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Proceedings of the Symposium on Global and Environmental Monitoring

atmosphcre-cryosphere-hydrosphere energy exchanges and can result
in accelerated ice formation or ice ablation, depending on the time of
year (Gudmandsen, 1985) and region. In addition, wind induced ice
floe motion creates a surface stress gradient which modifies the
ocean mixed layer. This may be the cause of water upwelling
observed at ice margins (Carsey and Zwally, 1986; Gudmandsen,
1985). These upwelling areas bring nutrient rich deep water, to the
euphotic zone, where photosynthetic processes make use of organic
carbon in the production of phytoplankton. Proliferation at this
level of the food chain stimulates production at all other levels,
making these areas biologically important. Where ice motion is
inhibited, a sea ice cover can act to reduce wind/wave surface stress
which, in turn, dampens surface turbulent exchange with the
atmosphere (OIES, 1988).
It has been proposed (Bums, 1990) that, since SAR is sensitive to
microscale surface roughness (particularly during the winter season),
there may be a relationship between the atmospheric drag over the
ice surface, and roughness of that surface measured by a Synthetic
Aperture Radar. Anderson (1987) summarized various observations
of the relationship between floe size, concentration, and ice
roughness with atmospheric drag. From observations made during
the Marginal Ice Zone Experiment (MIZEX) Anderson found that,
for a neutral atmospheric stratification, the coefficient of drag (Ctf)
increases as the ice concentration increases and that, if concentration
is held constant, Cj is a function of flow size and ice roughness.
Bums (1990) found that in areas of relatively smooth pack ice, the
backscattering information contained within SAR was at a scale
inappropriate to infer the atmospheric drag coefficient. The presence
of ridges in pack ice, and the sensitivity of SAR to the frequency
and height of ridges is presented as an area requiring future research.
However, SAR provided good estimates of Qj within a Marginal Ice
Zone (MIZ). Within SIMS we intend to validate the type and range
of roughness elements which can be detected in SAR imagery.
These measurements will be coupled with in situ observations of
wind profiles.
Snow cover is essentially transparent in the winter season at
microwavelengths (Kim et al., 1985). There is, however, evidence
to suggest that snow cover plays a role in determining SAR
backscatter by altering the thermal structure at the ice/snow
interface. It has also been suggested that during the fall season the
highly saline snow cover, created by the destructive metamorphosis
of frost flowers, has a strong influence on the backscatter from SAR
imaging system (Drinkwater and Crocker, 1988).
The primary objectives of SIMS is to conduct research on the
physical processes active in the floating ice regime and to develop
methods by which these variables can be extracted from various
remote sensing imagery. The development of appropriate data
management and analysis tools is integral to the success of this
project. In the remainder of this paper we concentrate on the design
of an advanced geographic information system, the Sea Ice
Information System (SIIS), which is being developed to assimilate
all of our data sets into a single, coherent package.
The primary objectives of SIIS is to provide a computer based
framework by which the various datasets from SIMS can be
efficiently manipulated and to develop a methodology by which
historical data sets can be integrated with current measurements for
monitoring climate change. Two principle tasks are required within
the framework of SIIS:
• Provide a flexible research environment whereby in situ
observations can be linked with concurrent remote sensing
imagery and where synergistic image datasets can be integrated for
reduction to Sea Ice geophysical or climatological variables.
• Provide a research environment whereby Sea Ice geophysical or
climatological variables, extracted from remote sensing imagery,
can be incorporated into climate modelling studies of the Arctic
floating ice regime at both local and regional scales.
3.4 Sea Ice-Cloud Interactions
Very little information is available regarding cloud cover and sea ice
extent, though there have been correlations between ice extent and
cloudiness (Crane and Barry, 1984). The problem is a difficult one
due to the compensatory effects clouds have on the shortwave and
longwave radiation balances; relative domination of either flux is
sensitive to the season and the nature of the underlying sea ice
surface (Shine and Crane, 1984) and cloud character. Maltese et al.
(1984) found that the simultaneous presence of ice-albedo and cloud
amount-temperature feedbacks greatly amplifies the magnitude of a
climatic response to an extreme forcing. Barry et al. (1984), Crane
and Barry (1984), Shine et al. (1984), Shine and Crane (1984), and
Shine and Henderson-Sellers (1984) provide detailed insights into
this issue.
The spectral albedo and thermal fluxes are altered by changing cloud
cover. The former by decreases in radiation (all wavelengths)
incident on the ice surface, the later by re-radiation to the surface
from the warmer cloud surface. Herman (1980) reports values for
net longwave radiation at the surface of -60 W m"^ versus -32 W m'
2 for cloud-free versus cloud covered conditions in January for a test
area in the central Arctic (Bany 1983).
Thermal infrared wavelength imagery coupled with SAR images of
the ice surface will allow identification of this cloud-over-ice
condition. Generalizations about the radiation balance over the ice
surface can be inferred by determining relationships between short
wave and long wave components associated with different ice
surfaces in overcast conditions.
3.5 Snow Cover
Sea ice snow cover is important due to its thermodynamic effects
(Carsey, 1984) and its control on surface albedo (Scharfen et al.,
1987). (The role of snow cover albedo has been discussed in the
Albedo sub-section). The timing of snow melt initiation varies,
causing significant impacts on the Arctic Basin heat and mass
balance (due to the strong control exerted by surface albedo) which
could ultimately affect the long term stability of the ice (Fletcher,
1966; Barry, 1985, in Robinson et al., 1987). To compound the
problem, SAR signatures of sea ice are also strongly controlled by
the onset of melt. The dielectric constant of the snow/ice matrix
changes dramatically as the amount of free water increases. This
causes a shift in the backscattering characteristics as recorded by
Synthetic Aperture Radar (Livingstone et al., 1987).
When complete, the Sea Ice Information System (SIIS) will be a
flexible research utility for the analysis of sea ice as an indicator of
climate change and variability. It will have the capability to
integrate data from in situ measurements with those acquired by
space-borne sensors into a single operation; it will be able to
compare current observations with trends derived from historical
charts and tables. Within the design objectives we intend to make
SIIS a multi-format, multi-temporal geographic information system
(Figure 2).
Remote Sensing |
In Situ h
Measurements |
¡d® 0©(§
Figure 2: Sea Ice Information System
4.1 Multi-Format Data Processing
Many scientists make use of a number of different systems in a
single project to take advantage of processing capabilities that some
systems have and others do not. In practice, this sharing of spatial
data among several different systems is difficult because of natural
incompatibilities in the techniques used to model our spatial
environment and because of designed incompatibilities in the
analysis systems we use. In the first case, differences in data
structures (i.e. vector vs. raster) and formats (e.g. row-order vs.
quadtree raster or topologically encoded vs. spaghetti vector) present
very real barriers even to experienced users. We should be free to
devote all of our analysis time on a system to analyzing our data;
we shouldn't be concerned with their formats. Secondly, most
system developers see their processing system as the ultimate
destination during analysis. Consequently, facilities for importing
data are much more common than facilities for exporting data.