Download The response of polar sea ice to climate variability and change

In: Lozán et al., Climate of the 21st century: Changes and risks. GEO, Hamburg/Germany, pp. 206-211,
2001.
The response of polar sea ice to climate variability and change
HAJO EICKEN1 & PETER LEMKE2
1
2
Geophysical Institute, University of Alaska, Fairbanks, USA
Institut für Meereskunde, Kiel, Germany
Abstract
The polar oceans' sea-ice cover plays an important role in the global climate
system. As the sea-ice albedo is much higher than that of open water, input of solar
shortwave energy is greatly reduced in the polar regions. Furthermore, the ice-covered
ocean is susceptible to feedback processes, potentially amplifying reductions in ice
coverage with important consequences for the global heat budget. With global climate
models predicting enhanced warming in the polar regions, sea ice may both indicate
and amplify natural and anthropogenic climate change. While satellite remote sensing
can now provide sea-ice data sets of sufficient spatial and temporal coverage,
variability inherent in the coupled system ocean-ice-atmosphere renders detection of
climate trends difficult, however. In the Antarctic, where a slight positive trend in
sea-ice extent is apparent in 1978-1996, different atmospheric and oceanic processes
tend to stabilize the present state of the seasonal ice cover. Arctic sea ice, on the
other hand, appears more susceptible to climatic change, with a decrease in ice extent
from 1978-1996. Focussing on the Siberian shelf seas and the major negative and
positive anomalies of 1995 and 1996, the difficulties in unraveling climate signals
from the sea-ice record are discussed in more detail.
Polar sea ice, ranging between 15 x106 km2 and 19 x 106 km2 in areal extent, is an
important component of the global climate system. Its growth and decay have a significant
impact on the world oceans‘ circulation and the large-scale oceanic heat and gas transport
(see Chapter 1.2). The heat budget of the polar regions is dominated by the presence of sea
ice, which reduces the amount of solar radiation absorbed at the earth’s surface by a factor
of five to eight. This is due to the high albedo of sea ice, i.e., the reflected fraction of the
incoming shortwave radiation at wavelengths from 0.4 to 0.7 µm, which ranges between
<0.5 for melting bare ice and >0.85 snow-covered, cold ice. In contrast, the ice-free ocean
has an albedo of below 0.1. As a result, reductions in ice extent due to, e.g., perturbations
in atmospheric heat transfer into the polar regions expose more of the ocean, which in turn
increases the amount of solar heating, further amplifying ice retreat.
Such ice-albedo feedback processes are capable of modulating the global energy
balance. Global circulation model (GCM) simulations indicate that the enhanced warming
by 3 to 6 ˚C predicted for the Arctic as a result of a doubling of atmospheric CO2 (from the
onset of industrialization to the year 2050) is at least in part driven by ice-albedo feedback
through the response of the sea-ice cover to changes in the amount of longwave radiation
reaching the surface during winter (KATTENBERG et al. 1996; see Chapter 3.4). Recent
observations of a significant decrease in Arctic sea-ice extent thus raise the question
whether these are the first signs of anthropogenic global warming. To be sure, owing to its
smaller mass (with mean thicknesses between 0.5 and 5 m) and its coupling to both the
atmosphere and the ocean, the sea-ice cover responds much faster to climate variability and
change than glaciers and ice sheets (see Chapter 1.7 and 3.13). For the same reasons,
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however, it is also more likely to exhibit oscillations in extent and thickness resulting from
the coupled response to atmospheric and oceanic variability. Moreover, observations
indicate that most of the retreat of Arctic sea ice occurs during the summer months (SERREZE
et al. 2000), further complicating the picture. Below, we discuss the recent record of seaice extent in the context of climate variability and change, assessing the strengths and
limitations of the available data sets and focussing on potential causes of ice retreat.
