Ice Thickness Estimates Research Articles

Arctic Snow Depth, Ice Thickness, and Volume From ICESat‐2 and CryoSat‐2: 2018–2021

AbstractUsing ICESat‐2 and CryoSat‐2 freeboards, we examine the variability of monthly Arctic sea ice snow depth, thickness and volume between October 2018 and April 2021. For the 3 years, satellite‐derived estimates captured a decrease in mean April snow depth (∼2.50 cm) and ice thickness (∼0.28 m) equivalent to an ice volume loss of ∼12.5%. Results show greater thinning of multiyear ice with an end‐of‐season thickness in 2021 that is lower by ∼16.1% (0.50 m), with negligible changes over first‐year ice. For the period, sea ice thickness estimates using snow depth from climatology result in thicker ice (by up to ∼0.22 m) with a smaller decrease in multiyear ice thickness (∼0.38 m). An 18‐year satellite record, since the launch of ICESat, points to a loss of ∼6,000 km3 or one‐third of the winter Arctic ice volume driven by decline in multiyear‐ice coverage in the multi‐decadal transition to a largely seasonal ice cover.

An observation-based approach to calculating ice-shelf calving mass flux

In order to determine whether the calving flux of an ice shelf is changing, the long-term calving flux needs to be established. Methods used to estimate the calving flux either take into account non-steady-state behaviour by capturing movement of the calving-front location (e.g., using satellite observations), or they assume the calving front is stationary and that the ice is in steady state (e.g., flux-gate methods). Non-steady-state methods are hampered by the issue of temporal aliasing, i.e., when the satellite observation frequency is insufficient to capture the cyclic nature of the calving-front position. Methods that assume a steady state to estimate the calving flux accrue uncertainties if the ice shelf changes its physical state. In order to overcome these limitations we propose and implement a new observation-based approach that combines a time series of calving-front locations with a flux-gate method. The approach involves the creation of a unique semi-temporal domain as a mechanism to overcome the issue of temporal aliasing, and only requires easily accessible ice thickness and surface velocity estimates of the ice shelf. This approach allows for complex calving-front geometries and captures calving events of all sizes that are visible within the satellite imagery. Application of the approach allows the long-term average calving flux to be estimated (provided sufficient temporal coverage by satellite imagery), as well as identification of the minimum temporal baseline needed to produce a representative estimate of the long-term average calving flux, for any ice shelf. Implementation of the approach to multiple ice shelves would enable comparisons to be made regarding the spatial variability in the long-term calving flux of Antarctica's ice shelves, thereby highlighting calving regime change around the continent.

Ice thickness estimation method of overhead line based on distributed optical fiber temperature sensing

In order to monitor icing condition of overhead line based on distributed optical fiber temperature sensing, the transient temperature field models of radial and axial heat transfer for iced overhead line are constructed. The finite element method is used to solve the model to obtain the axial and radial temperature distribution inside the overhead line. Based on the two models, the change of the optical fiber temperature with time when the ambient temperature changes at a certain rate is investigated. At the same time, the change of optical fiber temperature with time in the iced and uniced sections under different ice thicknesses is also investigated. On this basis, the change of temperature difference with time is investigated. The formula of ice thickness estimation according to the temperature difference between iced and uniced sections’ optical fibers is obtained. This work lays a foundation for the evaluation of ice condition of overhead line based on distributed optical fiber temperature sensing.

