MICROWAVE REMOTE SENSING, SEA ICE AND ARCTIC...

MICROWAVE REMOTE SENSING, SEA ICE ANDARCTIC CLIMATE

by David G. Barber

ARTICLE DE FOND ( MICROWAVE REMOTE SENSING, ... )

David G. Barber ([email protected]), CanadaResearch Chair (Arctic Systems Science), Faculty ofEnvironment, 476 Wallace, University of Manitoba,Winnipeg, MB. R3T 2N2

Contemporary research in polar regions is being drivenby uncertainties in the response of the Arctic to climate vari-ability and change. We expect polar regions to exhibit anearly and strong response to global warming [1]. It has beenargued that the anticipated change has already begun is nowevident within the ocean [2], sea ice [3]

and atmosphere [4]. Changes in seaice concentration and/or areal extentimpact all aspects of the Arcticmarine ecosystem, including thermo-haline circulation in the ocean andthe transfer of energy, mass andmomentum between the ocean andatmosphere.

Microwave remote sensing has longbeen viewed as an important tool inthe study of the Arctic snow/sea icesystem due to the strong contrast inelectromagnetic properties betweensea ice, snow and open ocean; its all-weather and diurnal sensing capabil-ities; and the relative inaccessibilityof polar regions to in situ measure-ment. The complex dielectric con-stant (ε*) of the sea ice volume (defined in [5]), snow/iceinterface and ocean are sufficiently different to allow sophis-ticated applications of both passive microwave radiometryand active microwave scattering to evolve [6]. For example,passive microwave radiometry is now routinely used tomeasure and monitor temporal changes over more than25 years in sea ice concentration and type on a regional tohemispheric scale [2], while Synthetic Aperture Radar (SAR)has traditionally been used for practical operations such asship navigation and more recently has found utility in cli-mate related sea ice process studies [5]. This includes esti-mates of the surface energy balance, presence of water in liq-uid phase, estimates of ice strength and the timing of ice for-mation and breakup [7].

Sea ice is an important component of Earth’s Cryosphereand is more appropriately considered as a multiphasic alloyrather than a single-phase pure ice form such as snow orlake ice [8]. The geophysical nature of sea ice above theocean-sea ice growth boundary is governed by temperaturewithin the sea ice volume, causing relationships between ice,liquid brine and solid salts to occur. Assur’s (1960) phasediagram (Figure 1) graphically portrays these temperature-dependent relationships, which are important in determin-ing the rate of energy, gas and mass fluxes through a sea icevolume. As the phase proportions change so does thedielectric constant of this material and thus the retrieval of

information from passive and active microwave remotesensing is possible.

The effective complex permittivity or ‘dielectric constant’ isan important variable in microwave remote sensing [5]

because it defines the electrical con-ductivity of the material relative tothe wavelength and polarization ofthe incident or emitted energy. Thetheory of microwave scattering andemission, and by design, the theory ofmicrowave interaction models, sepa-rate the scattering process into an air-snow interface, a snow volume, asnow-ice interface and an ice volume.Models allow for multiple layerswithin each of these units dependingon whether a signal is expected fromparticular depths. For example, com-putation of σ° (total average relativescattering coefficient to a SAR),requires the contributions of eachscattering layer to be weighted byits transmissivity coefficient (Ψ)

In this work we show howthe temporal dimension ofmicrowave scattering andemission can be used todevelop applications forretrieval of both geophysi-cal and thermodynamicstate information of Arcticsnow covered sea ice as ameans of assessing Arcticclimate processes.

LA PHYSIQUE AU CANADA septembre / octobre 2005 105

Fig. 1 Phase diagram of sea ice showing the relationshipsbetween ice in solid phase, brine, and solid salts as afunction of temperature (Adapted from [1]).

