Thin Ice Thickness Research Articles

Ross Sea polynyas: Response of ice concentration retrievals to large areas of thin ice

For a 3‐month period between May and July of 2005, we examine the response of the Advanced Microwave Scanning Radiometer (AMSR‐E) Enhanced NASA Team 2 (NT2) and AMSR‐E Bootstrap (ABA) ice concentration algorithms to large areas of thin ice of the Ross Sea polynyas. Coincident Envisat Synthetic Aperture Radar (SAR) coverage of the region during this period offers a detailed look at the development of the polynyas within several hundred kilometers of the ice front. The high‐resolution imagery and derived ice motion fields show bands of polynya ice, covering up to ∼105 km2 of the Ross Sea, that are associated with wind‐forced advection. In this study, ice thickness from AMSR‐E 36 GHz polarization information serves as the basis for examination of the response. The quality of the thickness of newly formed sea ice (<10 cm) from AMSR‐E is first assessed with thickness estimates derived from ice surface temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The effect of large areas of thin ice in lowering the ice concentration estimates from both NT2/ABA approaches is clearly demonstrated. Results show relatively robust relationships between retrieved ice concentrations and thin ice thickness estimates that differ between the two algorithms. These relationships define the approximate spatial coincidence of ice concentration and thickness isopleths. Using the 83% (ABA) and 91% (NT2) isopleths as polynya boundaries, we show that the computed coverage compares well with that using the estimated 10‐cm thickness contour. The thin ice response characterized here suggests that in regions with polynyas, the retrieval results could be used to provide useful geophysical information, namely thickness and coverage.

Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean

Abstract Antarctic coastal polynyas are important areas of high sea ice production and dense water formation, and thus their detection including an estimate of thin ice thickness is essential. In this paper, the authors propose an algorithm that estimates thin ice thickness and detects fast ice using Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data in the Antarctic Ocean. Detection and estimation of sea ice thicknesses of <0.2 m are based on the SSM/I 85- and 37-GHz polarization ratios (PR85 and PR37) through a comparison with sea ice thicknesses estimated from the Advanced Very High Resolution Radiometer (AVHRR) data. The exclusion of data affected by atmospheric water vapor is discussed. Because thin ice and fast ice (specifically ice shelves, glacier tongues, icebergs, and landfast ice) have similar PR signatures, a scheme was developed to separate these two surface types before the application of the thin ice algorithm to coastal polynyas. The probability that the algorithm correctly distinguishes thin ice from thick ice and from fast ice is ∼95%, relative to the ice thicknesses estimated from AVHRR. Although the standard deviation of the difference between the thin ice thicknesses estimated from the SSM/I algorithm and AVHRR is ∼0.05 m and thus not small, the estimated ice thicknesses from the microwave algorithm appear to have small biases and the accuracies are independent of region and season. A distribution map of thin ice occurrences derived from the SSM/I algorithm represents the Ross Sea coastal polynya being by far the largest among the Antarctic coastal polynyas; the Weddell Sea coastal polynyas are much smaller. Along the coast of East Antarctica, coastal polynyas frequently form on the western side of peninsulas and glacier tongues, downstream of the Antarctic Coastal Current.

Retrieval of thin-ice thickness using the L-band polarization ratio measured by the helicopter-borne scatterometer Heliscat

AbstractClimate 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.

Estimation of thin Sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica

AbstractAntarctic 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.

Estimation of the thin ice thickness and heat flux for the Chukchi Sea Alaskan coast polynya from Special Sensor Microwave/Imager data, 1990–2001

One of the largest Arctic polynyas occurs along the Alaskan coast of the Chukchi Sea between Cape Lisburne and Point Barrow. For this polynya, a new thin ice thickness algorithm is described that uses the ratio of the vertically and horizontally polarized Special Sensor Microwave/Imager (SSM/I) 37‐GHz channels to retrieve the distribution of thicknesses and heat fluxes at a 25‐km resolution. Comparison with clear‐sky advanced very high resolution radiometer data shows that the SSM/I thicknesses and heat fluxes are valid for ice thicknesses less than 10–20 cm, and comparison with several synthetic aperture radar (SAR) images shows that the 10‐cm ice SSM/I ice thickness contour approximately follows the SAR polynya edge. For the twelve winters of 1990–2001, the ice thicknesses and heat fluxes within the polynya are estimated from daily SSM/I data, then compared with field data and with estimates from other investigations. The results show the following: First, our calculated heat losses are consistent with 2 years of over‐winter salinity and temperature field data. Second, comparison with other numerical and satellite estimates of the ice production shows that although our ice production per unit area is smaller, our polynya areas are larger, so that our ice production estimates are of the same order. Because our salinity forcing occurs over a larger area than in the other models, the oceanic response associated with our forcing will be modified.