From sediment cores to satellite radiometers: Detecting variability and
change in sea-ice extent
The considerable variability of the ice cover mandates a high temporal and spatial
resolution of sea-ice climatological data sets, ideally extending back over timescales of
centuries. While these requirements are difficult to satisfy, there are a number of different
sources for sea-ice data, each with its own weaknesses and strengths. Paleo-records of
summer and winter ice extent can be gleaned from marine sediment cores going back
millions of years (see Chapter 3.7). While these data are associated with substantial errors
and integrate over millennia, they are unique in indicating changes in ice regimes between
glacial and interglacial periods. Historical records, such as whaling and sealing logs or
recordings of coastal drift ice in Iceland, extend back for a few centuries, providing
important regional information on changes in ice circulation and extent, in particular in the
critical North Atlantic sector.
For the past three decades, polar-orbiting satellites have provided us with data on
the sea-ice distribution of sufficient quality and density to establish detailed regional and
hemispheric sea-ice climatologies (CHAPMAN & WALSH 1993). While precursors operating
in the visible and infra-red range in the 1960‘s were mostly foiled by winter darkness and
high cloudiness in the polar regions, the satellite launch of the Electrically Scanning
Microwave Radiometer (ESMR) in 1972 ensured data acquisition largely unaffected by
lighting and cloud conditions. This single-channel instrument was replaced by multichannel radiometers operating at frequencies between 19 and 85 GHz in later years: the
Scanning Multichannel Microwave Radiometer (SMMR, 1978-1987) and the Special
Sensor Microwave/Imager (SSM/I, 1987-present). In contrast with radar, such passivemicrowave systems measure the thermal emission of different ice and ocean surfaces in the
millimeter- to centimeter-wavelength range, i.e., in a window where the atmosphere is
comparatively transparent and where weather effects can be corrected. The discrimination
between open water and different ice types at variable concentration is based on the strong
contrasts in emissivity ε (corresponding to the ratio of the brightness temperature measured
with a radiometer over a given frequency band to the physical temperature of a surface),
with ε ≈ 0.39 for open water, 0.91 for first-year and 0.71 for multi-year ice at 37 GHz
(horizontal polarization, EPPLER et al. 1992). By combining signals from different channels
(19 and 37 GHz, horizontal and vertical polarization), the ice concentration can be derived
independent from the physical surface temperature at a ground resolution of approximately
25 km.
As derived from satellite passive-microwave radiometers, climatological sea-ice data
are commonly based on the ice extent, defined as the ice-covered area enclosed by the 15-%
ice concentration contour. Even in winter, ice concentrations are mostly below 100 % due
to the constant opening of cracks and leads between ice floes. In summer, ice
concentrations in perennially ice-covered regions such as the central Arctic can drop below
80 %. Summertime data from the Arctic Ocean are compromised by larger errors (>10 %)
due to the presence of meltwater at the ice surface.
The overall accuracy of climatological parameters derived from passive-microwave
data depends on the method employed to compute ice concentration from the
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radiometrically determined surface brightness temperatures. The prevailing ice types and
their concentration as well as the time of year strongly impact the different derivation
techniques. In a case study in the Greenland Sea, SMITH (1996) found mean deviations
between different ice-concentration algorithms of 5.2 to 8.3 % in winter. During summer
and in the Antarctic the total error can be even larger. While ongoing research is aimed at
further refinement and validation of passive-microwave techniques, studies of ice
anomalies (i.e. deviations from the long-term, mean annual cycle) and trends are not as
much affected by these errors as long as data acquisition and processing have been
consistent. Systematic errors resulting from changes in the instrument parameters (such as
the transition from SMMR to SSM/I in 1987) can account for as much as a 5 % deviation in
apparent ice extent and need to be minimized through comparison of duplicate data sets and
radiative-transfer modelling.