FEATURE ARTICLE ( MICROWAVE REMOTE SENSING, ... )

106 PHYSICS IN CANADA September / October 2005

(equation 1) in the multi-layered snow/sea ice system andthe results summed. For example, the snow volume scatter-ing term (σ°sv) would be weighted by the transmissioncoefficient across the air-snow interface (Ψas), the sea icesurface (σ°s) by the transmissivity at the snow/ice interface(Ψs), continuing for each layer within the model. In a natural snow-covered sea ice system this equation wouldinclude terms for the snow surface, usually three layers inthe snow and between three and eight layers within the seaice depending on frequency, volume dielectrics, and thuspenetration depth.

(1)

Any transmission coefficient (Ψ) in (1) equals one minus theFresnel Reflection Coefficient (Γ) of each interface (2). TheFresnel Reflection Coefficient (Γ) is a measure of the amountof radiation reflected at the interface between adjacentmediums in a multi-layer system. It is computed as a com-plex ratio of the dielectric properties of the two materialscreating the interface (i.e., air-snow or snow-ice) and isdependent on whether the energy transmitted and receivedis vertically (VV) or horizontally (HH) polarized (3).

(2)

(3)

Where ξ1 and ξ2 are the complex dielectric constants of theair and snow.

Models of the dielectric constant require that we considerthe relative proportions of brine within the mixture and theproportion of salts within the brine (see Figure 1). Althoughthere is only a small quantity of brine present in the ice, itslarge dielectric constant (ε*=70 +j 34) has a significant influ-ence on the resulting dielectric properties of the ice-brinemixture. The salinity of the brine (Sb) is also a function ofthe snow/ice temperature. Decreased ice temperatureincreases the proportion of salts within the brine mixture(Figure 1). Further details on this modeling approach areavailable elsewhere [7,9].

THE PHYSICAL AND ELECTRICAL EVOLUTION OFTHE SYSTEMThe phase relationships between ice, liquid brine and solidsalts governed by the temperature profile within the sea iceis a function of air temperature and the thermal diffusivityof the snow/sea ice system, which refers to a material’s abil-ity to conduct energy in the form of heat in a specifieddirection. Following [10] we partition the system into thethermodynamic regimes of freeze-up, winter, early melt,melt onset and advanced melt:

Freeze-up – begins with new ice formation in the form offrazil or grease ice developing at or near the ocean surface.As the number of freezing degree days increases the icestarts to grow downward into the water column, a processtermed secondary ice growth which results in columnarshaped ice crystals. As the surface continues to cool, brine

between crystals in the frazil layer is extruded upwardsfrom the ice surface resulting in frost flower formation. Thesalinity of these features is considerably higher (45-100 ppt)than the bulk salinity of the ice and results in a rapiddecrease in ice surface salinity after initial freeze-up.Typically vertical profiles of salinity within newly grownsea ice follow a c-shape, with high salinities near surface,exponentially decreasing to a depth around 0.4 m (depend-ent upon the speed of ice growth) where the salinity profilebecomes near linear. Sea ice salinity increases at the sea icebottom due to constant contact with the saline ocean, andcontinual ice growth (however slow) occurs until the maxi-mum equilibrium thickness is reached. This occurs whenheat can no longer be conducted out of the ocean into theatmosphere through the sea ice volume. The electricalproperties of sea ice are found to be highly variable duringfreeze-up, primarily due to spatial and temporal variationsin the freezing rate, initial salinity of sea ice and the type ofmechanical environment present during formation.

Winter - generally lasts from December to April at a mini-mum. During this season first-year ice types cover a rangeof thickness conditions from >30 to <200 cm. Typically,first-year ice consists of a snow layer covering a primarylayer of frazil ice overlaying columnar ice crystals. Totalsnow cover increases due to increased precipitation in thefall and spring and range in depth from 5 cm to over 1.0 m.Ice density is relatively uniform between 0.90 to 0.92 g cm-3.Liquid brine is interspersed throughout the ice as inter crys-talline brine inclusions and brine drainage channels. Thesurface of multi-year ice typically consists of an array ofpreferential melt areas (melt ponds) and desalinated, lowporosity hummocks surrounding the melt ponds. Theupper layer of recrystallized ice (in the hummocks) haswidely varying densities, typically around 0.7 g cm-3. Thislayer merges into an intermediate layer of slightly higherdensity which in turn merges into a more solid layer withdensities in the range 0.8 to 0.9 g cm-3. The salinity of bothhummocks and melt ponds is considerably lower than first-year ice because of brine drainage during the summer. Thesnow cover is typically deep over frozen melt ponds andshallow over hummocks since the former create naturalentrapments for blowing snow. The electrical properties offirst-year and multiyear forms of sea ice are generally stablein the winter due to a lack of water in liquid phase and min-imal diurnal cycling in temperature.

