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 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.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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.

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>

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.

scholarly journals Suppression of Arctic sea ice growth by winter clouds and snowfall

2021 ◽  
Author(s):  
Won-il Lim ◽  
Hyo-Seok Park ◽  
Andrew Stewart ◽  
Kyong-Hwan Seo
Keyword(s):  
Sea Ice ◽  
Longwave Radiation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Downward Longwave Radiation ◽  
Arctic Sea ◽  
The Impact

Abstract The ongoing Arctic warming has been pronounced in winter and has been associated with an increase in downward longwave radiation. While previous studies have demonstrated that poleward moisture flux into the Arctic strengthens downward longwave radiation, less attention has been given to the impact of the accompanying increase in snowfall. Here, utilizing state-of-the art sea ice models, we show that typical winter snowfall anomalies of 1.0 cm, accompanied by positive downward longwave radiation anomalies of ~5 W m-2 can decrease sea ice thickness by around 5 cm in the following spring over the Eurasian Seas. This basin-wide ice thinning is followed by a shrinking of summer ice extent in extreme cases. In the winter of 2016–17, anomalously strong warm/moist air transport combined with ~2.5 cm increase in snowfall decreased spring ice thickness by ~10 cm and decreased the following summer sea ice extent by 5–30%. Projected future reductions in the thickness of Arctic sea ice and snow will amplify the impact of anomalous winter snowfall events on winter sea ice growth and seasonal sea ice thickness.

scholarly journals A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE)

2021 ◽  
Author(s):  
James Anheuser ◽  
Yinghui Liu ◽  
Jeffrey Key
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Dynamic Effects ◽  
Volumetric Growth ◽  
Growth Season ◽  
Retrieval Method ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Ice Mass Balance

Abstract. As changes to Earth’s polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By coupling a recently developed algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's Law, we have developed a new retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan’s Law Integrated Conducted Energy (SLICE). The advantages of the SLICE retrieval method include daily basin-wide coverage and a potential for use beginning in 1987. The method requires an initial condition at the beginning of the sea ice growth season in order to produce absolute sea ice thickness and cannot as yet capture dynamic sea ice thickness changes. Validation of the method against ten ice mass balance buoys using the ice mass balance buoy thickness as the initial condition show a mean correlation of 0.991 and a mean bias of 0.008 m over the course of an entire sea ice growth season. Estimated Arctic basin-wide sea ice thickness from SLICE for the sea ice growth seasons beginning between 2012 through 2019 capture a mean of 12.0 % less volumetric growth than a CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) merged sea ice thickness product (CS2SMOS) and a mean of 8.3 % more volumetric growth than the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS). The spatial distribution of the sea ice thickness differences between the retrieval results and those reference datasets show patterns consistent with expected sea ice thickness changes due to dynamic effects. This new retrieval method is a viable basis for a long–term sea ice thickness climatology, especially if dynamic effects can be captured through inclusion of an ice motion dataset.

scholarly journals Sea-ice thickness variability in Storfjorden, Svalbard

2011 ◽  
Vol 52 (57) ◽  
pp. 61-68 ◽  
Author(s):  
S. Hendricks ◽  
S. Gerland ◽  
L.H. Smedsrud ◽  
C. Haas ◽  
A.A. Pfaffhuber ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Meteorological Data ◽  
Ice Thickness ◽  
Central Arctic Ocean ◽  
Reanalysis Dataset ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Thickness Variations

AbstractResults from electromagnetic induction surveys of sea-ice thickness in Storfjorden, Svalbard, reveal large interannual ice-thickness variations in a region which is typically characterized by a reoccurring polynya. the surveys were performed in March 2003, May 2006 and March 2007 with helicopter- and ship-based sensors. the thickness distributions are influenced by sea-ice and atmospheric boundary conditions 2 months prior to the surveys, which are assessed with synthetic aperture radar (SAR) images, regional QuikSCAT backscatter maps and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset. Locally formed thin ice from the Storfjorden polynya was frequently observed in 2003 and 2007 (mean thickness 0.55 and 0.37 m, respectively) because these years were characterized by prevailing northeasterly winds. In contrast, the entire fjord was covered with thick external sea ice in 2006 (mean thickness 2.21 m), when ice from the Barents Sea was driven into the fjord by predominantly southerly winds. the modal thickness of this external ice in 2006 increased from 1.2m in the northern fjord to 2.4 m in the southern fjord, indicating stronger deformation in the southern part. This dynamically thickened ice was even thicker than multi-year ice advected from the central Arctic Ocean in 2003 (mean thickness 1.83 m). the thermodynamic ice thickness of fast ice as boundary condition is investigated with a one-dimensional sea-ice growth model (1DICE) forced with meteorological data from the weather station at the island of Hopen, southeast of Storfjorden. the model results are in good agreement with the modal thicknesses of fast-ice measurements in all years.

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

2018 ◽  
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 fast ice thickness have been extensively studied, there is little information about the sea 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 from helicopter- and ship-borne 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. In heavily ridged ice regions near the coast, mean ice thickness can be even manyfold thicker than in a pure thermodynamically grown fast ice. Drift ice exhibits larger inter-annual variability than fast ice.

scholarly journals Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification

2021 ◽  
Vol 15 (6) ◽  
pp. 2575-2591
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Tatiana A. Alekseeva ◽  
Gerit Birnbaum ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Arctic Sea ◽  
Transpolar Drift

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic.

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