scholarly journals Fast Ice Prediction System (FIPS) for land-fast sea ice at Prydz Bay, East Antarctica: an operational service for CHINARE

2020 ◽  
pp. 1-13
Author(s):  
Jiechen Zhao ◽  
Bin Cheng ◽  
Timo Vihma ◽  
Petra Heil ◽  
Fengming Hui ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Cold Bias ◽  
Prediction System ◽  
Atmospheric Forcing ◽  
Ice Melt ◽  
Fast Ice ◽  
Sar Imagery ◽  
Operational Service ◽  
Snow And Ice

Abstract A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

scholarly journals Compact Polarimetry Response to Modeled Fast Sea Ice Thickness

2020 ◽  
Vol 12 (19) ◽  
pp. 3240
Author(s):  
Mohammed Dabboor ◽  
Mohammed Shokr
Keyword(s):  
Sea Ice ◽  
Degree Of Polarization ◽  
Ice Thickness ◽  
Operational Mode ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Bay Area ◽  
Fast Ice ◽  
Preliminary Study ◽  
Sar Imagery

Compact Polarimetric (CP) Synthetic Aperture Radar (SAR) is expected to gain more and more ground for Earth observation applications in the coming years. This comes in light of the recently launched RADARSAT Constellation Mission (RCM), which uniquely provides CP SAR imagery in operational mode. In this study, we present observations about the sensitivity of CP SAR imagery to thickness of thermodynamically-grown fast sea ice during early ice growth (September–December 2017) in the Resolute Bay area, Canadian Central Arctic. Fast ice is most suitable to use for this preliminary study since it exhibits only thermodynamic growth in absence of ice mobility and deformation. Results reveal that ice thickness up to 30 cm can be retrieved using several CP parameters from the tested set. This ice thickness corresponds to the thickness of young ice. We found the surface scattering mechanism to be dominant during the early ice growth, exposing an increasing tendency up to 30 cm thickness with a correlation coefficient with the thickness equal to 0.86. The degree of polarization was found to be the parameter with the highest correlation up to 0.95. While thickness retrieval within the same range is also possible using parameters from Full Polarimetric (FP) SAR parameters as shown in previous studies, the advantage of using CP SAR mode is the much larger swath coverage, which is an operational requirement.

scholarly journals Improving the WRF model's simulation over sea ice surface through coupling with a complex thermodynamic sea ice model

2015 ◽  
Vol 8 (12) ◽  
pp. 10305-10337 ◽  
Author(s):  
Y. Yao ◽  
J. Huang ◽  
Y. Luo ◽  
Z. Zhao
Keyword(s):  
Sea Ice ◽  
Wrf Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Cold Bias ◽  
Sea Ice Thickness ◽  
The Impact ◽  
Ice Model ◽  
Snow And Ice

Abstract. Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice model, which has been widely used in the Weather Research and Forecasting (WRF) model, exhibits cold bias in simulating the Arctic sea ice temperature when validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations. According to sensitivity tests, this bias is attributed not only to the simulation of snow depth and turbulent fluxes but also to the heat conduction within snow and ice. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) has smaller bias in simulating the sea ice temperature. HIGHTSI is further coupled with the WRF model to evaluate the possible added value from better resolving the heat transport and solar penetration in sea ice from a complex thermodynamic sea ice model. The cold bias in simulating the surface temperature over sea ice in winter by the original Polar WRF is reduced when HIGHTSI rather than Noah is coupled with the WRF model, and this also leads to a better representation of surface upward longwave radiation and 2 m air temperature. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information would result in the best simulation among the available methods. If no observational information is available, using an empirical method based on the relationship between sea ice concentration and sea ice thickness could mimic the large-scale spatial feature of sea ice thickness. The potential application of a thermodynamic sea ice model in predicting the change in sea ice thickness in a RCM is limited by the lack of sea ice dynamic processes in the model and the coarse assumption on the initial value of sea ice thickness.

Assimilation of SMOS sea ice thickness in the regional ice prediction system

2021 ◽  
Vol 42 (12) ◽  
pp. 4583-4606
Author(s):  
Mukesh Gupta ◽  
Alain Caya ◽  
Mark Buehner
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Prediction System ◽  
Sea Ice Thickness

scholarly journals Interannual sea ice thickness variability in the Bay of Bothnia

2018 ◽  
Vol 12 (11) ◽  
pp. 3459-3476 ◽  
Author(s):  
Iina Ronkainen ◽  
Jonni Lehtiranta ◽  
Mikko Lensu ◽  
Eero Rinne ◽  
Jari Haapala ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

Land-fast ice microalgal and phytoplanktonic communities (Adélie Land, Antarctica) in relation to environmental factors during ice break-up