Early Melt – is a transition period starting with the begin-ning of snow pack metamorphism [11] and ending whenmoisture is continuously present in the snow. The most sig-nificant aspect of the early melt period thermodynamicallyis rapid changes in the physical properties of the snow vol-ume. Due to diurnal oscillations in the temperature profile,the early melt stage represents a rapid growth period forsnow grains throughout the snow layer. In the sea ice,increasing temperature means an increase in brine volumeand a ‘reconnection’ of brine drainage channels within theice matrix. The electrical properties in this period are con-trolled by diurnal variability of water in liquid phase withinthe snow cover and by the effect of changing volume tem-perature on the brine volumes in the snow and sea ice.

σ θ σ θ σ θ θ σ θtotalo

sso

as svo

s so( ) = + ( ) ′( ) + ( ) ′( )Ψ Ψ2 2* *

Ψ Γas VV= −( )1

ΓHH =× − × ′× + × ′

ξ θ ξ θξ θ ξ θ

2 1

2 1

cos coscos cos


THE MICROWAVE SCATTERING EVOLUTION OFTHE SYSTEMPrevious research results have shown that a distinct patternexists for the seasonal evolution of the microwave scatteringand emission over various sea ice types [12-16]. Althoughthese patterns are undoubtedly frequency and polarizationdependent, scattering from actively transmitted 5.3 GHzmicrowave energy over snow-covered thick first-year andmulti-year sea ice follows a form approximated in Figure 2and the 19, 37 and 85 GHz frequencies (V and H pol) typicalof many space borne passive microwave radiometers followsa form approximated in Figure 3. A summary of the scatter-ing/emission physics of these surfaces serves as a founda-tion for the development of applications which seek toretrieve geophysical and thermodynamic information frommicrowave remote sensing data seasonally:

Freeze-up - The high dielectric constant of the ocean surfacecauses variable scattering/emission over water as a functionof wind speed. Young ice types can have high σ° (due to thepresence of frost flowers) or low σ° (as grease ice reduceswind roughening of the ocean surface). As ice thickensthere is a decrease in scattering and an increase inmicrowave emission. Snow deposition further reduces scat-tering by breaking down frost flowers on the surface andreducing the first-year surface electromagnetic roughness.Remnant water in liquid phase causes low scattering frommulti-year sea ice. As water changes phase from liquid tosolid the hummock volume scattering increases; returningthe microwave response to winter norms (Figures 2 and 3).

Winter - Within multi-year sea ice, microwave scatteringand emission are relatively stable. Older forms of ice appearto have a higher magnitude of scattering due to larger anddeeper hummocks, which contain large bubble sizes spacedfurther apart. The variability in image texture of multi-yearsea ice is due to the spatial pattern of hummock and frozenmelt pond surfaces within the multi-year sea ice. The snowcover on multi-year sea ice is brine free and of sufficientlylow density and small grain size as to be transparent to bothactive and passive forms of EM interactions.


Melt Onset – once water in liquid phase occurs throughoutthe diurnal cycle (at about 2 percent water by volume) andthe snow-ice interface becomes damp. Pendular (water heldin the interstices of the snow pack) and funicular regimes(grain bonds break and gravity drainage occurs) are conven-ient as a means of categorizing how the water is held withinthe snow layer. The transition between the pendular andfunicular regimes occurs at approximately 7 percent waterby volume. Equitemperature metamorphosis occurs whenthe vertical temperature gradient is relatively small over thegiven volume of snow. Under this process there is a ten-dency for small faceted grains to grow together into largeraggregate grains. Complex permittivity is controlled by thedistribution of water in liquid phase (including percolationinto the snow and sea ice) and diurnal freeze-thaw cycles.The rapid desalination of the surface layers tends to reducethe dielectric constant.