Distribution and variability of Antarctic sea ice: Increasing ice extent
during the past three decades
An ice cover of up to 20 x 106 km2 extent girdles the Antarctic continent during
winter (Fig. 3.10-1). In summer, patches of perennial ice, covering 4 x 106 km2, remain in
the Weddell and Bellingshausen/Amundsen/Ross Sea sectors at 0-60˚W and 70-180˚W,
respectively. Interannual and longer-term ice-extent anomalies are approximately one order
of magnitude smaller than the seasonal variations (Figs. 3.10-2 and 3.10-3). Between 1978
and 1996, CAVALIERI et al. (1997) find a positive trend in ice extent of 1.3 % per decade.
Based on a different ice-concentration algorithm, BJØRGO et al. (1997) detected a decline in
ice extent. Including the ESMR data (associated with a larger error) starting in 1973 in the
analysis results in no significant overall trend. These discrepancies highlight the difficulties
associated with the derivation of linear trends from comparatively short time series
characterized by high seasonal variability.
While more recent work appears to confirm the increase in ice extent during this
time period, part of the anomalies apparent in Fig. 3.10-3 are due to phase shifts in
seasonal ice advance and retreat. The northernmost position of the ice edge in September is
coupled to the location of the polar front separating cold Antarctic surface water from
warmer subantarctic water masses. The position of the front, and hence to some degree
maximum ice extent, is primarily determined by the surface wind field and the submarine
topography. Large-scale coupled atmospheric circulation patterns such as the El NiñoSouthern Oscillation (ENSO, see Chapter 3.8) are also impacting regional ice distribution.
The Circumantarctic Wave is such an anomaly circling Antarctica roughly every eight years
and manifesting itself in regional anomalies in ice extent (Fig. 3.10-1). Its impact on the
total ice extent is small to negligible, however.
The retreat of the ice edge during summer is mostly controlled by solar heating of
the ice and upper ocean and atmospheric heat transfer to the ice surface, with the ice cover
shrinking by 80 %. In the mid-1980’s, the extent of perennial ice in the Bellingshausen and
Amundsen Seas began to decline, mostly due to regional atmospheric warming in the
vicinity of the Antarctic Peninsula (JACOBS & COMISO 1997). This is supported by a
positive correlation between air temperature (from longer-term records obtained at coastal
stations) and the monthly mean ice extent between June and November, with the former
preceding the latter. In the Antarctic, the strong coupling between ocean, ice and
atmosphere adds a further degree of complexity. The mass balance of the ice cover in the
Bellingshausen and Amundsen Seas is strongly impacted by high snow accumulation rates.
Whereas low to moderate snow deposition reduces ice growth due to enhanced thermal
insulation from the atmosphere, higher accumulation rates can compensate for these effects
through increases in summer ice albedo and formation of snow ice (EICKEN et al. 1995).
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Thus, increased precipitation can potentially buffer the impact of atmospheric warming on
the ice cover. In the Bellingshausen Sea, the sparse data base also indicates an increase in
the amount of heat transferred from the deeper ocean to the ice during the past decades,
which may further contribute to its thinning and retreat (JACOBS & COMISO, 1997).
The impact of high ocean heat fluxes on the ice cover is perhaps best demonstrated
by the Weddell Polynya, an area of open water several hundred thousand square kilometers
in extent, that opened up in the winter pack ice of the central Weddell Sea in the mid1970’s. With as much as 40 W m–2 of oceanic heat transferred to the underside of the ice in
parts of the Southern Ocean, the ice grows at much slower rates, or even melts, during the
Antarctic winter than in the Arctic, where the ocean heat flux amounts to a few Watts per
square meter. Maximum thicknesses of level ice thus only amount to between 0.5 and 0.8
m at the end of winter and can be reduced to few decimeters in regions of high oceanic heat
flux. With thin, weak ice, deformation may contribute significantly to the overall thickening
of the ice pack. All these processes are manifest in the ice-thickness distribution, which
could thus serve as a much more substantial mass-balance indicator than the ice extent.