Towards ice-thickness inversion: an evaluation of global digital elevation models (DEMs) in the glacierized Tibetan Plateau

Abstract. Accurate estimates of regional ice thickness, which are generally produced by ice-thickness inversion models, are crucial for assessments of available freshwater resources and sea level rise. A digital elevation model (DEM) derived from surface topography of glaciers is a primary data source for such models. However, the scarce in situ measurements of glacier surface elevation limit the evaluation of DEM uncertainty. Hence the influence of DEM uncertainty on ice-thickness modeling remains unclear over the glacierized area of the Tibetan Plateau (TP). Here, we examine the performance of six widely used and mainly global-scale DEMs: AW3D30 (ALOS – Advanced Land Observing Satellite – World 3D – 30 m; 30 m), SRTM-GL1 (Shuttle Radar Topography Mission Global 1 arc second; 30 m), NASADEM (NASA Digital Elevation Model; 30 m), TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement, synthetic-aperture radar; 90 m), SRTM v4.1 (Shuttle Radar Topography Mission; 90 m), and MERIT (Multi-Error-Removed Improved-Terrain; 90 m) over the glacierized TP by comparison with ICESat-2 (Ice, Cloud and land Elevation Satellite) laser altimetry data while considering the effects of glacier dynamics, terrain factors, and DEM misregistration. The results reveal NASADEM to be the best performer in vertical accuracy, with a small mean error (ME) of 0.9 m and a root mean squared error (RMSE) of 12.6 m, followed by AW3D30 (2.6 m ME and 11.3 m RMSE). TanDEM-X also performs well (0.1 m ME and 15.1 m RMSE) but suffers from serious errors and outliers on steep slopes. SRTM-based DEMs (SRTM-GL1, SRTM v4.1, and MERIT) (13.5–17.0 m RMSE) had an inferior performance to NASADEM. Errors in the six DEMs increase from the south-facing to the north-facing aspect and become larger with increasing slope. Misregistration of the six DEMs relative to the ICESat-2 footprint in most glacier areas is small (less than one grid spacing). In a next step, the influence of six DEMs on four ice-thickness inversion models – GlabTop2 (Glacier bed Topography), Open Global Glacier Model (OGGM), Huss–Farinotti (HF), and Ice Thickness Inversion Based on Velocity (ITIBOV) – is intercompared. The results show that GlabTop2 is sensitive to the accuracy of both elevation and slope, while OGGM and HF are less sensitive to DEM quality and resolution, and ITIBOV is the most sensitive to slope accuracy. NASADEM is the best choice for ice-thickness estimates over the whole TP.

Freezing Fraction in Freezing Rain

Abstract Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain, using models to estimate equivalent radial ice thicknesses from historical weather data. The conservative simple flux model assumes that all the freezing rain that impinges on a horizontal cylinder, representing vegetation or components of the built infrastructure, freezes. Here I present a simplified heat-balance formulation to calculate the fraction of the impinging precipitation that freezes, using parameters measured at ASOS weather stations and an estimate of solar heating. Radial ice thickness estimates from this approach are compared with the simple model and those generated from the ASOS icing sensor. These estimates can all be tested by comparing to measurements on cylinders at weather stations. A link to an Excel spreadsheet that calculates freezing fraction using user-input weather data is provided. In forecast freezing rain events, this tool could be used by utility crews and emergency response teams to estimate the likely range of equivalent radial ice thicknesses over the affected region and plan their response accordingly. Significance Statement Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain calculated from historical weather data. This paper provides an algorithm for computing ice loads on trees and power lines, using weather data to determine the fraction of the precipitation that freezes on them rather than dripping off. This freezing fraction result is compared to estimates reported by weather stations and to a simple model that assumes all the wind-blown freezing rain freezes on the wires, twigs, and branches. A link is provided to an Excel tool that calculates freezing fraction. This could be used with freezing rain forecasts to estimate the likely severity of the event.

Probabilistic Forecasts of Arctic Sea Ice Thickness

In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.

