Sea-ice thickness on the northern Canadian polar shelf: A second look after 40 years

This paper presents a systematic record of multi-year sea-ice thickness on the northern Canadian polar shelf, acquired during the winter of 2009–10. The data were acquired by submerged sonar positioned within Penny Strait where they measured floes drifting south from the notional “last ice area”. Ice was moving over the site until 10 December and fast thereafter. Old ice comprised about half of the 1669-km long survey. The average old-ice thickness within 25-km segments of the survey track was 3–4 m; maximum keels were 12–16 m deep. Floes with high average draft were of two types, one with interspersed low draft intervals and one without. The presence or absence of thin patches apparently distinguished aggregate floes comprised of sub-units of various ages and deformation states from units of more homogeneous age and deformation state. The former were larger and of somewhat lower mean thickness (1–5 km; 3.5–4.5 m) than the latter (400–600 m; 6.5–14 m). Calculated ice accretion onto the multi-year ice measured in autumn 2009 was used to seasonally adjust the observations to a date in late winter, when prior data are available. The adjusted mean thickness for all 25-km segments with 4 tenths or more old ice was 3.6 m (sample deviation of 0.4 m), a value indistinguishable within sampling error from values measured in the same area during the 1970s. The recently measured ice-draft distributions were also very similar to those from the 1970s.

Surface elevation and ice thickness data between 2012 and 2020 at the ablation area of Artesonraju Glacier, Cordillera Blanca, Perú

We present a representative set of data of interpreted ice thickness and ice surface elevation of the ablation area of the Artesonraju glacier between 2012 and 2020. The ice thickness was obtained by means of Ground Penetrating Radar (GPR), while the surface elevation was by means of automated total stations and mass balance stakes. The results from GPR data show a maximum depth of 235 ± 18 m and a decreasing mean depth ranging from 134 ± 18 m in 2013 to 110 ± 18 m in 2020. Additionally, we estimate a mean ice thickness change rate of −4.2 ± 3.2 m yr−1 between 2014 and 2020 with GPR data alone, which is in agreement with the elevation change in the same period. The latter was estimated with the more accurate surface elevation data, yielding a change rate of −3.2 ± 0.2 m yr−1, and hence, confirming a negative glacier mass balance. The data set can be valuable for further analysis when combined with other data types, and as input for glacier dynamics modeling, ice volume estimations, and GLOF (glacial lake outburst flood) risk assessment. The complete dataset is available at https://doi.org/10.5281/zenodo.5571081 (Oberreuter et al, 2021).

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Ice Cover, Subglacial Landscape, and Estimation of Bottom Melting of Mac. Robertson, Princess Elizabeth, Wilhelm II, and Western Queen Mary Lands, East Antarctica

This study demonstrates the results of Russian airborne radio-echo sounding (RES) investigations and also seismic reflection soundings carried out in 1971–2020 over a vast area of coastal part of East Antarctica. It is the first comprehensive summary mapping of these data. Field research, equipment, errors of initial RES data, and methods of gridding are discussed. Ice thickness, ice base elevation, and bedrock topography are presented. The ice thickness across the research area varies from a few meters to 3620 m, and is greatest in the local subglacial depressions. The average thickness is about 1220 m. The total volume of the ice is about 710,500 km3. The bedrock heights vary from 2860 m below sea level in the ocean bathyal zone to 2040 m above sea level in the Grove Mountains area (4900 m relief). The main directions of the bedrock orographic forms are concentrated mostly in three intervals: 345∘–30∘, 45∘–70∘, and 70∘–100∘. The bottom melting rate was estimated on the basis of the simple Zotikov model. Total annual melting under the study area is about 0.633 cubic meters. The total annual melting in the study area is approximately 1.5 mm/yr.

Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

Dynamic and Full-Time Acquisition Technology and Method of Ice Data of Yellow River

Regarding the ice periods of the Yellow River, it is difficult to obtain ice data information. To effectively grasp the ice evolution process in the ice periods of the typical reach of the Yellow River, a fixed-point air-coupled radar remote monitoring device is proposed in this paper. The device is mainly composed of an air-coupled radar ice thickness measurement sensor, radar water level measurement sensor, temperature measurement sensor, high-definition infrared night vision instrument, remote switch control, telemetry communication machine, solar and wind power supply, lightning protection, and slewing arm steel tower. The integrated monitoring device can monitor ice thickness, water level, air temperature, ice surface temperature, and other related parameters in real time. At present, devices have obtained the ice change process of fixed points in ice periods from 2020 to 2021. Through a comparison with manual data, the mean error of the monitoring results of the water level and ice thickness was approximately 1 cm. The device realizes the real-time monitoring of ice thickness and water level change in the whole cycle at the fixed position. Through video monitoring, it can take pictures and videos regularly and realize the connection between the visual river and monitoring data. The research results provide a new model and new technology for hydrological monitoring in the ice periods of the Yellow River, which has broad application prospects.

