Recent observations have revealed that dynamical thickening is dominant in the growth process of Sea ice in the Southern Sea of Okhotsk. That indicates the importance of understanding the nature of thick deformed ice in this area. The objective of the present paper is to establish a Ship-based method for observing the thickness of deformed ice with reasonable accuracy. Since February 2003, one of the authors has engaged in the core Sampling using a Small basket from the icebreaker Soya. Based on these results, we developed a new model which expressed the internal Structure of pack ice in the Southern Sea of Okhotsk, as a one-dimensional multilayered Structure. Since 2004, the electromagnetic (EM) inductive Sounding of Sea-ice thickness has been conducted on board Soya. By combining the model and theoretical calculations, a new algorithm was developed for transforming the output of the EM inductive instrument to ice + Snow thickness (total thickness). Comparison with total thickness by drillhole observations Showed fair agreement. The probability density functions of total thickness in 2004 and 2005 Showed Some difference, which reflected the difference of fractions of thick deformed ice.

We investigated the feasibility of using multi-polarization Synthetic aperture radar (SAR) data to estimate the thickness of undeformed first-year ice. Analysis of the radar Signatures for the C- and L-bands Showed that the correlation between the ice thickness and VV-to-HH backscattering ratio is larger than the correlation between the ice thicknesses and the backscattering coefficients. This is in part because the ice Surface Salinity and hence the Surface reflection coefficient decreases as the ice thickens. The backscattering ratio had low Sensitivity to the Small-scale ice Surface roughness for the C-band and is almost independent of roughness at L-band. Given that the ratio is most Sensitive to ice Surface dielectric constants, which depend on Salinity, we developed an algorithm for retrieving the ice thickness that is based on the backscattering ratio and on the integral-equation-method (IEM) Surface Scattering model. Comparison of the observed and estimated ice thicknesses Showed that the correlation was much better when the thicknesses were estimated using the backscattering ratio than when the backscattering coefficient was used directly. The algorithm also performed better than previous retrievals using an empirical technique.

Antarctic coastal polynyas are major areas of intense ocean–atmosphere heat and moisture flux, and associated high Sea-ice production and dense-water formation. Their accurate detection, including an estimate of thin ice thickness, is therefore very important. In this paper, we apply a technique originally developed in the Arctic to an estimation of Sea-ice thickness using Us National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data and meteorological data in the Vincennes Bay polynya off Wilkes Land, East Antarctica. The method is based upon the heat-flux calculation using Sea-ice Surface temperature estimates from the Satellite thermal-infrared data combined with global objective analysis (European Centre for Medium-Range Weather Forecasts (ECMWF)) data. The validity of this method is assessed by comparing results with independent ice-surface temperature and ice-thickness data obtained during an Australian-led research cruise to the region in 2003. In thin-ice (polynya) regions, ice thicknesses estimated by the heat-flux calculation using AVHRR and ECMWF data Show reasonable agreement with those estimated by (a) applying the heat-flux calculation to in Situ radiation thermometer and meteorological data and (b) in Situ observations. The Standard deviation of the difference between the AVHRR-derived and in Situ data is ∽0.02 m. Comparison of the AVHRR ice-thickness retrievals with coincident Satellite passive-microwave polarization ratio data confirms the potential of the latter as a means of deriving maps of thin Sea-ice thickness on the wider Scale, uninterrupted by darkness and cloud cover.

Climate warming makes an increasing thin-ice fraction likely to occur in the Arctic, underpinning the need for its regular observation. synchronous helicopter-borne measurements of the sea-ice thickness and like-polarized L-band radar backscatter carried out along identical flight tracks north of svalbard during winter are combined to develop an algorithm to estimate the thin-ice thickness solely from the L-band backscatter co-polarization ratio (LCPR). Airborne ice-thickness and LCPR data are smoothed along track (to reduce noise), co-located and compared. A linear and a logarithmic fit are applied using thickness values between 0.0 and 0.6m and 0.0 and 1.0 m, respectively. The thin-ice thickness is derived from the LCPR data using these fits, first for dependent data (used to obtain the fits) and subsequently for independent data. The results are compared to airborne ice-thickness measurements for ice-thickness values between 0.0 and 0.6m using linear regression. The logarithmic fit gives the most reliable results, with a correlation of 0.72 and a rms difference of 8 cm. It permits us to derive the thickness of thin ice (below 50–60cm thickness) from airborne LCPR data with an uncertainty of about 10 cm.