Estimating extreme level ice and ridge thickness for offshore wind turbine design: Case study Kriegers Flak

AbstractWhen designing offshore wind turbines in ice‐covered seas, site‐specific ice conditions present crucial input for the structural design. In this study, methods of estimating the maximum level ice thickness occurring once in 50 years and parameters of a design ice ridge in an area where no direct ice thickness measurements exists are presented. The site of Kriegers Flak at the Southern Baltic Sea is taken as a case study. Rather than just applying basic equations found from the standards, the method gives more detailed estimates by utilizing available ice chart information, ice reports and atlases together with measured air temperature data to estimate the starting and ending date of ice growth. The maximum level ice thickness occurring once in 50 years at the site of Kriegers Flak wind farm was estimated to be between 0.26 and 0.44 m depending on the assumptions. The effect of the studied history length, the utilized weather station and the use of snow thicknesses to the 50‐year ice thickness estimates are compared and discussed. Interaction with ice ridges was found to be a relevant but infrequent, load scenario for the site of Kriegers Flak. For this reason, a representative ridge for determining ice ridge loads is presented. The most important parameter in a ridge, the thickness of the consolidated layer, was found based on scenario analyses to be in a range between 0.43 and 0.67 m can be used to calculate ice loads against the wind turbine substructures.

Estimation of thermodynamic and dynamic contributions to sea ice growth in the Central Arctic using ICESat-2 and MOSAiC SIMBA buoy data

The fine spatial resolution of the ICESat-2 (IS2) satellite altimeter allows monitoring the evolution of sea ice thickness with detailed dynamic information (e.g. ridges and leads). In this study, we first assess the ability of IS2 to estimate thermodynamic ice growth and dynamic thickening during the ice-growing season in the central Arctic Ocean. As an indicator of the thermodynamic ice growth, we use 10 thermistor string-based sea ice mass balance array (SIMBA) buoys deployed at a scale of ~50 km from the Icebreaker Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. We collect IS2 data within 20 km buffer distance from the individual buoys, and calculate the mode, median, and mean of the IS2-derived ice thickness. The IS2 modal thickness shows the least bias (−0.169 m) with the buoy ice thickness, representing level ice thickness. In addition, the increasing rate of the IS2 modal thickness is close to the thermodynamic ice growth with a small bias of −0.054 cm/day. However, the increasing rates of the IS2 median and mean thickness are greater than the thermodynamic ice growth by about 0.114 cm/day and 0.198 cm/day, respectively, because they also include ice growth caused by thickness redistribution during dynamic deformation. The dynamic contributions may account for 26.1 ± 10.3% and 34.4 ± 10.1% of the total increase of the IS2 median and mean thickness, respectively. Within a ~ 50 km radius area from the MOSAiC Central Observatory, IS2 measurements exhibit that the ridge fraction increased from <2% in November to ~4% in March (~0.029%/day of average increasing rate) and ridge height increased about 0.047 cm/day during the same period. However, lead formation does not show significant contributions to the dynamic ice thickening because leads are temporary features lasting only 2–3 days. Although there are considerable uncertainties in IS2 ice thickness estimation and IS2-buoy thickness comparison, this study emphasizes the importance of combining measurements by IS2 and SIMBA buoys to explain the regional sea ice mass balance with separating the thermodynamic and dynamic contributions.

Modelling ice thickness and storage volume of svalbard glaciers monitored through Indian Arctic Programme

In this study, glacier ice thickness distribution and storage volume has been modelled for two glaciers viz., Vestre Broggerbreen and Feiringbreen situated across the Kongsfjorden around Ny-Ålesund in Svalbard. These two high Arctic glaciers are being monitored through the ongoing efforts of the Indian Arctic Program. Here, the capability of a glacier specific shape factor parametrization approach was evaluated when paired with a parsimonoious, simple stress-driven, slope-dependent ice thickness estimation model called Glacier bed Topography2_Indian Institute of Technology Bombay version (GlabTop2_IITB). The modelled average thickness of ice for Vestre Broggerbreen and Feiringbreen Glaciers was 59 m and 92 m respectively during the study period. The obtained ice thickness estimates were found to be reliable when compared with the recent field estimates obtained through a Ground Penetrating Radar (GPR) survey. Moreover, the modelled results of both the glaciers were in good agreement with the consensus estimates of ice thickness reported in a recent study. The obtained results suggest that the modelling approach presented here can be an alternative to complex models, which requires lot of other data such as surface mass balance, surface velocity etc. Based on the modelled ice thickness distribution pertained to the year 2015, the total volume of the glacier-stored ice estimated for Vestre Broggerbreen Glacier and Freingbreen Glacier was 0.29 ± 0.04 km3and 0.65 ± 0.09 km3, respectively.