2003 ◽  
Vol 15 (3) ◽  
pp. 353-364 ◽  
Author(s):  
C. RIAUX-GOBIN ◽  
M. POULIN ◽  
R. PRODON ◽  
P. TREGUER
Keyword(s):  
Sea Ice ◽  
Early Spring ◽  
Ice Melt ◽  
Fast Ice ◽  
Maximum Cell ◽  
Cell Densities ◽  
Ice Water ◽  
Water Species ◽  
Very High ◽  
Pennate Diatoms

Annual land-fast ice, particularly an unconsolidated layer or “platelet ice-like” layer (PLI), was sampled in spring 1995 to study the spatial and short-term variations of ice-associated diatoms. Under-ice water, a lead and small polynyas were also sampled. Along a 7 km seaward transect a geographical gradient was evident, with some rare diatom species present only in the offshore PLI, whereas others (mainly pennate diatoms) were ubiquitous. The dense microphytic PLI community as well as the phytoplankton was diatom-dominated, but, within these two communities, marked differences appeared. First, the sea-ice communities (PLI and solid bottom ice) were moderately diverse (36 species), mostly composed of pennate diatoms, of which many were chain forming or tube-dwelling. Dominant taxa were Navicula glaciei, Berkeleya adeliensis, Nitzschia stellata, Amphiprora kufferathii and Nitzschia lecointei. Some differences in the distribution of the most dominant species appeared within the bottom ice and the PLI, attesting to differences in the origin or/and growing capability of these diatoms in these two ice compartments. Under-ice water species composition was mixed with sea-ice communities only on the most coastal sites and during ice melt. Maximum cell numbers were mostly noticed in the PLI, reaching up to 1010 cells l−1 and very high Chl a concentrations (exceptionally up to 9.8 mg Chl a l−1 or 1.9 g Chl a m−2, from a 10 to 20 cm thick PLI layer, close to the continent). Secondly, the phytoplankton in the lead and small polynyas had a low diversity, very low standing stocks (on an average 0.69 μg Chl a l−1) and cell densities (2 × 104 cells l−1). Some species from the polynyas were similar to those of the PLI, such as Navicula glaciei, but others were typically planktonic, such as Chaetoceros cf. neglectus. The presence of encysted cells (Chaetoceros and Chrysophytes) was also noticeable in the polynya water. In early spring no seeding process was obvious from the PLI to polynya water. A comparison with similar fast-ice diatom communities in other parts of coastal Antarctica, is presented.

Early detection of the Weddell polynya re-opening using SAR imagery

2020 ◽  
Author(s):  
Adriano Lemos ◽  
Céline Heuzé
Keyword(s):  
Early Detection ◽  
Sea Ice ◽  
Open Water ◽  
Weddell Sea ◽  
Small Scale ◽  
Ice Thickness ◽  
Sar Images ◽  
Sea Ice Thickness ◽  
Sar Imagery ◽  
Microwave Sensors

<p>The sea ice thickness in the Weddell Sea during the austral winter normally exceeds 1 m, but in the case of a polynya, this thickness decreases to 10 cm or less. There are two theories as to why the Weddell Polynya opens: 1) comparatively warm oceanic water upwelling from its nominal depth of several hundred metres to the surface where it melts the sea ice from underneath; or 2) opening of a lead by a passing storm, lead which will then be maintained open either by the atmosphere or ocean and grow. The objective of this study is to estimate how long in advance the recent Weddell Polynya opening could have been detected by synthetic aperture radar (SAR) images due to the decrease of the sea ice thickness and/or early appearance of leads. We use high temporal and spatial resolution SAR images from the Sentinel-1 constellation (C-band) and ALOS2 (L-band) during the austral winters 2014-2018. We use an adapted version of the algorithm developed by Aldenhoff et al. (2018) to monitor changes in sea ice thickness over the polynya region. The algorithm detects the transition of the sea ice thickness through changes in small scale surface roughness and thus reduced backscatter, and allowing us to distinguish three different categories: ice, thin ice, and open water. The transition from ice to thin ice and then to open water indicates that the polynya is melted from under, whereas a direct transition from ice to open water will reveal leads. The high resolution and good coverage of the SAR imagery, and a combined effort of different satellites sensors (e.g. infrared and microwave sensors), opens the possibility of an early detection of Weddell Polynya opening.</p>

Winter Arctic sea ice bottom evolution detected by thermistor string-based ice mass balance buoys (SIMBA)

2020 ◽  
Author(s):  
Bin Cheng ◽  
Timo Vihma ◽  
Zeling Liao ◽  
Ruibo Lei ◽  
Mario Hoppmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
Heating Element ◽  
The Arctic ◽  
Ice Thickness ◽  
The Arctic Ocean ◽  
Ice Mass Balance ◽  
Snow And Ice

