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.

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

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 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 Seasonal changes of sea-ice characteristics off East Antarctica

1994 ◽  
Vol 20 ◽  
pp. 195-201 ◽  
Author(s):  
Ian Allison ◽  
Anthony Worby
Keyword(s):  
Sea Ice ◽  
Ice Cores ◽  
Open Water ◽  
Ice Formation ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Growth Season ◽  
Ice Growth ◽  
Antarctic Sea Ice ◽  
Ice Edge

Data on Antarctic sea‐ice characteristics, and their spatial and temporal variability, are presented from cruises between 1986 and 1993 for the region spanning 60°−150° E between October and May. In spring, the sea‐ice zone is a variable mixture of different thicknesses of ice plus open water and in some regions only 30−40% of the area is covered with ice >0.3 m thick. The thin‐ice and open‐water areas are important for air‐sea heat exchange. Crystallographic analyses of ice cores, supported by salinity and stable‐isotope measurements, show that approximately 50% of the ice mass is composed of small frazil crystals. These are formed by rapid ice growth in leads and polynyas and indicate the presence of open water throughout the growth season. The area‐averaged thickness of undeformed ice west of 120° E is typically less than 0.3 m and tends to‐increase with distance south of the ice edge. Ice growth by congelation freezing rarely exceeds 0.4 m, with increases in ice thickness beyond this mostly attributable to rafting and ridging. While most of the total area is thin ice or open water, in the central pack much of the total ice mass is contained in ridges. Taking account of the extent of ridging, the total area‐averaged ice thickness is estimated to be about 1m for the region 60°−90° E and 2 m for the region 120°−150° E. By December, new ice formation has ceased in all areas of the pack and only floes >0.3 m remain. In most regions these melt completely over the summer and the new season's ice formation starts in late February. By March, the thin ice has reached a thickness of 0.15 0.30 m, with nilas formation being an important mechanism for ice growth within the ice edge

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 About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

2015 ◽  
Vol 9 (5) ◽  
pp. 4893-4923 ◽  
Author(s):  
S. Schwegmann ◽  
E. Rinne ◽  
R. Ricker ◽  
S. Hendricks ◽  
V. Helm
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Altimeter Data ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Growth Season ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Abstract Knowledge ◽  
Modal Values

Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, data come from different sources, and the characteristics of individual sensors differ. Hence, it is important to study the consistency between single sensors in order to develop long and consistent time series over the potentially available measurement period. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on-board Envisat and freeboard measurements from the Synthetic-Aperture Interferometric Radar Altimeter on-board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are comparable over the sea-ice growth season (May–October) and partly also beyond. In general, Envisat data shows higher freeboards in the seasonal ice zone while CryoSat-2 freeboards are higher in the perennial ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regional separated) Southern Ocean differs generally by not more than 2 cm (5 cm, except for the Amundsen/Bellingshausen Sea) between Envisat and CryoSat-2, and the differences between modal freeboard lie generally within ±10 cm and often even below.

scholarly journals A scatterometer record of sea ice extents and backscatter: 1992–2016

2018 ◽  
Author(s):  
Maria Belmonte Rivas ◽  
Ines Otosaka ◽  
Ad Stoffelen ◽  
Anton Verhoef
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Climate Data ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
New Evidence ◽  
Climate Data Record ◽  
Higher Sensitivity

Abstract. This paper presents the first long-term climate data record of sea ice extents and backscatter derived from inter-calibrated satellite scatterometer missions (ERS, QuikSCAT and ASCAT) extending from 1992 to present date. This record provides a valuable independent account of the evolution of Arctic and Antarctic sea ice extents, one that is in excellent agreement with the passive microwave records during the fall and winter months but shows higher sensitivity to lower concentration and melting sea ice during the spring and summer months, providing a means to correct for summer melt ponding errors. The scatterometer record also provides a depiction of sea ice backscatter at C and Ku-band, allowing the separation of seasonal and perennial sea ice in the Arctic, and further differentiation between second year (SY) and older multiyear (MY) ice classes, revealing the emergence of SY ice as the dominant perennial ice type after the record sea ice loss in 2007, and bearing new evidence on the loss of multiyear ice in the Arctic over the last 25 years. The relative good agreement between the backscatter-based sea ice (FY, SY and older MY) classes and the ice thickness record from Cryosat suggests its applicability as a reliable proxy in the historical reconstruction of sea ice thickness in the Arctic.

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 About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

2016 ◽  
Vol 10 (4) ◽  
pp. 1415-1425 ◽  
Author(s):  
Sandra Schwegmann ◽  
Eero Rinne ◽  
Robert Ricker ◽  
Stefan Hendricks ◽  
Veit Helm
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Altimeter Data ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Growth Season ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Abstract Knowledge ◽  
Modal Values

Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on board Envisat and freeboard measurements from the Synthetic-Aperture Interferometric Radar Altimeter on board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are in reasonable agreement over the sea-ice growth season (May–October) and partly also beyond. In general, Envisat data show higher freeboards in the first-year ice zone while CryoSat-2 freeboards are higher in the multiyear ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regionally separated) Southern Ocean differs generally by not more than 3 cm (8 cm, with few exceptions) between Envisat and CryoSat-2, and the differences between modal freeboards lie generally within ±10 cm and often even below.

scholarly journals Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

2019 ◽  
Vol 13 (4) ◽  
pp. 1409-1422
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack ◽  
Ethan Dale
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Mcmurdo Sound ◽  
Sea Ice Thickness ◽  
Antarctic Sea 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 the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound 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 in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the 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 what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

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