The impact of snow products on detecting trends in sea ice thickness during the CryoSat-2 era

2020 ◽  
Author(s):  
Heidi Sallila ◽  
Samantha Buzzard ◽  
Eero Rinne ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Age ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Seasonal Snow ◽  
Uncertainty Estimates ◽  
The One ◽  
The Impact ◽  
First Time

<p>Retrieval of sea ice depth from satellite altimetry relies on knowledge of snow depth in the conversion of freeboard measurements to sea ice thickness. This remains the largest source of uncertainty in calculating sea ice thickness. In order to go beyond the use of a seasonal snow climatology, namely the one by Warren created from measurements collected during the drifting stations in 1937 and 1954–1991, we have developed as part of an ESA Arctic+ project several novel snow on sea ice pan-Arctic products, with the ultimate goal to resolve for the first time inter-annual and seasonal snow variability.</p><p><span>Our products are inter-compared and calibrated with each other to guarantee multi-decadal continuity, and also compared with other recently developed snow on sea ice modelling </span><span>and satellite based </span><span>products. Quality assessment and uncertainty estimates are provided at a gridded level and as a function of sea ice cover characteristics such as sea ice age, and sea ice type.</span></p><p>We investigate the impact of the spatially and temporally varying snow products on current satellite estimates of sea ice thickness and provide an update on the sea ice thickness uncertainties. We pay particular attention to potential biases of the seasonal ice growth and inter-annual trends.</p>

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 Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

2021 ◽  
Vol 15 (4) ◽  
pp. 1811-1822
Author(s):  
Rasmus T. Tonboe ◽  
Vishnu Nandan ◽  
John Yackel ◽  
Stefan Kern ◽  
Leif Toudal Pedersen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Scattering Model ◽  
Radar Scattering ◽  
The Impact ◽  
Ku Band

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

scholarly journals Simulated Ka- and Ku-band radar altimeter scattering horizon on snow-covered Arctic sea ice

2020 ◽  
Author(s):  
Rasmus T. Tonboe ◽  
Vishnu Nandan ◽  
John Yackel ◽  
Stefan Kern ◽  
Leif Toudal Pedersen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Scattering Model ◽  
Radar Scattering ◽  
The Impact ◽  
Ku Band

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and its impact on the Ka- and Ku-band radar scattering horizon or the "track point" (i.e. the scattering layer depth detected by the radar re-tracker), are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and Archimedes’ principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round robin data package, NASA’s Operation Ice Bridge (OIB) data and climatology, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth, however, the simulated track point difference is much smaller than what is reported in the literature from the CryoSat-2 Ku-band and SARAL/AltiKa Ka-band satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences are sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track-point difference when the snow is thin and then decreasing the difference when the snow is thick (> 10 cm). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for 1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and 2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small on the measured freeboard, by only raising the measured freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals from combining currently operational and upcoming Ka- and Ku-band dual-frequency radar altimeter missions, such as ESA’s Copernicus High Priority Candidate Mission CRISTAL.

scholarly journals Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2

2018 ◽  
Vol 12 (8) ◽  
pp. 2789-2801 ◽  
Author(s):  
Ron Kwok ◽  
Sahra Kacimi
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Basal Layer ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Mean Differences ◽  
The Impact ◽  
Snow Thickness

Abstract. We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2.40±1.07 (2011) to 2.60±1.15 m (2014) and snow depth from 35.8±11.5 (2016) to 43.6±10.2 cm (2014), suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the western Weddell. The impact of undersampling the thin end of the snow depth distribution on the regional statistics, due to the resolution of the snow radar, is assessed. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2) sampled along the same transect are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow–ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thicknesses computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore generally higher; mean differences in sea ice thickness along a transect are up to ∼0.6 m higher (in 2014). This analysis is relevant to the use of differences between ICESat-2 and CS-2 freeboards to estimate snow depth for ice thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, with which the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding the limits of retrievals of Weddell Sea ice thickness from satellite altimeters.

scholarly journals Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

2012 ◽  
Vol 6 (6) ◽  
pp. 4771-4827 ◽  
Author(s):  
N. T. Kurtz ◽  
S. L. Farrell ◽  
M. Studinger ◽  
N. Galin ◽  
J. P. Harbeck ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Thickness ◽  
Data Sets ◽  
Climate Data ◽  
Sea Ice Thickness ◽  
Sensing Platforms ◽  
Long Term Trends ◽  
Using Data ◽  
The Impact

Abstract. The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multi-sensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets which demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns which are currently available in product form via the National Snow and Ice Data Center.

scholarly journals Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

2013 ◽  
Vol 7 (4) ◽  
pp. 1035-1056 ◽  
Author(s):  
N. T. Kurtz ◽  
S. L. Farrell ◽  
M. Studinger ◽  
N. Galin ◽  
J. P. Harbeck ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Thickness ◽  
Data Sets ◽  
Climate Data ◽  
Sea Ice Thickness ◽  
Sensing Platforms ◽  
Long Term Trends ◽  
Using Data ◽  
The Impact

Abstract. The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multi-sensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Center.

scholarly journals Antarctic sea-ice thickness and volume estimates from ice charts between 1995 and 1998

2015 ◽  
Vol 56 (69) ◽  
pp. 383-393 ◽  
Author(s):  
E. Rachel Bernstein ◽  
Cathleen A. Geiger ◽  
Tracy L. Deliberty ◽  
Mary D. Lemcke-Stampone
Keyword(s):  
Sea Ice ◽  
Regional Scale ◽  
Weighted Average ◽  
Ice Thickness ◽  
Average Thickness ◽  
Sea Ice Thickness ◽  
Subjective Analysis ◽  
Stage Of Development ◽  
Volume Estimates ◽  
The Impact

AbstractThis work evaluates two distinct calculations of central tendency for sea-ice thickness and quantifies the impact such calculations have on ice volume for the Southern Ocean. The first calculation, area-weighted average thickness, is computed from polygonal ice features and then upscaled to regions. The second calculation, integrated thickness, is a measure of the central value of thickness categories tracked across different scales and subsequently summed to chosen regions. Both methods yield the same result from one scale to the next, but subsequent scales develop diverging solutions when distributions are strongly non-Gaussian. Data for this evaluation are sea-ice stage-of-development records from US National Ice Center ice charts from 1995 to 1998, as proxy records of ice thickness. Results show regionally integrated thickness exceeds area-weighted average thickness by as much as 60% in summer with as few as five bins in thickness distribution. Year-round, the difference between the two calculations yields volume differences consistently >10%. The largest discrepancies arise due to bimodal distributions which are common in ice charts based on current subjective-analysis protocols. We recommend that integrated distribution be used for regional-scale sea-ice thickness and volume estimates from ice charts and encourage similar testing of other large-scale thickness data archives.

scholarly journals Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

2006 ◽  
Vol 44 ◽  
pp. 281-287 ◽  
Author(s):  
Shotaro Uto ◽  
Haruhito Shimoda ◽  
Shuki Ushio
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Snow Depth ◽  
East Antarctica ◽  
Ice Thickness ◽  
First Year ◽  
Total Thickness ◽  
Sea Ice Thickness ◽  
Landfast Ice ◽  
Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

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.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

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