Advanced Melt – occurs when the snow cover is first water-saturated throughout its volume and then melts rapidly.During this period liquid is drained from the saturatedsnow resulting in a gradient of water volume with a surfaceminimum and basal maximum. The ice surface undergoesmelting, often in a cyclical diurnal fashion. Preferentialsnowmelt occurs whereby shallower inter-drift patches meltfirst. This is because the shallower snow cover generallyresults in larger snow grains (which have a lower albedo),created during cold season temperature gradient metamor-phosis. The drifts themselves are preserved longer to even-tually become the snow patches evident on melt-ponded seaice surfaces. The bulk salinity of the sea ice and the basallayer of snow both decrease over the advanced melt periodin first-year sea ice. Fresh melt water from melting snowcan enhance this brine drainage mechanism thereby increas-ing the desalination process over both first-year and multi-year forms of sea ice. Breakup of the ice sheet follows atsome point beyond the desalination of the ice surface andformation of surface ponds. The electrical properties of thistype of surface are dominated by the snow patch to meltpond fractions and the distribution of water in liquid phasein the snow patches.

Fig. 2 Phenomenological summary of the seasonal evolutionof s° at 5.3 GHz (based on ERS and RADARSAT data)for thick first year and multi-year sea ice over the sea-sonal periods spanning the annual sea ice cycle.

Fig. 3 Phenomenological summary of the seasonal evolu-tion of brightness temperatures (Tb) for 19, 37 and85 GHz data based on in situ radiometer measure-ments over landfast first-year sea ice over severaldifferent field seasons in the Canadian Arctic.



Within first year ice, microwave scattering appears to oscil-late according to changes in the oceanic and atmosphericheat fluxes but still provides a stable surface in whichremote sensing can be useful. It has been shown [9] that thisoscillation is driven by atmospheric forcing of the snow/iceinterface temperature. As an increase in temperature is con-ducted through the snow to the base of the snow cover itincreases the brine volume in the basal layer. This increaseis physically collocated with large kinetic growth grainswhich in combination create sufficiently large volume scat-tering. This causes an increase the total scattering abovethat created by the sea ice surface (i.e., these oscillations areonly found over smooth thick first-year sea ice). Evidencesuggests that these grains also create oscillations inmicrowave emission as the relative amount of microwaveemission from the basal layer increases within the overallvolume emission term (see equation 1). This informationcan be used to estimate the snow water equivalent (SWE) asit affects the ability of the temperature wave to propagate tothe snow/ice interface.

Early Melt - In multi-year sea ice, microwave scattering andemission remains stable into the early melt period becausehummock volumes dominate (Figure 2 and 3). Diurnal dif-ferences may be present as small amounts of water in liquidphase become available near the surface of the snow coverat or near solar noon. Because the snow cover is brine freeand the grains are fairly small we do not expect significantscattering or emission from the snow volume during thisperiod, except for the case of 85 GHz. Early evidence andmodeling studies show that this high frequency may in factbe sufficiently sensitive to measure small changes in waterin liquid phase in a brine free environment (Figure 3).

In first-year sea ice, microwave scattering and emission aredominated by a combination of snow basal layer volumescattering and ice surface scattering. During this period wefind a significant difference between observations at solarnoon versus solar midnight as small amounts of water inliquid phase contribute both to grain growth and the elevat-ed temperatures significantly increase the brine volume ofthe snow basal layer. The overall magnitude of emissionand scattering is dependent on the ice surface microscaleroughness, snow grain size and brine volume. Large diur-nal variations in both passive and active microwave sensingare a function of frequency with higher frequencies beingmore sensitive to the role of water in liquid phase(c.f., Figures 2 and 3).