Unfortunately, however, ice thickness currently cannot be reliably measured by
instruments onboard satellites and the lack of submarine sonar measurements, an important
data source in the Arctic, leaves us with a restricted data set of changes in the thickness of
the Antarctic ice pack.
Distribution and variability of Arctic sea ice: Decreasing ice extent during
the past three decades
Arctic sea-ice extent varies between 8 x 106 and 15 x 106 km2 during the annual
cycle (Figs. 3.10-4 and 3.10-5). In summer, the ice edge retreats towards the shelf break,
with a perennial ice cover of 3 to 5 m thickness residing in the central Arctic Ocean. The
anticyclonic (clockwise) Beaufort Gyre dominates ice circulation in the North American
Arctic, whereas the Transpolar Drift exporting ice from Siberia across the Pole into the
Greenland Sea is fed mostly by ice produced in the Laptev Sea. Arctic ice extent declined at
approximately 2.9 % per decade between 1978 and 1996 (Fig. 3.10-6), with a record low
attained in 1995. During the 1990’s, negative ice anomalies were particularly pronounced
during summer in the Laptev and East Siberian Sea sectors of the Arctic Ocean (Fig. 3.107; SERREZE et al. 2000). This region also contributed disproportionately to a 40 % decrease
in mean ice thickness observed between the 1950’s and 1990’s based on submarine sonar
data (ROTHROCK et al. 1999). In 1997 and 1998, record low ice concentrations were
observed in the Beaufort Sea sector of the Arctic Ocean. It is currently not clear to what an
extent these observations are already part of an anthropogenic signal predicted for the Arctic
in GCM simulations (KATTENBERG et al. 1996) or whether they can solely be explained by
natural variability. Recent evidence points towards amplifications of inherent longer-period
oscillations (such as the Arctic Oscillation, SERREZE et al. 2000) as a result of enhanced
greenhouse-gas forcing. These phenomena are modulated by a decadal to secular trend of
decreasing pressure in the central Arctic, however. The complex interplay between
atmospheric dynamics (enhanced as a result of the latter), radiative forcing and the coupled
sea-ice/ocean response add to the difficulty of looking to the ice cover as a bellwether (and
amplifier) of anthropogenic climate change. Focussing on the Siberian Arctic, we will
briefly discuss why this problem is not quite as straightforward as it is often made out to
be.
In contrast with the Antarctic and the European Nordic Seas, direct oceanic forcing
of sea-ice anomalies in the Arctic Ocean is not as pronounced, since the surface waters are
freshened by river run-off as well as snow and ice melt, effectively insulating the ice from
warmer waters at intermediate depths. Atmospheric forcing of ice anomalies plays a key
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role, as testified by a strong correlation between air temperature anomalies and sea-ice
extent (CHAPMAN & WALSH, 1993). In the Siberian Arctic, these linkages are amplified by
advection of warm air masses from the continent, which has been subject to a warming of
0.5-1 ˚C per decade between 1966 and 1995 (SERREZE et al., 2000). Reductions in winter
snow cover, particularly during the spring of 1990 and 1995, contributed further to a
regional warming by reducing surface albedos (see Chapter 3.13). Along the Siberian
shelves, advection of warmer air in spring and summer helps to advance seasonal ice
retreat, both by accelerating reductions in ice albedo and through direct transfer of sensible
heat. Earlier and more extensive ice melt increases the amount of solar heating of the mixed
layer, which in turn delays onset of fall freeze-up and results in an overall thinner ice cover.
This chain of events was particularly pronounced in the Laptev Sea during the summer of
minimum ice extent in 1995. A stable high-pressure system over the New Siberian Islands
promoted inflow of warm air from the Siberian continent throughout June, at the same time
advecting ice northwards into the central Arctic (HAAS & EICKEN 2000). Enhanced solar
heating of the surface waters contributed to a four-week delay in fall ice formation (as
compared to the long term mean for 1979-1996). Similar to changes in air temperature or
longwave forcing, such anomalies can only be tracked as ice-thickness signals, however,
since the Siberian shelves are completely ice-covered in winter.