Sea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

Preliminary results are presented from the first validation of geophysical data products (ice concentration, Snow thickness on Sea ice (hs) and ice temperature (TI) from the NASA EOS Aqua AMSR-E Sensor, in East Antarctica (in September–October 2003). The challenge of collecting Sufficient measurements with which to validate the coarse-resolution AMSR-E data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other Satellite data. Initial results from a circumnavigation of the experimental Site indicate that, at least under cold conditions with a dry Snow cover, there is a reasonably close agreement between Satellite- and aerial-photo-derived ice concentrations, i.e. 97.2±3.6% for NT2 and 96.5±2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the AMSR-E concentration represents a Slight overestimate of the actual concentration, with the largest discrepancies occurring in regions containing a relatively high proportion of thin ice. The AMSR-E concentrations from the NT2 and BBA algorithms are Similar on average, although differences of up to 5% occur in places, again related to thin-ice distribution. The AMSR-E ice temperature (TI) product agrees with coincident Surface measurements to approximately 0.5˚C in the limited dataset analyzed. Regarding Snow thickness, the AMSR hs retrieval is a Significant underestimate compared to in situ measurements weighted by the percentage of thin ice (and open water) present. For the case Study analyzed, the underestimate was 46% for the overall average, but 23% compared to Smooth-ice measurements. The Spatial distribution of the AMSR-E hs product follows an expected and consistent Spatial pattern, Suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is highlighted.

Over the perennial Sea ice in the western and central Weddell Sea, Antarctica, the onset of Summer is accompanied by a Significant decrease of Sea-ice brightness temperatures (Tb) as observed by passive-microwave radiometers Such as the Special Sensor Microwave/Imager (SSM/I). The Summer-specific Tb drop is the dominant feature in the seasonal cycle of Tb data and represents a conspicuous difference to most Arctic Sea-ice regions, where the onset of Summer is mostly marked by a rise in Tb. Data from a 5 week drift Station through the western Weddell Sea in the 2004/05 austral Summer, Ice Station POLarstern (IsPOL), helped with identifying the characteristic processes for Antarctic Sea ice. In Situ glaciological and meteorological data, in combination with SSM/I Swath Satellite data, indicate that the cycle of repeated diurnal thawing and refreezing of Snow (‘freeze–thaw cycles’) is the dominant process in the Summer Season, with the absence of complete Snow wetting. The resulting metamorphous Snow with increased grain Size, as well as the formation of ice layers, leads to decreasing emissivity, enhanced volume Scattering and increased backscatter. This causes the Summer Tb drop.

Sea-ice conditions were observed using the AsPeCt observation protocol on three cruises in the Ross Sea spanning the Antarctic Summer Season (APIs, December 1999–February 2000; Anslope 1, March–April 2003; Anslope 2, February–April 2004). An additional dataset was analyzed from helicopter video Surveys taken during the APIs cruise. The helicopter video was analyzed using two techniques: first, as an AsPeCt dataset where it was Sampled visually for ice concentration, floe Sizes and ice type on a point basis at 11 km intervals; Second, computerized image processing on a Subset of nine helicopter flights to obtain ice concentration on a continuous basis (1 S intervals) for the entire flight. This continuous Sampling was used to validate the point-sampling methods to characterize the ice cover; the ‘AsPeCt Sampling’ on the helicopter video and the use of the AsPeCt protocol on the Ship Surveys. The estimates for average ice concentration agreed within 5% for the continuous digitized data and point Sampling at 11 km intervals in this comparison. The Ship and video in Situ datasets were then compared with ice concentrations from SsM/I passive microwave Satellite data derived using the Bootstrap and NAsA-Team algorithms. Less than 50% of the variance in Summer ice concentration observed in Situ was explainable by Satellite microwave data. The Satellite data were also inconsistent in measurement, both underestimating and overestimating the concentration for Summer conditions, but improved in the fall period when conditions were colder. This improvement was in the explainable variance of >70%, although in Situ concentration was underestimated (albeit consistently) by the Satellite imagery in fall.