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 d are required in order to generate pan-Arctic coverage from CryoSat-2 and 27 d are required from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly timescales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A, and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian process regression to learn functional mappings between radar freeboard observations in space and time and to subsequently retrieve pan-Arctic freeboard as well as uncertainty estimates. We also employ an empirical Bayesian approach towards learning the free (hyper)parameters of the model, which allows us to derive daily estimates related to radar freeboard spatial and temporal correlation length scales. The estimated daily radar freeboard predictions are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 1 mm (standard deviations &lt;6 cm), and cross-validation experiments show that errors in predictions are, on average, ≤ 4 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with 31 d running means from CryoSat-2 and Sentinel-3 freeboards, across nine sectors of the Arctic, as well as making comparisons with daily ERA5 snowfall data. Pearson correlations between daily radar freeboard anomalies and snowfall are as high as +0.52 over first-year ice and +0.41 over multi-year ice, suggesting that the estimated daily fields are able to capture real physical radar freeboard variability at sub-weekly timescales.

Implications of surface flooding on airborne estimates of snow depth on sea ice

Abstract. Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

On the errors involved in ice-thickness estimates I: ground-penetrating radar measurement errors – ERRATUM

On the errors involved in ice-thickness estimates I: ground-penetrating radar measurement errors – ERRATUM

Decadal terminus position changes and ice thickness measurement of Menthosa Glacier in Lahaul region of North-Western Himalaya

Glacier ice-thickness measurement and distribution is one of the essential variables to assess present status of glacier-water equivalent and its volumetric reserve as well as to model the future glacier dynamics under the climate changing scenario. Yet, substantial gaps in ice thickness information exist for the Himalayan glaciers. The present study provides a long-term assessment (1965–2016) of recessional and area change patterns, as well as the detailed field-based (2016–2017) Ground Penetrating Radar(GPR), derived ice-thickness measurement of the Menthosa Glacier, Lahaul Himalaya. Additionally, the study examines whether the modelled ice thickness from remote sensing data is consistent with the field-based GPR measurement and how can it be improved. The extensive field surveys coupled with the multi-temporal high (Corona KH-4A) to medium resolution (Landsat Enhanced Thematic Mapper+ (ETM+)/Operational Land Imager (OLI), Sentinel 2A-Multispectral Instrument (MSI)) remote sensing data and cross-sectional GPR surveyed profile measurements have been used to examine past half a century (1965–2016) glacier fluctuation and the recent ice-thickness estimations, respectively. The results show that the Menthosa Glacier receded by 301.5 ± 19.2 m during the past half a century (1965–2016) with an average annual retreat of 5.9 ± 0.4 m a−1, whereas glacier lost 0.09 km2 ice in the frontal section. Field measurement over the past one decade (2006–2017) also conforms to a continuous recessional pattern and substantial glacier degeneration particularly the extensive surface lowering and significant appearance of ice-cliffs in the ablation and lateral zones over this period. The GPR measurements (2017) show the minimum glacier ice thickness of 24 meters at 4691 m a.s.l. (in the lower part of ablation area) and maximum glacier ice thickness of 55 meters measured at 4758 m a.s.l. (in the upper left-side tributary part of ablation area). Moreover, the modelled ice thickness derived from remotely sensed data is having Root Mean Square Error (RMSE) between 38 to 72 ± 10 m as compared with GPR measured ice thickness.