Melt Onset - In multi-year sea ice, Melt Onset is denoted bya rapid decrease in σ° (Figure 2). The mechanism responsi-ble is the absorption of microwave energy by the water inliquid phase within the snow cover and by the presence ofwater in liquid phase within the hummock structures free ofa snow cover. An increase in permittivity and in particularan increase in the dielectric loss effectively mask the volumescattering from the hummocks thereby decreasing σ°. Thedecrease in σ° proceeds over both the pendular and funicu-lar regimes of snow ablation. We expect passive microwaveemissions to increase during this period due to the addition-al emissions from water in liquid phase relative to the lowerlayers in the snow/sea ice system (not shown).

In first-year sea ice, melt onset is denoted by a rapidincrease in active microwave scattering (Figure 2). Thereare two mechanisms responsible for the observed increase.At relatively low water volumes (1 to 3 percent) the largebrine wetted snow grains in the basal layer contribute a sig-nificant volume scattering term to σ°. As the water in liquidphase continues to increase (but is maintained within thependular regime) it is likely that the snow surface con-tributes a surface scattering term to σ° [12,15,17]. A distinctdip in σ° over first year ice corresponds with the transitionfrom the pendular and funicular regimes. This transitionmarks the reduction of brine within the basal layer to nearzero, an increase in the water in liquid phase at the base ofthe snow cover and a reduction of water in liquid phase inthe top parts of the snow volume (as the surface begins todrain). These processes lead to a reduction of both the vol-ume scattering and snow surface scattering hypothesized todominate the pendular regime. In the case of microwaveemission we observe an initial increase in emission followedby a cyclical pattern which is associated with the diurnalcycling of water in liquid phase (Figure 3). The details ofthis diurnal cycling are the topic of ongoing research butearly evidence suggests a complex diurnal/weekly processthat is driven by water presence, grain size and subsequentdrainage of liquid water leaving very large snow grains inthe upper layers (thus masking increased emissions fromdepth).

Advanced Melt - In multi-year sea ice, advanced melt isdenoted by a rapid increase in active microwave scattering(figure 2). As surface water forms in the previous yearsmelt ponds there is an increase in the discontinuity at theair/water interface. The complex permittivity of this fresh-water (which has a surface temperature of about +0.5°C) issufficiently large that we find a penetration depth on theorder of 1 mm [18]. We can expect an increase in scattering ifthe melt pond surfaces are wind roughened [19,20]. A reduc-tion in the water in liquid phase within the hummocks alsocauses an increase in σ° by increasing the volume scatteringcomponent of σ°. Once the ice surface begins to drain thereis a pronounced decrease in the multi-year σ°. This periodcoincides with a reduction in the spatial extent of the meltponds and cyclical changes in the water in liquid contentwithin the hummocks.

In first-year sea ice, σ° increases over the ponding period(Figure 2) and decreases as the ice begins to drain, analo-gous to that described for the multi-year ice surface. Pondsurface scattering increases σ° when wind roughened.Breakup initiation is often directly evident as the ablated icesheet opens due to oceanic or atmospheric momentum forc-ing. These late season scattering processes create very simi-lar returns for both ERS [20] and RADARSAT data [19]. Thepassive microwave response sees an increase in the 85 GHzreturns which is likely due to the effect of the high dielectriccontrast of the melt ponded surfaces. The 19 and 37 GHzreturns decrease substantially, mostly due to the fact that atthis frequencies we see a larger surface area, which in thecase of field deployed radiometers means a mixture of meltponds and patches (Figure 3). Further research is ongoingto segment these very late season surfaces into mixtures ofdielectric surfaces.


been sustained over the past 30 years with significant spatialand temporal variability in the estimated reductions in seaice concentration (SIC) areal extent. Geophysical retrievalalgorithms have been developed (e.g., NASA team, NASATeam II, Bootstrap, etc, see [6]) for passive microwaveremote sensing instruments which are able to extract sea icetype and concentration throughout the annual cycle. Thispassive microwave dataset now represents the most consis-tent Arctic climate database available, with daily data begin-ning in 1979 and continuing through to today.