<p>A thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) has been developed in recent years and used for monitoring snow and ice mass balance in the Arctic Ocean. SIMBA measures vertical environment temperature (ET) profiles through the air-snow-sea ice-ocean column using a thermistor string (5 m long, sensor spacing 2cm). Each thermistor sensor equipped with a small identical heating element. A small voltage was applied to the heating element so that the heat energy liberated in the vicinity of each sensor is the same. The heating time intervals lasted 60 s and 120 s, respectively. The heating temperatures (HT) after these two intervals were recorded. The ET was measured 4 times a day and once per day for the HT.</p><p>A total 15 SIMBA buoys have been deployed in the Arctic Ocean during the Chinese National Arctic Research Expedition (CHINARE) 2018 and the Nansen and Amundsen Basins Observational System (NABOS) 2018 field expeditions in late autumn. We applied a recently developed SIMBA algorithm to retrieve snow and ice thickness using SIMBA ET and HT temperature data. We focus particularly on sea ice bottom evolution during Arctic winter.</p><p>In mid-September 2018, 5 SIMBA buoys were deployed in the East Siberian Sea (NABOS2018) where snow was in practical zero cm and ice thickness ranged between 1.8 m – 2.6 m. By the end of May, those SIMBA buoys were drifted in the central Arctic where snow and ice thicknesses were around 0.05m - 0.2m and 2.6m – 3.2m, respectively. For those 10 SIMBA buoys deployed by the CHINARE2018 in the Chukchi Sea and Canadian Basin, the initial snow and ice thickness were ranged between 0.05m – 0.1cm and 1.5m – 2.5m, respectively.  By the end of May, those SIMBA buoys were drifted toward the north of Greenland where snow and ice thicknesses were around 0.2m - 0.3m and 2.0m – 3.5m, respectively. The ice bottom evolution derived by SIMBA algorithm agrees well with SIMBA HT identified ice-ocean interfaces. We also perform a preliminary investigation of sea ice bottom evolution measured by several SIMBA buoys deployed during the MOSAiC leg1 field campaign in winter 2019/2020.  </p>

Early winter ice and snow thickness distribution, ice structure and development of the western Ross Sea pack ice between the ice edge and the Ross Ice Shelf

1997 ◽  
Vol 9 (2) ◽  
pp. 188-200 ◽  
Author(s):  
Martin O. Jeffries ◽  
Ute Adolphs
Keyword(s):  
Sea Ice ◽  
Layer Thickness ◽  
Ross Sea ◽  
Ice Thickness ◽  
First Year ◽  
Ross Ice Shelf ◽  
Antarctic Sea Ice ◽  
Pack Ice ◽  
Ice Edge ◽  
Snow And Ice

A study of early winter first-year sea ice conditions and development in the western Ross Sea in May and June 1995 included measurements of snow and ice thickness, freeboard, ice core structure and stable isotopic composition. These variables showed strong spatial variability between the Ross Ice Shelf and the ice edge 1400 km to the north, and indicate that the development of the Ross Sea pack ice is quite different from that observed in other Antarctic sea ice zones. The thinnest snow and ice occurred in a 200 km wide coastal zone. The thickest snow and ice were observed in a continental shelf zone 200–600 km from the coast where the average ice thickness (0.8 m) determined by drilling is as thick as first-year sea ice later in winter elsewhere in Antarctica. A zone of moderate snow and ice thickness occurred on the deep ocean from 600 km to the ice edge at 1400 km. Thermodynamic thickening of the ice in the inner pack ice, <800 km from the coast, was dominated by congelation ice growth, which occurred in a greater amount (65%) and in thicker layers (mean: 20 cm) than was observed in the outer pack ice >800 km from the coast (amount: 22%; mean layer thickness: 12 cm) and elsewhere in the Antarctic pack ice. The preponderance of congelation ice in the inner pack ice might be due to a low oceanic heat flux on the Ross Sea continental shelf, and a colder, less stormy environment which favours the more frequent and prolonged calm conditions necessary for significant congelation ice growth. In the outer pack ice, thermodynamic thickening occurred mainly by snow ice formation (mean layer thickness: 20 cm) while dynamic processes, i.e., rafting and ridging, caused the thickening of frazil ice and columnar ice (mean layer thickness: 14 cm and 12 cm respectively). A greater amount of snow ice (37%) occurred in the outer pack ice than in the inner pack ice (15%), and both values indicate that in the Ross Sea, unlike other Antarctic sea ice zones, there can be significant seawater flooding of the snow/ice interface and snow ice formation before midwinter.

scholarly journals Snow depth uncertainty and its implications on satellite derived Antarctic sea ice thickness

2018 ◽  
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Fast Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.

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