Assessment of Arctic Sea Ice Thickness Estimates From ICESat-2 Using IceBird Airborne Measurements

The successful launch of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a new and advanced tool for sea ice thickness (SIT) estimations in the Arctic. However, the performance of ICESat-2 for SIT estimations still remains unknown. In the present study, SIT estimates derived from ICESat-2 are examined using three retrieval methods, namely, two buoyancy methods with the merged snow depth and empirical snow depth (BMA and BME, respectively) and one empirical estimation method (EEM), and these estimates are compared to near-simultaneous airborne measurements from the IceBird mission in April 2019. Overall, the ICESat-2 total freeboard registers quite well with that from the near-concurrent IceBird mission with a mean bias of 2.5 cm, which demonstrates the high reliability of ICESat-2 data for SIT estimation. However, the much more evident difference between SIT estimations than total freeboard from ICESat-2 and IceBird indicates that other parameters (e.g., snow depth and snow/ice densities) may bring increased uncertainties to the SIT estimation. Overall, BMA is the best method for SIT estimation and has the closest thickness distribution to that of IceBird data with a mean bias of 0.11 m, followed by the BME and EEM methods. The dominate error sources for SIT estimation using the buoyancy method are ice density and snow depth that require further investigation in future studies.

Ice thickness estimates of Lemon Creek Glacier, Alaska, from active-source seismic imaging

AbstractLemon Creek Glacier, a temperate valley glacier in the Juneau Icefield of Southeast Alaska, is the site of long running (&gt;60 years) glaciological studies. However, the most recent published estimates of its thickness and subglacial topography come from two ~50 years old sources that are not in agreement and do not account for the effects of years of negative mass balance. We collected a 1-km long active-source seismic line on the upper section of the glacier parallel and near to the centerline of the glacier, roughly straddling the equilibrium-line altitude. We used these data to perform joint reflection-refraction velocity modeling and reflection imaging of the glacier bed. We find that this upper section of Lemon Creek Glacier is as much as 150 m (~65%) thicker than previously suggested with a large overdeepening in an area previously believed to have a uniform thickness. Our results lead us to reinterpret the impact of basal motion on ice flow and have a significant impact on expectations of subglacial hydrology. We suggest that further efforts to develop a whole-glacier model of subglacial topography are necessary to support studies that require accurate models of ice thickness and subglacial topography.

Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India

Abstract Knowledge of glacier volume is crucial for ice flow modelling and predicting the impacts of climate change on glaciers. Rugged terrain, harsh weather conditions and logistic costs limit field-based ice thickness observations in the Himalaya. Remote-sensing applications, together with mathematical models, provide alternative techniques for glacier ice thickness and volume estimation. The objective of the present research is to assess the application of artificial neural network (ANN) modelling coupled with remote-sensing techniques to estimate ice thickness on individual glaciers with direct field measurements. We have developed two ANN models and estimated the ice thickness of Chhota Shigri Glacier (western Himalaya) on ten transverse cross sections and two longitudinal sections. The ANN model estimates agree well with ice thickness measurements from a ground-penetrating radar, available for five transverse cross sections on Chhota Shigri Glacier. The overall root mean square errors of the two ANN models are 24 and 13 m and the mean bias errors are ±13 and ±6 m, respectively, which are significantly lower than for other available models. The estimated mean ice thickness and volume for Chhota Shigri Glacier are 109 ± 17 m and 1.69 ± 0.26 km3, respectively.