Comparison of thin ice thickness distributions derived from RADARSAT Geophysical Processor System and advanced very high resolution radiometer data sets

Thin ice thickness distributions estimated from advanced very high resolution radiometer (AVHRR) and RADARSAT Geophysical Processor System (RGPS) data sets were compared over the Beaufort Sea and the Canada Basin for the period December 1996 to February 1997. The comparisons show a compelling agreement. High correlations were found in cases where thin ice grew in large, wide leads extending several hundred kilometers. At these large scales, estimates from AVHRR images and RGPS showed similar amounts of thin ice in leads. However, when major surface deformation occurred on small scales (100 m to 10 km), the finer spatial resolution (100 m) of RADARSAT images enabled the RGPS algorithm to derive more thin ice than that of AVHRR. Under such conditions the correlation between the two dropped, and a small negative bias (about 1%) was observed in the estimates from AVHRR. This bias, mostly concentrated at the very thin end of the thickness distribution, caused a further deficit in the AVHRR‐derived thin ice growth, roughly 0.2 cm/d. Although the AVHRR and RGPS algorithms treat snowfall differently in the ice thickness calculations, both snow assumptions appear reasonable. However, RGPS may underestimate the thin ice production because of the 3‐day sampling interval. With a better understanding of the sources of uncertainty and improved satellite estimates, a combination of these two satellite data could offer a wider coverage of thin ice thickness observations in the Arctic Basin.

Thin ice impacts on surface salt flux and ice strength: Inferences from advanced very high resolution radiometer

Temperatures and albedos derived from satellite imagery are combined with a thermodynamic ice model to estimate thin ice thickness distributions over the Beaufort and the northern Greenland seas. The study shows that thin ice (thinner than 1 m) occupied over half the area in the seasonal ice zones in November and December of 1990 but dropped significantly in April 1991; the Beaufort Shelf showed the largest seasonal change. The aggregate properties of surface salt flux and compressive ice strength, both strongly dependent on the thin end of the ice thickness distribution, were estimated with data from advanced very high resolution radiometer to reveal the spatial and temporal variations over a large scale. The salt flux from growing thin ice was 1–2 orders of magnitude larger on the Beaufort and Greenland Shelves than in the deep basins. On the shelves, flux from thin ice accounted for over 90% of the total surface salt budget. These satellite‐derived estimates provided detailed spatial information on salt flux, which can be of great use in studies of surface patterns of salinity forcing and shelf‐basin interaction. Also revealed by the satellite data was the wide range in values of compressive strength, which affects how freely the ice cover can deform. Strengths were low in early winter and in seasonal ice zones and nearly doubled in spring. Both thin ice fraction and compressive ice strength estimated from satellite imagery were in good agreement with those simulated by a coupled ice‐ocean model.

Characteristics of the surface heat budget during the ice-growth season in the southern Sea of Okhotsk

AbstractThe heat budget over the ice-covered area of the southern Sea of Okhotsk is estimated from in situ meteorological and ice observation for four years, 1996−99. The data are from about 1 week in early February in each of four years. Ice-thickness distributions required for calculating the heat budget are quantitatively obtained from video analysis. A one-dimensional thermodynamical model is used to calculate the heat flux. The total heat flux is obtained by summing up the area-weighted heat flux of each ice-thickness category. In addition, to determine the characteristics of the heat budget in this region, we also calculated the heat budget in the northern Sea of Okhotsk using Special Sensor Microwave/Imager ice-extent data and European Centre for Medium-range Weather Forecasts meteorological data, and compared the results. Our investigations show the following characteristics in the southern Sea of Okhotsk: (1) Due to relatively thin ice thickness, the average turbulent heat flux is upward. (2) Thin ice and open water contribute significantly to the total turbulent heat flux. (3) Thermodynamic ice growth is limited to about 1 cm d−1 on average. (4) The heat budget is largely characterized by abundant solar radiation. The first, third and fourth results are characteristic of this region located at a relatively low latitude, while the second one is similar to that for polar regions.