Further factors, often not addressed sufficiently in interpretations of ice-extent
anomalies are the drift and deformation of sea ice. Through rafting and ridging of ice
grown to lesser thicknesses, ice deformation can help compensate for the effects of reduced
ice accretion. Until the recent advent of sophisticated passive- and active-microwave
remote-sensing techniques, ice-dynamics data have been lacking over the seasonal ice
zones, however. Moreover, GCM’s are only now beginning to include less simplistic
formulations of ice kinematics and dynamics.These simplifications may have contributed to
a non-negligible degree to the predictions of greatly reduced ice extent and enhanced
warming over the Arctic Ocean. Finally, advection and compaction processes need to be
taken into account in the interpretation of both positive and negative anomalies. Thus, in
1995 southerly winds helped compact the ice edge in the central Siberian Arctic, whereas in
1996 the pack extended further south but was much less compact in the central Arctic
(HAAS & EICKEN 2000).
Conclusions: The impact of natural variability and anthropogenic climate
change on the sea-ice cover
The importance of sea ice both as an indicator and amplifier of climate variability
and change is undisputed. While the past decades have presented us with intriguing, and to
some of the stakeholders alarming, signals in the records of Arctic ice thickness and extent,
interpreting and acting on these is not straightforward. To improve upon this situation, we
need to gain a better understanding of the the complex interplay between atmosphere, ice
and ocean in the Arctic and the Southern Ocean. Close coupling between these components
explains their importance in the global climate system and at the same time results in
variability on interannual, decadal and possibly longer timescales. Given the substantial
variability in atmospheric heat transport into the Arctic, the interannual variability in ice
extent is actually smaller than to be expected and might be indicative of negative feedback
processes that have not been adequately addressed to date (UNTERSTEINER, unpubl.).
The Southern Ocean’s ice cover appears to have been quite stable in the past, with
the ice shrinking to a few million square kilometers in extent during summer and advancing
to roughly five times this area in winter. Historical data from the Weddell Sea contain no
evidence of more severe ice conditions at the end of the “Little Ice Age” in the late 18th and
early 19th century (PARKINSON, 1990). Sediment cores furthermore attest that the presentday summer and winter ice extent are comparable to conditions during previous interglacial
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periods (see Chapter 1.7). Predictions of climate models for the next 50 to 70 years range
between a significant decrease in ice extent to a slight increase.
In contrast, models predict a strong decrease in Arctic sea-ice extent, coupled with
an enhanced atmospheric warming north of about 60˚N. Given some of the problems
associated with the collection of meteorological data over the Arctic Ocean, sea-ice data can
be of considerable value in assessing the state of Arctic climate. While evidence for
substantial, recent change in the Arctic, including but not limited to key sea-ice variables, is
mounting, so is our awareness of the extent of natural variability on different time scales.
In fact, it appears highly likely that anthropogenic climate change will manifest itself by
locking onto and amplifying the major modes of variability in the system. Such variability
includes changes in the oceanic circulation apparent in the European Nordic Seas, which
have been tentatively linked to reductions in ice extent in the Greenland and Barents Seas
during the past century. On time scales of centuries to millennia, cores obtained from the
Greenland Ice Sheet have demonstrated that changes in the thermohaline circulation and
sea-ice cover of the Nordic Seas were at the root of major, rapid climate change (see
Chapter 3.7). Possibly more important, however, is variability in the atmosphere, which
has been shown to drive much of the upper ocean/ice system in the Arctic Ocean
(PROSHUTINSKY et al. 1999). Here, a comprehensive, efficient observational program in
combination with improved modelling efforts is likely to substantially further our insight in
the coming years.