On the errors involved in ice-thickness estimates I: ground-penetrating radar measurement errors - ERRATUM

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Ice thickness and volume of the Renland Ice Cap, East Greenland

Abstract To assess the amount of ice volume stored in glaciers or ice caps, a method to estimate ice thickness distribution is required for glaciers where no direct observations are available. In this study, we use an existing inverse method to estimate the bedrock topography and ice thickness of the Renland Ice Cap, East Greenland, using satellite-based observations of the surface topography. The inverse approach involves a procedure in which an ice dynamical model is used to build-up an ice cap in steady state with climate forcing from a regional climate model, and the bedrock is iteratively adjusted until the modelled and observed surface topography match. We validate our model results against information from airborne radar data and satellite observed surface velocity, and we find that the inferred ice thickness and thereby the stored total volume of the ice cap is sensitive to the assumed ice softness and basal slipperiness. The best basal model parameters for the Renland Ice Cap are determined and the best estimated total ice volume of 384 km3 is found. The Renland Ice Cap is particularly interesting because of its location at a high elevation plateau and hence assumed low sensitivity to climate change.

Evaluation of sea-ice thickness from four reanalyses in the Antarctic Weddell Sea

Abstract. Ocean–sea-ice coupled models constrained by various observations provide different ice thickness estimates in the Antarctic. We evaluate contemporary monthly ice thickness from four reanalyses in the Weddell Sea: the German contribution of the project Estimating the Circulation and Climate of the Ocean Version 2 (GECCO2), the Southern Ocean State Estimate (SOSE), the Ensemble Kalman Filter system based on the Nucleus for European Modelling of the Ocean (NEMO-EnKF) and the Global Ice–Ocean Modeling and Assimilation System (GIOMAS). The evaluation is performed against reference satellite and in situ observations from ICESat-1, Envisat, upward-looking sonars and visual ship-based sea-ice observations. Compared with ICESat-1, NEMO-EnKF has the highest correlation coefficient (CC) of 0.54 and lowest root mean square error (RMSE) of 0.44 m. Compared with in situ observations, SOSE has the highest CC of 0.77 and lowest RMSE of 0.72 m. All reanalyses underestimate ice thickness near the coast of the western Weddell Sea with respect to ICESat-1 and in situ observations even though these observational estimates may be biased low. GECCO2 and NEMO-EnKF reproduce the seasonal variation in first-year ice thickness reasonably well in the eastern Weddell Sea. In contrast, GIOMAS ice thickness performs best in the central Weddell Sea, while SOSE ice thickness agrees most with the observations from the southern coast of the Weddell Sea. In addition, only NEMO-EnKF can reproduce the seasonal evolution of the large-scale spatial distribution of ice thickness, characterized by the thick ice shifting from the southwestern and western Weddell Sea in summer to the western and northwestern Weddell Sea in spring. We infer that the thick ice distribution is correlated with its better simulation of northward ice motion in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis for understanding the recent variability of sea-ice volume in the Antarctic.

Spatio-temporal analysis of glacier surface velocity in dhauliganga basin using geo-spatial techniques

Glacier velocity is one of the significant parameter for the assessment of the glacier dynamics, which directly signifies the response of the glacier health with the respect to climate change. The present work focuses on analysing the spatio-temporal characteristics of the surface velocity in the Dhauliganga basin for seven glaciers. The cross-correlation method was applied on the panchromatic scenes of Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A Multispectral Instrument (MSI) sensors to estimate the velocity. The mean velocity of the seven glaciers along the central flow line ranges from 59.57 to 5.34 m/year while the average of mean velocity ranges from 30.82 to 11.04 m/year during 1993–2018. Whereas, across the flow line, mean velocity ranges from 63.58 to 4.74 m/year. The lowest velocity was observed in the T1–T2 locations near the snout, where the velocity highly dominated by mass loss and debris cover; the error in velocity estimation ranges from ± 3.67 to ± 4.57 m/year. Decreasing trend of surface velocity was observed for all the glaciers. The present work demonstrates the application of cross-correlation technique to monitor the dynamics of a group of glaciers on a basin scale having similar climatic setting. The velocity profiles of various glaciers can be used as input to methods estimating ice thickness and volume and to prioritize the glaciers in view of glacier-dynamics linked events such as surges and retreat induced to rise to pro-glacial lakes and glacier melt contribution to the river flows.