A comparison between Envisat and ICESat sea ice thickness in the Antarctic

Abstract. The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While field observations are too sparse in the Antarctic to determine long-term trends of the Antarctic sea ice thickness (SIT) on a hemispheric scale, satellite radar altimetry data can be applied with a promising prospect. European Space Agency Climate Change Initiative – Sea Ice Project (ESA SICCI) includes sea ice freeboard and sea ice thickness derived from Envisat, covering the entire Antarctic year-round from 2002 to 2012. In this study, the SICCI Envisat SIT in the Antarctic is first compared with a conceptually new ICESat SIT product retrieved from an algorithm employing modified ice density. Both data sets are compared to SIT estimates from upward-looking sonar (ULS) in the Weddell Sea, showing mean differences (MD) and standard deviations (SD) of 1.29 (0.65) m for Envisat-ULS, while we find 1.11 (0.81) m for ICESat-ULS, respectively. The inter-comparisons are conducted for three seasons except winter, based on the ICESat operating periods. According to the results, the differences between Envisat and ICESat SIT reveal significant temporal and spatial variations. More specifically, the smallest seasonal SIT MD (with SD shown in brackets) of 0.00 m (0.39 m) for Envisat-ICESat for the entire Antarctic is found in spring (October–November) while larger MD of 0.52 m (0.68 m) and 0.57 m (0.45 m) exist in summer (February–March) and autumn (May–June), respectively. It is also shown that from autumn to spring, mean Envisat SIT decreases while mean ICESat SIT increases. Our findings suggest that overestimation of Envisat sea ice freeboard, potentially caused by radar backscatter originating from inside the snow layer, primarily accounts for the differences between Envisat and ICESat SIT in summer and autumn, while the uncertainties of snow depth product are not the dominant cause of the differences.To get a better understanding of the characteristics of the Envisat-derived sea ice thickness product, we firstly conduct a comprehensive comparison between Envisat and ICESat-1 sea ice thickness. Their differences reveal significant temporal and spatial variations. Our findings suggest that overestimation of Envisat sea ice freeboard primarily accounts for the differences in summer and autumn, while the uncertainties of snow depth product are not the dominant cause of the differences.

Download Full-text

A comparison between Envisat and ICESat sea ice thickness in the Antarctic

10.5194/tc-2020-48 ◽

2020 ◽

Author(s):

Jinfei Wang ◽

Chao Min ◽

Robert Ricker ◽

Qinghua Yang ◽

Qian Shi ◽

...

Keyword(s):

Sea Ice ◽

European Space Agency ◽

Weddell Sea ◽

Ice Thickness ◽

Radar Altimeter ◽

Average Deviation ◽

Absolute Deviation ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

The Antarctic

Abstract. The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While in situ observations are too sparse in the Antarctic to determine long-term trends of the Antarctic sea ice thickness on a global scale, satellite radar altimetry data can be applied with a promising prospect. A newly released Envisat-derived product from the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI), including sea ice freeboard and sea ice thickness, covers the entire Antarctic year-round from 2002 to 2012. In this study, the SICCI Envisat sea ice thickness in the Antarctic is firstly compared with a conceptually new proposed ICESat ice thickness that has been derived from an algorithm employing modified ice density. Both data sets have been validated with the Weddell Sea upward looking sonar measurements (ULS), indicating that ICESat agrees better with field observations. The inter-comparisons are conducted for three seasons except winter based on the ICESat operating periods. According to the results, the deviations between Envisat and ICESat sea ice thickness are different considering different seasons, years and regions. More specifically, the smallest average deviation between Envisat and ICESat sea ice thickness exists in spring by −0.03 m while larger deviations exist in summer and autumn by 0.86 m and 0.62 m, respectively. Although the smallest absolute deviation occurs in spring 2005 by 0.02 m, the largest correlation coefficient appears in autumn 2004 by 0.77. The largest positive deviation occurs in the western Weddell Sea by 1.03 m in summer while the largest negative deviation occurs in the Eastern Antarctic by −0.25 m in spring. Potential reasons for those deviations mainly deduce from the limitations of Envisat radar altimeter affected by the weather conditions and the surface roughness as well as the different retrieval algorithms. The better performance in spring of Envisat has a potential relation with relative humidity.

Download Full-text

Uncertainties in Antarctic sea-ice thickness retrieval from ICESat

Annals of Glaciology ◽

10.3189/2015aog69a736 ◽

2015 ◽

Vol 56 (69) ◽

pp. 107-119 ◽

Cited By ~ 31

Author(s):

Stefan Kern ◽

Gunnar Spreen

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Sensitivity Study ◽

Weddell Sea ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Earth Observing System ◽

Antarctic Sea Ice ◽

Snow Properties ◽

The Mean

AbstractA sensitivity study was carried out for the lowest-level elevation method to retrieve total (sea ice + snow) freeboard from Ice, Cloud and land Elevation Satellite (ICESat) elevation measurements in the Weddell Sea, Antarctica. Varying the percentage (P) of elevations used to approximate the instantaneous sea-surface height can cause widespread changes of a few to ˃10cm in the total freeboard obtained. Other input parameters have a smaller influence on the overall mean total freeboard but can cause large regional differences. These results, together with published ICESat elevation precision and accuracy, suggest that three times the mean per gridcell single-laser-shot error budget can be used as an estimate for freeboard uncertainty. Theoretical relative ice thickness uncertainty ranges between 20% and 80% for typical freeboard and snow properties. Ice thickness is computed from total freeboard using Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow depth data. Average ice thickness for the Weddell Sea is 1.73 ± 0.38 m for ICESat measurements from 2004 to 2006, in agreement with previous work. The mean uncertainty is 0.72 ± 0.09 m. Our comparison with data of an alternative approach, which assumes that sea-ice freeboard is zero and that total freeboard equals snow depth, reveals an average sea-ice thickness difference of ∼0.77m.