The dilemma that we may be facing is perhaps best summed up by the negative and
positive ice anomalies of 1995 and 1996, which span almost the entire amplitude of
variability observed in the Arctic during the past 25 years, with more than half of the signal
originating from within a tenth of the total area (Figs. 3.10-6 and 3.10-7). If enhanced
greenhouse-gas forcing were in fact to amplify the natural modes of variability, both
regional and temporal variability would increase, thereby increasing the impact of
anthropogenic contributions to climate change while at the same time rendering their signal
more elusive.
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Figure captions
Fig. 3.10-1: Maximum (black lines) and minimum (grey lines) Antarctic sea ice extent
during austral summer (Max.: Feb. 3-9 1988, Min.: Feb. 10-16 1993, thin lines) and
austral winter (Max.: Sep. 8-14 1980, Min.: Sep. 1-7 1986, solid lines) during the period
1978-1995. Data are based on weekly mean ice concentration maps derived from SMMR
and SSM/I brightness temperature data sets provided by the National Snow and Ice Data
Center (NSIDC) in Boulder, Colorado. Note the regional differences in the maximum and
minimum winter ice extent in the sectors between 60 and 180˚W (Bellingshausen,
Amundsen and Ross Seas) and between 30˚E and 60˚W (Weddell Sea; see also discussion
of Circumantarctic Wave in the text). Similar patterns are evident in the summer ice edge in
the Weddell and Ross Seas.
Fig. 3.10-2: Time series of Antarctic sea ice extent between 1978 and 1996 based on
SMMR and SSM/I data (from CAVALIERI et al. 1997).
Fig. 3.10-3: Time series of the Antarctic sea-ice extent anomaly (based on the mean annual
cycle) as derived from SMMR and SSM/I data between 1978 and 1996 (cf. Fig. 3.10-2,
Figure from CAVALIERI et al. 1997; the dotted curve shows the 12-month running mean, the
dashed line the results of a band-limited regression and the solid straight line those of an
ordinary least-squares regression).
Fig. 3.10-4: Maximum (black lines) and minimum (grey lines) Arctic sea ice extent during
summer (Max.: Sep. 28-Oct. 4 1981, Min.: July 24-30 1995, thin lines) and winter (Max.:
March 26-April 1 1979, Min.: Feb. 21-27 1994, solid lines) during the period 1979-1995.
Data are based on weekly mean ice concentration maps derived from SMMR and SSM/I
brightness temperature data sets provided by the National Snow and Ice Data Center
(NSIDC) in Boulder, Colorado.
Fig. 3.10-5: Time series of Arctic sea ice extent between 1978 and 1996 based on SMMR
and SSM/I data (from CAVALIERI et al. 1997).
Fig. 3.10-6: Time series of the Arctic sea-ice extent anomaly (based on the mean annual
cycle) as derived from SMMR and SSM/I data between 1978 and 1996 (cf. Fig. 3.10-5,
Figure from CAVALIERI et al. 1997; the dotted curve shows the 12-month running mean, the
dashed line the results of a band-limited regression and the solid straight line those of an
ordinary least-squares regression).
Fig. 3.10-7: Time series (30-day running mean) of the sea-ice extent anomaly (based on
the mean annual cycle) in the Laptev and East Siberian Sea sector of the Arctic Basin as
derived from SMMR and SSM/I data between 1979 and 1996.
9
0°
60°W
60°E
Weddell
Sea
Bellingshausen
Sea
80°S
70°S
Amundsen
Sea
60°S
120°E
120°W
Ross Sea
50°S
180°
Fig. 3.10.1
Fig. 3.10.2
Fig. 3.10.3
10
180°
60°N
120°W
Fr
Str am
ait
70°N
Beaufort
Sea
80°N
Arctic
Ocean
120°E
East Siberian
Sea
Laptev Sea
Kara Sea
60°E
60°W
Fig. 3.10.4
0°
Fig. 3.10.5
Fig. 3.10.6
600,000
Ice extent anomaly, km 2
400,000
200,000
0
-200,000
Fig. 3.10.7
-400,000
-600,000
1980
1984
1988
Year
1992
1996