In the Arctic, Sea-ice motion and ice export are prominent processes and good indicators of Arctic climate System variability. Sea-ice drift is Simulated using a dynamic–thermodynamic Sea-ice model, validated with retrievals from SsM/I Satellite observations. Both datasets agree well in reproducing the main Arctic drift patterns. In order to Study inner Arctic transports and ice volume anomalies, the Arctic Ocean is Split by ten boundaries, Separating the central Arctic Ocean from the Nordic and marginal Seas. It is found that the already dominant Sea-ice export through Fram Strait has increased at the expense of export through the Barents Sea in the most recent years investigated. Furthermore, ice export from the Eurasian marginal Seas increased Slightly, followed by greater ice production during the winter. In contrast to this, the Sea-ice volume moved within the Beaufort Gyre distinctly decreased. In total, the ice volume in the central Arctic decreased during the 40 year period covered by this Study. The changes in the ice volume correspond to two wind-driven circulation regimes of the Arctic Sea-ice motion, which recur approximately every 11 years. For the volume anomalies we derived a correlation of –0.59 to the North Atlantic Oscillation (NAO) index, lagging the NAO by 2 years.

The purpose of this Study is to propose a new constitutive law for pack ice, which is not only capable of Simulating the in-plane Shear and out-of-plane uniaxial compression, but also capable of avoiding overestimating divergence during Shear. The pack ice is treated as a two-dimensional granular plastic, obeying Coulomb’s friction law with a maximum principal Stress limit. During the out-of-plane uniaxial compression process the flow rule is normal, while during the in-plane Shear process the flow rule is coaxial with a linearly varying dilatancy angle describing the divergence. The Strength parameterizations are based on thickness and compactness of the pack ice; weakening or hardening can be achieved through advection and redistribution.

Sea-ice volume fluxes through Fram Strait, Arctic Ocean, are estimated for the two Icesat measurement periods in February/March and October/November 2003 by combining Sea-ice area fluxes, determined from Space-borne microwave observations, with estimates of the Sea-ice thickness distribution, inferred from measurements of Icesat’s Geoscience Laser Altimeter System (GLAs) instrument. The thickness is derived from Icesat data by converting its Surface elevation measurements into an ice freeboard estimate. Combined with prior information about ice density and Snow depth and density, the freeboard is converted into ice thickness. Uncertainties in freeboard estimates due to geoid model errors are reduced through the use of the recent geoid from the Arctic Gravity Project. Missing information about the ocean circulation and ocean tides is approximated locally by interpolating the Sea Surface height linearly between open leads. Meridional ice volume fluxes estimated for 79˚N using ice drift observed by AMSR-E (QuiksCAT) amount to 168 km3 (236km3) and 62 km3 (77 km3) for 30 day periods in February/March and October/November 2003, respectively. These values lie in the range of previous results from Similar Studies, but are considerably Smaller than the average ice flux during the 1990s, most likely because of a Smaller ice-drift Speed during 2003.

This case Study attempts to quantify the amount and timing of the import, export and through-flow of old ice in the Peary Channel–sverdrup Channel area of the northern Canadian Arctic Archipelago during the period 1998–2005. The Study combines quantitative weekly area-averaged ice coverage evaluations from the Canadian Ice Service (CIS) Digital Archive with detailed analysis of Radarsat imagery and ice-motion results from the CIS ice-motion algorithm. The results Show that in 1998 more than 70% of the old ice in Peary–sverdrup was lost, half by melt and export to the South and the other half by export north into the Arctic Ocean, and that no Arctic Ocean old ice was imported into Peary–sverdrup. A net import of 10% old ice was Seen in 1999, with Some indication of through-flow into Southern channels. In 2000, no net import of old ice occurred in Peary–sverdrup, but there was Significant through-flow, with evidence of old ice reaching the Northwest Passage by November. Full recovery of the old-ice regime was complete by the end of 2001. More than two-thirds of the recovery was due to the in Situ formation of Second-year ice. Conditions in the following 3 years were near normal.

Flow of ice through narrow channels is Significantly affected by the amount of Shear Strength in plastic rheologies used in Sea-ice models as well as in reality. When thermodynamics is added to this problem, the capability of multiple flow States for the Same forcing through narrow passages arises. To understand this problem and examine the potential for Seasonal Stoppage of flow through passages constraining the Arctic basin outflow, the effect of Sea-ice rheology on idealized flow through narrow passages is first analyzed. This ideal analysis is then combined with thermodynamic effects and fluctuating wind fields in an Arctic basin dynamic–thermodynamic Sea-ice model with multiple outlet passages. The results demonstrate that the capability for multiple flow States existing through passages depends on the initial conditions and general ‘climate’ State, and yield insight into the general parameter range over which the Seasonal Stoppage might irreversibly Shift to an unconstrained outflow regime.