Download Full-text

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

Remote Sensing ◽

10.3390/rs12091494 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1494

Author(s):

M. Jeffrey Mei ◽

Ted Maksym

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Thickness Distribution ◽

Radar Data ◽

Weddell Sea ◽

Ice Thickness ◽

Two Dimensional ◽

Snow Surface ◽

Sea Ice Thickness ◽

Antarctic Sea Ice

The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.

Download Full-text

Review of The Cryosphere Discussion submission tc-2020-71 ‘Evaluation of Sea-Ice Thickness from four reanalyses in the Antarctic Weddell Sea’

10.5194/tc-2020-71-rc3 ◽

2020 ◽

Author(s):

Daniel Price

Keyword(s):

Sea Ice ◽

Weddell Sea ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Antarctic

Download Full-text

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

10.5194/tc-2018-92 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text

Influence of the Southern Annular Mode on the sea ice-ocean system: the role of the thermal and mechanical forcing

Ocean Science ◽

10.5194/os-1-145-2005 ◽

2005 ◽

Vol 1 (3) ◽

pp. 145-157 ◽

Cited By ~ 37

Author(s):

W. Lefebvre ◽

H. Goosse

Keyword(s):

Sea Ice ◽

Air Temperature ◽

Wind Stress ◽

Southern Annular Mode ◽

Weddell Sea ◽

The Other ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Annular Mode ◽

The Antarctic

Abstract. The global sea ice-ocean model ORCA2-LIM is used to investigate the impact of the thermal and mechanical forcing associated with the Southern Annular Mode (SAM) on the Antarctic sea ice-ocean system. The model is driven by idealized forcings based on regressions between the wind stress and the air temperature at one hand and the SAM index the other hand. The wind-stress component strongly affects the overall patterns of the ocean circulation with a northward surface drift, a downwelling at about 45° S and an upwelling in the vicinity of the Antarctic continent when the SAM is positive. On the other hand, the thermal forcing has a negligible effect on the ocean currents. For sea ice, both the wind-stress (mechanical) and the air temperature (thermal) components have a significant impact. The mechanical part induces a decrease of the sea ice thickness close to the continent and a sharp decrease of the mean sea ice thickness in the Weddell sector. In general, the sea ice area also diminishes, with a maximum decrease in the Weddell Sea. On the contrary, the thermal part tends to increase the ice concentration in all sectors except in the Weddell Sea, where the ice area shrinks. This thermal effect is the strongest in autumn and in winter due to the larger temperature differences associated with the SAM during these seasons. The sum of the thermal and mechaninal effects gives a dipole response of sea ice to the SAM, with a decrease of the ice area in the Weddell Sea and around the Antarctic Peninsula and an increase in the Ross and Amundsen Seas during high SAM years. This is in good agreement with the observed response of the ice cover to the SAM.

Download Full-text

Improved sea-ice prediction in the Weddell Sea using sea-ice thickness initialization

10.5194/egusphere-egu21-3638 ◽

2021 ◽

Author(s):

Yushi Morioka ◽

Doroteaciro Iovino ◽

Andrea Cipollone ◽

Simona Masina ◽

Swadhin Behera

Keyword(s):

Sea Ice ◽

General Circulation ◽

Climate Models ◽

Circulation Model ◽

Weddell Sea ◽

Ice Thickness ◽

Austral Spring ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

The Antarctic

Skillful sea-ice prediction in the Antarctic Ocean remains a big challenge due to paucity of sea-ice observations and insufficient representation of sea-ice processes in climate models. This study demonstrates that the Antarctic sea-ice concentration (SIC) prediction is significantly improved using a coupled general circulation model (SINTEX-F2) in which the model&#8217;s SIC and sea-ice thickness (SIT) are initialized with the ocean/sea-ice reanalysis product (C-GLORSv7). It is found that the wintertime SIT initialization adds positive values to the prediction skills of the summertime SIC, most effectively in the Weddell Sea where the SIT climatology and variability are the largest among the Antarctic Seas. Examination of the SIT balance during low sea-ice years of the Weddell Sea shows that negative SIT anomalies initialized in June retain the memory throughout austral winter (July-September) owing to horizontal advection of the SIT anomalies by sea-ice velocities. The negative SIT anomalies continue to develop in austral spring (October-December) owing to more incoming solar radiation via ice-albedo feedback and the associated warming of mixed layer. This results in further sea-ice decrease during austral summer (January-March). Concomitantly, the model reasonably reproduces atmospheric circulation anomalies in the Amundsen-Bellingshausen Seas as well as the Weddell Sea during the development of the negative sea-ice anomalies. These results provide solid evidence that the wintertime SIT initialization benefits skillful summertime sea-ice prediction in the Antarctic Seas.

Download Full-text

Farewell to IceBridge: 10 years of polar sea ice remote sensing

10.5194/egusphere-egu2020-12019 ◽

2020 ◽

Author(s):

Linette Boisvert ◽

Joseph MacGregor ◽

Brooke Medley ◽

Nathan Kurtz ◽

Ron Kwok ◽

...

Keyword(s):

Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Ice Drift ◽

Laser Altimeters ◽

Sea Ice Drift ◽

To Come ◽

The Antarctic

NASA&#8217;s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by &#8216;bridging the gap&#8217; between NASA&#8217;s ICESat (2003&#8211;2009) and ICESat-2 (launched September 2018) satellite missions. OIB&#8217;s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community&#8217;s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA&#8217;s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane&#8217;s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.&#160;

Download Full-text

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

The Cryosphere ◽

10.5194/tc-13-1409-2019 ◽

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.

Download Full-text