衛星搭載マイクロ波放射計を用いた薄氷域の海氷密接度推定に氷厚が及ぼす影響に関する研究

Ice concentration derived from satellite passive microwave sensor data is a key parameter for monitoring the global sea ice cover areas. Since the sea surface heat flux is quite sensitive to the existence of thin ice, accurate calculation of ice concentration within thin ice area is very important for heat exchange between ocean and atmosphere. In this study, two sea ice concentration algorithms, namely NASA Team algorithm and Bootstrap algorithm, were compared for the thin sea ice area of the Okhotsk Sea to evaluate the effect of sea ice thickness to the calculation of ice concentration from SSM/I data. Firstly, 100% sea ice concentration areas were extracted by using simultaneously collected NOAA/AVHRR/2 data. Then the ice concentrations of those areas were calculated with using SSM/I. The result shows that the NASA Team algorithm was likely to underestimate the ice concentration within the 100% thin ice area. The ice thickness of those areas was estimated with using AVHRR/ 2 data, and they were compared with the measured brightness temperatures of each SSM/I channels. It has become clear that the wavebands of 19GHz and 37GHz at horizontal polarization were sensitive to the thickness of thin ice. This explains why ice concentrations of thin ice area were underestimated with the NASA Team algorithm.

オホーツク海南部の海氷域における熱収支の見積もり

From in-situ meteorological and ice observation in early February of 1996 and 1997, we estimate the heat budget in the ice cover of the southern Okhotsk Sea. Ice concentration and ice thickness distribution required for its calculation are obtained quantitatively from video analysis. One dimensional-thermodynamical model is used to calculate heat flux. The total heat flux is obtained by summing up the areally weighted heat flux on each ice thickness. As a result, the following characteristics are found in this region. (1) Due to relatively thin ice thickness, turbulent heat flux (sensible + latent) is upward, showing that the sea ice region gives turbulent heat to atmosphere. (2) Thin ice and open area contribute significantly to the total turbulent heat flux over the whole area. (3) Thermodynamical ice growth is limited to below 1 cm per day on average. The first and third results are the characteristics of this area located at a relatively low latitude, while the second one is generally observed for polar regions. The results also suggest that surface melting occur at daytime mainly due to solar radiation. The salinity of sea ice samples shows the values lower than that of the first-year ice in the polar regions and rather similar to that of the multi-year ice, which is lowered by surface melting during summer; this may support this suggestion.

Laboratory measurements of sea ice: connections to microwave remote sensing

The connections between laboratory measurements and remote-sensing observations of sea ice are explored. The focus of this paper is on thin ice, which is more easily simulated in a laboratory environment. The authors summarize results of C-band scatterometer measurements and discuss how they may help in the interpretation of remote-sensing data. They compare the measurements with observations of thin ice from ERS and airborne radar data sets. They suggest that laboratory backscatter signatures should serve as bounds on the interpretation of remote-sensing data. They examine these bounds from the perspective of thin ice signatures, the effect of temperature, and surface processes, such as frost flowers and slush on these signatures. Controlled experiments also suggest new directions in remote-sensing measurements. The potential of polarimetric radar measurements in the retrieval of thickness of thin ice is discussed. In addition to the radar results, the authors discuss the importance of low-frequency passive measurements with respect to the thickness of thin ice.

Retrieval of thin ice thickness from multifrequency polarimetric SAR data

We discuss the observed C- and L-band polarimetric signatures of thin lead ice in one Synthetic Aperture Radar (SAR) image based on the expected ice properties and results from a scattering model. In this article, we focus on thin ice with thicknesses in the range of 0–10 cm. The layered scattering model used here allows for the inclusion of surface and volume scattering contributions from a slush layer, an ice layer, and roughness at the interfaces. The sensitivity of the signatures to the model parameters is explored. A highly saline surface skim formed on the top surface during ice growth significantly affects the electromagnetic properties of the medium and helps to explain the magnitude of the copolarized returns at high incidence angles. Based on these model predictions, we demonstrate an approach to retrieve the ice thickness from polarimetric SAR observations. The approach includes the training of a neural network with model predictions and using this neural network to estimate the ice thickness distribution using polarimetric observations from SAR data. The results from this ice thickness retrieval process are discussed.