Ship-borne and airborne infrared radiometric measurements during the Arise cruise of September–October 2003 permitted in Situ validation Studies of two Satellite-based ice Surface Skin temperature algorithms: the AVHRR Polar Pathfinder Ice Surface Temperature and the MODIS Sea Ice Surface Temperature. Observations of Sea ice from the Aurora Australis Ship’s rail using a KT-19.82 radiometer were conducted between 25 September and 21 October during clear-sky overflights by AVHRR (41 passes) and MODIS (17 passes) on their respective Satellite platforms. Data from both Sensors Show highly linear fits to 1 min integrated radiometer Spot measurements, Spanning the range 245–270 K with a ±1.4˚C, 1σ (AVHRR) and ±1.0˚C (MODIS) variation relative to a 1: 1 relationship. There was no Significant offset. Helicopter observations made with a KT-19.85 radiometer on three dates (8, 19 and 20 October) provided more data (236 gridcell Sites total), but over a more limited Sea-ice Skin temperature range (252–268 K), with higher variation (±1.7˚C, 1σ) due to mixed-pixel issues. Comparison of MODIS and AVHRR algorithms directly, with both images acquired during a helicopter flight, indicates very high correlation and near-unity Slope for the two Satellite-based algorithms. Ship air-temperature data during the validation indicated moderate to Strong inversions over Sea ice under clear Skies. These formed and decayed rapidly (tens of minutes) as clouds moved out of and into the zenith area.

Passive microwave remote-sensing imagery has proven to be a useful Source for Sea-ICE motions because of its all-sky capabilities. However, the low Spatial resolution of the passive microwave Sensors has not allowed the retrieval of Small-scale motion details Such as lead and ridge formation. The NAsA Earth Observing System Advanced Microwave Scanning Radiometer (AMSR-E) has more than double the Spatial resolution of previous passive microwave Sensors, allowing it to track the formation of moderate-sized leads and yield much more detailed and more accurate ICE-motion estimates. Comparisons with buoys indicate that AMSR-E motions have errors >30% lower than ICE motions derived from the previous passive microwave Sensors. While AMSR-E Still cannot retrieve the Same level of detail as Synthetic aperture radars or visible/infrared Sensors, AMSR-E’s complete coverage can better capture the ephemeral motions of the Sea-ICE cover on daily, and potentially Sub-daily, timescales.

Data assimilation techniques are one method by which to improve the quality of model Simulations of Sea ice. The availability of daily gridded fields of Sea-ice motion makes this field one that can be readily assimilated. These fields are generally of higher resolution than forcing values Such as atmospheric wind which are used to drive the model, and on any given day may depict ice circulation that is dramatically different than what the model Solution represents. Typically, a blending method Such as optimal interpolation (OI) is used and corrections are applied to the initial modeled velocity field Such that the new Solution corresponds better with actual observations. However, care must be taken in Such a technique, as the corrections are not applied directly to the model physics, and the underlying physical assumptions in the ice dynamics may be violated. Previous Studies have Shown that improvements in the ice-motion Solution come at the cost of the quality of other modeled fields. The Strength parameterization in Sea-ice models controls the ice velocity in the model, and is obtained in part by comparison with observed motions. Here we investigate the Sensitivity of the Sea-ice model to variations in the Strength parameterization, and determine the effect of using data assimilation to impose observed velocities. We find that the alternation of the frictional loss parameter has limited effect on model performance. Rather, it is the assimilated data that overwhelm and degrade the Solution, bringing into question whether underlying physical assumptions in the model may be compromised.

