
temperature ratios to remove the effect of physical
temperature; the BT algorithm uses seasonally
adjusted reference T
s to improve performance during
summer melt. Each method has advantages and
disadvantages and compromises must be made
because the spatial, temporal, and spectral resolution
cannot resolve all features of the complex sea ice
surface. This is particularly true for thin ice, whose
emissivity varies with thickness depending on the
frequency. This can cause considerable uncertainties
in such regions because the algorithms are based on
general conditions over the whole hemisphere, which
is dominated overall by thicker ice. Each algorithm
makes different compromises, as discussed in a joint
study by the NASA Goddard authors .
features of each algorithm product is that they both
use internally consistent methods, consistent
microwave frequencies, and consistent quality control.
Differences in sea ice records are further illuminated when looking at other sea ice data
records for September monthly means (Figure 3) from a variety of time series, described in
Table 1. The Goddard and NSIDC data sets are pure passive microwave products from the
NASA Team or Bootstrap and are more consistent. The NIC , Hadley , and ESMR
Merged  datasets are combined products using different sources through time to create
longer time series, but this leads to differences in data quality through time and possible
inconsistencies in the time series. This is most notable in the NIC and Hadley data sets after
1996. The addition of Radarsat SAR as a source allowed more thin ice to be detected and
increased estimates from the NIC charts compared to earlier years. The Hadley time series
is based on passive microwave fields, adjusted by the 1973
1994 NIC climatology to correct
the passive microwave summer low
bias; however, after 1996, the Hadley data set switched
from the Goddard NT product to NCEP sea ice fields. Thus, the NIC and Hadley post
fields are possibly more accurate than the pure passive microwave, but they are not
consistent with their earlier period. The change in the Hadley product is particularly evident
where the 1987
2002 decadal trend in the Arctic is actually positive (Table 1), inconsistent
with all other time series.
A climate data record of sea ice cover should be one consistent, quality
controlled data set
that is able to track climate variability and change . As of now there is no single clear
superior sea ice data product. Data fusion approaches, similar to that used in the NIC,
Hadley, and ESMR Merged timeseries, can be used to produce an optimal data product. The
E, with higher resolution and higher
quality sensor properties, can further
improve the SMMR
SSM/I timeseries as we approach the NPOESS era.
Great care must be
taken to avoid inconsistencies due to differing data sources and different processing methods,
as demonstrated by the NIC and Hadley time series. However, intelligent data fusion of the
data sets, with strict standards for consistency, could yield high quality time series. Another
important component of a climate data record is a quality assessment. There have been
several studies of the errors in the various passive microwave algorithm products, e.g. ,
but there is not yet a data product with complete quality flags. This is the next step toward a
based sea ice climate data record.
mean sea ice concentration fields for the
Arctic (top row) and Antarctic (bottom row)
for the month of annual maximum (March
for Arctic, September for Antarctic) and
minimum (September for Arctic, February
for Antarctic) ice cover.
September minimum sea
ice extent from various
sources. Goddard NT
2004 & 2005 estimated
from the NSIDC Sea Ice
calibration and QC
calibration and QC
Manual analysis based on a variety of
satellite imagery and other sources
NIC climatology used to adjust passive
microwave NT estimates
1978 NIC charts used to fill gaps
calibrate with ESMR
Data sources for
Figure 4. NT = NASA Team,
BT = Bootstrap. Time period
varies for each, but all
products overlap for
2002. Decadal trends
are for September means.
 Gloersen, P., C.L. Parkinson, D.J. Cavalieri, J. Comiso, and H.J. Zwally, 1999. Spatial distribution of trends and season
ty in the hemispheric sea ice covers: 1978
J. Geophys. Res.
, 104(C9), 20,827
 Parkinson, C.L., D.J. Cavalieri, P. Gloersen, H.J. Zwally, and J.C. Comiso, 1999. Arctic sea ice extents, areas, and tre
J. Geophys. Res.
, 104(C9), 20,837
 Stroeve, J.C., M.C. Serreze, F. Fetterer, T. Arbetter, W.N. Meier, J. Maslanik, and K. Knowles, 2005. Tracking the Arcti
shrinking ice cover: Another extreme minimum in
Geophys. Res. Lett.
, 32, L04501, doi: 10.1029/2004GL021810.
 Cavalieri, D.J., P. Gloersen, and W.J. Campbell, 1984. Determination of sea ice parameters with the Nimbus 7 SMMR,
J. Geophys. Res.
, 89(C3), 5355
 Comiso, J., 1986. Characteristics of Arctic winter sea
ice from passive microwave and infrared observations,
J. Geophys. Res.
, 91(C1), 975
 Dedrick, K., K. Partington, M. Van Woert, C. Bertoia, and D. Benner, 2001. U.S. National/Naval Ice Center Digital Sea Ic
ata and Climatology,
Can. J. Remote Sensing
 Rayner, N.A., D.E. Parker, E.B. Horton, C.K. Folland, L.V. Alexander, D.P. Rowell, E.C. Kent, and A. Kaplan. 2003. Global
alysis of sea surface temperature, sea ice, and
night marine air temperature since the late nineteenth century.
Journal of Geophysical Research
. 108 (D14). doi:10.1029/2002JD002670.
 Cavalieri, D.J., C.L. Parkinson, and K.Y. Vinnikov, 2003. 30
year satellite record reveals contrasting Arctic and Antarctic
Geophys. Res. Lett.
, 30(18), 1970, doi:
 Comiso, J.C., D.J. Cavalieri, C.L. Parkinson, and P. Gloersen, 1997. Passive microwave algorithms for sea ice concentrat
: A comparison of two techniques,
, 60, 357
 National Research Council, 2004. Climate data records from environmental satellites, National Academies Press, Washingt
DC, 116 pp.
 Meier, W.N., 2005. Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in the Arc
IEEE Trans. Geosci. and
, 43(6), 1324
Potential Climate Data Records