An example of SIC variability at a single (pixel) location(Figure 4A) shows a general tendency for positive SIC anom-aly to dominate in the decade of the 1980’s and negative SICanomalies to dominate in the decade of the 1990’s and2000’s. The average trend (line in Figure 4A) indicates anegative trend in SIC anomalies over significant interannualvariability. There is a broad scale trend towards a decreasein SIC anomalies over most of the Arctic (Figure 4B), withespecially large reductions within the Chukchi Shelf and theBarents Sea. Reductions are evident throughout most ofBaffin Bay and Hudson Bay but the slope values are general-ly less negative in these regions. Positive SIC anomalies arerestricted to the NW coast of Greenland and the CanadianArctic Archipelago. This pattern is closely linked to thephases of the Arctic Oscillation (AO) over this time peri-od [21] and result in the central Arctic pack tending to moveacross the pole into this sector of the Arctic.

A principal interest climatologically in the Arctic is the tim-ing and location of melt onset and pond onset. The theoryfor the approaches to geophysical extraction of melt relatedconditions of the snow/sea ice system are described aboveand in more detail in the cited literature. The utility of thismelt onset and pond onset from microwave remote sensingis that we are now able to estimate the climatological(i.e., shortwave) albedo of the sea ice surface during thespring transition. In situ measurements of albedo duringthis spring transition show that surface albedo is statisticallylinked to the time series of SAR scattering (Figure 5). ThisSAR derived shortwave albedo can then be used in numeri-cal process models of the climate system associated withocean-sea ice-atmosphere interface [5] and is an importantelement in estimating breakup timing for fast ice [22].


REMOTE SENSING APPLICATIONSThe theory presented above is based on several years offield investigations. Satellite-based remote sensing algo-rithms and geophysical inversion techniques have evolvedfrom this theory to allow the assessment of a variety of geo-physical and thermodynamic state inversions from timeseries data sets of active and passive microwave remotesensing instruments.

Compelling evidence for Arctic climate change is theobserved and sustained reduction in sea ice areal extent inthe northern hemisphere (IPCC, 2001). This reduction has

Fig. 4 A single pixel from a 25 year average weekly sea iceconcentration (SIC) anomaly dataset (A) showing thelarge degree of interannual variability and a trendline showing a statistically significant slope in thistrend. Plots of SIC anomaly concentration slopes forthe entire northern hemisphere (B) illustrating thespatial variability in SIC anomaly concentrationreductions (blue) and the small increases in SICanomaly concentrations (red) along the NW coast ofthe Canadian Arctic Archipelago (Adapted from [2]).

Fig. 5 Empirical relationship between RADARSAT-1 aver-age scattering coefficient and the climatologicalshortwave albedo of a ponded FY ice surface fromC-ICE'97 (Adapted from [18])



Time series SAR data can alsobe used to estimate the melt fluxto the ocean surface. This meltflux leads to a stabilization ofthe mixed layer depth (MLD)which in turn allows for pri-mary and secondary producersto remain in the euphotic zonethus stimulating overall produc-tivity (Figure 6). In the case ofthe NOW polynya this meltonset has a distinct SE to NWtransition and is highly variableinterannually. SAR provides ameans to directly measure theonset of melt ponds and predictthe timing of stabilization of theupper ocean mixed layer, whichcreates the necessary conditionsto stimulate primary productionin the NOW polynya. SeaWifsdata can be used to monitor thedevelopment of the phytoplank-ton in the NOW polynya result-ing from this process (Figure 6).

Snow water equivalent (SWE) isalso a climate state variable ofconsiderable interest as it con-trols the thermodynamic growthof the sea ice and dominates theradiative transfer of solar radia-tion within the system. SWEcan be estimated using both

passive [23] and active [9]

forms of microwave remotesensing. Operational algo-rithms are still a ways off butresearch suggests that sur-face temperature informationcoupled to multifrequencypassive microwave data hasgood predictive skill(Figure 7) in estimating SWE,particularly at 37 GHz forsmooth first-year sea ice [23].