Drone Surveying of Volumetric Ice Growth in a Steep River

Representative ice thickness data is essential for accurate hydraulic modelling, assessing the potential for ice induced floods, understanding environmental conditions during winter and estimation of ice-run forces. Steep rivers exhibit complex freeze-up behaviour combining formation of columnar ice with successions of anchor ice dams to build a complete ice cover, resulting in an ice cover with complex geometry. For such ice covers traditional single point measurements are unrepresentative. Gathering sufficiently distributed measurements for representativeness is labour intensive and at times impossible with hard to access ice. Structure from Motion (SfM) software and low-cost drones have enabled river ice mapping without the need to directly access the ice, thereby reducing both the workload and the potential danger in accessing the ice. In this paper we show how drone-based photography can be used to efficiently survey river ice and how these photographic surveys can be processed into digital elevation models (DEMs) using Structure from Motion. We also show how DEMs of the riverbed, riverbanks and ice conditions can be used to deduce ice volume and ice thickness distributions. A QGIS plugin has been implemented to automate these tasks. These techniques are demonstrated with a survey of a stretch of the river Sokna in Trøndelag, Norway. The survey was carried out during the winter 2020–2021 at various stages of freeze-up using a simple quadcopter with camera. The 500 m stretch of river studied was estimated to have an ice volume of up to 8.6 × 103 m3 (This corresponds to an average ice thickness of ∼67 cm) during the full ice cover condition of which up to 7.2 × 103 m3 (This corresponds to an average ice thickness of ∼57 cm) could be anchor ice. Ground Control Points were measured with an RTK-GPS and used to determine that the accuracy of these ice surface geometry measurements lie between 0.03 and 0.09 m. The ice thicknesses estimated through the SfM methods are on average 18 cm thicker than the manual measurements. Primarily due to the SfM methods inability to detect suspended ice covers. This paper highlights the need to develop better ways of estimating the volume of air beneath suspended ice covers.

Uncertainties in mass balance estimation of the Antarctic Ice Sheet using the input and output method

The input-output method (IOM) is one of the most popular methods of estimating the ice sheet mass balance (MB), with a significant advantage in presenting the dynamics response of ice to climate change. Assessing the uncertainties of the MB estimation using the IOM is crucial to gaining a clear understanding of the Antarctic ice-sheet mass budget. Here, we introduce a framework for assessing the uncertainties in the MB estimation due to the methodological differences in the IOM, the impact of the parameterization and scale effect on the modeled surface mass balance (SMB, input), and the impact of the uncertainties of ice thickness, ice velocity, and grounding line data on ice discharge (D, output). For the assessment of the D’s uncertainty, we present D at a fine scale. Compared with the goal of determining the Antarctic MB within an uncertainty of 15 Gt yr−1, we found that the different strategies employed in the methods cause considerable uncertainties in the annual MB estimation. The uncertainty of the RACMO2.3 SMB caused by its parameterization can reach 20.4 Gt yr−1, while that due to the scale effect is up to 216.7 Gt yr−1. The observation precisions of the MEaSUREs InSAR-based velocity (1–17 m yr−1), the airborne radio-echo sounder thickness (±100 m), and the MEaSUREs InSAR-based grounding line (±100 m) contribute uncertainties of 17.1 Gt yr−1, 10.5 ± 2.7 Gt yr−1 and 8.0~27.8 Gt yr−1 to the D, respectively. However, the D’s uncertainty due to the remarkable ice thickness data gap, which is represented by the thickness difference between the BEDMAP2 and the BedMachine reaches 101.7 Gt yr−1, which indicates its dominant cause of the future D’s uncertainty. In addition, the interannual variability of D caused by the annual changes in the ice velocity and ice thickness are considerable compared with the target uncertainty of 15 Gt yr−1, which cannot be ignored in annual MB estimations.

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.