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

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.

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

10.5194/egusphere-egu21-12240 ◽

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

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.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.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.

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

10.5194/tc-2018-159 ◽

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.

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Better constraints on the sea-ice state using global sea-ice data assimilation

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-1627-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 1627-1667 ◽

Cited By ~ 1

Author(s):

P. Mathiot ◽

C. König Beatty ◽

T. Fichefet ◽

H. Goosse ◽

F. Massonnet ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Ice Thickness ◽

Initial State ◽

Concentration Data ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Ice Concentration ◽

The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

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

10.5194/egusphere-egu2020-18304 ◽

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

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&#8211;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.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 and satellite based 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.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.

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Suppression of Arctic sea ice growth by winter clouds and snowfall

10.21203/rs.3.rs-84779/v1 ◽

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.

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

The Cryosphere ◽

10.5194/tc-15-1811-2021 ◽

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.

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The Impact of Milankovitch Solar Radiation Variations on Sea-Ice and Air Temperature in a Coupled Energy-Balance Climate-Sea-Ice Model

Annals of Glaciology ◽

10.3189/s0260305500008454 ◽

1990 ◽

Vol 14 ◽

pp. 144-147 ◽

Cited By ~ 1

Author(s):

Tamara Shapiro Ledley

Keyword(s):

Solar Radiation ◽

Sea Ice ◽

Air Temperature ◽

Energy Transport ◽

Model Simulation ◽

Surface Air Temperature ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Mean ◽

The Impact

The sensitivity of thermodynamically-varying sea-ice and surface air temperature to variations in solar radiation on the 104 to 105 time scales is examined in this study. Model simulation results show the mean annual sea-ice thickness is very sensitive to the magnitude of midsummer solar radiation. During periods of high midsummer solar radiation between 115 ka B.P. and the present the sea ice is thinner, producing larger summer time leads and longer periods of open ocean. This has an effect on the mean annual sea-ice thickness, but not on the mean annual air temperature. However, the changes in sea ice are accompanied by similar variations in the summer surface air temperature, which are the result of the variations in the solar radiation and meridional energy transport.

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The Impact of Milankovitch Solar Radiation Variations on Sea-Ice and Air Temperature in a Coupled Energy-Balance Climate-Sea-Ice Model

Annals of Glaciology ◽

10.1017/s0260305500008454 ◽

1990 ◽

Vol 14 ◽

pp. 144-147 ◽

Cited By ~ 5

Author(s):

Tamara Shapiro Ledley

Keyword(s):

Solar Radiation ◽

Sea Ice ◽

Air Temperature ◽

Energy Transport ◽

Model Simulation ◽

Surface Air Temperature ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Mean ◽

The Impact

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Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

The Cryosphere ◽

10.5194/tc-10-2745-2016 ◽

2016 ◽

Vol 10 (6) ◽

pp. 2745-2761 ◽

Cited By ~ 26

Author(s):

Jiping Xie ◽

François Counillon ◽

Laurent Bertino ◽

Xiangshan Tian-Kunze ◽

Lars Kaleschke

Keyword(s):

Sea Ice ◽

Cold Season ◽

The Arctic ◽

Ice Thickness ◽

Data Set ◽

Sea Ice Thickness ◽

Ice Concentration ◽

Ice Edge ◽

Anomaly Analysis ◽

The Impact

Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. This product is available in near-real time, at daily frequency, during the cold season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ coupled ocean and sea ice forecasting system, which is the Arctic component of the Copernicus marine environment monitoring services. The TOPAZ system assimilates sea surface temperature (SST), altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the ensemble Kalman filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two parallel Observing System Experiments (OSE) have been performed in March and November 2014, in which the thicknesses from SMOS-Ice (thinner than 0.4 m) are assimilated in addition to the standard observational data sets. It is found that the root mean square difference (RMSD) of thin sea ice thickness is reduced by 11 % in March and 22 % in November compared to the daily thin ice thicknesses of SMOS-Ice, which suggests that SMOS-Ice has a larger impact during the beginning of the cold season. Validation against independent observations of ice thickness from buoys and ice draft from moorings indicates that there are no degradations in the pack ice but there are some improvements near the ice edge close to where the SMOS-Ice has been assimilated. Assimilation of SMOS-Ice yields a slight improvement for ice concentration and degrades neither SST nor sea level anomaly. Analysis of the degrees of freedom for signal (DFS) indicates that the SMOS-Ice has a comparatively small impact but it has a significant contribution in constraining the system (> 20 % of the impact of all ice and ocean observations) near the ice edge. The areas of largest impact are the Kara Sea, Canadian Archipelago, Baffin Bay, Beaufort Sea and Greenland Sea. This study suggests that the SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system.

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Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model

Annals of Glaciology ◽

10.1017/aog.2020.13 ◽

2020 ◽

Vol 61 (82) ◽

pp. 97-105

Author(s):

Jun Ono ◽

Yoshiki Komuro ◽

Hiroaki Tatebe

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Predictive Skill ◽

The Pacific ◽

The Impact

AbstractThe impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

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