Other approaches to geo-physical and thermodynamicretrievals are in variousforms of research and test-ing. Scientists are investigat-ing ways to provide bettergeophysical information onthe distribution of brine insnow and sea ice, better esti-mates of various ages of mul-tiyear sea ice, estimate sur-face energy fluxes and toprovide estimates of icethickness over a full range ofthickness conditions in theArctic. New developments

are also underway with improvedsensor calibration, polarimetry andsynthetic aperture imaging radiome-ters. Suffice it to say that the develop-ment cycle of microwave remote sens-ing is very much alive and well in thevarious Earth ObservationsLaboratories working on polar sea iceand climate processes.

CONCLUSIONSMicrowave remote sensing hasbecome increasingly important inhigh latitude cryospheric science aswe become more knowledgeableabout information contained withintime series datasets. The relationshipbetween the thermo-physical evolu-tion of the system and the control thishas on complex permittivity hasallowed researchers to use time seriesscattering and emission to estimateaspects of both the geophysical andthermodynamic state of the snow/seaice system. The future of microwaveremote sensing will see an increasedreliance on both active and passivemicrowave sensors. The ways inwhich radiative transfer happenswithin the snow/sea ice volume sug-gests there are several places wheredata fusion would be beneficial

Fig. 6 Radarsat SAR data merged with ocean colour data from SeaWifs for the North OpenWater (NOW) polynya (left). Seasonal evolution of MLD in the NOW Polynya (right)calculated from multiple CTD casts over the period April - July, 1998. The dramaticreduction in the depth of the mixed layer is created by increase melt water flux fromthe sea ice in the spring which is detectable in RADARSAT data (Adapted from [22]).

Fig. 7 SWE geophysical retrieval algorithms areable to predict SWE on landfast first-year sea ice with precision ranging from20 to 90 percent. The relationships arebest for 85 GHz V polarization (H wasnot available for this field program).Predictive capacity was also high for 37GHz (V and H pol) and poor for 10 GHz.


9. D.G. Barber, S.V. Nghiem, “The role of snow on the thermaldependence of backscatter over sea ice”, Journal ofGeophysical Research, 104(C11), 25789-25803 (1999).

10. C.E. Livingstone, R.G. Onstott, L.D. Arsenault, A.L. Gray,K.P. Singh, “Microwave Sea-Ice Signatures Near the Onsetof Melt”, IEEE Transactions on Geoscience and Remote Sensing,GE-25(2), 174-187 (1987).

11. S.C. Colbeck, “An overview of seasonal snow metamor-phism”, Rev. Geophys. Space Phys., 20(1), 45-61 (1982).

12. M.R. Drinkwater, “LIMEX’87 Ice surface characteristics:implications for C-Band SAR backscatter signatures”, IEEEtransactions on Geoscience and Remote Sensing, 27(5), 501-513(1989).

13. R. Kwok, G.F. Cunningham, “Backscatter characteristics ofthe winter ice cover in the Beaufort Sea”, Journal ofGeophysical Research, 99(C4), 7787-7802 (1994).

14. J.C. Comiso, R. Kwok, “Surface and RadiativeCharacteristics of the Summer Arctic Sea Ice Cover fromMultisensor Satellite Observations”, Journal of GeophysicalResearch, 101(C12), 28397-28416 (1996).

15. D.G. Barber, E.F. LeDrew, “On the links between microwaveand solar wavelength interactions with snow-covered first-year ice”, Arctic, 47, 298-309 (1994).

16. I. Harouche, D.G. Barber, “Seasonal Characterization ofMicrowave Emissions Over Snow-Covered First-Year SeaIce”, Hydrological Processes, 15, 3571-3583 (2001).

17. C.E. Livingstone, M.R. Drinkwater, “Springtime C-bandSAR backscatter signatures of Labrador Sea marginal ice:measurements versus modeling predictions”, IEEETransactions on Geoscience and Remote Sensing, 29, 29-41(1991).