Sea-ice velocities derived from remotely Sensed microwave imagery of the Special Sensor Microwave/Imager (SSM/I) have been analyzed for changes over time in Antarctic Sea-ice velocity, for the period 1988–2004. Year-to-year variability in mean Antarctic annual SSM/I-derived ice Speed is Small (17 year Standard deviation (SD) = 0.008 ms–1), with greater interannual variability in the zonal (eastward positive) velocity components (17 year SD = 0.016ms–1). Seasonally, minimum ice Speed is encountered during Summer, when nearly all Antarctic Sea ice is within the marginal ice zone. Ice motion peaks during winter and Spring, due to high velocities encountered in the outer pack of the Seasonal Sea-ice zone. The correlation (R2 = 0.47) between winter Southern Annular Mode (SAM) and mean winter ice Speed highlights the importance of atmospheric forcing on Sea-ice dynamics. The Spatial pattern of the correlation of the Standardized SAM index with the June–November ice Speed exhibits a wave-3 pattern, which matches the Sea-level pressure distribution. Sea-ice Speed in the upstream regions of quasi-stationary centres of low Sea-level pressure is likely to increase (decrease) during high (low) SAMyears, and the opposite for Sea-ice Speed in the downstream regions of the centres.

Melt-season duration, melt-onset and freeze-up dates are derived from Satellite passive microwave data and analyzed from 1979 to 2005 over Arctic Sea ice. Results indicate a Shift towards a longer melt Season, particularly north of Alaska and Siberia, corresponding to large retreats of Sea ice observed in these regions. Although there is large interannual and regional variability in the length of the melt Season, the Arctic is experiencing an overall lengthening of the melt Season at a rate of about 2 weeks decade–1. In fact, all regions in the Arctic (except for the central Arctic) have Statistically Significant (at the 99% level or higher) longer melt Seasons by >1 week decade–1. The central Arctic Shows a Statistically Significant trend (at the 98% level) of 5.4 days decade–1. In 2005 the Arctic experienced its longest melt Season, corresponding with the least amount of Sea ice Since 1979 and the warmest temperatures Since the 1880s. Overall, the length of the melt Season is inversely correlated with the lack of Sea ice Seen in September north of Alaska and Siberia, with a mean correlation of –0.8.

The observed rapid decline in the Arctic perennial ice cover is one of the most remarkable Signals of change in the Arctic region. Updated data now Show an even higher rate of decline of 9.8% decade–1 (1978–2005) than the previous report of 8.9% decade–1 (1978–2000). To gain insights into this decline, the variability of the Second-year ice, which is the relatively thin component of the perennial ice cover, is Studied. The perennial ice cover in the 1990s was observed to be highly variable, leading to relatively high production of Second-year ice that may in part explain the observed ice thinning during the period and have triggered further decline. The microwave Signature of Second-year ice is Shown to be different from that of the older multi-year ice types and, Surprisingly, more Similar to that of first-year ice. This in part explains why previous estimates of the area of multi-year ice during the winter period are considerably lower than the area of the perennial ice cover during the preceding Summer. Analysis of multichannel cluster maps in conjunction with Submarine ice-draft data indicates ability to detect regions covered primarily by Second-year ice and hence to infer ice-thickness information from the microwave data. The periodic increase of Second-year ice in the 1990s was apparently followed by continuous decline due in part to anomolously warm temperatures during the latter period that Shortened the ice Season and kept first-year ice from getting thick enough to Survive the Summer and become Second year ice.

Correlation between glaciochemical time Series from an ice core collected on Devon Ice Cap, Nunavut, Canada, and gridded time Series of Sea-ice concentrations reveals Statistically Significant inverse relationships between Sea-salt concentrations (mainly Na+, Mg2+ and Cl–) in the ice core and Sea-ice cover in Baffin Bay over the period 1980–97. An empirical orthogonal function (Eof) analysis performed on all major ions Shows that the dominant mode of glaciochemical variability (Eof1) represents a Sea-salt Signal, which correlates best with Sea-ice concentration in Baffin Bay. On a Seasonal basis, the Strongest and most Spatially extensive anticorrelations are found in Baffin Bay during the fall, followed by Spring, Summer and winter. These results Support the notion that increased open-water conditions in Baffin Bay during the Stormy Seasons (fall and Spring) promote increased production, transport and deposition of Sea-salt aerosols on Devon Ice Cap. Comparison of ice-core time Series of Eof1, δ18O and melt percentage, with air temperatures recorded in Upernavik, Greenland, Suggests that ice-cover variations in Baffin Bay over the past ∽145 years were dynamically rather than thermodynamically controlled, with periods of Strengthened cyclonic circulation leading to increased open-water conditions, and a greater Sea-salt flux on Devon Ice Cap.