18. F.T. Ulaby, R.K. Moore, A.K. Fung, Microwave RemoteSensing: Active and Passive volume 3, Addison-WesleyPublishing Company, (1986).

19. J.J. Yackel, D.G. Barber, “Melt ponds on sea ice in theCanadian Arctic Archipelago. Part 2. Extraction of meltpond information from RADARSAT-1 SAR”, Journal ofGeophysical Research. 105(C9):22,061-22,070 (2000).

20. D.G. Barber, J.J. Yackel, “The physical, radiative andmicrowave scattering characteristics of melt ponds on seaice”, International Journal of Remote Sensing 20(10), 2069-2090(1999).

21. A.Y. Prushutinsky and M.A. Johnson. Two circulationregimes of the wind-driven Arctic Ocean., Journal ofGeophysical Research,102(C6),12493-12514. (1997).

22. R.A. DeAbreu, J. Yackel, D. Barber and M. Arkett.Operational Satellite Sensing of Arctic First Year Sea IceMelt. Canadian Journal of Remote Sensing. 24:487-501 (2003)

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(fusion of microwave radiometer data with that fromradars). There is also ample evidence that a transfer func-tion exists through which we could measure the microwaveradiative transfer and through this estimate how optical orthermal IR frequencies should be interacting with the samesurface/volume. There is also considerable potential fordata assimilation into numerical climate models whichexamine physical aspects of the snow/sea ice system. Thisassimilation may also extend to coupled physical-biologicalmodels as the physical forcing of the ocean-sea ice-atmos-phere interface plays a significant role in how the marineecosystem responds to Arctic climate variability and change.

ACKNOWLEDGEMENTSMany graduate students and collaborators have contributedto this work over the years. Thanks in particular toJ. Yackel, P. Hwang, R. Galley, A. Thomas, I. Harouche andS. Drobot for direct contributions. This work was funded bythe Natural Sciences and Engineering Research Council(NSERC), Canada Foundation of Innovation (CFI) and theCanada Research Chairs (CRC) programs. Thanks to NASAfor access to Canadian Radarsat data.

REFERENCES1. IPCC, Climate Change. Impacts, Adaptations and Mitigation of

Climate Change: Scientific-Technical Analyses IntergovernmentalPanel on Climate Change, Working Group II second assessmentreport, Cambridge University Press, 2001.

2. E. Carmack, D. Barber, J. Christensen, R. Macdonald,B. Rudels and E. Sakshaug. “Climate Variability andPhysical Forcing of the Food Webs and the Carbon Budgeton Panarctic Shelves’’; DeepSea Research. In review(Feb’05)

3. Y. Yu, G.A. Maykut and D.A. Rothrock, “Changes in thethickness distribution of Arctic sea ice between 1958 – 1970and 1993 – 1997”, Journal of Geophysical Research., 109,C08004, doi: 10.1029/2003JC001982. (2004)

4. J.E. Walsh, W.L. Chapman, T. Shy, “Recent decrease of sealevel pressure in the central Arctic”, J. Climate, 9, 480-486(1996).

5. D.G. Barber, J. Yackel, J. Hanesiak, “Perspectives on Sea Ice,RadarSat-1, and Arctic Climate Processes”, Canadian Journalof Remote Sensing, 27(1), 51-61 (2000).

6. F. Carsey, (Editor), Microwave Remote Sensing of Sea Ice,Geophysical Monograph #68, American Geophysical Union,1993.

7. D.G. Barber, A. Thomas, T. Papakyriakou, “The role of syn-thetic aperture radar (SAR) in surface energy flux measure-ments over sea ice”, invited Chapter 3 in C. Tsatsoulis,R.Kwok, Synthetic Aperture Radar Remote Sensing of Sea ice,John Wiley and Sons, 1998.

8. A. Assur, “Composition of sea ice and its tensile strength”,SIPRE Res. Rep., 44 (1960).

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